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I_kwDOArmXAs52qBq0
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[Apple ARM architecture] - Golang app crashes when use TensorFlow in M1 Mac (ARM-based).
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[
"@joseangel-mm To fix this issue , you need to install the TensorFlow library that is specifically designed for ARM-based Macs. You can download this library from the TensorFlow website.\r\nPlease make sure that you are using the latest version of TensorFlow and the latest version of the Golang toolchain. When you build your Golang app, use the` -ldflags=\"-r -w\" `flag to link against the TensorFlow libraries. \r\nFor any further queries could you please post your issue in this [repository](https://github.com/golang/go/issues) or TF [forum](https://discuss.tensorflow.org/) where there is a larger community to get you the right help?\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hello @sushreebarsa, thank you for the response.\r\n\r\nI will try it.",
"Hello, @joseangel-mm! Is there any update on this issue?\r\nThank you!",
"> Hello, @joseangel-mm! Is there any update on this issue? Thank you!\r\n\r\nHi @sushreebarsa, not yet sorry. We are busy with other topics 😞.",
"Hi @sushreebarsa, I've been trying your approach with `-ldflags=\"-r -w\"`, but the issue keeps there.\r\nI've run two different commands:\r\n`go build -ldflags=\"-r -w\"`\r\n`go build -ldflags=\"-r=\"/usr/local/lib\" -w\"`\r\n\r\nIn both of them I get the following error:\r\n<details><summary>Error</summary>\r\n<p>\r\n\r\n/usr/local/go/pkg/tool/darwin_arm64/link: running clang failed: exit status 1\r\nUndefined symbols for architecture arm64:\r\n \"_TFE_ContextListDevices\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_ContextListDevices in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_ContextListDevices)\r\n \"_TFE_ContextOptionsSetAsync\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_ContextOptionsSetAsync in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_ContextOptionsSetAsync)\r\n \"_TFE_ContextOptionsSetConfig\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_ContextOptionsSetConfig in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_ContextOptionsSetConfig)\r\n \"_TFE_DeleteContext\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_DeleteContext in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_DeleteContext, __cgo_aec64e91903f_Cfunc_TFE_DeleteContextOptions )\r\n \"_TFE_DeleteContextOptions\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_DeleteContextOptions in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_DeleteContextOptions)\r\n \"_TFE_DeleteTensorHandle\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_DeleteTensorHandle in 000013.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_DeleteTensorHandle)\r\n \"_TFE_NewContext\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_NewContext in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_NewContext, __cgo_aec64e91903f_Cfunc_TFE_NewContextOptions )\r\n \"_TFE_NewContextOptions\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_NewContextOptions in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_NewContextOptions)\r\n \"_TFE_NewTensorHandle\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_NewTensorHandle in 000013.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_NewTensorHandle)\r\n \"_TFE_TensorHandleBackingDeviceName\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_TensorHandleBackingDeviceName in 000013.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_TensorHandleBackingDeviceName)\r\n \"_TFE_TensorHandleCopyToDevice\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_TensorHandleCopyToDevice in 000013.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_TensorHandleCopyToDevice)\r\n \"_TFE_TensorHandleDataType\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_TensorHandleDataType in 000013.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TFE_TensorHandleDataType)\r\n \"_TFE_TensorHandleDeviceName\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TFE_TensorHandleDeviceName in 000013.o\r\n (maybe you meant: 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referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_AddInput in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_AddInput, __cgo_aec64e91903f_Cfunc_TF_AddInputList )\r\n \"_TF_AddInputList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_AddInputList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_AddInputList)\r\n \"_TF_AllocateTensor\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_AllocateTensor in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_AllocateTensor)\r\n \"_TF_CloseSession\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_CloseSession in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_CloseSession)\r\n \"_TF_DeleteBuffer\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteBuffer in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteBuffer)\r\n \"_TF_DeleteDeviceList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteDeviceList in 000004.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteDeviceList)\r\n \"_TF_DeleteGraph\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteGraph in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteGraph)\r\n \"_TF_DeleteImportGraphDefOptions\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteImportGraphDefOptions in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteImportGraphDefOptions)\r\n \"_TF_DeleteLibraryHandle\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteLibraryHandle in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteLibraryHandle)\r\n \"_TF_DeletePRunHandle\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeletePRunHandle in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeletePRunHandle)\r\n \"_TF_DeleteSession\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteSession in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteSession, __cgo_aec64e91903f_Cfunc_TF_DeleteSessionOptions )\r\n \"_TF_DeleteSessionOptions\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteSessionOptions in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteSessionOptions)\r\n \"_TF_DeleteStatus\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteStatus in 000011.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteStatus)\r\n \"_TF_DeleteTensor\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeleteTensor in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeleteTensor)\r\n \"_TF_DeviceListCount\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeviceListCount in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeviceListCount)\r\n \"_TF_DeviceListMemoryBytes\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeviceListMemoryBytes in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeviceListMemoryBytes)\r\n \"_TF_DeviceListName\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeviceListName in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeviceListName)\r\n \"_TF_DeviceListType\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_DeviceListType in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_DeviceListType)\r\n \"_TF_Dim\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_Dim in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_Dim)\r\n \"_TF_FinishOperation\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_FinishOperation in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_FinishOperation)\r\n \"_TF_GetCode\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GetCode in 000011.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GetCode)\r\n \"_TF_GraphGetTensorNumDims\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphGetTensorNumDims in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphGetTensorNumDims)\r\n \"_TF_GraphGetTensorShape\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphGetTensorShape in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphGetTensorShape)\r\n \"_TF_GraphImportGraphDef\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphImportGraphDef in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphImportGraphDef)\r\n \"_TF_GraphNextOperation\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphNextOperation in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphNextOperation)\r\n \"_TF_GraphOperationByName\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphOperationByName in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphOperationByName)\r\n \"_TF_GraphToGraphDef\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_GraphToGraphDef in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_GraphToGraphDef)\r\n \"_TF_ImportGraphDefOptionsAddInputMapping\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsAddInputMapping in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsAddInputMapping)\r\n \"_TF_ImportGraphDefOptionsSetDefaultDevice\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsSetDefaultDevice in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsSetDefaultDevice)\r\n \"_TF_ImportGraphDefOptionsSetPrefix\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsSetPrefix in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_ImportGraphDefOptionsSetPrefix)\r\n \"_TF_LoadLibrary\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_LoadLibrary in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_LoadLibrary)\r\n \"_TF_LoadSessionFromSavedModel\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_LoadSessionFromSavedModel in 000008.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_LoadSessionFromSavedModel)\r\n \"_TF_Message\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_Message in 000011.o\r\n (maybe you meant: 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000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_OperationOpType)\r\n \"_TF_OperationOutputConsumers\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_OperationOutputConsumers in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_OperationOutputConsumers)\r\n \"_TF_OperationOutputListLength\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_OperationOutputListLength in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_OperationOutputListLength)\r\n \"_TF_OperationOutputNumConsumers\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_OperationOutputNumConsumers in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_OperationOutputNumConsumers)\r\n \"_TF_OperationOutputType\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_OperationOutputType in 000007.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_OperationOutputType)\r\n \"_TF_SessionListDevices\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SessionListDevices in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SessionListDevices)\r\n \"_TF_SessionPRun\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SessionPRun in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SessionPRunSetup, __cgo_aec64e91903f_Cfunc_TF_SessionPRun )\r\n \"_TF_SessionPRunSetup\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SessionPRunSetup in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SessionPRunSetup)\r\n \"_TF_SessionRun\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SessionRun in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SessionRun)\r\n \"_TF_SetAttrBool\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrBool in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrBoolList, __cgo_aec64e91903f_Cfunc_TF_SetAttrBool )\r\n \"_TF_SetAttrBoolList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrBoolList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrBoolList)\r\n \"_TF_SetAttrFloat\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrFloat in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrFloatList, __cgo_aec64e91903f_Cfunc_TF_SetAttrFloat )\r\n \"_TF_SetAttrFloatList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrFloatList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrFloatList)\r\n \"_TF_SetAttrInt\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrInt in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrIntList, __cgo_aec64e91903f_Cfunc_TF_SetAttrInt )\r\n \"_TF_SetAttrIntList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrIntList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrIntList)\r\n \"_TF_SetAttrShape\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrShape in 000005.o\r\n (maybe you meant: _TF_SetAttrShapeList_Helper, __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList , __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList_Helper , __cgo_aec64e91903f_Cfunc_TF_SetAttrShape )\r\n \"_TF_SetAttrShapeList\", referenced from:\r\n _TF_SetAttrShapeList_Helper in 000005.o\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList in 000005.o\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList_Helper in 000005.o\r\n (maybe you meant: _TF_SetAttrShapeList_Helper, __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList , __cgo_aec64e91903f_Cfunc_TF_SetAttrShapeList_Helper )\r\n \"_TF_SetAttrString\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrString in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrString, __cgo_aec64e91903f_Cfunc_TF_SetAttrStringList )\r\n \"_TF_SetAttrStringList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrStringList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrStringList)\r\n \"_TF_SetAttrTensor\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrTensor in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrTensor, __cgo_aec64e91903f_Cfunc_TF_SetAttrTensorList )\r\n \"_TF_SetAttrTensorList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrTensorList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrTensorList)\r\n \"_TF_SetAttrType\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrType in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrTypeList, __cgo_aec64e91903f_Cfunc_TF_SetAttrType )\r\n \"_TF_SetAttrTypeList\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetAttrTypeList in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetAttrTypeList)\r\n \"_TF_SetConfig\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetConfig in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetConfig)\r\n \"_TF_SetDevice\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetDevice in 000005.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetDevice)\r\n \"_TF_SetTarget\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_SetTarget in 000009.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_SetTarget)\r\n \"_TF_TensorBitcastFrom\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_TensorBitcastFrom in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_TensorBitcastFrom)\r\n \"_TF_TensorByteSize\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_TensorByteSize in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_TensorByteSize)\r\n \"_TF_TensorData\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_TensorData in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_TensorData)\r\n \"_TF_TensorType\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_TensorType in 000012.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_TensorType)\r\n \"_TF_Version\", referenced from:\r\n __cgo_aec64e91903f_Cfunc_TF_Version in 000014.o\r\n (maybe you meant: __cgo_aec64e91903f_Cfunc_TF_Version)\r\nld: symbol(s) not found for architecture arm64\r\nclang: error: linker command failed with exit code 1 (use -v to see invocation)\r\n\r\n</p>\r\n</details> \r\n\r\n\r\nMoreover, I read in some posts that changing `GOOS` env to `ios` instead of `darwin`, could help, but I got the same error.\r\nThis is my go env:\r\n<details><summary>Go env</summary>\r\n<p>\r\n\r\nGO111MODULE='on'\r\nGOARCH='arm64'\r\nGOBIN=''\r\nGOCACHE='/Users/martinjo/Library/Caches/go-build'\r\nGOENV='/Users/martinjo/Library/Application Support/go/env'\r\nGOEXE=''\r\nGOEXPERIMENT=''\r\nGOFLAGS=''\r\nGOHOSTARCH='arm64'\r\nGOHOSTOS='darwin'\r\nGOINSECURE=''\r\nGOMODCACHE='/Users/martinjo/go/pkg/mod'\r\nGONOPROXY=''\r\nGONOSUMDB=''\r\nGOOS='ios'\r\nGOPATH='/Users/martinjo/go'\r\nGOPRIVATE=''\r\nGOPROXY='“https://proxy.golang.org,direct”'\r\nGOROOT='/usr/local/go'\r\nGOSUMDB='sum.golang.org'\r\nGOTMPDIR=''\r\nGOTOOLCHAIN='auto'\r\nGOTOOLDIR='/usr/local/go/pkg/tool/darwin_arm64'\r\nGOVCS=''\r\nGOVERSION='go1.21.3'\r\nGCCGO='gccgo'\r\nAR='ar'\r\nCC='clang'\r\nCXX='clang++'\r\nCGO_ENABLED='1'\r\nGOMOD='/Users/martinjo/go/src/yoti/ai-service-on-device/go.mod'\r\nGOWORK=''\r\nCGO_CFLAGS='-O2 -g'\r\nCGO_CPPFLAGS=''\r\nCGO_CXXFLAGS='-O2 -g'\r\nCGO_FFLAGS='-O2 -g'\r\nCGO_LDFLAGS='-O2 -g'\r\nPKG_CONFIG='pkg-config'\r\nGOGCCFLAGS='-fPIC -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -ffile-prefix-map=/var/folders/fn/btj24j1n5k3cv201gk1dcgvw0000gq/T/go-build4131197003=/tmp/go-build -gno-record-gcc-switches -fno-common'\r\n\r\n</p>\r\n</details> \r\n\r\nAs a detail, when I have CGO_ENABLED to 0, I have this error instead that is like the code doesn't recognize well the TensorFlow [library](github.com/wamuir/graft/tensorflow):\r\n<details><summary>Alternative error</summary>\r\n<p>\r\n\r\ninternal/tensor/prepare_tensor.go:13:77: undefined: tf.Tensor\r\ninternal/tensor/prepare_tensor.go:20:22: undefined: tf.NewTensor\r\n\r\n</p>\r\n</details> ",
"Finally I got TensorFlow working following this [comment](https://gist.github.com/wangjia184/f9ffb2782d0703ef3dbceec9b2bbc4b4?permalink_comment_id=4269188#gistcomment-4269188).",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62383\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62383\">No</a>\n"
] | 2023-11-13T14:04:23 | 2024-01-12T13:15:55 | 2024-01-12T13:15:52 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
MacOS Ventura 13.5
### Mobile device
Mac M1 Pro
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Apple clang version 14.0.0 (clang-1400.0.29.102)
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I've tried to run my Golang app which uses TensorFlow in Mac ARM-based. I've researched, and in a first glance, it seems TensorFlow is not very supported for Golang apps in this kind of Mac.
Moreover, I've seen there are some fixes for Python but not for Golang.
Would it be possible an alternative to using it?
### Standalone code to reproduce the issue
```shell
I've tested the basic test in C, and the result is exactly the same as Golang:
#include <stdio.h>
#include <tensorflow/c/c_api.h>
int main() {
printf("Hello from TensorFlow C library version %s\n", TF_Version());
return 0;
}
```
### Relevant log output
```shell
go run main.go
# command-line-arguments
/usr/local/go/pkg/tool/darwin_arm64/link: running clang failed: exit status 1
ld: warning: ignoring file /usr/local/lib/libtensorflow.so, building for macOS-arm64 but attempting to link with file built for unknown-unsupported file format ( 0x7F 0x45 0x4C 0x46 0x02 0x01 0x01 0x00 0x00 0x00 0x00 0x00 0x00 0x00 0x00 0x00 )
Undefined symbols for architecture arm64:
"_TFE_ContextListDevices", referenced from:
__cgo_13f1810e087e_Cfunc_TFE_ContextListDevices in 000004.o
(maybe you meant: __cgo_13f1810e087e_Cfunc_TFE_ContextListDevices)
...
```
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I_kwDOArmXAs52ovrH
| 62,382 |
Understand thread_sync_cost and framework_cost calculation in mkl_heuristics.h
|
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[
"Hi, @akote123!\r\nCould you please fill the [template ](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml)which will help us to analyze the issue.\r\nThank you!",
"@Venkat6871 , Iam here going through the latest Tensorflow code base in github , in tensorflow/core/util/mkl_heuristics.h file and trying to understand the heristic concepts . Is there any documentation available on how the constants are taken in to consider in this file.\r\n\r\nThanks\r\n",
"Hi @cfRod ,\r\n Can you please help me in understanding these concepts , like how the threshold value is been decided for matmul and above case.\r\n\r\nThank You\r\n",
"Hi **@akote123** ,\r\n\r\nThe constants in tensorflow/core/util/mkl_heuristics.h are used to tune the performance of the Intel Math Kernel Library (MKL) when used with TensorFlow. These constants are based on a variety of factors, such as the type of operation being performed, the size of the input and output tensors, and the available hardware resources. \r\nIf you have further questions on this issue please feel free to ask. Could you please fill the [template ](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml)which will help us to analyze the issue.\r\n\r\n\r\nThank you!",
"Hi @Venkat6871 ,\r\n Thank you . Actually this is not a issue or bug .Here I was going through the code and I wanted to understand the how the value is being chosen like is there any formula to get this",
"Hi **@akote123** ,\r\nThis is not a bug or feature request, for any further queries you may open this issue in tf discussion [forum](https://discuss.tensorflow.org/) as there is a larger community there. \r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62382\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62382\">No</a>\n"
] | 2023-11-13T10:48:22 | 2023-12-05T03:38:08 | 2023-12-05T03:38:04 |
NONE
| null | null | null |
Hi,
I am trying to understand the mkl_heuristcs from tensorflow/core/util/mkl_heuristics.h .In rewrite_thresholds list the const values are given to framework cost and thread_sync_cost as :
{"Conv2D", 0x41, 0xd40, {0.9349, 22.603}},
{"_FusedConv2D", 0x41, 0xd40, {0.9349, 22.603}},
{"FusedBatchNormV3", 0x41, 0xd40, {0.3223, -0.8822}},
{"Sigmoid", 0x41, 0xd40, {0.0, 0.064736}}
Here how these constants are being calculated or taken into consider for rewrite thresholds?
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I_kwDOArmXAs52or3G
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Tensorflow Lite: Build installable package fails
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[
"I was able to reproduce this issue in TF 2.14 and nightly.\r\n\r\n<img width=\"1304\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/118897289/bd5e8838-2886-42ec-8b3e-2a81899da4ba\">\r\n\r\n@pkgoogle Could you please look into this?\r\n\r\nThanks.",
"I am able to replicate on nightly:\r\n\r\n```\r\n# install prereqs\r\nsudo apt-install cmake libabsl-dev libeigen3-dev flatbuffers-compiler libflatbuffers-dev libgemmlowp-dev libneon-2-sse-dev libcpuinfo-dev libruy-dev libfarmhash-dev libpthreadpool-dev\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\ngit switch nightly\r\ngit pull\r\ncd ..\r\nmkdir tflite_build\r\ncd tflite_build\r\ncmake ../tensorflow_src/tensorflow/lite -DTFLITE_ENABLE_INSTALL=ON \\\r\n -DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON \\\r\n -DSYSTEM_FARMHASH=ON \\\r\n -DSYSTEM_PTHREADPOOL=ON \\\r\n -Dabsl_DIR=/usr/lib/x86_64-linux-gnu/cmake/absl \\\r\n -DEigen3_DIR=/usr/share/eigen3/cmake \\\r\n -DFlatBuffers_DIR=/usr/lib/x86_64-linux-gnu/cmake/flatbuffers \\\r\n -Dgemmlowp_DIR=/usr/lib/x86_64-linux-gnu/cmake/gemmlowp \\\r\n -DNEON_2_SSE_DIR=/usr/lib/cmake/NEON_2_SSE \\\r\n -Dcpuinfo_DIR=/usr/lib/x86_64-linux-gnu/cmake/cpuinfo \\\r\n -Druy_DIR=/usr/lib/x86_64-linux-gnu/cmake/ruy\r\n```\r\n\r\n```\r\n...\r\n-- Configuring done (1.4s)\r\nCMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:\r\n Target \"ml_dtypes\" INTERFACE_INCLUDE_DIRECTORIES property contains path:\r\n\r\n \"/usr/local/google/home/xxxxxx/tflite_build/ml_dtypes\"\r\n\r\n which is prefixed in the build directory.\r\n\r\n\r\nCMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:\r\n Target \"ml_dtypes\" INTERFACE_INCLUDE_DIRECTORIES property contains path:\r\n\r\n \"/usr/local/google/home/xxxxxx/tflite_build/ml_dtypes/ml_dtypes\"\r\n\r\n which is prefixed in the build directory.\r\n\r\n\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"pthreadpool\" that is not in any export set.\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"XNNPACK\" that is not in any export set.\r\n-- Generating done (0.7s)\r\nCMake Generate step failed. Build files cannot be regenerated correctly.\r\n\r\n```\r\n\r\n@terryheo, can you please take a look? Thanks.",
"any news on this issue?",
"What is the path in the error message supposed to be anyways?\r\n\r\n`/usr/local/google/home/xxxxxx/tflite_build/ml_dtypes/ml_dtypes`\r\n\r\nIt does not exist on my machine and I might misinterpret things here, but it seems to be kind of hardcoded assuming that my home directory lies in `/usr/local/google/home/USERNAME` and that the build directory `tflite_build` lies in the root of it. Both not the case.",
"Hi @jagiella, \r\nI had the same issue. I did the following change to fix it.\r\n\r\nIn /home/xxx/dev/tensorflow/tensorflow/lite/tools/cmake/modules/ml_dtypes/CMakeLists.txt:\r\n[tensorflow v2.15.0]\r\n\r\nI replaced:\r\n```\r\ntarget_include_directories(ml_dtypes INTERFACE\r\n \"${ML_DTYPES_SOURCE_DIR}\"\r\n \"${ML_DTYPES_SOURCE_DIR}/ml_dtypes\")\r\n```\r\nby\r\n```\r\ntarget_include_directories(ml_dtypes INTERFACE\r\n \"$<BUILD_INTERFACE:${ML_DTYPES_SOURCE_DIR}>\" \"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>\"\r\n \"$<BUILD_INTERFACE:${ML_DTYPES_SOURCE_DIR}/ml_dtypes>\" \"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/ml_dtypes>\")\r\n```\r\nNote that this will put the ml_dtypes includes directly into your /usr/local/include folder.\r\nI don't know if that's how it is intended to be used. But anyway, this works for me.\r\n\r\nsources:\r\nhttps://stackoverflow.com/questions/25676277/cmake-target-include-directories-prints-an-error-when-i-try-to-add-the-source\r\nhttps://cmake.org/cmake/help/latest/command/target_include_directories.html\r\nhttps://cmake.org/cmake/help/latest/manual/cmake-generator-expressions.7.html#export-and-install-expressions\r\n\r\n\r\n\r\n\r\n",
"Well that solves the first part of the problem :+1: \r\n\r\nBut pthreadpool and XNNPACK errors remain:\r\n\r\n```bash\r\n$ cmake ../tensorflow_src/tensorflow/lite -DTFLITE_ENABLE_INSTALL=ON -DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON -DSYSTEM_FARMHASH=ON -DSYSTEM_PTHREADPOOL=ON -Dabsl_DIR=/usr/lib/x86_64-linux-gnu/cmake/absl -DEigen3_DIR=/usr/share/eigen3/cmake -DFlatBuffers_DIR=/usr/lib/x86_64-linux-gnu/cmake/flatbuffers -Dgemmlowp_DIR=/usr/lib/x86_64-linux-gnu/cmake/gemmlowp -DNEON_2_SSE_DIR=/usr/lib/cmake/NEON_2_SSE -Dcpuinfo_DIR=/usr/lib/x86_64-linux-gnu/cmake/cpuinfo -Druy_DIR=/usr/lib/x86_64-linux-gnu/cmake/ruy\r\n-- Setting build type to Release, for debug builds use'-DCMAKE_BUILD_TYPE=Debug'.\r\nCMake Warning (dev) at /usr/share/cmake-3.28/Modules/FetchContent.cmake:1331 (message):\r\n The DOWNLOAD_EXTRACT_TIMESTAMP option was not given and policy CMP0135 is\r\n not set. The policy's OLD behavior will be used. When using a URL\r\n download, the timestamps of extracted files should preferably be that of\r\n the time of extraction, otherwise code that depends on the extracted\r\n contents might not be rebuilt if the URL changes. The OLD behavior\r\n preserves the timestamps from the archive instead, but this is usually not\r\n what you want. Update your project to the NEW behavior or specify the\r\n DOWNLOAD_EXTRACT_TIMESTAMP option with a value of true to avoid this\r\n robustness issue.\r\nCall Stack (most recent call first):\r\n tools/cmake/modules/OverridableFetchContent.cmake:398 (FetchContent_Declare)\r\n tools/cmake/modules/fft2d.cmake:22 (OverridableFetchContent_Declare)\r\n tools/cmake/modules/Findfft2d.cmake:18 (include)\r\n CMakeLists.txt:152 (find_package)\r\nThis warning is for project developers. Use -Wno-dev to suppress it.\r\n\r\nCMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/FP16-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):\r\n Compatibility with CMake < 3.5 will be removed from a future version of\r\n CMake.\r\n\r\n Update the VERSION argument <min> value or use a ...<max> suffix to tell\r\n CMake that the project does not need compatibility with older versions.\r\n\r\n\r\nCMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/psimd-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):\r\n Compatibility with CMake < 3.5 will be removed from a future version of\r\n CMake.\r\n\r\n Update the VERSION argument <min> value or use a ...<max> suffix to tell\r\n CMake that the project does not need compatibility with older versions.\r\n\r\n\r\n-- Configuring done (0.8s)\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"pthreadpool\" that is not in any export set.\r\nCMake Error: install(EXPORT \"tensorflow-liteTargets\" ...) includes target \"tensorflow-lite\" which requires target \"XNNPACK\" that is not in any export set.\r\n-- Generating done (0.4s)\r\nCMake Generate step failed. Build files cannot be regenerated correctly.\r\n\r\n```",
"Did you try to build it despite the remaining errors at the configuration step ? \r\nFrom what I understand, the build system (CMake) will install the missing dependencies alongside Tensorflow, if they were not installed.\r\n\r\nFYI, I documented the installation process of Tensorflow Lite for my project:\r\nhttps://github.com/bastien-sagetat/photohead/blob/main/doc/soft_requirements.md\r\n\r\n"
] | 2023-11-13T10:39:12 | 2024-05-10T16:13:16 | null |
NONE
| null | null | null |
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 23.10
- **GCC/Compiler version (if compiling from source)**: 13.2.0
- **CUDA/cuDNN version**: 11.8
- **installed dependencies**:
```
cmake/mantic,now 3.27.4-1 amd64 [installiert]
cuda-11-8/unbekannt,now 11.8.0-1 amd64 [installiert]
g++/mantic,now 4:13.2.0-1ubuntu1 amd64 [Installiert,automatisch]
gcc/mantic,now 4:13.2.0-1ubuntu1 amd64 [Installiert,automatisch]
libabsl-dev/mantic,now 20220623.1-3 amd64 [installiert]
libcpuinfo-dev/mantic,now 0.0~git20220819.8ec7bd9-3 amd64 [installiert]
libeigen3-dev/mantic,now 3.4.0-4 all [installiert]
libfarmhash-dev/mantic,now 0~git20190513.0d859a8-2 amd64 [installiert]
libflatbuffers-dev/mantic,now 2.0.8+dfsg1-6 amd64 [installiert]
libgemmlowp-dev/mantic,now 0.0~git20211220.e844ffd-1 amd64 [installiert]
libneon-2-sse-dev/mantic,now 0.0.0~git20220912.a15b489-2 all [installiert]
libpthreadpool-dev/mantic,now 0.0~git20210507.1787867-1 amd64 [installiert]
libruy-dev/mantic,now 0.0.0~git20230215.21a85fe-1 amd64 [installiert]
libxnnpack-dev/mantic,now 0.0~git20221221.51a9875-1build1 amd64 [installiert]
```
- **Exact command to reproduce**:
```bash
$ cmake ../tensorflow/tensorflow/lite -DTFLITE_ENABLE_INSTALL=ON \
-DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON \
-DSYSTEM_FARMHASH=ON \
-DSYSTEM_PTHREADPOOL=ON \
-Dabsl_DIR=/usr/lib/x86_64-linux-gnu/cmake/absl \
-DEigen3_DIR=/usr/share/eigen3/cmake \
-DFlatBuffers_DIR=/usr/lib/x86_64-linux-gnu/cmake/flatbuffers \
-Dgemmlowp_DIR=/usr/lib/x86_64-linux-gnu/cmake/gemmlowp \
-DNEON_2_SSE_DIR=/usr/lib/cmake/NEON_2_SSE \
-Dcpuinfo_DIR=/usr/lib/x86_64-linux-gnu/cmake/cpuinfo \
-Druy_DIR=/usr/lib/x86_64-linux-gnu/cmake/ruy
```
### Describe the problem
Building an installable package of tensorflow lite with cmake following the instructions in the [documentation](https://www.tensorflow.org/lite/guide/build_cmake#build_installable_package) fails.
### Source code / logs
```bash
-- Setting build type to Release, for debug builds use'-DCMAKE_BUILD_TYPE=Debug'.
-- The C compiler identification is GNU 13.2.0
-- The CXX compiler identification is GNU 13.2.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
-- Found farmhash: /usr/lib/x86_64-linux-gnu/libfarmhash.so
CMake Warning (dev) at /usr/share/cmake-3.27/Modules/FetchContent.cmake:1316 (message):
The DOWNLOAD_EXTRACT_TIMESTAMP option was not given and policy CMP0135 is
not set. The policy's OLD behavior will be used. When using a URL
download, the timestamps of extracted files should preferably be that of
the time of extraction, otherwise code that depends on the extracted
contents might not be rebuilt if the URL changes. The OLD behavior
preserves the timestamps from the archive instead, but this is usually not
what you want. Update your project to the NEW behavior or specify the
DOWNLOAD_EXTRACT_TIMESTAMP option with a value of true to avoid this
robustness issue.
Call Stack (most recent call first):
tools/cmake/modules/OverridableFetchContent.cmake:398 (FetchContent_Declare)
tools/cmake/modules/fft2d.cmake:22 (OverridableFetchContent_Declare)
tools/cmake/modules/Findfft2d.cmake:18 (include)
CMakeLists.txt:150 (find_package)
This warning is for project developers. Use -Wno-dev to suppress it.
-- The ASM compiler identification is GNU
-- Found assembler: /usr/bin/cc
-- Downloading cpuinfo to /home/me/Documents/GitHub/tflite_build/cpuinfo-source (define CPUINFO_SOURCE_DIR to avoid it)
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/cpuinfo-download
[ 11%] Creating directories for 'cpuinfo'
[ 22%] Performing download step (download, verify and extract) for 'cpuinfo'
-- Downloading...
dst='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'
timeout='none'
inactivity timeout='none'
-- Using src='https://github.com/pytorch/cpuinfo/archive/959002f82d7962a473d8bf301845f2af720e0aa4.zip'
-- [download 0% complete]
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-- verifying file...
file='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'
-- Downloading... done
-- extracting...
src='/home/me/Documents/GitHub/tflite_build/cpuinfo-download/cpuinfo-prefix/src/959002f82d7962a473d8bf301845f2af720e0aa4.zip'
dst='/home/me/Documents/GitHub/tflite_build/cpuinfo-source'
-- extracting... [tar xfz]
-- extracting... [analysis]
-- extracting... [rename]
-- extracting... [clean up]
-- extracting... done
[ 33%] No update step for 'cpuinfo'
[ 44%] No patch step for 'cpuinfo'
[ 55%] No configure step for 'cpuinfo'
[ 66%] No build step for 'cpuinfo'
[ 77%] No install step for 'cpuinfo'
[ 88%] No test step for 'cpuinfo'
[100%] Completed 'cpuinfo'
[100%] Built target cpuinfo
-- Downloading FP16 to /home/me/Documents/GitHub/tflite_build/FP16-source (define FP16_SOURCE_DIR to avoid it)
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/FP16-download
[ 11%] Creating directories for 'fp16'
[ 22%] Performing download step (download, verify and extract) for 'fp16'
-- Downloading...
dst='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'
timeout='none'
inactivity timeout='none'
-- Using src='https://github.com/Maratyszcza/FP16/archive/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'
-- [download 14% complete]
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file='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'
-- Downloading... done
-- extracting...
src='/home/me/Documents/GitHub/tflite_build/FP16-download/fp16-prefix/src/0a92994d729ff76a58f692d3028ca1b64b145d91.zip'
dst='/home/me/Documents/GitHub/tflite_build/FP16-source'
-- extracting... [tar xfz]
-- extracting... [analysis]
-- extracting... [rename]
-- extracting... [clean up]
-- extracting... done
[ 33%] No update step for 'fp16'
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[100%] Completed 'fp16'
[100%] Built target fp16
-- Downloading FXdiv to /home/me/Documents/GitHub/tflite_build/FXdiv-source (define FXDIV_SOURCE_DIR to avoid it)
-- Configuring done (0.0s)
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-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/FXdiv-download
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-- Downloading...
dst='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'
timeout='none'
inactivity timeout='none'
-- Using src='https://github.com/Maratyszcza/FXdiv/archive/b408327ac2a15ec3e43352421954f5b1967701d1.zip'
-- [download 65% complete]
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-- verifying file...
file='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'
-- Downloading... done
-- extracting...
src='/home/me/Documents/GitHub/tflite_build/FXdiv-download/fxdiv-prefix/src/b408327ac2a15ec3e43352421954f5b1967701d1.zip'
dst='/home/me/Documents/GitHub/tflite_build/FXdiv-source'
-- extracting... [tar xfz]
-- extracting... [analysis]
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[ 33%] No update step for 'fxdiv'
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[100%] Built target fxdiv
-- Downloading pthreadpool to /home/me/Documents/GitHub/tflite_build/pthreadpool-source (define PTHREADPOOL_SOURCE_DIR to avoid it)
-- Configuring done (0.0s)
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-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/pthreadpool-download
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-- Downloading...
dst='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'
timeout='none'
inactivity timeout='none'
-- Using src='https://github.com/Maratyszcza/pthreadpool/archive/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'
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file='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'
-- Downloading... done
-- extracting...
src='/home/me/Documents/GitHub/tflite_build/pthreadpool-download/pthreadpool-prefix/src/4fe0e1e183925bf8cfa6aae24237e724a96479b8.zip'
dst='/home/me/Documents/GitHub/tflite_build/pthreadpool-source'
-- extracting... [tar xfz]
-- extracting... [analysis]
-- extracting... [rename]
-- extracting... [clean up]
-- extracting... done
[ 33%] No update step for 'pthreadpool'
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[ 77%] No install step for 'pthreadpool'
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[100%] Completed 'pthreadpool'
[100%] Built target pthreadpool
CMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/FP16-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):
Compatibility with CMake < 3.5 will be removed from a future version of
CMake.
Update the VERSION argument <min> value or use a ...<max> suffix to tell
CMake that the project does not need compatibility with older versions.
-- Downloading PSimd to /home/me/Documents/GitHub/tflite_build/psimd-source (define PSIMD_SOURCE_DIR to avoid it)
CMake Deprecation Warning at CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):
Compatibility with CMake < 3.5 will be removed from a future version of
CMake.
Update the VERSION argument <min> value or use a ...<max> suffix to tell
CMake that the project does not need compatibility with older versions.
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /home/me/Documents/GitHub/tflite_build/psimd-download
[ 11%] Creating directories for 'psimd'
[ 22%] Performing download step (git clone) for 'psimd'
Klone nach 'psimd-source' …
Ihr Branch ist auf demselben Stand wie 'origin/master'.
Bereits auf 'master'
[ 33%] Performing update step for 'psimd'
[ 44%] No patch step for 'psimd'
[ 55%] No configure step for 'psimd'
[ 66%] No build step for 'psimd'
[ 77%] No install step for 'psimd'
[ 88%] No test step for 'psimd'
[100%] Completed 'psimd'
[100%] Built target psimd
CMake Deprecation Warning at /home/me/Documents/GitHub/tflite_build/psimd-source/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED):
Compatibility with CMake < 3.5 will be removed from a future version of
CMake.
Update the VERSION argument <min> value or use a ...<max> suffix to tell
CMake that the project does not need compatibility with older versions.
-- Configuring done (35.6s)
CMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:
Target "ml_dtypes" INTERFACE_INCLUDE_DIRECTORIES property contains path:
"/home/me/Documents/GitHub/tflite_build/ml_dtypes"
which is prefixed in the build directory.
CMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:
Target "ml_dtypes" INTERFACE_INCLUDE_DIRECTORIES property contains path:
"/home/me/Documents/GitHub/tflite_build/ml_dtypes/ml_dtypes"
which is prefixed in the build directory.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "pthreadpool" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "XNNPACK" that is not in any export set.
-- Generating done (0.4s)
CMake Generate step failed. Build files cannot be regenerated correctly.
```
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I_kwDOArmXAs52nMNd
| 62,380 |
Unable to build cpu debug on windows
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[
"Hi @ksapozhn ,\r\n\r\nTF2.6v is quiet older and not actively supported. Please test with latest versions.\r\n\r\nAlso please try including the flags`--linkopt=/DEBUG --host_linkopt=/DEBUG` for windows build with latest version and let us know the outcome.",
"Unfortunatly I can't test newer version as I'm working with an embeded device that has TF2.6.",
"@ksapozhn ,\r\n\r\nPlease refer to .[bazlerc](https://github.com/tensorflow/tensorflow/blob/r2.6/.bazelrc) file of Tf2.6v if it helps.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62380\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62380\">No</a>\n"
] | 2023-11-13T06:41:22 | 2023-11-30T01:49:10 | 2023-11-30T01:49:06 |
NONE
| null | null | null |
Hello,
I'm trying to build TF 2.6 using the following command
bazel --output_base="D:/bazel_output" build --config=dbg //tensorflow/tools/lib_package:libtensorflow
### System information
- I'm using source code with tag v2.6.0
- Windows 10 Enterprise
- Trying to build TF 2.6 from source
- Python 3.9.18
- bazel 3.7.2
- VS2019
- Exact command to reproduce: bazel build --config=dbg //tensorflow/tools/lib_package:libtensorflow
I get the following error (attached the full log):
ERROR: E:/git/tensorflow/tensorflow/BUILD:984:20: Linking of rule '//tensorflow:tensorflow.dll' failed (Exit 1120): link.exe failed: error executing command
cd D:/bazel_output/execroot/org_tensorflow
SET LIB=C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\ATLMFC\lib\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\lib\x64;C:\Program Files (x86)\Windows Kits\NETFXSDK\4.8\lib\um\x64;C:\Program Files (x86)\Windows Kits\10\lib\10.0.19041.0\ucrt\x64;C:\Program Files (x86)\Windows Kits\10\lib\10.0.19041.0\um\x64
SET PATH=C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\\Extensions\Microsoft\IntelliCode\CLI;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\TestWindow;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\TeamFoundation\Team Explorer;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\MSBuild\Current\bin\Roslyn;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Team Tools\Performance Tools\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Team Tools\Performance Tools;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\\x64;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\;C:\Program Files (x86)\Microsoft SDKs\Windows\v10.0A\bin\NETFX 4.8 Tools\x64\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\FSharp\Tools;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\Tools\devinit;C:\Program Files (x86)\Windows Kits\10\bin\10.0.19041.0\x64;C:\Program Files (x86)\Windows Kits\10\bin\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\\MSBuild\Current\Bin;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\Tools\;;C:\WINDOWS\system32;;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\VC\Linux\bin\ConnectionManagerExe
SET PWD=/proc/self/cwd
SET PYTHON_BIN_PATH=C:/Users/ksapozhn/Miniconda3/envs/tf2.6_build/python.exe
SET PYTHON_LIB_PATH=C:/Users/ksapozhn/Miniconda3/envs/tf2.6_build/lib/site-packages
SET RUNFILES_MANIFEST_ONLY=1
SET TEMP=C:\Users\ksapozhn\AppData\Local\Temp
SET TF2_BEHAVIOR=1
SET TMP=C:\Users\ksapozhn\AppData\Local\Temp
C:/Program Files (x86)/Microsoft Visual Studio/2019/Professional/VC/Tools/MSVC/14.29.30133/bin/HostX64/x64/link.exe @bazel-out/x64_windows-dbg/bin/tensorflow/tensorflow.dll-2.params
Execution platform: @local_execution_config_platform//:platform
LINK : warning LNK4044: unrecognized option '/lm'; ignored
LINK : warning LNK4044: unrecognized option '/lpthread'; ignored
bazel-out/x64_windows-dbg/bin/tensorflow/tensorflow_filtered_def_file.def : warning LNK4197: export '?_GraphDef_default_instance_@tensorflow@@3VGraphDefDefaultTypeInternal@1@A' specified multiple times; using first specification
bazel-out/x64_windows-dbg/bin/tensorflow/tensorflow_filtered_def_file.def : warning LNK4197: export '?_TensorShapeProto_default_instance_@tensorflow@@3VTensorShapeProtoDefaultTypeInternal@1@A' specified multiple times; using first specification
Creating library bazel-out/x64_windows-dbg/bin/tensorflow/tensorflow.dll.if.lib and object bazel-out/x64_windows-dbg/bin/tensorflow/tensorflow.dll.if.exp
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)'
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'grappler.lib(grappler.obj)' in function '"class tensorflow::Status __cdecl tensorflow::grappler::InitGraphPlugin(void (__cdecl*)(struct TP_OptimizerRegistrationParams * const,struct TF_Status * const))" (?InitGraphPlugin@grappler@tensorflow@@YA?AVStatus@2@P6AXQEAUTP_OptimizerRegistrationParams@@QEAUTF_Status@@@Z@Z)'
LINK : warning LNK4217: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'modular_filesystem.lib(modular_filesystem_registration.obj)' in function '"private: void __cdecl std::list<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > >::_Alloc_sentinel_and_proxy(void)" (?_Alloc_sentinel_and_proxy@?$list@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@@2@@std@@AEAAXXZ)'
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'modular_filesystem.lib(modular_filesystem.obj)'
LINK : warning LNK4286: symbol 'TF_NewStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'stream_executor.lib(stream_executor.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'grappler.lib(grappler.obj)' in function '"public: void __cdecl tensorflow::grappler::TFStatusDeleter::operator()(struct TF_Status *)const " (??RTFStatusDeleter@grappler@tensorflow@@QEBAXPEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'modular_filesystem.lib(modular_filesystem_registration.obj)' in function '"public: static void __cdecl std::_List_node<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,void *>::_Freenode0<class std::allocator<struct std::_List_node<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,void *> > >(class std::allocator<struct std::_List_node<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,void *> > &,struct std::_List_node<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,void *> *)" (??$_Freenode0@V?$allocator@U?$_List_node@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@PEAX@std@@@std@@@?$_List_node@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@PEAX@std@@SAXAEAV?$allocator@U?$_List_node@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@PEAX@std@@@1@PEAU01@@Z)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'modular_filesystem.lib(modular_filesystem.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'stream_executor.lib(stream_executor.obj)'
LINK : warning LNK4286: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'grappler.lib(grappler.obj)' in function 'TF_GetNodesToPreserveListSize'
LINK : warning LNK4286: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)'
LINK : warning LNK4286: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'tf_tensor.lib(tf_tensor.obj)'
LINK : warning LNK4286: symbol 'TF_SetStatus' defined in 'tf_status.lib(tf_status.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4217: symbol 'TF_NewBuffer' defined in 'c_api_no_xla.lo.lib(c_api.obj)' is imported by 'grappler.lib(grappler.obj)' in function '"public: virtual class tensorflow::Status __cdecl tensorflow::grappler::CGraphOptimizer::Optimize(class tensorflow::grappler::Cluster *,struct tensorflow::grappler::GrapplerItem const &,class tensorflow::GraphDef *)" (?Optimize@CGraphOptimizer@grappler@tensorflow@@UEAA?AVStatus@3@PEAVCluster@23@AEBUGrapplerItem@23@PEAVGraphDef@3@@Z)'
LINK : warning LNK4217: symbol 'TF_DeleteBuffer' defined in 'c_api_no_xla.lo.lib(c_api.obj)' is imported by 'grappler.lib(grappler.obj)' in function '"public: void __cdecl tensorflow::grappler::TFBufferDeleter::operator()(struct TF_Buffer *)const " (??RTFBufferDeleter@grappler@tensorflow@@QEBAXPEAUTF_Buffer@@@Z)'
LINK : warning LNK4286: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'modular_filesystem.lib(modular_filesystem.obj)' in function '"public: virtual class tensorflow::Status __cdecl tensorflow::ModularFileSystem::NewRandomAccessFile(class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,struct tensorflow::TransactionToken *,class std::unique_ptr<class tensorflow::RandomAccessFile,struct std::default_delete<class tensorflow::RandomAccessFile> > *)" (?NewRandomAccessFile@ModularFileSystem@tensorflow@@UEAA?AVStatus@2@AEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@PEAUTransactionToken@2@PEAV?$unique_ptr@VRandomAccessFile@tensorflow@@U?$default_delete@VRandomAccessFile@tensorflow@@@std@@@5@@Z)'
LINK : warning LNK4217: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'stream_executor.lib(stream_executor.obj)' in function '"void __cdecl std::_Destroy_range<class std::allocator<struct tensorflow::StackFrame> >(struct tensorflow::StackFrame *,struct tensorflow::StackFrame * const,class std::allocator<struct tensorflow::StackFrame> &)" (??$_Destroy_range@V?$allocator@UStackFrame@tensorflow@@@std@@@std@@YAXPEAUStackFrame@tensorflow@@QEAU12@AEAV?$allocator@UStackFrame@tensorflow@@@0@@Z)'
LINK : warning LNK4286: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)'
LINK : warning LNK4286: symbol 'TF_GetCode' defined in 'tf_status.lib(tf_status.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'captured_function.lib(captured_function.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'bfc_allocator.lib(bfc_allocator.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'grpc_remote_master.lo.lib(grpc_remote_master.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'batch_kernels.lo.lib(batch_kernels.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'batch_resource_base.lib(batch_resource_base.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'snapshot_utils.lib(snapshot_utils.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'xla_device_no_jit_rewrite_registration.lo.lib(xla_device.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'execute.lib(execute.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'remote_tensor_handle_data.lib(remote_tensor_handle_data.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'graph_mgr.lib(graph_mgr.obj)'
LINK : warning LNK4217: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'cpu_runtime.lib(cpu_runtime.obj)' in function '"public: static bool __cdecl tensorflow::profiler::TraceMeRecorder::Active(int)" (?Active@TraceMeRecorder@profiler@tensorflow@@SA_NH@Z)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'grpc_master_service.lo.lib(grpc_master_service.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'eager_service_impl.lib(eager_service_impl.obj)'
LINK : warning LNK4286: symbol '?g_trace_level@internal@profiler@tensorflow@@3U?$atomic@H@std@@A (struct std::atomic<int> tensorflow::profiler::internal::g_trace_level)' defined in 'traceme_recorder_impl.lo.lib(traceme_recorder.obj)' is imported by 'xla_ops_no_jit_rewrite_registration.lo.lib(xla_ops.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'utils.lib(utils.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'captured_function.lib(captured_function.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'arithmetic_optimizer.lib(arithmetic_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'memory_optimizer.lib(memory_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'pin_to_host_optimizer.lib(pin_to_host_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'batch_kernels.lo.lib(batch_kernels.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'window_dataset.lib(window_dataset.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'ragged_tensor_variant.lib(ragged_tensor_variant.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'snapshot_utils.lib(snapshot_utils.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'tpu_rewrite_device_util.lib(tpu_rewrite_device_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_op_registry.lo.lib(xla_op_registry.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_helpers.lo.lib(xla_helpers.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'collective_param_resolver_distributed.lib(collective_param_resolver_distributed.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'device_util.lib(device_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_device_no_jit_rewrite_registration.lo.lib(xla_platform_info.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_device_no_jit_rewrite_registration.lo.lib(xla_ops_on_regular_devices.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_launch_util.lib(xla_launch_util.obj)'
LINK : warning LNK4217: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_ops_no_jit_rewrite_registration.lo.lib(xla_ops.obj)' in function '"void __cdecl tensorflow::`dynamic initializer for 'register_kernel_10''(void)" (??__Eregister_kernel_10@tensorflow@@YAXXZ)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'compilation_passes.lib(mark_for_compilation_pass.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'compilation_passes.lib(encapsulate_xla_computations_pass.obj)'
LINK : warning LNK4286: symbol '?DEVICE_CPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_CPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'compilation_passes.lib(clone_constants_for_better_clustering.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'memory_optimizer.lib(memory_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'pin_to_host_optimizer.lib(pin_to_host_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'gpu_id_impl.lib(gpu_id_manager.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'utils.lib(utils.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_op_registry.lo.lib(xla_op_registry.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_cluster_util.lib(xla_cluster_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'batch_kernels.lo.lib(batch_kernels.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'arithmetic_optimizer.lib(arithmetic_optimizer.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_device_no_jit_rewrite_registration.lo.lib(xla_ops_on_regular_devices.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_launch_util.lib(xla_launch_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'device_util.lib(device_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'tensorflow_ops_a_m.lib(tf_ops_a_m.obj)'
LINK : warning LNK4217: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_ops_no_jit_rewrite_registration.lo.lib(xla_ops.obj)' in function '"void __cdecl tensorflow::`dynamic initializer for 'register_kernel_11''(void)" (??__Eregister_kernel_11@tensorflow@@YAXXZ)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'compilation_passes.lib(encapsulate_xla_computations_pass.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'device_util.lib(device_util.obj)'
LINK : warning LNK4286: symbol '?DEVICE_GPU@tensorflow@@3QEBDEB (char const * const tensorflow::DEVICE_GPU)' defined in 'tensor.lo.lib(types.obj)' is imported by 'xla_device_no_jit_rewrite_registration.lo.lib(xla_platform_info.obj)'
LINK : warning LNK4217: symbol 'TF_NumDims' defined in 'tf_tensor.lib(tf_tensor.obj)' is imported by 'tensor_shape_utils.lib(tensor_shape_utils.obj)' in function '"class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl tensorflow::ShapeDebugString(struct TF_Tensor *)" (?ShapeDebugString@tensorflow@@YA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@PEAUTF_Tensor@@@Z)'
LINK : warning LNK4217: symbol 'TF_Dim' defined in 'tf_tensor.lib(tf_tensor.obj)' is imported by 'tensor_shape_utils.lib(tensor_shape_utils.obj)' in function '"class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl tensorflow::ShapeDebugString(struct TF_Tensor *)" (?ShapeDebugString@tensorflow@@YA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@PEAUTF_Tensor@@@Z)'
LINK : warning LNK4286: symbol 'TF_Message' defined in 'tf_status.lib(tf_status.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_Message' defined in 'tf_status.lib(tf_status.obj)' is imported by 'stream_executor.lib(stream_executor.obj)' in function '"public: virtual bool __cdecl stream_executor::`anonymous namespace'::CStreamExecutor::SynchronizeAllActivity(void)" (?SynchronizeAllActivity@CStreamExecutor@?A0x5ddabdab@stream_executor@@UEAA_NXZ)'
LINK : warning LNK4286: symbol 'TF_Message' defined in 'tf_status.lib(tf_status.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)'
LINK : warning LNK4286: symbol 'TF_Message' defined in 'tf_status.lib(tf_status.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_Message' defined in 'tf_status.lib(tf_status.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4217: symbol 'TF_DataTypeSize' defined in 'tf_datatype.lo.lib(tf_datatype.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4286: symbol 'TF_DataTypeSize' defined in 'tf_datatype.lo.lib(tf_datatype.obj)' is imported by 'tf_tensor.lib(tf_tensor.obj)'
LINK : warning LNK4217: symbol 'TF_NewOpDefinitionBuilder' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_NewOpDefinitionBuilder' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_NewOpDefinitionBuilder' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_NewOpDefinitionBuilder' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_RegisterOpDefinition' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_RegisterOpDefinition' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_RegisterOpDefinition' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_RegisterOpDefinition' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_OpDefinitionBuilderAddAttr' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddAttr' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddAttr' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddAttr' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_OpDefinitionBuilderAddInput' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddInput' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddInput' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddInput' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_OpDefinitionBuilderAddOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderAddOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_OpDefinitionBuilderSetShapeInferenceFunction' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl RegisterBitcastOp(void)" (?RegisterBitcastOp@@YAXXZ)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderSetShapeInferenceFunction' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderSetShapeInferenceFunction' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_OpDefinitionBuilderSetShapeInferenceFunction' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_NewShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextGetInput' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextSetOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4286: symbol 'TF_ShapeInferenceContextSetOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_ShapeInferenceContextSetOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_ShapeInferenceContextSetOutput' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextVectorFromSize' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_NewDimensionHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContext_GetAttrType' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextRankKnown' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextWithRankAtLeast' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextDim' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextSubshape' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextSetUnknownShape' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl bitcast_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?bitcast_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DimensionHandleValueKnown' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DimensionHandleValue' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextConcatenateShapes' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4286: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_DeleteDimensionHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)' in function '"void __cdecl histogram_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?histogram_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)' in function '"void __cdecl merge_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?merge_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)' in function '"void __cdecl scalar_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?scalar_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@E$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBE$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@E$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@E@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<float>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@M@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<float> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@M@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,double,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<double const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<double,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBN$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,double>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,float,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<float const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<float,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@M$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBM$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@M$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,float>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@M@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Ubfloat16@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUbfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Ubfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Ubfloat16@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::half,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Uhalf@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Uhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::half>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Uhalf@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,signed char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<signed char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<signed char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@C$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBC$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@C$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@F$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBF$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@F$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@F@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@G$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBG$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@G$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@G@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@I$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBI$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@I$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned int>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@I@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,__int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<__int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<__int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_J$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,__int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_J@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned __int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_K$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned __int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_K@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
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In function: xnn_f16_avgpool_minmax_ukernel_9p8x__neonfp16arith_c8, clang: note: diagnostic msg: /var/folders/cj/15jvtbpn3z15rcddhg75_h_r0000gn/T/neonfp16arith-cd34d7.c
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[
"@Qinlong275 Could you please try to update your clang compiler to a newer version that supports the neonfp16arith instruction set. You may also need to modify the compiler flags that are being used to compile XNNPACK.\r\nThank you!",
"> @Qinlong275 Could you please try to update your clang compiler to a newer version that supports the neonfp16arith instruction set. You may also need to modify the compiler flags that are being used to compile XNNPACK. Thank you!\r\n\r\nnice bro, I update the android ndk version from 19.2.5345600 to 22.1.7171670, and then build success! thx~",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62379\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62379\">No</a>\n",
"@Qinlong275 Thank you for the confirmation!\r\nGlad it worked for you.\r\nThank you!"
] | 2023-11-13T03:24:16 | 2023-11-16T13:46:38 | 2023-11-16T11:22:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.12.0
### Custom code
Yes
### OS platform and distribution
mac apple m1; android studio
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
build in androidstudio use ndk r19
### GCC/compiler version
clang version 8.0.2
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
run android gradle task assemble to build release TFLite SO。
### Standalone code to reproduce the issue
```shell
run android gradle task assemble to build release TFLite SO。
```
### Relevant log output
```shell
[715/901] Building ASM object _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/qs8-igemm/gen/qs8-igemm-4x8-minmax-rndnu-asm-aarch32-neon-mlal-lane-prfm-cortex-a7.S.o
[716/901] Building ASM object _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/qs8-igemm/gen/qs8-igemm-4x8-minmax-rndnu-asm-aarch32-neon-mlal-lane-prfm-cortex-a53.S.o
[717/901] Building ASM object _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/qs8-igemm/gen/qs8-igemm-4x8-minmax-rndnu-asm-aarch32-neon-mlal-lane-prfm-ld64.S.o
FAILED: /Users/qinlong/Library/Android/sdk/ndk/19.2.5345600/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang --target=armv7-none-linux-androideabi19 --gcc-toolchain=/Users/qinlong/Library/Android/sdk/ndk/19.2.5345600/toolchains/llvm/prebuilt/darwin-x86_64 --sysroot=/Users/qinlong/Library/Android/sdk/ndk/19.2.5345600/toolchains/llvm/prebuilt/darwin-x86_64/sysroot -DEIGEN_MPL2_ONLY -DFXDIV_USE_INLINE_ASSEMBLY=0 -DNOMINMAX=1 -DPTHREADPOOL_NO_DEPRECATED_API=1 -DXNN_ENABLE_ARM_BF16=1 -DXNN_ENABLE_ARM_DOTPROD=1 -DXNN_ENABLE_ARM_FP16_SCALAR=1 -DXNN_ENABLE_ARM_FP16_VECTOR=1 -DXNN_ENABLE_ASSEMBLY=1 -DXNN_ENABLE_DWCONV_MULTIPASS=0 -DXNN_ENABLE_GEMM_M_SPECIALIZATION=1 -DXNN_ENABLE_JIT=0 -DXNN_ENABLE_MEMOPT=1 -DXNN_ENABLE_RISCV_VECTOR=1 -DXNN_ENABLE_SPARSE=1 -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../jni -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../jni/benchmark -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../jni/common -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/Interface -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/base -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/base/misc -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/mnn -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/onnx -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/hdrs -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/hdrs/coreML -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/hdrs/gpu -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/hdrs/liteC -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/hdrs/metal -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/libs -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/libs/android -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/libs/android/arm64-v8a -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/libs/android/armeabi-v7a -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni/../../../../../src/tflite/libs/iOS -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/opencl_headers -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/vulkan_headers/include -I/Users/qinlong/aiInterpret/FrontAIFramework/3rdparty/TNetTensorflow/tensorflow/lite/delegates/gpu/common -I/Users/qinlong/aiInterpret/FrontAIFramework/3rdparty/TNetTensorflow/tensorflow/lite/delegates/gpu/common/task -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/xnnpack/src -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/pthreadpool-source/include -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/FXdiv-source/include -I/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/FP16-source/include -g -DANDROID -fdata-sections -ffunction-sections -funwind-tables -fstack-protector-strong -no-canonical-prefixes -mfpu=vfpv3-d16 -fno-addrsig -march=armv7-a -mthumb -mfpu=neon -Wa,--noexecstack -Wformat -Werror=format-security -Oz -DNDEBUG -std=c99 -fPIC -O2 -pthread -marm -march=armv8.2-a+fp16 -mfpu=neon-fp-armv8 -mfloat-abi=softfp -MD -MT _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/amalgam/neonfp16arith.c.o -MF _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/amalgam/neonfp16arith.c.o.d -o _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/amalgam/neonfp16arith.c.o -c /Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/xnnpack/src/amalgam/neonfp16arith.c
fatal error: error in backend: Cannot select: 0x7f895987f7a8: v8f16 = bitcast 0x7f895987c468, /Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/xnnpack/src/amalgam/neonfp16arith.c:50:28
0x7f895987c468: v8i16,ch = ARMISD::VLD1DUP<(load 2 from %ir.17)> 0x7f8959712ac8, 0x7f895987f608, Constant:i32<2>, /Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/xnnpack/src/amalgam/neonfp16arith.c:50:50
0x7f895987f608: i32 = add nuw 0x7f895987ccf0, Constant:i32<2>, /Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a/xnnpack/src/amalgam/neonfp16arith.c:50:50
0x7f895987ccf0: i32,ch = load<(load 4 from %fixed-stack.0, align 8)> 0x7f8959712ac8, FrameIndex:i32<-7>, undef:i32
0x7f895987cc88: i32 = FrameIndex<-7>
0x7f895987c7a8: i32 = undef
0x7f895987f5a0: i32 = Constant<2>
0x7f895987f5a0: i32 = Constant<2>
In function: xnn_f16_avgpool_minmax_ukernel_9p8x__neonfp16arith_c8
clang: error: clang frontend command failed with exit code 70 (use -v to see invocation)
Android (5058415 based on r339409) clang version 8.0.2 (https://android.googlesource.com/toolchain/clang 40173bab62ec746213857d083c0e8b0abb568790) (https://android.googlesource.com/toolchain/llvm 7a6618d69e7e8111e1d49dc9e7813767c5ca756a) (based on LLVM 8.0.2svn)
Target: armv7-none-linux-android19
Thread model: posix
InstalledDir: /Users/qinlong/Library/Android/sdk/ndk/19.2.5345600/toolchains/llvm/prebuilt/darwin-x86_64/bin
clang: note: diagnostic msg: PLEASE submit a bug report to https://bugs.llvm.org/ and include the crash backtrace, preprocessed source, and associated run script.
clang: note: diagnostic msg:
********************
PLEASE ATTACH THE FOLLOWING FILES TO THE BUG REPORT:
Preprocessed source(s) and associated run script(s) are located at:
clang: note: diagnostic msg: /var/folders/cj/15jvtbpn3z15rcddhg75_h_r0000gn/T/neonfp16arith-cd34d7.c
clang: note: diagnostic msg: /var/folders/cj/15jvtbpn3z15rcddhg75_h_r0000gn/T/neonfp16arith-cd34d7.sh
clang: note: diagnostic msg: Crash backtrace is located in
clang: note: diagnostic msg: /Users/qinlong/Library/Logs/DiagnosticReports/clang_<YYYY-MM-DD-HHMMSS>_<hostname>.crash
clang: note: diagnostic msg: (choose the .crash file that corresponds to your crash)
clang: note: diagnostic msg:
********************
ninja: build stopped: subcommand failed.
C++ build system [build] failed while executing:
/Users/qinlong/Library/Android/sdk/cmake/3.6.4111459/bin/ninja \
-C \
/Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/60191d14/armeabi-v7a \
benchmark_model \
flatc \
flathash \
tNetInterpret
from /Users/qinlong/aiInterpret/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk
```
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I_kwDOArmXAs52lTB8
| 62,378 |
Least squares is 50% slower when activating JIT compile
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[
"Hi **@lonidard** ,\r\nI have replicated the reported behavior with jit_compile=True with 2.14,2.13 and tf-nightly. Here I attached a [gist1](https://colab.research.google.com/gist/Venkat6871/1d590ef56e562b957d22fdce5b2a03e6/62378_2-14-2-13-v.ipynb),[ gist2](https://colab.research.google.com/gist/Venkat6871/1d590ef56e562b957d22fdce5b2a03e6/62378_2-14-2-13-v.ipynb) for your reference.\r\n\r\nThank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62378\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62378\">No</a>\n",
"Thank you Venkat! If there's anything I can do to assist in its resolution, please let me know.",
"Hi @lonidard ,\r\n\r\nI have checked the code with Tf2.14v and Tf2.15v and also increased the no of runs and I found in Tf2.15v this API indeed performs better with `jit_compile`. Please find the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/14eb751255b83a3f5c7e6255cfcb0eb5/62378_2-14v-vs-2-15v.ipynb#scrollTo=V54msX3TWU_e) for reference.\r\n\r\n```\r\n**In TF2.15v for 10 runs:**\r\n`lstsq_no_jit`\r\n29.5 ms ± 4.69 ms per loop\r\n`lstsq_jit`\r\n27.6 ms ± 575 µs per loop\r\n\r\n**In TF2.14v for 10 runs:**\r\n`lstsq_no_jit`\r\n20.4 ms ± 2.25 ms per loop\r\n`lstsq_jit`\r\n29.2 ms ± 1.11 ms per loop\r\n```\r\n\r\nFrom the above logs TF2.15v don't have this issue.Thanks!",
"Hi @SuryanarayanaY,\r\n\r\nDoesn’t this mean that TF2.15 slowed down the non-jit version without impacting the JIT compiled one, rather than fixing the bug?\r\n\r\nFor a proper resolution, I’d expect the JIT compiled version to perform better than the no-jit in 2.14.",
"Are there any updates?",
"Hi @lonidard ,\r\n\r\nXLA performance depends upon the no of fusions,optimizations for an Op.\r\n\r\nAccelerators like GPUs make it even faster.Please refer to attached [gpu-gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/f3e6c8f2d7a5f2d3c181191adc8368af/62378_2-14v-vs-2-15v-r1_gpu.ipynb).\r\n\r\n```\r\n**In TF2.15v for 1000 runs:**\r\n`lstsq_no_jit`\r\n2.29 s ± 346 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\r\n`lstsq_jit`\r\n1.46 s ± 11.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\r\n\r\n**In TF2.14v for 10 runs:**\r\n`lstsq_no_jit`\r\n1.43 s ± 465 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\r\n`lstsq_jit`\r\n1.45 s ± 29.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\r\n```\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62378\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62378\">No</a>\n"
] | 2023-11-12T17:01:11 | 2023-12-30T01:48:04 | 2023-12-30T01:48:00 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Activating JIT compile makes tf.linalg.lstsq up to 100% slower.
If we benchmark `tf.linalg.lstsq` on a matrix of shape (2**18, 5), we see considerable slowdown when activating JIT compile. Expectation would be that JIT actually speeds up solving the least squares problem, but seeing the opposite across multiple platforms and setups. JIT version is consistently slower, taking up to twice as much depending on the platform.
### Standalone code to reproduce the issue
See here:
https://colab.research.google.com/drive/1MIBRIYik2ZVpy1eIIalokMQxE2GA2G9D#scrollTo=7YrOyhUaZD24
or equivalently:
```shell
import tensorflow as tf
@tf.function(jit_compile=False)
def lstsq_no_jit(X,y):
return tf.linalg.lstsq(X,y)
@tf.function(jit_compile=True)
def lstsq_jit(X,y):
return tf.linalg.lstsq(X,y)
m, n, k = (2**18, 5, 1)
shape_X = (m, n)
shape_y = (m, k)
X = tf.random.normal(shape_X)
y = tf.random.normal(shape_y)
# Run once to avoid compilation overhead.
lstsq_no_jit(X,y)
lstsq_jit(X,y)
```
At this point, just time
```shell
lstsq_no_jit(X,y)
```
vs
```shell
lstsq_jit(X,y)
```
### Relevant log output
In the colab version, see the following:
* Running lstsq with no JIT: 6.01 ms ± 401 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
* Running lstsq with JIT: 11.4 ms ± 3.45 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
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I_kwDOArmXAs52kX29
| 62,377 |
tflite_runtime v2.14.0 not inside apt installation repository
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[
"@pkgoogle, Please look into this issue.\r\nThis issue is reproduced on Ubuntu 22.04 as well. Please find the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/87143c7cbd6eafc272e664b1d2b315fb/-62377.ipynb#scrollTo=yKdq5HjwfvyD).\r\n\r\nThank You\r\n\r\n",
"Hi @tribui141108, can you try using pip instead?:\r\n\r\n```\r\npip install tflite-runtime\r\nCollecting tflite-runtime\r\n Obtaining dependency information for tflite-runtime from https://files.pythonhosted.org/packages/9e/1f/aade0d066bacbe697946ae21f0467a702d81adb939bb64515e9abebae9ed/tflite_runtime-2.14.0-cp310-cp310-manylinux2014_x86_64.whl.metadata\r\n Downloading tflite_runtime-2.14.0-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.4 kB)\r\nRequirement already satisfied: numpy>=1.23.2 in ./.local/lib/python3.10/site-packages (from tflite-runtime) (1.24.3)\r\nDownloading tflite_runtime-2.14.0-cp310-cp310-manylinux2014_x86_64.whl (2.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 37.5 MB/s eta 0:00:00\r\nInstalling collected packages: tflite-runtime\r\nSuccessfully installed tflite-runtime-2.14.0\r\n```",
"I tried, but I don't think pip is allowed on a Raspberry Pi as it may break its Python Interface.\r\n\r\nNormally, I think installing tflite-runtime using apt package is possible on a Raspberry Pi 4. Raspberry Pi 5 has new hardware features, so I think that is the reason it doesn't work on my Raspberry Pi 5.",
"Hi @tribui141108, are you following any particular resource that gave you that command? I just want to ensure we are following instructions correctly for your particular environment.\r\n\r\n```\r\nE: Unable to correct problems, you have held broken packages.\r\n```\r\nTells me something in your environment seems broken\r\n\r\nCan you try uninstalling and reinstalling python, also it seems like we expect python < 3.10 ... according to the error message, but your description says 3.10 or newer.. I'm not sure if you accidentally misunderstood this? or are you referring to some other information? I would try installing with python=3.9 though to see if it resolves your issue.\r\n\r\nAlternatively, you can just try doing the --break-system-packages flag that is stated in your error message, though I suspect this will lead to other problems down the road (perhaps something similar to this current issue).\r\n\r\nIs there any reason you don't want to do this in a venv as described in the message above?\r\n\r\nIf none of the above work, you can also try installing from source: https://www.tensorflow.org/install/source",
"Yes, I'm following the instructions to install the Coral USB Accelerator on my Raspberry Pi.\r\nhttps://coral.ai/docs/accelerator/get-started/\r\nI'm currently trying to get pycoral installed, which requires tflite_runtime to be installed as well.\r\n\r\nI've also tried installing in a python virtual environment, but normally the package would not detect pycoral there.",
"Hi @tribui141108, I went through that document (not in full detail) but I couldn't find where it suggests installing that python package? It's possible I missed it, but if you can point me to where it suggests installing python3-tflite-runtime? I primarily see it suggesting to install python3-pycoral\r\n\r\nAlso I see an explicit Note here:\r\n\r\n```\r\nNote: PyCoral currently supports Python 3.6 through 3.9. If your default version is something else, we suggest you [install Python 3.9 with pyenv](https://realpython.com/intro-to-pyenv/).\r\n```\r\n\r\nThat suggests to me you should probably use python=3.9",
"I guess I have to wait until it supports python 3.10. Installing python 3.9 requires me to uninstall python 3.10, which will immediately break raspbian os since raspbian os is built on a python interface.",
"No worries, @tribui141108, if you have have no other open items regarding this, can you please close as completed? Thanks.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62377\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62377\">No</a>\n"
] | 2023-11-12T05:26:48 | 2023-11-23T09:21:35 | 2023-11-23T09:21:31 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
No
### OS platform and distribution
Raspbian OS (64-bit)
### Mobile device
_No response_
### Python version
3.11.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
4GB RAM, 4 cores
### Current behavior?
tflite_runtime should be installed correctly on the Raspberry Pi 5. However, the it says the version only supports Python 3.10 or newer, since the latest version of it is 2.5.0. I expected it to install with version 2.14.0. libedgetpu1-std has also been successfully installed onto the Raspberry Pi.
### Standalone code to reproduce the issue
```shell
sudo apt install python3-tflite-runtime
```
### Relevant log output
```shell
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:
The following packages have unmet dependencies:
python3-tflite-runtime : Depends: python3 (< 3.10) but 3.11.2-1 is to be installed
E: Unable to correct problems, you have held broken packages.
```
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I_kwDOArmXAs52kEnM
| 62,376 |
I have an error in predict command (InvalidArgumentError: Graph execution error:)
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[
"Hi @hunnder ,\r\n\r\nWe need `input_file=\"DS.csv\"` to debug the issue.\r\n\r\nAs per my understanding,It seems the problem is you are feeding different shape of input than the model trained on. Could you please verify the shapes of `trainX` in `model.fit()` and `x_input` in `x_input = x_input.reshape((1, n_steps,1))` are same ??\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62376\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62376\">No</a>\n"
] | 2023-11-12T00:15:32 | 2023-11-30T01:49:27 | 2023-11-30T01:49:08 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14.0 and 2.13.0
### Custom code
Yes
### OS platform and distribution
window 10
### Mobile device
jupyter notebook
### Python version
Python 3.9.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
i want to forcast the model forward
[DS.csv](https://github.com/tensorflow/tensorflow/files/13326819/DS.csv)
### Standalone code to reproduce the issue
```shell
I used tensorflow 2.14.0 and then 2.14.0 same problem # demonstrate prediction forward
my code.....
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from tensorflow.keras.layers import Dense, Activation, Dropout
import time #helper libraries
input_file="DS.csv"
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
# fix random seed for reproducibility
np.random.seed(5)
# load the dataset
df = read_csv(input_file, header=None, index_col=None, delimiter=',')
# take close price column[5]
all_y = df[5].values
dataset=all_y.reshape(-1, 1)
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets, 50% test data, 50% training data
train_size = int(len(dataset) * 0.5)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1, timestep 240
look_back = 240
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# create and fit the LSTM network, optimizer=adam, 25 neurons, dropout 0.1
model = Sequential()
model.add(LSTM(25, input_shape=(1, look_back)))
model.add(Dropout(0.1))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam')
model.fit(trainX, trainY, epochs=1000, batch_size=64, verbose=1)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
print('testPrices:')
testPrices=scaler.inverse_transform(dataset[test_size+look_back:])
print('testPredictions:')
print(testPredict)
# export prediction and actual prices
df = pd.DataFrame(data={"prediction": np.around(list(testPredict.reshape(-1)), decimals=2), "test_price": np.around(list(testPrices.reshape(-1)), decimals=2)})
df.to_csv("lstm_result.csv", sep=';', index=None)
# plot the actual price, prediction in test data=red line, actual price=blue line
plt.plot(testPredictPlot)
plt.show()
lst_output=[]
n_steps=200
i=0
while(i<30):
if(len(temp_input)>200):
#print(temp_input)
x_input=np.array(temp_input[1:])
print("{} day input {}".format(i,x_input))
x_input=x_input.reshape(1,-1)
x_input = x_input.reshape((1, n_steps, 1))
#print(x_input)
yhat = model.predict(x_input, verbose=0)
print("{} day output {}".format(i,yhat))
temp_input.extend(yhat[0].tolist())
temp_input=temp_input[1:]
#print(temp_input)
lst_output.extend(yhat.tolist())
i=i+1
else:
x_input = x_input.reshape((1, n_steps,1))
yhat = model.predict(x_input, verbose=0)
print(yhat[0])
temp_input.extend(yhat[0].tolist())
print(len(temp_input))
lst_output.extend(yhat.tolist())
i=i+1
print(lst_output)
```
### Relevant log output
```shell
InvalidArgumentError Traceback (most recent call last)
Input In [6], in <cell line: 7>()
23 else:
24 x_input = x_input.reshape((1, n_steps,1))
---> 25 yhat = model.predict(x_input, verbose=0)
26 print(yhat[0])
27 temp_input.extend(yhat[0].tolist())
File ~\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'while/MatMul' defined at (most recent call last):
File "C:\Users\MR\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\MR\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Users\MR\anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
app.start()
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Users\MR\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Users\MR\anaconda3\lib\asyncio\base_events.py", line 601, in run_forever
self._run_once()
File "C:\Users\MR\anaconda3\lib\asyncio\base_events.py", line 1905, in _run_once
handle._run()
File "C:\Users\MR\anaconda3\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 471, in dispatch_queue
await self.process_one()
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 460, in process_one
await dispatch(*args)
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 367, in dispatch_shell
await result
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 662, in execute_request
reply_content = await reply_content
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 360, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\MR\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
return super().run_cell(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
result = self._run_cell(
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
return runner(coro)
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "C:\Users\MR\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\MR\AppData\Local\Temp\ipykernel_1348\665417727.py", line 25, in <cell line: 7>
yhat = model.predict(x_input, verbose=0)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2554, in predict
tmp_batch_outputs = self.predict_function(iterator)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2341, in predict_function
return step_function(self, iterator)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2327, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2315, in run_step
outputs = model.predict_step(data)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 2283, in predict_step
return self(x, training=False)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\training.py", line 569, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\base_layer.py", line 1150, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\sequential.py", line 405, in call
return super().call(inputs, training=training, mask=mask)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\functional.py", line 512, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\functional.py", line 669, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\base_rnn.py", line 556, in __call__
return super().__call__(inputs, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\engine\base_layer.py", line 1150, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 749, in call
) = lstm_with_backend_selection(**normal_lstm_kwargs)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 1339, in lstm_with_backend_selection
last_output, outputs, new_h, new_c, runtime = defun_standard_lstm(
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 981, in standard_lstm
last_output, outputs, new_states = backend.rnn(
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 5170, in rnn
final_outputs = tf.compat.v1.while_loop(
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 5149, in _step
output, new_states = step_function(
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\layers\rnn\lstm.py", line 967, in step
z = backend.dot(cell_inputs, kernel)
File "C:\Users\MR\anaconda3\lib\site-packages\keras\src\backend.py", line 2465, in dot
out = tf.matmul(x, y)
Node: 'while/MatMul'
Matrix size-incompatible: In[0]: [1,1], In[1]: [240,100]
[[{{node while/MatMul}}]]
[[sequential/lstm/PartitionedCall]] [Op:__inference_predict_function_119476]
```
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I_kwDOArmXAs52jCli
| 62,375 |
Can't recognize GPU in tensorflow.
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[
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62375\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62375\">No</a>\n"
] | 2023-11-11T11:10:41 | 2023-11-11T11:29:49 | 2023-11-11T11:29:47 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.14
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.1
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2/8.1
### GPU model and memory
nvidia GTX 1060 6GB
### Current behavior?
I installed tensorflow and cuda/cudnn but cannot see GPU in tensorflow.
print('GPU', tf.config.list_physical_devices('GPU'))
The output is:
GPU []
The output of 'print(tf.config.list_physical_devices())' is
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print(tf.config.list_physical_devices())
print('GPU', tf.config.list_physical_devices('GPU'))
```
### Relevant log output
_No response_
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I_kwDOArmXAs52i_Rp
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Tensorflow compiler error in bazel
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[
"Hi @BiophiliaSWDA ,\r\n\r\nThis seems to be issue related to linker ids on Macos. Please have a look into the bazel repo issues [1](https://github.com/bazelbuild/bazel/issues/16286), [2](https://github.com/bazelbuild/bazel/issues/16053) and let us know if this is of help.",
"> Hi @BiophiliaSWDA ,\r\n> \r\n> This seems to be issue related to linker ids on Macos. Please have a look into the bazel repo issues [1](https://github.com/bazelbuild/bazel/issues/16286), [2](https://github.com/bazelbuild/bazel/issues/16053) and let us know if this is of help.\r\n\r\nThanks for the reply, I have finished compiling on ubuntu. But I didn't find a way to calculate the `c++ code coverage`. For example, I now have a tensorflow network written in python, can I use `bazel coverage` to get the `c++ code coverage`.",
"Hi @BiophiliaSWDA ,\r\n\r\nCould you please refer this [documentation](https://www.tensorflow.org/community/contribute/tests) on TF tests is of any help. I find out similar issue #51091 where author tried same but couldn't succeed in older TF versions. Could you try and let us know the outcome with latest versions? Also all tests coverage shall be available on [tensorflow/tensorflow.bazel](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorflow.bzl). \r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62374\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62374\">No</a>\n"
] | 2023-11-11T10:32:53 | 2023-12-15T01:49:31 | 2023-12-15T01:49:25 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf2.12.0
### Custom code
No
### OS platform and distribution
MacOS12.6
### Mobile device
_No response_
### Python version
3.10
### Bazel version
5.3.0
### GCC/compiler version
clang14 llvm14
### CUDA/cuDNN version
no
### GPU model and memory
no
### Current behavior?
I use `python3 configure.py`, and `bazel build //tensorflow/tools/pip_package:build_pip_package` atfer. I get an error :
```
clang: error: invalid linker name in argument '-fuse-ld=-debugger-tuning=lldb'
```
All logs is here:
```
(tensorflowcov) my@mydeMacBook-Pro tensorflow % python3 configure.py
You have bazel 5.3.0 installed.
Please specify the location of python. [Default is /Users/my/miniconda3/envs/tensorflowcov/bin/python3]:
Found possible Python library paths:
/Users/my/miniconda3/envs/tensorflowcov/lib/python3.10/site-packages
Please input the desired Python library path to use. Default is [/Users/my/miniconda3/envs/tensorflowcov/lib/python3.10/site-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.
Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Do you wish to build TensorFlow with iOS support? [y/N]: n
No iOS support will be enabled for TensorFlow.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
(tensorflowcov) my@mydeMacBook-Pro tensorflow % bazel build //tensorflow/tools/pip_package:build_pip_package
WARNING: Ignoring JAVA_HOME, because it must point to a JDK, not a JRE.
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=121
INFO: Reading rc options for 'build' from /Users/my/tensorflowcov/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /Users/my/tensorflowcov/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /Users/my/tensorflowcov/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/Users/my/miniconda3/envs/tensorflowcov/bin/python3 --action_env PYTHON_LIB_PATH=/Users/my/miniconda3/envs/tensorflowcov/lib/python3.10/site-packages --python_path=/Users/my/miniconda3/envs/tensorflowcov/bin/python3
INFO: Reading rc options for 'build' from /Users/my/tensorflowcov/tensorflow/.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file /Users/my/tensorflowcov/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /Users/my/tensorflowcov/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:macos in file /Users/my/tensorflowcov/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17
WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/10939d1d580b9d3c9c2f3539c6bdb39f408179c0.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found
WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/91d765cad5599f9710973d3e34d4dc22583e2e79.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found
INFO: Build options --action_env and --python_path have changed, discarding analysis cache.
WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/659147817805d17c7be2d60bd7bbca7e780f9c82.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found
WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/boringssl/archive/b9232f9e27e5668bc0414879dcdedb2a59ea75f2.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (583 packages loaded, 42968 targets configured).
INFO: Found 1 target...
ERROR: /private/var/tmp/_bazel_my/d373406bd1736cab8f8b5a2e209dce51/external/com_google_protobuf/BUILD.bazel:459:10: Linking external/com_google_protobuf/protoc failed: (Exit 1): cc_wrapper.sh failed: error executing command external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt-exec-50AE0418/bin/external/com_google_protobuf/protoc-2.params
clang: error: invalid linker name in argument '-fuse-ld=-debugger-tuning=lldb'
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 51.060s, Critical Path: 4.94s
INFO: 398 processes: 195 internal, 203 local.
FAILED: Build did NOT complete successfully
```
After searching I found [ISSUE](https://github.com/tensorflow/tensorflow/issues/59739)
At the end of this issue, the author also mentioned this problem, but did not fix yet.
Sorry to interrupt.
I want to use `bazel coverage` to get the `c++` operator coverage of my network after compiling `tensorflow2.12.0` with `bazel`. I don't know if this is ok, maybe you can give me some advice on this point? I would really appreciate it.
Or, you can give me the algorithm to get the `c++ operators`, because AFAIK, pytorch provides [code_coverage](https://github.com/pytorch/pytorch/tree/main/tools/code_coverage) to get the coverage of c++ operators
### Standalone code to reproduce the issue
```shell
No code
```
### Relevant log output
_No response_
|
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[
"@tilakrayal \r\nDo you think this issue is a bug in the TensorFlow Profiler?\r\nPlease let me know your opinion.",
"@y-vectorfield,\r\nI tried to execute the given tutorial and it was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/c1d0879b01c06230fc2bdf0926cdfe47/tensorboard_profiling_keras.ipynb?hl=ja) and the screenshot for the reference.\r\n\r\n\r\n\r\n\r\nThank you!\r\n\r\n",
"Same problem for me. My local setup with TF2.14 produces the same problem \"No profile data was found.\"\r\n\r\n\r\nIf I execute the official notebook https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/docs/tensorboard_profiling_keras.ipynb\r\nthen I encounter the same problem.\r\n\r\n@tilakrayal What you show in the screenshot is not the profiler ! \r\n\r\n\r\n\r\n",
"Hello, @tilakrayal \r\n@TillBeemelmanns and I could implement TensorBoard.\r\nHowever, we could not watch the result of the TensorFlow Profiler using TensorBoard.\r\nI think this is a problem of the TensorFlow Profiler.\r\n@zzzaries , @qiuminxu , what do you think about this problem?\r\nPlease let us know your opinions.\r\n\r\nWe could not confirm the following content.\r\n>The **Profile** tab opens the Overview page which shows you a high-level summary of your model performance. Looking at the Step-time Graph on the right, you can see that the model is highly input bound (i.e., it spends a lot of time >in the data input piepline). The Overview page also gives you recommendations on potential next steps you can follow to optimize your model performance. \r\n",
"Hello @tilakrayal , \r\n I wanted to use tensorboard for inference, \r\ntf.profiler.experimental.start('logs')\r\noutput = model.predict(encoded_input, verbose=False)[0]\r\ntf.profiler.experimental.stop()\r\nI have tried this way . But When the profiler tab opened its saying no profile data. May I know how we can get profile data for model inference\r\n\r\n",
"@yisitu , @ronshapiro ,\r\nWhat do you think about the above problem?\r\nThe TensorFlow Profiler(v2) did not work correctly and did not watch the measurement results.",
"So I could resolve the problem. The solution for me was to manually copy and paste this file https://github.com/tensorflow/profiler/blob/master/demo/events.out.tfevents.1583461681.localhost.profile-empty\r\n\r\ninto the Tensorboard log directory of my experiment, then suddenly Tensorboard and the profiler-plugin load the results of the Tensorflow profiler. \r\n",
"@TillBeemelmanns ,\r\n Did you try for inference as well?\r\n",
"@TillBeemelmanns \r\nThank you very much for your report.\r\n\r\n> The solution for me was to manually copy and paste this file\r\n\r\nI think the profiler should create this event file automatically, not manually.",
"Hello, @sachinprasadhs ,\r\nWhat do you think about this issue??\r\nI think this profiler should create an event file(event.out.tfevents.xxx.xxxx.profile-empty) automatically.\r\nHowever, the latest version of this does not create it.",
"Hi,\r\n\r\nThanks for reporting the issue, I have reported the issue to the concerned team, I was able to find similar issues which is reported here https://github.com/tensorflow/tensorflow/issues/61212",
"Hello, @sachinprasadhs \r\nI confirmed #61212.\r\nThis is a similar issue of this.",
"Hi y-vectorfield, for the \"No data Found\" issue, could you please share \r\n- the screenshot of your log directory after collecting the profile so we can check on the profile data.\r\n- How you collect the profile (if possible, the code structure)\r\nThanks!\r\n\r\nAnd the profile inactive issue should be fixed soon with a new change.\r\n",
"Hello, @zzzaries, thank you very much for your message.\r\n@akote123 and I tried to measure the inference performance using this.\r\nThe example of measurement code is as follows.\r\n\r\n```python\r\nlog_dir = \"./logs\"\r\nwith tf.profiler.experimental.Profile(log_dir, options):\r\n some inference codes are implemented\r\n```\r\n\r\nIf we implement the above code, we get the following directory structure.\r\n\r\n```bash\r\nlogs/\r\n└── 20231116-004551\r\n └── plugins\r\n └── profile\r\n └── 2023_11_16_00_46_09\r\n └── intel-gpu-tensorflow-stable-diffusion-v2140-yvectorfield.xplane.pb\r\n```\r\n\r\nWe can not get some files of measurement results. Hence, we can not watch the measurement results using TensorBoard. ",
"Hi @zzzaries ,\r\n We also tried with \r\ntf.profiler.experimental.start('logs')\r\noutput = model.predict(encoded_input, verbose=False)[0]\r\n\r\nwhen I do tensorboard --logdir=logs\r\nI get profile page as attached .\r\n<img width=\"926\" alt=\"tensorboard\" src=\"https://github.com/tensorflow/tensorflow/assets/133775732/ff25c873-e096-4f5f-be6e-34291a592917\">\r\n",
"@akote123 , thank you very much for your info sharing.",
"Thanks @y-vectorfield @akote123 for the detailed description!\r\n\r\nAnd @akote123 could you also share the directory structure like what y-vectorfield mentioned above? This could be very helpful for us to debug the issue as the directory structure and files inside will potentially determine if tensorboard can identify valid profiler runs data there, Thank you!",
"@zzzaries, thank you for your message.\r\nCan I help you to fix this issue?\r\nI also try to investigate the root cause of this!",
"@zzzaries , Thank you. The directory structure looks like:\r\n```\r\nlogs/\r\n -plugins\r\n -profile\r\n -2023_11_17_10_16_20\r\n -ip-172-31-93-51.xplane.pb\r\n\r\n```\r\nThis how the directory structure looks like.\r\n\r\nThank You",
"@akote123, thank you very much for your sharing.",
"@zzzaries , Do you have any example inference code where tensorboard shows the profile data? \r\nCan you please share any working example for model inference tensorboard code, so that it will be helpful for us to understand whether we are doing any mistake while using profiler API.\r\n\r\nThanks",
"I used the following code to investigate the root cause of this issue.\r\nReference: [Extract features with VGG16](https://keras.io/api/applications/#extract-features-with-vgg16)\r\n\r\n```python\r\nfrom pathlib import Path \r\n\r\nimport numpy as np\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.applications.vgg16 import VGG16\r\nfrom tensorflow.keras.applications.vgg16 import preprocess_input\r\nfrom tensorflow.keras.preprocessing import image\r\n\r\n\r\ndef main(args):\r\n model = VGG16(weights='imagenet', include_top=False)\r\n img_path = 'common/elephant.jpg'\r\n img = image.load_img(img_path, target_size=(224, 224))\r\n x = image.img_to_array(img)\r\n x = np.expand_dims(x, axis=0)\r\n x = preprocess_input(x)\r\n log_path = Path(args.log_path)\r\n if not log_path.exists():\r\n raise FileNotFoundError(str(log_path))\r\n with tf.profiler.experimental.Profile(str(log_path)):\r\n model.predict(x)\r\n\r\n\r\nif __name__ == '__main__':\r\n from argparse import ArgumentParser\r\n arg_parser = ArgumentParser()\r\n arg_parser.add_argument(\"--log_path\", required=True)\r\n args = arg_parser.parse_args()\r\n main(args)\r\n```",
"I got the following results. I used each version of TF official container image from Docker Hub.\r\nhttps://hub.docker.com/r/tensorflow/tensorflow/tags?page=1\r\n\r\n|TF version|eventfile|profiler results|\r\n|:---:|:---:|:---:|\r\n|2.8.0|○|○|\r\n|2.8.1|○|○|\r\n|2.8.2|○|○|\r\n|2.8.4|○|○|\r\n|2.9.0|○|○|\r\n|2.9.1|○|○|\r\n|2.9.2|○|○|\r\n|2.9.3|○|○|\r\n|2.10.0|○|○|\r\n|2.10.1|○|○|\r\n|2.11.0|○|✕|\r\n|2.11.1|○|✕|\r\n|2.12.0|✕|✕|\r\n|2.13.0|✕|✕|\r\n|2.14.0|✕|✕|\r\n|2.15.0|✕|✕|\r\n\r\nThe result eventfile: ○, profiler results: ○ means the following dir structure.\r\n\r\n```bash\r\nlogs/\r\n├── events.out.tfevents.1700568471.a0a96e93ba93.profile-empty\r\n└── plugins\r\n └── profile\r\n └── 2023_11_21_12_07_51\r\n ├── a0a96e93ba93.input_pipeline.pb\r\n ├── a0a96e93ba93.kernel_stats.pb\r\n ├── a0a96e93ba93.memory_profile.json.gz\r\n ├── a0a96e93ba93.overview_page.pb\r\n ├── a0a96e93ba93.tensorflow_stats.pb\r\n ├── a0a96e93ba93.trace.json.gz\r\n └── a0a96e93ba93.xplane.pb\r\n\r\n3 directories, 8 files\r\n```\r\n\r\nThe result eventfile: ○, profiler results: ✕ means the following dir structure.\r\n\r\n```bash\r\nlogs/\r\n├── events.out.tfevents.1700520805.fa34d7d6c817.profile-empty\r\n└── plugins\r\n └── profile\r\n └── 2023_11_20_22_53_25\r\n └── fa34d7d6c817.xplane.pb\r\n\r\n3 directories, 2 files\r\n```\r\n\r\nThe result eventfile: ✕, profiler results: ✕ means the following dir structure.\r\n\r\n```bash\r\nlogs/\r\n└── plugins\r\n └── profile\r\n └── 2023_11_21_05_12_44\r\n └── d12c6b2d4b00.xplane.pb\r\n\r\n3 directories, 1 file\r\n```",
"thanks @y-vectorfield for the detailed debugging and that's really helpful!\r\n\r\nfor the 3 types of directory structure you showed, the 1 and 2 should both work (2 is intended change), meaning until 2.11 it should be working fine. We've identified some suspicious code snippet and has been working on a fix for this, will update you ASAP. ",
"> @zzzaries , Do you have any example inference code where tensorboard shows the profile data? Can you please share any working example for model inference tensorboard code, so that it will be helpful for us to understand whether we are doing any mistake while using profiler API.\r\n> \r\n> Thanks\r\n\r\n@akote123 I'm not an expert in performance tuning, but from what I know, you are not doing anything wrong, and this should be a known issue on our side.\r\n\r\nHowever want to double check, as you mentioned \r\n> We also tried with\r\n> tf.profiler.experimental.start('logs')\r\n> output = model.predict(encoded_input, verbose=False)[0]\r\n> when I do tensorboard --logdir=logs\r\n> I get profile page as attached .\r\n\r\ndoes the above code create the directory of following but you can see the data in the profiler?\r\nand this is all the files within /logs dir?\r\n\r\n```\r\nlogs/\r\n -plugins\r\n -profile\r\n -2023_11_17_10_16_20\r\n -ip-172-31-93-51.xplane.pb\r\n```",
"Hello, @sachinprasadhs, @zzzaries, \r\nI think we should attach the bug label to this issue and remove the support label.\r\nWhat do you think of my opinion?",
"@zzzaries, thank you very much for your advice.\r\n\r\n> the 1 and 2 should both work (2 is intended change), meaning until 2.11 it should be working fine. \r\n\r\nOK, I will check this using TensorBoard.\r\nBy the way, I would like to contribute to solving this problem.\r\nI will investigate the source codes deeply and submit some modification codes(Pull Request) as soon as possible.\r\nOK?",
"@sachinprasadhs, thank you!",
"> > @zzzaries , Do you have any example inference code where tensorboard shows the profile data? Can you please share any working example for model inference tensorboard code, so that it will be helpful for us to understand whether we are doing any mistake while using profiler API.\r\n> > Thanks\r\n> \r\n> @akote123 I'm not an expert in performance tuning, but from what I know, you are not doing anything wrong, and this should be a known issue on our side.\r\n> \r\n> However want to double check, as you mentioned\r\n> \r\n> > We also tried with\r\n> > tf.profiler.experimental.start('logs')\r\n> > output = model.predict(encoded_input, verbose=False)[0]\r\n> > when I do tensorboard --logdir=logs\r\n> > I get profile page as attached .\r\n> \r\n> does the above code create the directory of following but you can see the data in the profiler? and this is all the files within /logs dir?\r\n> \r\n> ```\r\n> logs/\r\n> -plugins\r\n> -profile\r\n> -2023_11_17_10_16_20\r\n> -ip-172-31-93-51.xplane.pb\r\n> ```\r\n\r\n@zzzaries ,these are the files created and in the profiler its not showing any data.",
"Im expperimenting the same issue I tryed the solution provided by @TillBeemelmanns but this still doesn't work for me\r\n\r\n> So I could resolve the problem. The solution for me was to manually copy and paste this file https://github.com/tensorflow/profiler/blob/master/demo/events.out.tfevents.1583461681.localhost.profile-empty\r\n> \r\n> into the Tensorboard log directory of my experiment, then suddenly Tensorboard and the profiler-plugin load the results of the Tensorflow profiler.\r\n\r\n"
] | 2023-11-11T04:49:16 | 2024-04-19T14:37:29 | null |
NONE
| null | null | null |
I used the TensorFlow profiler to measure large-scale DL models' CPU/GPU performance. However, this tool did not work correctly. I did not see a graph for the results of measurement using TensorBoard. Hence, I tried a tutorial of [this](https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tensorboard/tensorboard_profiling_keras.ipynb?hl=ja), and this problem occurred again. I think the current version of the TensorFlow profiler does not save all of the files for measurement results.
The following image shows the current status.
<img width="1986" alt="TensorBoard" src="https://github.com/tensorflow/tensorflow/assets/30323722/b043d495-6999-4383-bac9-6f4fbe255885">
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I_kwDOArmXAs52hv_R
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Issue created for Rollback of PR #61276: return assert_shapes for debugging.assert_shapes
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[] | 2023-11-10T23:31:12 | 2023-11-10T23:46:22 | 2023-11-10T23:46:22 |
NONE
| null | null | null |
Merged PR #61276 is rolled back in 9c492018a641ac6b5a03b65a77ff9d1ce60699eb.
Please follow up with the reviewer and close this issue once its resolved.
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Segmentation Fault when using Coral Edge Tpu
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[
"@Skillnoob Could you please make sure that you are using the correct TensorFlow Lite model and interpreter for your Edge TPU device?\r\nThe Edge TPU hardware and firmware needs to be up to date to avoid such issues.\r\nThank you!",
"@sushreebarsa The model has been converted to tflite with int8 and compiled using the latest version of the edgetpu compiler. The edgetpu runtime is also on the latest version. The ultralytics module uses the tflite interpreter from the tensorflow module iirc.",
"@Skillnoob , Could you please provide the model_edgetpu.tflite file to better understand the issue and to investigate further?\r\n\r\nThank You",
"@LakshmiKalaKadali\r\nhttps://drive.google.com/file/d/1Vua1ujK07FXpMdM34uFltA0AchEvC0lH/view?usp=sharing",
"@Skillnoob, I have verified the Tflite file given. It seems that your model is using custom ops. Please find the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/3296c04175e20ee378d5234853f18c79/-62371.ipynb). Could you please check these instructions([documentation](https://www.tensorflow.org/lite/guide/ops_custom)) for custom ops and let us know if they have been followed?\r\n\r\nThank You",
"@LakshmiKalaKadali the error you got inside the notebook is to be expected as the model relies on the delegate from the [coral usb accelerator](https://coral.ai/docs/accelerator/get-started#1-install-the-edge-tpu-runtime)",
"Hi @pkgoogle ,\r\nPlease look into the issue.\r\n\r\nThank you",
"Hi @Skillnoob, it seems like there is a dependency on some installation instructions from Coral? Can you let us know the Coral instructions you followed prior to receiving this error? Also please note that the website explicitly states:\r\n\r\n```\r\nPython 3.6 - 3.9\r\n```\r\n\r\nAs a requirement.\r\n\r\nApparently you are using: 3.11.2, you might want to try using python=3.9 and see if it resolves your issue. Thanks for your help.",
"@pkgoogle Hi. I've tried the same program previously on a raspberry pi 4B with python 3.9.2 and had the same crash but couldn't run it through gdb as the pi would just freeze. I also tried it on the pi 5 using a anaconda virtual env with python 3.9.18 and the same crash occured.",
"Hi @Skillnoob, that settles the version issue but can you also please let us know the coral installation steps you followed?",
"@pkgoogle i followed the installation instructions exactly as written on the installation page",
"need a coral to reproduce, Hi @nutsiepully, can you please take a look? Thanks.\r\n",
"Recently tried to run this using tensorflow-aarch64 2.13.1 and 2.15.0 and the crash still occurs",
"easier way of reproducing the issue:\r\n```\r\npip install ultralytics\r\n\r\nyolo export model=yolov8n.pt format=edgetpu\r\n```\r\nInstall the edge tpu runtime as explained [here](https://coral.ai/docs/accelerator/get-started/#1-install-the-edge-tpu-runtime)\r\n```\r\nyolo detect val model=yolov8n_saved_model/yolov8n_full_integer_quant_edgetpu.tflite data=coco128.yaml batch=1\r\n```\r\n",
"also tried this with every tensorflow version going from the current latest to 2.12.1 on a raspberry pi 4B with python 3.9.2",
"This is now resolved due to https://github.com/feranick updating the libedgetpu runtime to support newer tflite_runtime versions in https://github.com/google-coral/edgetpu/issues/812 .",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62371\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62371\">No</a>\n",
"I would still keep this issue open. While there is a solution from [my forked repository](https://github.com/feranick/libedgetpu), Google should update the main one, and until then the issue is not solved. ",
"@feranick i agree. But it seems that google has completly abandoned the coral project.",
"Hi @feranick I don't work with that repo but can you perhaps link your PR that fixes the issue? Maybe I can help push it from here.\r\n\r\nEdit: nevermind.. found it. I think this is it? https://github.com/google-coral/libedgetpu/pull/59",
"@pkgoogle thats the correct pr",
"New builds are finally available against [Tensorflow `v2.15.0` (current)](https://github.com/tensorflow/tensorflow/releases/tag/v2.15.0). I had to refactor the WORKSPACE to conform to the [deprecation of the TF/Toolchain](https://discuss.tensorflow.org/t/tensorflow-toolchains-has-been-deprecated-and-moved-into-tensorflow-tensorflow/7713). All seem to be working for me (including on `armhf` where it only worked with `tflite_runtime v2.13.1`). \r\n\r\nI plan to do a new PR soon.\r\n\r\nhttps://github.com/feranick/libedgetpu/releases/tag/v16.0-TF2.15.0-1",
"> Hi @feranick I don't work with that repo but can you perhaps link your PR that fixes the issue? Maybe I can help push it from here.\r\n> \r\n> Edit: nevermind.. found it. I think this is it? [google-coral/libedgetpu#59](https://github.com/google-coral/libedgetpu/pull/59)\r\n\r\nThanks. That is the PR, but I plan to make a new one based on the last few commits hat bring support to TensorFlow 2.15.0. I'll post here.",
"BTW, in case someone needs updated `tflite_runtime` wheels, I prepared a few here.\r\n\r\nhttps://github.com/feranick/TFlite-builds/releases/tag/v2.15.0",
"@feranick Thanks a lot! Finally after a long journey in the rabbit hole, **this** is the only solution to get working with 3.11 currently",
"> Hi @feranick I don't work with that repo but can you perhaps link your PR that fixes the issue? Maybe I can help push it from here.\r\n> \r\n> Edit: nevermind.. found it. I think this is it? [google-coral/libedgetpu#59](https://github.com/google-coral/libedgetpu/pull/59)\r\n\r\n[pkgoogle](https://github.com/pkgoogle) Just wondering whether you had a chance to move this forward. The correct and current PR is this:\r\n\r\nhttps://github.com/google-coral/libedgetpu/pull/60",
"Thanks very much to @pkgoogle and @namburger at Google for merging PR. The libedgetpu library is now fully updated, and I hope binaries will be made available soon through the official channel.",
"Hi @Skillnoob, as @feranick mentioned, the libedgetpu library is now updated. Can you test your case against master and see if it resolves your issue?",
"@pkgoogle I have already tested with @feranick 's builds a while ago and they work without any issues on my rpi 5 with python 3.11",
"@Skillnoob, awesome, if you are confident this issue is resolved and you have no more open items, please feel free to close this issue as completed. Thanks."
] | 2023-11-10T22:11:05 | 2024-02-29T22:52:01 | 2024-02-29T22:51:59 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Raspberry Pi Os Bookworm
### Mobile device
_No response_
### Python version
3.11.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The program crashes as soon as it tries to load the edge tpu model.
I already asked in the YoloV8 discord and they told me to open a issue here from looking at the stacktrace.
The hardware is a raspberry pi 5 with 8Gb of ram but the same issue also happens on a raspberry pi 4B with 2Gb of ram
### Standalone code to reproduce the issue
```python
import cv2
from ultralytics import YOLO
def main():
model = YOLO('model_edgetpu.tflite', task='detect') # has to be a YoloV8 model converted to tflite and compiled to the coral edge tpu
camera = cv2.VideoCapture(0)
while camera.isOpened():
_, frame = camera.read()
results = model.predict(frame)
annotated_frame = results[0].plot()
cv2.imshow("YOLOv8 Inference", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
camera.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
```
### Relevant log output
Crash when running through gdb to get the stacktrace:
```shell
(gdb) run ai_model_copy.py run ai_model_copy.py
Starting program: /usr/bin/python3 ai_model_copy.py run ai_model_copy.py
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
[New Thread 0x7ffff5a4f180 (LWP 2502)]
[New Thread 0x7ffff523f180 (LWP 2503)]
[New Thread 0x7ffff0a2f180 (LWP 2504)]
[New Thread 0x7fffd9bef180 (LWP 2505)]
[New Thread 0x7fffd73df180 (LWP 2506)]
[New Thread 0x7fffd4bcf180 (LWP 2507)]
[Detaching after vfork from child process 2508]
[Detaching after vfork from child process 2509]
[Detaching after vfork from child process 2510]
[New Thread 0x7fff94aaf180 (LWP 2517)]
[New Thread 0x7fff9229f180 (LWP 2518)]
[New Thread 0x7fff91a8f180 (LWP 2519)]
Loading mode_edgetpu.tflite for TensorFlow Lite Edge TPU inference...
[New Thread 0x7fff89bef180 (LWP 2521)]
[Thread 0x7fff89bef180 (LWP 2521) exited]
[New Thread 0x7fff89bef180 (LWP 2522)]
[Thread 0x7fff89bef180 (LWP 2522) exited]
[New Thread 0x7fff89bef180 (LWP 2523)]
[New Thread 0x7fff893df180 (LWP 2524)]
[New Thread 0x7fff88bcf180 (LWP 2525)]
[Thread 0x7fff88bcf180 (LWP 2525) exited]
[Thread 0x7fff893df180 (LWP 2524) exited]
[New Thread 0x7fff893df180 (LWP 2526)]
[New Thread 0x7fff88bcf180 (LWP 2527)]
[New Thread 0x7fff83fff180 (LWP 2528)]
Thread 1 "python3" received signal SIGSEGV, Segmentation fault.
0x00007fff8bbee22c in tflite::Subgraph::ReplaceNodeSubsetsWithDelegateKernels(TfLiteRegistration, TfLiteIntArray const*, TfLiteDelegate*) ()
from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
```
Stacktrace made using gdb:
```shell
#0 0x00007fff8bbee22c in tflite::Subgraph::ReplaceNodeSubsetsWithDelegateKernels(TfLiteRegistration, TfLiteIntArray const*, TfLiteDelegate*) ()
from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#1 0x00007fff8bbee1e0 in tflite::Subgraph::ReplaceNodeSubsetsWithDelegateKernels(TfLiteContext*, TfLiteRegistration, TfLiteIntArray const*, TfLiteDelegate*) ()
from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#2 0x00007fff89c6be24 in ?? () from /lib/aarch64-linux-gnu/libedgetpu.so.1
#3 0x00007fff8bbf3058 in tflite::Subgraph::ModifyGraphWithDelegateImpl(TfLiteDelegate*) () from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#4 0x00007fff8bbf3624 in tflite::Subgraph::ModifyGraphWithDelegate(TfLiteDelegate*) () from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#5 0x00007fff8bbe5ce8 in tflite::impl::Interpreter::ModifyGraphWithDelegateImpl(TfLiteDelegate*) () from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#6 0x00007fff8b92af70 in tflite::interpreter_wrapper::InterpreterWrapper::ModifyGraphWithDelegate(TfLiteDelegate*) ()
from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#7 0x00007fff8b9273a4 in pybind11::cpp_function::initialize<pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module_&)::$_23, pybind11::object, tflite::interpreter_wrapper::InterpreterWrapper&, unsigned long, pybind11::name, pybind11::is_method, pybind11::sibling, char [60]>(pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module_&)::$_23&&, pybind11::object (*)(tflite::interpreter_wrapper::InterpreterWrapper&, unsigned long), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, char const (&) [60])::{lambda(pybind11::detail::function_call&)#1}::__invoke(pybind11::detail::function_call&) ()
from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#8 0x00007fff8b91772c in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) () from /home/pi/.local/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#9 0x00000000004c9d5c in ?? ()
#10 0x0000000000494548 in _PyObject_MakeTpCall ()
#11 0x00000000004aa23c in _PyEval_EvalFrameDefault ()
#12 0x00000000004e2cec in _PyFunction_Vectorcall ()
#13 0x000000000049c1d8 in _PyObject_FastCallDictTstate ()
#14 0x00000000004edc24 in ?? ()
#15 0x00000000004944d8 in _PyObject_MakeTpCall ()
#16 0x00000000004aa23c in _PyEval_EvalFrameDefault ()
#17 0x00000000004e2cec in _PyFunction_Vectorcall ()
#18 0x00000000004f3390 in PyObject_Call ()
#19 0x00000000004ae388 in _PyEval_EvalFrameDefault ()
#20 0x00000000004e2cec in _PyFunction_Vectorcall ()
#21 0x000000000049c1d8 in _PyObject_FastCallDictTstate ()
#22 0x00000000004edc24 in ?? ()
#23 0x00000000004944d8 in _PyObject_MakeTpCall ()
#24 0x00000000004aa23c in _PyEval_EvalFrameDefault ()
#25 0x00000000004a0b60 in PyEval_EvalCode ()
#26 0x00000000005fafa8 in ?? ()
#27 0x00000000005f7bd0 in ?? ()
#28 0x0000000000608760 in ?? ()
#29 0x0000000000608308 in _PyRun_SimpleFileObject ()
#30 0x0000000000608070 in _PyRun_AnyFileObject ()
#31 0x000000000060631c in Py_RunMain ()
#32 0x00000000005d0154 in Py_BytesMain ()
#33 0x00007ffff7ce7780 in __libc_start_call_main (main=main@entry=0x5cfff4 <_start+52>, argc=argc@entry=4, argv=argv@entry=0x7ffffffff0f8) at ../sysdeps/nptl/libc_start_call_main.h:58
#34 0x00007ffff7ce7858 in __libc_start_main_impl (main=0x5cfff4 <_start+52>, argc=4, argv=0x7ffffffff0f8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=<optimized out>) at ../csu/libc-start.c:360
#35 0x00000000005cfff0 in _start ()
```
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PR_kwDOArmXAs5fL5Px
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r2.14 cherry-pick: ea45e14c926 "Change jaxlib version to the next earliest version for MacOS + Linux CI builds."
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PR_kwDOArmXAs5fLv1L
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r2.15 cherry-pick: ea45e14c926 "Change jaxlib version to the next earliest version for MacOS + Linux CI builds."
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Change jaxlib version to the next earliest version for MacOS + Linux
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Cherry pick for https://github.com/tensorflow/tensorflow/commit/ea45e14c926b19cba205588e0cb55a6bf8f38ea1
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I_kwDOArmXAs52gX8W
| 62,367 |
[TF 2.14][aarch64]Memory footprint increased by almost 2.5x for inference (eg: I've tested MLPerf Resnet50 offline mode)
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"@sachinprasadhs \r\nAWS c7g is ARM-based CPU, so it might not use oneDNN (TensorFlow-MKL)"
] | 2023-11-10T18:55:51 | 2023-11-29T00:18:47 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
TF2.14
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
on a machine with around 32GB memory (for example, AWS c7g.4xl), mlperf Resnet50 offline inference fails with OutOfMemory on TF 2.14 and nightly wheels. The same benchmark works fine on TF 2.13.
I've root-caused the issue to the following commit that introduced inter op scheduler to improve performance for models with parallel ops. While this is improving the perf by 15% for MLPerf Resnet50 batch mode on r7g.16xl, it is increasing the memory footprint by 2.5x (from 25GB to 67GB).
~~~
commit d0cb12441747ef9fb14137cb99f0b6a17e22b5e4
Author: David Svantesson <[email protected]>
Date: Tue Jul 25 09:33:40 2023 -0700
PR #61235: Add inter scheduler support on AArch64
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/61235
This PR adds support for inter op scheduler in the oneDNN + ACL build. It enables the creation of more than 1 scheduler inside ACL to increase performance of models with parallel ops.
For benchmarked NLP models the average performance increase is 9%, for CV classification models its around 2%.
The below benchmarks were done with the following PR’s applied as patches:
#60026, #60723, #61110, #61114, #61093, #61123
~~~
We need to reduce the memory footprint or let the max limit be set at runtime, something similar to LRU cache capacity.
### Standalone code to reproduce the issue
```shell
# install MLcomons inference repo
cd $HOME
git clone https://github.com/mlcommons/inference.git
cd inference
git checkout v2.0
cd inference/loadgen
CFLAGS="-std=c++14" python3 setup.py bdist_wheel
pip3 install dist/*.whl
# download the resnet50 model and the dataset
wget https://zenodo.org/record/2535873/files/resnet50_v1.pb
ck pull repo:ck-env
echo 0 | ck install package --tags=image-classification,dataset,imagenet,aux
echo 1 | ck install package --tags=image-classification,dataset,imagenet,val
cp /CK-TOOLS/dataset-imagenet-ilsvrc2012-aux-from.berkeley/val.txt \
/CK-TOOLS/dataset-imagenet-ilsvrc2012-val-min/val_map.txt
# Run resnet50 inference in offline mode
export DATA_DIR=/CK-TOOLS/dataset-imagenet-ilsvrc2012-val-min
export MODEL_DIR=$HOME/
cd $HOME/inference/vision/classification_and_detection$ ./run_local.sh tf resnet50 cpu --scenario=Offline
```
### Relevant log output
```shell
INFO:main:starting TestScenario.Offline
./run_local.sh: line 13: 50519 Killed python python/main.py --profile $profile $common_opt --model $model_path $dataset --output $OUTPUT_DIR $EXTRA_OPS $@
```
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I_kwDOArmXAs52fYOc
| 62,366 |
Tensorflow weird GPU memory usage universal-sentence-encoder
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[
"@nitishvu,\r\nI was facing a different issue while executing the above mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1e0f62846cab4956ca733d98b81bb4a3/untitled1512.ipynb). Also clear_session() is not enough to reset states and ensure reproducibility. Also needed the Set (& reset) random seeds, Reset TensorFlow default graph and delete the previous model.\r\n\r\nhttps://github.com/keras-team/keras/issues/9379\r\nhttps://stackoverflow.com/questions/58453793/the-clear-session-method-of-keras-backend-does-not-clean-up-the-fitting-data\r\n\r\nThank you!\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62366\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62366\">No</a>\n"
] | 2023-11-10T16:19:25 | 2023-11-30T01:49:30 | 2023-11-30T01:49:10 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.14.0-rc1-21-g4dacf3f368e 2.14.0
### Custom code
Yes
### OS platform and distribution
ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9.17
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current behavior?
Hi I am new to TensorFlow, GPU,Models everything :)
I am using the below code to generate text embeddings
```
texts = [ "This is the first sentence.","Another sentence for embedding.","Embeddings are useful for NLP."]
import tensorflow as tf
import numpy as np
# Path to the saved USE model directory
use_model_path = "universal-sentence-encoder"
# Configure GPU memory growth
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
use_model = tf.saved_model.load(use_model_path)
embeddings_list = []
embeddings = use_model(texts)
embeddings_list = embeddings.numpy().tolist()
#clear session
tf.keras.backend.clear_session()
print(embeddings_list)
```
If I execute this in a for loop with a list of 512kb texts, GPU memory increases exponentially. Even after adding tf.keras.backend.clear_session() in every iteration, I see the same behavior.
I am expecting it to release memory on each iteration.
I know we can do this in batches but I am planning to us this as a REST API.
how can I optimize this?
### Standalone code to reproduce the issue
```shell
texts = [ "This is the first sentence.","Another sentence for embedding.","Embeddings are useful for NLP."]
import tensorflow as tf
import numpy as np
# Path to the saved USE model directory
use_model_path = "universal-sentence-encoder"
# Configure GPU memory growth
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
use_model = tf.saved_model.load(use_model_path)
embeddings_list = []
embeddings = use_model(texts)
embeddings_list = embeddings.numpy().tolist()
#clear session
tf.keras.backend.clear_session()
print(embeddings_list)
```
### Relevant log output
_No response_
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no classess in AAR of tensorflow-lite-support in versions 0.3.+
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[
"It is intentional that tensorflow-lite-support is empty.\r\nThe Java classes have moved into tensorflow-lite-support-api which tensorflow-lite-support has a dependency on.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-11-10T13:58:46 | 2023-11-30T01:49:12 | 2023-11-30T01:49:11 |
NONE
| null | null | null |
I wanted to use AAR file from Maven for version 0.4.4
https://repo1.maven.org/maven2/org/tensorflow/tensorflow-lite-support/0.4.4/
but in Android it couldn't find class TensorBuffer.
i checked AAR (changed to ZIP and unpacked and looked into Jar and found no class), also low volume of file <1KB made me suspicious.
when I rolled back to 0.2.0-rc2 the code worked ok. this was the latest AAR file that was heavier than 1KB.
maybe something wrong in the AAR deployer for this part of TensorFlowLite?
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Issue created for Rollback of PR #6657: fix zlib dependency (current one fails with 403 on ./configure)
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[] | 2023-11-09T18:24:00 | 2023-11-09T18:24:05 | null |
NONE
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Merged PR #6657 is rolled back in 28d130930b6b5c1dc62447edc3b8e435719b7470.
Please follow up with the reviewer and close this issue once its resolved.
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Can't load model with tf v2.14 if the model was saved with previous tf versions
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[
"Hi **@ofirgo**,\r\nCould you please check with the latest stable version. I checked with versions Tf=2.13, 2.14 and tf-nightly too. It is working fine for me for both the models. It is recommended to save and load the models in the same TF version which will not conflict in the outcome. I attached a [gist ](https://colab.research.google.com/gist/Venkat6871/ec236a020b77af3f13e5bffb43348869/62363_2-13-2-14-nightly.ipynb)for your reference.\r\n\r\nThank you!",
"Hi @Venkat6871, \r\nThank you for your response.\r\n\r\nSaving and loading with the same TF version works fine for me (in all 3 aforementioned versions).\r\n\r\nMy issue is when trying to load with TF 2.14 a model that was originally saved in TF 2.13 (I have a large database of saved models that I wish to not be needing to recreate when upgrading a TF version).\r\n\r\nIs there a way to solve this issue to preserve backward compatibility?",
"Have you tried \"fitting\" this model instead of loading previous weights?\r\n\r\nI ask this because I get input_shape errors on fit() even though they are correctly specified in a keras.layer. I can \"build\" the model and display its summary(). However, when I use fit(), whether designed through subclassing or functional API, the model seems to _forget_ that I specified input_shape.\r\n\r\nI don't get this issue when I downgrade TF to v2.12.0 (i.e. the last time I ran it).\r\n\r\nI wonder if this is related???\r\n\r\nOf note, I've been running this in Colab, if that's of any importance.",
"Hi @ofirgo ,\r\n\r\nThe issue addressed in Keras #[18830](https://github.com/keras-team/keras/issues/18830) already. Could you please close the issue if resolved.If not please track it at keras repo itself. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62363\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62363\">No</a>\n"
] | 2023-11-09T10:16:47 | 2024-02-07T01:46:43 | 2024-02-07T01:46:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.14
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Trying to load a Keras model that was saved using tf version < 2.14 (e.g., 2.13), with tf 2.14, gives the following error:
> File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_api.py", line 254, in load_model
return saving_lib.load_model(
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 281, in load_model
raise e
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 269, in load_model
_load_state(
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 466, in _load_state
_load_container_state(
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 534, in _load_container_state
_load_state(
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 435, in _load_state
trackable.load_own_variables(weights_store.get(inner_path))
File "/data/projects/swat/envs/eladc/mct_tf_python310/lib/python3.10/site-packages/keras/src/engine/base_layer.py", line 3531, in load_own_variables
raise ValueError(
ValueError: Layer 'conv2d_1' expected 2 variables, but received 0 variables during loading. Expected: ['conv2d_1/kernel:0', 'conv2d_1/bias:0']
Trying to load the same model with tf-nightly gives a different error:
> TypeError: Could not deserialize class 'Functional' because its parent module keras.src.engine.functional cannot be imported. Full object config: {'module': 'keras.src.engine.functional', 'class_name': 'Functional', 'config': {'name': 'tf213', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': [None, 8, 8, 3], 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Conv2D', 'config': {'name': 'conv2d', 'trainable': True, 'dtype': 'float32', 'filters': 3, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': [None, 8, 8, 3]}, 'name': 'conv2d', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}, {'module': 'keras.layers', 'class_name': 'Conv2D', 'config': {'name': 'conv2d_1', 'trainable': True, 'dtype': 'float32', 'filters': 3, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': [None, 6, 6, 3]}, 'name': 'conv2d_1', 'inbound_nodes': [[['conv2d', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['conv2d_1', 0, 0]]}, 'registered_name': 'Functional', 'build_config': {'input_shape': [None, 8, 8, 3]}}
The issue is also reproduced when saving and loading models (MobileNetV2) from keras.applications.
This only happens if the model has at least two layers of the same type.
### Standalone code to reproduce the issue
```shell
Run this code with tf v2.13:
`
import tensorflow as tf
_in = tf.keras.layers.Input(shape=(8, 8, 3))
x = _in
x = tf.keras.layers.Conv2D(3, 3)(x)
x = tf.keras.layers.Conv2D(3, 3)(x)
_out = x
model = tf.keras.Model(inputs=_in, outputs=_out)
model.save('model_213.keras')
`
Run this code with tf v2.14:
`
loaded_model = tf.keras.models.load_model('model_213.keras')
`
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5e_RMQ
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Pass call context for GEMM calls
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[
"> Please write a complete PR description - I have no context for this change\r\n\r\nSorry, missed that. Just added that info. \r\n\r\nPlease let me know if you have any other questions. \r\n\r\nThanks!",
"@akuegel I can see some failing CIs, but they are all internal. Can you please share what's failing?\r\nThanks!",
"> @akuegel I can see some failing CIs, but they are all internal. Can you please share what's failing? Thanks!\r\n\r\nThe internal comment on the change:\r\n\r\n\"This triggers the full fallback mode of the TF2XLA bridge from any use of tf.MatMul in a program (rather than just running the XlaBuilder kernels for the MatMul Ops).\"\r\n\r\nBut it seems this would need to be fixed internally by updating the TF dialect.",
"> > @akuegel I can see some failing CIs, but they are all internal. Can you please share what's failing? Thanks!\r\n> \r\n> The internal comment on the change:\r\n> \r\n> \"This triggers the full fallback mode of the TF2XLA bridge from any use of tf.MatMul in a program (rather than just running the XlaBuilder kernels for the MatMul Ops).\"\r\n> \r\n> But it seems this would need to be fixed internally by updating the TF dialect.\r\n\r\n@akuegel ok, let me know if any changes are needed from me. Thanks!",
"@rahulbatra85, Could you provide a draft for the docstring of the newly added grad_a, grad_b arguments in this CL? The linter is complaining about lack of Args entry in the docstring, and I'll just add the docstring in the internal clone of the PR before submitting it.",
"It turns out the newly added attribute interacts with a bunch of other systems, such as quantization and TFL. I suppose the correct behavior is to just ignore the new attributes in those cases?",
"> It turns out the newly added attribute interacts with a bunch of other systems, such as quantization and TFL. I suppose the correct behavior is to just ignore the new attributes in those cases?\r\n\r\n@rainwoodman \r\nyes, the default is false.",
"> @rahulbatra85, Could you provide a draft for the docstring of the newly added grad_a, grad_b arguments in this CL? The linter is complaining about lack of Args entry in the docstring, and I'll just add the docstring in the internal clone of the PR before submitting it.\r\n\r\ngrad_a, grad_b can be set to indicate if the op is done as part of backward pass ie gradient calculation. The idea is if it's set/unset then some optimizations can be done by the library, etc.",
"Thanks! I'll go with the following then:\r\n ```\r\n grad_a: Set it to `True` to hint that Tensor `a` is for the backward pass.\r\n grad_b: Set it to `True` to hint that Tensor `b` is for the backward pass.\r\n```\r\n\r\nIt may take a few additional days for it to go through the review process because of the larger footprint of the changes.\r\n\r\nBe aware that the MLIR bridge (which is the default) doesn't yet default to use the XLABuilder kernels you have added. However the TableGen HLO lowering rules (preferred by the MLIR bridge) do not know how to pass down the new grad attributes -- I only fixed them to ignore the new attrs on the internal change. There is ongoing work to change the MLIR bridge to prefer XLABuilder kernels. (CC @changm )",
"> Thanks! I'll go with the following then:\r\n> \r\n> ```\r\n> grad_a: Set it to `True` to hint that Tensor `a` is for the backward pass.\r\n> grad_b: Set it to `True` to hint that Tensor `b` is for the backward pass.\r\n> ```\r\n> \r\n> It may take a few additional days for it to go through the review process because of the larger footprint of the changes.\r\n> \r\n> Be aware that the MLIR bridge (which is the default) doesn't yet default to use the XLABuilder kernels you have added. However the TableGen HLO lowering rules (preferred by the MLIR bridge) do not know how to pass down the new grad attributes -- I only fixed them to ignore the new attrs on the internal change. There is ongoing work to change the MLIR bridge to prefer XLABuilder kernels. (CC @changm )\r\n\r\n@rainwoodman ok, thanks!",
"We will rollback the changes because some targets are failing due to the newly added attributes apparently become mandatory in certain cases. I will post an update for the rollforward before next week."
] | 2023-11-09T03:37:58 | 2023-12-07T17:30:37 | 2023-12-06T18:42:38 |
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Adds gradient flags to ops to indicate whether forward or backward pass
Passes this call context to XLA for GEMM calls(See this XLA PR)
https://github.com/openxla/xla/pull/6533
XLA passes this information to library call
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Discrepancy in training accuracy for CNN on MNIST dataset between Apple Silicon and colab
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[
"colab training output:\r\n```\r\nEpoch 1/20\r\n938/938 [==============================] - 9s 8ms/step - loss: 0.3648 - accuracy: 0.8979 - val_loss: 0.2319 - val_accuracy: 0.9322\r\nEpoch 2/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.1963 - accuracy: 0.9444 - val_loss: 0.1679 - val_accuracy: 0.9508\r\nEpoch 3/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.1470 - accuracy: 0.9582 - val_loss: 0.1314 - val_accuracy: 0.9609\r\nEpoch 4/20\r\n938/938 [==============================] - 3s 3ms/step - loss: 0.1187 - accuracy: 0.9663 - val_loss: 0.1143 - val_accuracy: 0.9648\r\nEpoch 5/20\r\n938/938 [==============================] - 3s 3ms/step - loss: 0.0998 - accuracy: 0.9720 - val_loss: 0.1046 - val_accuracy: 0.9674\r\nEpoch 6/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0866 - accuracy: 0.9757 - val_loss: 0.0940 - val_accuracy: 0.9706\r\nEpoch 7/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0766 - accuracy: 0.9787 - val_loss: 0.0872 - val_accuracy: 0.9735\r\nEpoch 8/20\r\n938/938 [==============================] - 3s 4ms/step - loss: 0.0686 - accuracy: 0.9811 - val_loss: 0.0834 - val_accuracy: 0.9744\r\nEpoch 9/20\r\n938/938 [==============================] - 3s 3ms/step - loss: 0.0617 - accuracy: 0.9831 - val_loss: 0.0809 - val_accuracy: 0.9746\r\nEpoch 10/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.0562 - accuracy: 0.9844 - val_loss: 0.0786 - val_accuracy: 0.9746\r\nEpoch 11/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0511 - accuracy: 0.9861 - val_loss: 0.0760 - val_accuracy: 0.9764\r\nEpoch 12/20\r\n938/938 [==============================] - 3s 3ms/step - loss: 0.0467 - accuracy: 0.9872 - val_loss: 0.0768 - val_accuracy: 0.9757\r\nEpoch 13/20\r\n938/938 [==============================] - 3s 4ms/step - loss: 0.0426 - accuracy: 0.9886 - val_loss: 0.0761 - val_accuracy: 0.9760\r\nEpoch 14/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.0396 - accuracy: 0.9895 - val_loss: 0.0731 - val_accuracy: 0.9757\r\nEpoch 15/20\r\n938/938 [==============================] - 3s 4ms/step - loss: 0.0361 - accuracy: 0.9906 - val_loss: 0.0706 - val_accuracy: 0.9780\r\nEpoch 16/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0335 - accuracy: 0.9913 - val_loss: 0.0705 - val_accuracy: 0.9781\r\nEpoch 17/20\r\n938/938 [==============================] - 3s 3ms/step - loss: 0.0310 - accuracy: 0.9922 - val_loss: 0.0697 - val_accuracy: 0.9777\r\nEpoch 18/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 0.0286 - accuracy: 0.9932 - val_loss: 0.0692 - val_accuracy: 0.9770\r\nEpoch 19/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0265 - accuracy: 0.9938 - val_loss: 0.0689 - val_accuracy: 0.9780\r\nEpoch 20/20\r\n938/938 [==============================] - 4s 4ms/step - loss: 0.0245 - accuracy: 0.9943 - val_loss: 0.0673 - val_accuracy: 0.9787\r\n```",
"mac training output:\r\n```\r\nEpoch 1/20\r\n 10/938 [..............................] - ETA: 5s - loss: 1.9703 - accuracy: 0.3406 2023-11-08 20:41:34.161484: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n938/938 [==============================] - ETA: 0s - loss: 0.4114 - accuracy: 0.88262023-11-08 20:41:38.610013: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n938/938 [==============================] - 5s 5ms/step - loss: 0.4114 - accuracy: 0.8826 - val_loss: 0.3143 - val_accuracy: 0.9107\r\nEpoch 2/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.3101 - accuracy: 0.9133 - val_loss: 0.3021 - val_accuracy: 0.9165\r\nEpoch 3/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 0.2998 - accuracy: 0.9161 - val_loss: 0.2989 - val_accuracy: 0.9155\r\nEpoch 4/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.2983 - accuracy: 0.9166 - val_loss: 0.3063 - val_accuracy: 0.9144\r\nEpoch 5/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.3038 - accuracy: 0.9149 - val_loss: 0.3682 - val_accuracy: 0.8932\r\nEpoch 6/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.3150 - accuracy: 0.9122 - val_loss: 0.3739 - val_accuracy: 0.8932\r\nEpoch 7/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.3397 - accuracy: 0.9064 - val_loss: 0.4372 - val_accuracy: 0.8834\r\nEpoch 8/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.3819 - accuracy: 0.8998 - val_loss: 0.4067 - val_accuracy: 0.8965\r\nEpoch 9/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.4602 - accuracy: 0.8902 - val_loss: 0.8379 - val_accuracy: 0.8237\r\nEpoch 10/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.6021 - accuracy: 0.8800 - val_loss: 1.0389 - val_accuracy: 0.8353\r\nEpoch 11/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.7349 - accuracy: 0.8748 - val_loss: 1.0670 - val_accuracy: 0.8420\r\nEpoch 12/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 0.9430 - accuracy: 0.8689 - val_loss: 1.2278 - val_accuracy: 0.8605\r\nEpoch 13/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 1.1816 - accuracy: 0.8641 - val_loss: 1.4626 - val_accuracy: 0.8539\r\nEpoch 14/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 1.5326 - accuracy: 0.8634 - val_loss: 1.5809 - val_accuracy: 0.8603\r\nEpoch 15/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 1.9404 - accuracy: 0.8633 - val_loss: 1.8023 - val_accuracy: 0.8647\r\nEpoch 16/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 2.4296 - accuracy: 0.8607 - val_loss: 3.9961 - val_accuracy: 0.8077\r\nEpoch 17/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 3.2530 - accuracy: 0.8572 - val_loss: 3.5723 - val_accuracy: 0.8546\r\nEpoch 18/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 4.0268 - accuracy: 0.8612 - val_loss: 5.4699 - val_accuracy: 0.8384\r\nEpoch 19/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 5.1973 - accuracy: 0.8587 - val_loss: 7.9400 - val_accuracy: 0.7539\r\nEpoch 20/20\r\n938/938 [==============================] - 4s 5ms/step - loss: 6.6197 - accuracy: 0.8568 - val_loss: 14.9388 - val_accuracy: 0.7909\r\n```",
"@N33MO,\r\nThank you for reporting the issue. We are currently investigating the issue, and I kindly request some time to thoroughly analyze the problem in order to offer a resolution and also could you please confirm is this happening in tf v2.14 as well? Thank you!",
"Hi @tilakrayal,\r\nThe issue still show on v2.14: https://colab.research.google.com/drive/1bQe1F4PO2G-kaLWvDQcnqzLMsddoUux9\r\nHere's the package installed on the Mac machine.\r\n<img width=\"709\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/27294839/b2f1b65a-8517-40b4-a783-851b07947746\">\r\n",
"@N33MO @tilakrayal Seems like the issue is happening when using the Apple Silicon GPU as I get nearly identical results as colab on my M1 Max if I hide the GPU using the below code(tf v2.14):\r\n`hw = tf.config.get_visible_devices()`\r\n`tf.config.set_visible_devices(hw[0])`\r\n\r\nMac output:\r\n\r\n> [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\nWARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.SGD` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.SGD`.\r\nEpoch 1/20\r\n938/938 [==============================] - 1s 824us/step - loss: 0.3648 - accuracy: 0.8979 - val_loss: 0.2319 - val_accuracy: 0.9322\r\nEpoch 2/20\r\n938/938 [==============================] - 1s 745us/step - loss: 0.1963 - accuracy: 0.9443 - val_loss: 0.1677 - val_accuracy: 0.9510\r\nEpoch 3/20\r\n938/938 [==============================] - 1s 743us/step - loss: 0.1471 - accuracy: 0.9583 - val_loss: 0.1313 - val_accuracy: 0.9605\r\nEpoch 4/20\r\n938/938 [==============================] - 1s 736us/step - loss: 0.1187 - accuracy: 0.9662 - val_loss: 0.1142 - val_accuracy: 0.9647\r\nEpoch 5/20\r\n938/938 [==============================] - 1s 752us/step - loss: 0.0998 - accuracy: 0.9719 - val_loss: 0.1045 - val_accuracy: 0.9673\r\nEpoch 6/20\r\n938/938 [==============================] - 1s 744us/step - loss: 0.0866 - accuracy: 0.9757 - val_loss: 0.0937 - val_accuracy: 0.9708\r\nEpoch 7/20\r\n938/938 [==============================] - 1s 743us/step - loss: 0.0766 - accuracy: 0.9787 - val_loss: 0.0872 - val_accuracy: 0.9733\r\nEpoch 8/20\r\n938/938 [==============================] - 1s 745us/step - loss: 0.0685 - accuracy: 0.9812 - val_loss: 0.0835 - val_accuracy: 0.9746\r\nEpoch 9/20\r\n938/938 [==============================] - 1s 731us/step - loss: 0.0617 - accuracy: 0.9831 - val_loss: 0.0809 - val_accuracy: 0.9750\r\nEpoch 10/20\r\n938/938 [==============================] - 1s 737us/step - loss: 0.0561 - accuracy: 0.9845 - val_loss: 0.0788 - val_accuracy: 0.9750\r\nEpoch 11/20\r\n938/938 [==============================] - 1s 732us/step - loss: 0.0510 - accuracy: 0.9860 - val_loss: 0.0755 - val_accuracy: 0.9763\r\nEpoch 12/20\r\n938/938 [==============================] - 1s 752us/step - loss: 0.0467 - accuracy: 0.9872 - val_loss: 0.0766 - val_accuracy: 0.9763\r\nEpoch 13/20\r\n938/938 [==============================] - 1s 741us/step - loss: 0.0426 - accuracy: 0.9886 - val_loss: 0.0762 - val_accuracy: 0.9761\r\nEpoch 14/20\r\n938/938 [==============================] - 1s 745us/step - loss: 0.0395 - accuracy: 0.9894 - val_loss: 0.0727 - val_accuracy: 0.9761\r\nEpoch 15/20\r\n938/938 [==============================] - 1s 745us/step - loss: 0.0360 - accuracy: 0.9906 - val_loss: 0.0707 - val_accuracy: 0.9779\r\nEpoch 16/20\r\n938/938 [==============================] - 1s 745us/step - loss: 0.0334 - accuracy: 0.9913 - val_loss: 0.0707 - val_accuracy: 0.9773\r\nEpoch 17/20\r\n938/938 [==============================] - 1s 740us/step - loss: 0.0310 - accuracy: 0.9921 - val_loss: 0.0700 - val_accuracy: 0.9773\r\nEpoch 18/20\r\n938/938 [==============================] - 1s 747us/step - loss: 0.0286 - accuracy: 0.9933 - val_loss: 0.0698 - val_accuracy: 0.9769\r\nEpoch 19/20\r\n938/938 [==============================] - 1s 742us/step - loss: 0.0265 - accuracy: 0.9937 - val_loss: 0.0690 - val_accuracy: 0.9783\r\nEpoch 20/20\r\n938/938 [==============================] - 1s 754us/step - loss: 0.0246 - accuracy: 0.9942 - val_loss: 0.0674 - val_accuracy: 0.9792",
"I'm encountering not exactly the same but I think related issue. \r\nI use MacBook Air with M2. Running the same code on CPU and on GPU (with `tensorflow-metal`) gives different results. On GPU it causes to model training fail. \r\nPlease see all the details in related [issue](https://github.com/keras-team/tf-keras/issues/140#issuecomment-1824331706).\r\n",
"@tilakrayal I tested the code on v2.15 as well, the issue still exists and is not resolved as mentioned [here](https://github.com/keras-team/tf-keras/issues/140).\r\n @romanilchyshyn Regarding the running time of CPU vs GPU, in order to take advantage of GPU you should have sufficient load. For basic learning CPU is faster.",
"I have reproduced the same issue with tf2.15 as well using M2 MacBook Air GPU training... increasing loss, compared to CPU mode where everything seems fine...",
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.14, v2.15 and also on the Mac-OS M1. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/713489165dd196cc354db8d64589ec02/tf_cnn_mnist_example_colab.ipynb) and the logs below.\r\n\r\nEpoch 11/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 0.7349 - accuracy: 0.8748 - val_loss: 1.0670 - val_accuracy: 0.8420\r\nEpoch 12/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 0.9430 - accuracy: 0.8689 - val_loss: 1.2278 - val_accuracy: 0.8605\r\nEpoch 13/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 1.1816 - accuracy: 0.8641 - val_loss: 1.4626 - val_accuracy: 0.8539\r\nEpoch 14/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 1.5326 - accuracy: 0.8634 - val_loss: 1.5809 - val_accuracy: 0.8603\r\nEpoch 15/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 1.9404 - accuracy: 0.8633 - val_loss: 1.8023 - val_accuracy: 0.8647\r\nEpoch 16/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 2.4296 - accuracy: 0.8607 - val_loss: 3.9961 - val_accuracy: 0.8077\r\nEpoch 17/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 3.2530 - accuracy: 0.8572 - val_loss: 3.5723 - val_accuracy: 0.8546\r\nEpoch 18/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 4.0268 - accuracy: 0.8612 - val_loss: 5.4699 - val_accuracy: 0.8384\r\nEpoch 19/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 5.1973 - accuracy: 0.8587 - val_loss: 7.9400 - val_accuracy: 0.7539\r\nEpoch 20/20\r\n938/938 [==============================] - 5s 5ms/step - loss: 6.6197 - accuracy: 0.8568 - val_loss: 14.9388 - val_accuracy: 0.7909",
"I am also seeing radically different (and worse) results with `tensorflow-metal` using the GPU on an M3 Max than I do just with the CPU.\r\n\r\nIt is worth noting that the problems get worse as the network gets deeper. If I test using a simple model like:\r\n```python\r\nmodel = tf.keras.models.Sequential([\r\n tf.keras.Input(name=\"x\", shape=(28, 28, 1)),\r\n tf.keras.layers.Flatten(),\r\n tf.keras.layers.Dense(128, activation='relu'),\r\n tf.keras.layers.Dropout(0.2),\r\n tf.keras.layers.Dense(10)\r\n])\r\n```\r\nthen I get similar results to the ones posted above. If I try a more complex model such as:\r\n```python\r\nmodel2 = tf.keras.models.Sequential([\r\n tf.keras.Input(name=\"x\", shape=(28, 28, 1)),\r\n tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\r\n tf.keras.layers.MaxPooling2D((2, 2)),\r\n tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\r\n tf.keras.layers.MaxPooling2D((2, 2)),\r\n tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\r\n tf.keras.layers.Flatten(),\r\n tf.keras.layers.Dense(64, activation='relu'),\r\n tf.keras.layers.Dropout(0.2),\r\n tf.keras.layers.Dense(10, name=\"y\")\r\n])\r\n```\r\nthen with the CPU I'm getting 99.5% accuracy after 10 epochs whereas with the GPU using Metal the accuracy starts on the 85% range but plummets to around 11% after 10 epochs and doesn't recover.",
"> I am also seeing radically different (and worse) results with `tensorflow-metal` 2.1.1 using the GPU on an M3 Max than I do just with the CPU.\r\n> \r\n> It is worth noting that the problems get worse as the network gets deeper. If I test using a simple model like:\r\n> \r\n> ```python\r\n> model = tf.keras.models.Sequential([\r\n> tf.keras.Input(name=\"x\", shape=(28, 28, 1)),\r\n> tf.keras.layers.Flatten(),\r\n> tf.keras.layers.Dense(128, activation='relu'),\r\n> tf.keras.layers.Dropout(0.2),\r\n> tf.keras.layers.Dense(10)\r\n> ])\r\n> ```\r\n> \r\n> then I get similar results to the ones posted above. If I try a more complex model such as:\r\n> \r\n> ```python\r\n> model2 = tf.keras.models.Sequential([\r\n> tf.keras.Input(name=\"x\", shape=(28, 28, 1)),\r\n> tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\r\n> tf.keras.layers.MaxPooling2D((2, 2)),\r\n> tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\r\n> tf.keras.layers.MaxPooling2D((2, 2)),\r\n> tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\r\n> tf.keras.layers.Flatten(),\r\n> tf.keras.layers.Dense(64, activation='relu'),\r\n> tf.keras.layers.Dropout(0.2),\r\n> tf.keras.layers.Dense(10, name=\"y\")\r\n> ])\r\n> ```\r\n> \r\n> then with the CPU I'm getting 99.5% accuracy after 10 epochs whereas with the GPU using Metal the accuracy starts on the 85% range but plummets to around 11% after 10 epochs and doesn't recover.\r\n\r\nRegarding `model2` as mentioned above:\r\n\r\nFor me, on Apple M1 Pro w/ Ventura, using GPU, val_loss stays pegged at 5.0967 across 15 epochs, while w/o GPU it goes from 13.5264 - 14.4612.\r\n\r\n",
"There appears to be something significantly wrong with the back propagation implementation on Metal. Running inference on snapshots from elsewhere delivers identical results with useful performance improvements. When training categorisation models that happily converge on the CPU, when you try using Metal the accuracy degenerates towards not much better than guesswork. The time it takes to degenerate depends on the model depth. \r\n\r\nAs it stands, training on Metal is simply broken and `tensorflow-metal` is only really useful for inference.",
"Same here.\r\nM3 Pro \r\nTF 2.15.0\r\ntensorflow-metal 1.1.0\r\n\r\nVery annoing..."
] | 2023-11-09T02:46:34 | 2024-02-11T13:00:18 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13
### Custom code
Yes
### OS platform and distribution
macOS 14.1
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Running a simple example training a CNN using MNIST dataset usnig tensorflow, the training accuracy/loss yield very different results.
### Standalone code to reproduce the issue
```shell
colab output: https://colab.research.google.com/drive/1jE9qmdaGXbC9lt69kUUzzzJq7dZJnVJ8?usp=sharing
mac output: https://colab.research.google.com/drive/1mr9JPqX7XzVVevdyueiGpQu8Hc7eBHw1
```
### Relevant log output
_No response_
|
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TFLite Interpreter fails to load fp32/ fp16 model on iPhone with CoreML or Metal Delegate in Swift
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[
"Hi @hemangchawla, I'm trying to recreate the converted model but I don't know how your params are defined, please review this colab: https://colab.sandbox.google.com/gist/pkgoogle/4ddd628342ee82a0a0eaf946a34f5ca4/62360.ipynb. If you can finish how you get the converted model and revert it back to me, that'll be the easiest. Thanks!",
"Hi @pkgoogle, I have updated the collab. In summary, I use the ResNet50 retinanetmodel from the tf model garden. \r\nI convert it to tflite and load into the iOS app (have provided reference to an example iOS app from google which can be used). And when using the coreml or tf-metal delegates, I get the error that `Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors`",
"Hi @hemangchawla, I think you forgot to share your colab? You can save a new gist and share it.",
"Hi @pkgoogle , please find the notebook [here](https://colab.research.google.com/gist/hemangchawla/865e06ea33b2925ed5e19ed15d206b34/62360.ipynb)",
"Hi @hemangchawla, I was able to create the model file, but my edits to the project are running into some issues importing the MetalDelegate. Can you do me a favor and go ahead and fork https://github.com/https-deeplearning-ai/tensorflow-2-public/tree/main, make the changes to the project in question and then let me know where the edited repo/code is? This will help me best mirror your environment to reproduce this. Thanks for your help.",
"Hi @pkgoogle,\r\n\r\nYou need to replace `pod 'TensorFlowLiteSwift'` in the podfile with either\r\n`pod 'TensorFlowLiteSwift', '~>2.13.0, :subspecs => ['Metal', 'CoreML']` or \r\n`pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['Metal', 'CoreML']`, \r\nand then run `pod install` again.\r\n\r\nIn the`ModelDataHandler`, you need to replace\r\n```\r\nvar options = InterpreterOptions()\r\noptions.threadCount = threadCount\r\ndo {\r\n // Create the `Interpreter`.\r\n interpreter = try Interpreter(modelPath: modelPath, options: options)\r\n // Allocate memory for the model's input `Tensor`s.\r\n try interpreter.allocateTensors()\r\n} catch let error {\r\n print(\"Failed to create the interpreter with error: \\(error.localizedDescription)\")\r\n return nil\r\n}\r\n```\r\nwith \r\n```\r\nvar options = InterpreterOptions()\r\noptions.threadCount = threadCount\r\nvar delegate = MetalDelegate()\r\ndo {\r\n // Create the `Interpreter`.\r\n interpreter = try Interpreter(modelPath: modelPath, options: options, delegates: [delegate])\r\n // Allocate memory for the model's input `Tensor`s.\r\n try interpreter.allocateTensors()\r\n} catch let error {\r\n print(\"Failed to create the interpreter with error: \\(error.localizedDescription)\")\r\n return nil\r\n}\r\n```\r\n\r\nOf course you need to add in `ModelDataHandler` your retinanet model enum\r\n```\r\n/// Information about the MobileNet SSD model.\r\nenum MobileNetSSD {\r\n static let modelInfo: FileInfo = (name: \"detect\", extension: \"tflite\")\r\n static let labelsInfo: FileInfo = (name: \"labelmap\", extension: \"txt\")\r\n}\r\n\r\n// ADD THIS:\r\n/// Information about RetinaNet model. \r\nenum RetinaNet {\r\n static let modelInfo: FileInfo = (name: \"retinanet_model\", extension: \"tflite\")\r\n static let labelsInfo: FileInfo = (name: \"labelmap\", extension: \"txt\")\r\n}\r\n```\r\n\r\nand the `retinanet_model.tflite` file must be in the appropriate location similar to the `detect.tflite` file (see `RunScripts/download_models.sh`)\r\n\r\nFinally, you need to update the `ViewController` where you replace `MobileNetSSD.modelInfo` and `MobileNetSSD.labelsInfo` with `RetinaNet.modelInfo` and `RetinaNet.labelsInfo` respectively. ",
"Thanks for that, I was able to replicate your issue with those steps, summarized here for convenience:\r\n\r\n1. Create The RetinaNet model via this colab: [here](https://colab.research.google.com/gist/hemangchawla/865e06ea33b2925ed5e19ed15d206b34/62360.ipynb)\r\n2. Get the source code for the project:\r\n```\r\ngit clone https://github.com/https-deeplearning-ai/tensorflow-2-public.git\r\ncd tensorflow-2-public/C2_Device-based-TF-lite/W3/ungraded?labs/ios_apps/object_detection # this is the root of the project in question\r\n```\r\n3. Add the retina_net model to the project:\r\n```\r\n# If you're at the root of the project\r\ncp path/to/retinanet_model.tflite ObjectDetection/Model\r\n```\r\n4. Edit the files as stated in the above comment (I am unable to upload the modified files, but your comment is sufficiently detailed)\r\n5. reinstall the pod\r\n```\r\npod install\r\n```\r\n6. run the project, error in the console:\r\n\r\n\r\n@yishuangP, can you please take a look? Thanks.",
"Hi @pkgoogle , @yishuangP could you please share if you had some time to take a look at this? Would really appreciate your support on this. ",
"@yishuangP @pkgoogle could it be the while loop within the non maximal suppression that is causing it to have issues with converting the dynamic sizes? ",
"Hi @hemangchawla, can you link the code in question for your hypothesis?",
"Hi @pkgoogle, \r\n\r\nPlease see:\r\nhttps://github.com/tensorflow/models/blob/master/official/vision/modeling/factory.py#L321\r\nin \r\nhttps://github.com/tensorflow/models/blob/master/official/vision/modeling/factory.py#L260\r\n\r\nThe nms is applied here:\r\nhttps://github.com/tensorflow/models/blob/master/official/vision/modeling/retinanet_model.py#L187\r\n\r\nThe detection_generator is found here:\r\nhttps://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/detection_generator.py#L1114"
] | 2023-11-09T00:07:09 | 2023-12-11T00:11:58 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.13
- Mobile Device: iPhone 13 Pro
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
I have finetuned RetinaNet detection model from the model garden.
The saved model takes input `(1, None, None, 3)`, even though I saved it as follows:
```
from official.vision.serving import export_saved_model_lib
export_saved_model_lib.export_inference_graph(
input_type='image_tensor',
batch_size=1,
input_image_size=list(args.inp_size),
params=exp_config,
checkpoint_path=tf.train.latest_checkpoint(args.model_dir),
export_dir=args.export_dir)
```
where `arg.inp_size` is `[256, 256, 3]`.
However I convert using concrete functions to tflite model that takes input (1, 256, 256, 3)
```
with open(os.path.join(saved_model_dir, "params.yaml"), "r") as stream:
params = yaml.safe_load(stream)
h, w, c = tuple(params['task']['model']['input_size'])[:3]
b = 1 # batch size is 1.
print(f"Saved model size will be set to [{b, h, w, c}]")
model = tf.saved_model.load(saved_model_dir)
concrete_func = model.signatures[ tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape([b, h, w, c])
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
tflite_model = converter.convert()
```
The result of `tf.lite.experimental.Analyzer.analyze(model_content=tflite_model, gpu_compatibility=True)` can be found [here](https://drive.google.com/file/d/1z9575V_rFLvZ_IGtX9tuO8BTyj-Pqizn/view?usp=sharing).
### 3. Failure after conversion
- Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors
- Note that the model works fine on iPhone CPU.
|
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PR_kwDOArmXAs5e8ZxC
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[Linaro:ARM_CI] Bump tag used for docker container
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CONTRIBUTOR
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Need to bump the tag used for the docker container to pull in the updates for the next release
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Build tensorflow lite problem accessing https://gitlab.com/libeigen/eigen.git
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[
"additionally, from bazel\r\n```\r\n\r\nbazel build --config=elinux_aarch64 -c opt //tensorflow/lite/c:libtensorflowlite_c.so\r\n\r\nINFO: Reading 'startup' options from /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --windows_enable_symlinks\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=156\r\nINFO: Reading rc options for 'build' from /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Found applicable config definition build:short_logs in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:elinux_aarch64 in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --config=elinux --cpu=aarch64\r\nINFO: Found applicable config definition build:elinux in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --crosstool_top=@local_config_embedded_arm//:toolchain --host_crosstool_top=@bazel_tools//tools/cpp:toolchain\r\nINFO: Found applicable config definition build:macos in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --features=archive_param_file --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=no_tfrt\r\nINFO: Found applicable config definition build:no_tfrt in file /Users/juan.crescente/Documents/rand/tensorflow-or/.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils\r\nINFO: Build options --cpu and --crosstool_top have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/c:libtensorflowlite_c.so (40 packages loaded, 11257 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /private/var/tmp/_bazel_juan.crescente/31d150060e009750e7a00c4e6ef76e44/external/ruy/ruy/BUILD:900:11: Compiling ruy/ctx.cc failed: (Exit 126): aarch64-none-linux-gnu-gcc failed: error executing command (from target @ruy//ruy:ctx) /private/var/tmp/_bazel_juan.crescente/31d150060e009750e7a00c4e6ef76e44/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections ... (remaining 54 arguments skipped)\r\n/private/var/tmp/_bazel_juan.crescente/31d150060e009750e7a00c4e6ef76e44/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc: /private/var/tmp/_bazel_juan.crescente/31d150060e009750e7a00c4e6ef76e44/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc: cannot execute binary file\r\nTarget //tensorflow/lite/c:libtensorflowlite_c.so failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 145.417s, Critical Path: 5.59s\r\nINFO: 276 processes: 236 internal, 40 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\n```",
"@juancresc Could you please check if the Eigen library is installed on your system. Please make sure that the TensorFlow Lite build process is able to find the Eigen library.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62358\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62358\">No</a>\n"
] | 2023-11-08T15:33:52 | 2023-11-30T01:49:21 | 2023-11-30T01:49:13 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.8
### Custom code
No
### OS platform and distribution
arm64
### Mobile device
macOS
### Python version
_No response_
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
CMake Warning at /Users/juan.crescente/Documents/rand/tensorflow-or/tflite_build/abseil-cpp/CMakeLists.txt:77 (message):
A future Abseil release will default ABSL_PROPAGATE_CXX_STD to ON for CMake
3.8 and up. We recommend enabling this option to ensure your project still
builds correctly.
-- Performing Test ABSL_INTERNAL_AT_LEAST_CXX17
-- Performing Test ABSL_INTERNAL_AT_LEAST_CXX17 - Success
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
The authenticity of host 'gitlab.com (172.65.251.78)' can't be established.
ED25519 key fingerprint is SHA256:eUXGGm1YGsMAS7vkcx6JOJdOGHPem5gQp4taiCfCLB8.
This key is not known by any other names
Are you sure you want to continue connecting (yes/no/[fingerprint])? yes
[ 11%] Creating directories for 'eigen-populate'
[ 22%] Performing download step (git clone) for 'eigen-populate'
Cloning into 'eigen'...
Warning: Permanently added 'gitlab.com' (ED25519) to the list of known hosts.
[email protected]: Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.
Cloning into 'eigen'...
[email protected]: Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.
Cloning into 'eigen'...
[email protected]: Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.
-- Had to git clone more than once: 3 times.
CMake Error at tmp/eigen-populate-gitclone.cmake:39 (message):
Failed to clone repository: 'https://gitlab.com/libeigen/eigen.git'
make[2]: *** [/Users/juan.crescente/Documents/rand/tensorflow-or/tflite_build/src/eigen-populate-stamp/eigen-populate-download] Error 1
make[1]: *** [CMakeFiles/eigen-populate.dir/all] Error 2
make: *** [all] Error 2
CMake Error at /opt/homebrew/Cellar/cmake/3.24.2/share/cmake/Modules/FetchContent.cmake:1604 (message):
Build step for eigen failed: 2
Call Stack (most recent call first):
/opt/homebrew/Cellar/cmake/3.24.2/share/cmake/Modules/FetchContent.cmake:1744:EVAL:2 (__FetchContent_directPopulate)
/opt/homebrew/Cellar/cmake/3.24.2/share/cmake/Modules/FetchContent.cmake:1744 (cmake_language)
tools/cmake/modules/OverridableFetchContent.cmake:537 (FetchContent_Populate)
tools/cmake/modules/eigen.cmake:39 (OverridableFetchContent_Populate)
tools/cmake/modules/FindEigen3.cmake:18 (include)
CMakeLists.txt:148 (find_package)
-- Configuring incomplete, errors occurred!
See also "/Users/juan.crescente/Documents/rand/tensorflow-or/tflite_build/CMakeFiles/CMakeOutput.log".
See also "/Users/juan.crescente/Documents/rand/tensorflow-or/tflite_build/CMakeFiles/CMakeError.log".
### Standalone code to reproduce the issue
```shell
as described here
https://www.tensorflow.org/lite/guide/build_cmake
mkdir tflite_build
cd tflite_build
cmake ../tensorflow/lite
```
### Relevant log output
_No response_
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[v2.14.0] Build failed on linux/arm64 w/ clang
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[
"build succeeded when compiling with `gcc`. Closing the issue.\r\n```\r\n[11,350 / 11,354] Compiling tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc; 77s local\r\n[11,351 / 11,354] [Prepa] Linking tensorflow/libtensorflow.so.2.14.0\r\n[11,351 / 11,354] Linking tensorflow/libtensorflow.so.2.14.0; 1s local\r\n[11,351 / 11,354] Linking tensorflow/libtensorflow.so.2.14.0; 11s local\r\n[11,351 / 11,354] Linking tensorflow/libtensorflow.so.2.14.0; 41s local\r\n[11,352 / 11,354] [Prepa] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/clib.tar\r\n[11,352 / 11,354] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/clib.tar; 0s local\r\n[11,353 / 11,354] [Prepa] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/libtensorflow.tar.gz\r\n[11,353 / 11,354] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/libtensorflow.tar.gz; 1s local\r\n[11,353 / 11,354] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/libtensorflow.tar.gz; 11s local\r\n[11,353 / 11,354] Writing: bazel-out/aarch64-opt/bin/tensorflow/tools/lib_package/libtensorflow.tar.gz; 41s local\r\nTarget //tensorflow/tools/lib_package:libtensorflow up-to-date:\r\n bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz\r\n[11,354 / 11,354] checking cached actions\r\nINFO: Elapsed time: 25574.510s, Critical Path: 3464.21s\r\nINFO: 1000 processes: 2 internal, 998 local.\r\nINFO: Build completed successfully, 1000 total actions\r\n```",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62357\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62357\">No</a>\n",
"Same behavior observed on v2.15.0. Linking step failed with clang, however, all good with gcc. Leaving this comment here to hopefully get this issue addressed in future versions if clang would be the preferred compiler toolchain."
] | 2023-11-08T13:35:41 | 2023-11-27T14:06:43 | 2023-11-09T21:28:26 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf v2.14.0
### Custom code
No
### OS platform and distribution
Linux arm64
### Mobile device
_No response_
### Python version
3.11.2
### Bazel version
6.4.0
### GCC/compiler version
Debian clang version 14.0.6 aarch64-unknown-linux-gnu
### CUDA/cuDNN version
n/a
### GPU model and memory
_No response_
### Current behavior?
When trying to build `libtensorflow` the linking step failed. Is there a way to fix this?
### Standalone code to reproduce the issue
```shell
Run `bazel build --config opt //tensorflow/tools/lib_package:libtensorflow`
```
### Relevant log output
```shell
[5,037 / 5,041] Compiling tensorflow/core/kernels/conv_grad_input_ops_3d.cc; 246s local
[5,037 / 5,041] Compiling tensorflow/core/kernels/conv_grad_input_ops_3d.cc; 276s local
[5,037 / 5,041] Compiling tensorflow/core/kernels/conv_grad_input_ops_3d.cc; 336s local
[5,038 / 5,041] [Prepa] Linking tensorflow/libtensorflow.so.2.14.0
[5,038 / 5,041] Linking tensorflow/libtensorflow.so.2.14.0; 1s local
[5,038 / 5,041] Linking tensorflow/libtensorflow.so.2.14.0; 11s local
[5,038 / 5,041] Linking tensorflow/libtensorflow.so.2.14.0; 41s local
ERROR: /mnt/sandisk/tensorflow/tensorflow/tensorflow/BUILD:1243:20: Linking tensorflow/libtensorflow.so.2.14.0 failed: (Exit 1): clang failed: error executing command (from target //tensorflow:libtensorflow.so.2.14.0) /usr/lib/llvm-14/bin/clang @bazel-out/aarch64-opt/bin/tensorflow/libtensorflow.so.2.14.0-2.params
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
/usr/bin/ld.gold: error: Stub is too far away, try a smaller value for '--stub-group-size'. The current value is 0x7ffbffc.
clang: error: linker command failed with exit code 1 (use -v to see invocation)
[5,039 / 5,041] checking cached actions
Target //tensorflow/tools/lib_package:libtensorflow failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 50743.078s, Critical Path: 1006.17s
INFO: 5039 processes: 38 internal, 5001 local.
FAILED: Build did NOT complete successfully
```
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PR_kwDOArmXAs5e5bsA
| 62,356 |
Fix memory leak in giflib
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[
"@cantonios \r\nCan you review this please?"
] | 2023-11-08T08:45:11 | 2023-11-10T07:47:52 | 2023-11-10T07:47:52 |
CONTRIBUTOR
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Memory leak was discovered in gif library in the part of the code that was patched by me in #61913. I've forgotten to free color map if it was allocated earlier.
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I_kwDOArmXAs52MpEt
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Building Tensorflow with custom Onednn
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[
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62355\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62355\">No</a>\n",
"@vishwascm,\r\nCOuld you please provide complete steps you have followed to install the tensorflow and also provide the code and the error log which you are encountered which helps us to debug the issue. Thank you!",
"Hi @tilakrayal these are the steps I followed:\r\n\r\n**1**. git clone https://github.com/tensorflow/tensorflow.git \r\n**2**. cd tensorflow\r\n**3**. git checkout v2.13.0\r\n**4.** In tensorflow/workspace2.bzl replace following line of code:\r\n `tf_http_archive(\r\n name = \"mkl_dnn_acl_compatible\",\r\n build_file = \"//third_party/mkl_dnn:mkldnn_acl.BUILD\",\r\n patch_file = [ ... ],\r\n sha256 = \"a50993aa6265b799b040fe745e0010502f9f7103cc53a9525d59646aef006633\",\r\n strip_prefix = \"oneDNN-2.7.3\",\r\n urls = tf_mirror_urls(\"https://github.com/oneapi-src/oneDNN/archive/v2.7.3.tar.gz\"),\r\n )\r\nWith\r\n\r\n tf_http_archive(\r\n name = \"mkl_dnn_acl_compatible\",\r\n build_file = \"//third_party/mkl_dnn:mkldnn_acl.BUILD\",\r\n patch_file = [ ... ],\r\n sha256 = \"b57f83a60518ad919493ecd71ed06d38881654bd90fc62016efdcb718ca1143e\",\r\n strip_prefix = \"oneDNN-3.1.1\",\r\n urls = tf_mirror_urls(\"https://github.com/deepeshfujitsu/oneDNN/archive/refs/tags/v3.1.1.tar.gz\"),\r\n )\r\n\r\nHere sha256, strip_prefix and urls are changed accordingly to our modified onednn repo.\r\n\r\n **5.** Also made following change in mkldnn_acl.BUILD file:\r\n expand_template(\r\n name = \"dnnl_version_h\",\r\n out = \"include/oneapi/dnnl/dnnl_version.h\",\r\n substitutions = {\r\n \"@DNNL_VERSION_MAJOR@\": \"2\", \r\n \"@DNNL_VERSION_MINOR@\": \"7\",\r\n \"@DNNL_VERSION_PATCH@\": \"3\",\r\n \"@DNNL_VERSION_HASH@\": \"N/A\",\r\n },\r\n template = \"include/oneapi/dnnl/dnnl_version.h.in\",\r\n) \r\n \r\nwith\r\n expand_template(\r\n name = \"dnnl_version_h\",\r\n out = \"include/oneapi/dnnl/dnnl_version.h\",\r\n substitutions = {\r\n \"@DNNL_VERSION_MAJOR@\": \"3\", \r\n \"@DNNL_VERSION_MINOR@\": \"1\",\r\n \"@DNNL_VERSION_PATCH@\": \"1\",\r\n \"@DNNL_VERSION_HASH@\": \"N/A\",\r\n },\r\n template = \"include/oneapi/dnnl/dnnl_version.h.in\",\r\n) \r\n\r\n**6.** Bazel build: \r\n bazel build -s --config=mkl_aarch64 --features=-layering_check --copt=-O3 --copt=-march=armv8-a+sve --copt=-msve-vector-bits=256 --copt=-Wno-gnu-offsetof-extensions --copt=-Wno-unused-but-set-variable //tensorflow/tools/pip_package:build_pip_package --verbose_failures \r\n\r\n**7 .** Build is getting failed and getting error:\r\n ERROR: An error occurred during the fetch of repository 'mkl_dnn_acl_compatible':\r\n Traceback (most recent call last):\r\n File \"/home/vishwas/Graviton/tensorflow/third_party/repo.bzl\", line 83, column 30, in _tf_http_archive_impl\r\n ctx.patch(patch_file, strip = 1)\r\n \r\nIt is not able to fetch repo, repo is public.\r\n\r\n\r\n\r\n",
"@vishwascm Could you build successfully with the original code without the change in steps 4 and 5 ? And what's your taget platform info?",
"@feng-intel yes I can build successfully without changes of step 4 and 5. The issue is resolved now, I was able to build by using custom onednn which is based on onednn v2.7.3."
] | 2023-11-08T08:30:56 | 2023-12-05T06:38:18 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.13.0
### Custom code
Yes
### OS platform and distribution
ubuntu 22.04.1 on aarch64
### Mobile device
_No response_
### Python version
python 3.10.12
### Bazel version
6.1.0
### GCC/compiler version
clang version 16.0.6
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
I am trying to build tensorflow with custom Onednn. For this I have replaced following code of lines in /tensorflow/workspace2.bzl folder:
tf_http_archive(
name = "onednn",
build_file = "//third_party/mkl_dnn:mkldnn_v1.BUILD",
sha256 = "2f76b407ef8893cca71340f88cd800019a1f14f8ac1bbdbb89a84be1370b52e3",
strip_prefix = "oneDNN-3.2.1",
urls = tf_mirror_urls("https://github.com/oneapi-src/oneDNN/archive/refs/tags/v3.2.1.tar.gz"),
)
Here urls is replaced by url to my custom onednn and also sha256 is changed.
So bazel build is successful, but if I run some deeplearning models it is not running on onednn itself.
So it will be great if someone helpme with this.
### Standalone code to reproduce the issue
```shell
Results are that my custom onednn is not getting used.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs52MJek
| 62,354 |
About tensorflowlite c api
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[
"Hi @panhu ,\r\n\r\nCould you please refer to the below note from the documentation.\r\n\r\n> Note: This generates a static library libtensorflow-lite.a in the current directory but the library isn't self-contained since all the transitive dependencies are not included. To use the library properly, you need to create a CMake project. Please refer the [\"Create a CMake project which uses TensorFlow Lite\"](https://tensorflow.google.cn/lite/guide/build_cmake#create_a_cmake_project_which_uses_tensorflow_lite) section.\r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62354\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62354\">No</a>\n"
] | 2023-11-08T07:05:01 | 2023-11-24T01:48:13 | 2023-11-24T01:48:10 |
NONE
| null | null | null |
**System information**
- Linux
**Standalone code to reproduce the issue**
I want to use cmake to build TensorFlow Lite C library. But based on the tutorial(https://tensorflow.google.cn/lite/guide/build_cmake#build_tensorflow_lite_c_library) content, I obtained an ". a" static file, but what I needed was an ". so" file. The original file content is as follows:
cmake_minimum_required(VERSION 3.16)
project(tensorflow-lite-c C CXX)
option(TFLITE_C_BUILD_SHARED_LIBS "Build shared libraries" ON)
set(TENSORFLOW_SOURCE_DIR "" CACHE PATH
"Directory that contains the TensorFlow project"
)
if (NOT TENSORFLOW_SOURCE_DIR)
get_filename_component(TENSORFLOW_SOURCE_DIR
"${CMAKE_CURRENT_LIST_DIR}/../../../"
ABSOLUTE
)
endif()
add_subdirectory(
"${TENSORFLOW_SOURCE_DIR}/tensorflow/lite"
"${CMAKE_CURRENT_BINARY_DIR}/tensorflow-lite"
EXCLUDE_FROM_ALL
)
set(CMAKE_CXX_STANDARD 17)
if(CMAKE_SYSTEM_NAME MATCHES "Windows"
AND (MSVC AND (CMAKE_SIZEOF_VOID_P EQUAL 4)))
message("Disabling MSVC /O2 optimization for Win32")
set(CompFlags
CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_MINSIZEREL
CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL
CMAKE_C_FLAGS_RELWITHDEBINFO
)
foreach (CompFlag ${CompFlags})
string(REGEX REPLACE "(\/Ob. )" "" ${CompFlag} "${${CompFlag}}")
string(REPLACE "/O2" "/O1" ${CompFlag} "${${CompFlag}}")
list(REMOVE_DUPLICATES ${CompFlag})
set(${CompFlag} "${${CompFlag}}" CACHE INTERNAL "")
endforeach()
endif()
set(TFLITE_C_LIBTYPE STATIC)
if (TFLITE_C_BUILD_SHARED_LIBS)
set(TFLITE_C_LIBTYPE SHARED)
endif()
add_library(tensorflowlite_c ${TFLITE_C_LIBTYPE}
builtin_op_data.h
common.h
common.c
c_api_types.h
c_api.h
c_api.cc
c_api_experimental.h
c_api_experimental.cc
c_api_internal.h
)
if (TFLITE_C_BUILD_SHARED_LIBS)
if (WIN32)
target_compile_definitions(tensorflowlite_c PRIVATE TFL_COMPILE_LIBRARY)
elseif (APPLE)
target_link_options(tensorflowlite_c PRIVATE "-Wl,-exported_symbols_list,${TENSORFLOW_SOURCE_DIR}/tensorflow/lite/c/exported_symbols.lds")
else ()
target_link_options(tensorflowlite_c PRIVATE "-Wl,--version-script,${TENSORFLOW_SOURCE_DIR}/tensorflow/lite/c/version_script.lds")
endif()
endif()
target_link_libraries(tensorflowlite_c
tensorflow-lite
)
What should I do if I want to obtain the “. so” file.
Thanks!
|
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I_kwDOArmXAs52LZ2q
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TensorFlow Lite in Play Services issue
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[
"Hi, @Maja-GHub!\r\nCould you please make sure that the TensorFlow Lite modules are installed at the same time your application is installed or updated from the Play Store?\r\nIn order to expedite the trouble-shooting process here,Could you please fill the issue [template](https://github.com/tensorflow/tensorflow/issues/new/choose),\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62353\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62353\">No</a>\n"
] | 2023-11-08T04:16:06 | 2023-11-24T01:48:16 | 2023-11-24T01:48:12 |
NONE
| null | null | null |
**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version
(`Settings` > `Apps` > `Google Play Services` > `App details`):
**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to or attach code demonstrating
the problem.
**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached.
|
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I_kwDOArmXAs52LBUG
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Callbacks - tf.keras.callbacks.ModelCheckpoint and tf.keras.callbacks.EarlyStopping -> set_params not working
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[
"The problem is that `tf.keras.callbacks.ModelCheckpoint` and `tf.keras.callbacks.EarlyStopping` uses `set_params` from parent class `tf.keras.callbacks.Callback` which is not working well. So solution to add separate `set_params` method in both (I have also added `set_params`) source (tf.keras.callbacks.ModelCheckpoint): https://github.com/keras-team/keras/blob/v2.14.0/keras/callbacks.py#L1224-L1740 and source (tf.keras.callbacks.EarlyStopping): https://github.com/keras-team/keras/blob/v2.14.0/keras/callbacks.py#L1973-L2146.\r\n\r\n```python \r\nimport tensorflow as tf # load tensorflow \r\nimport warnings # load warnings \r\nclass UpdatedModelCheckpoint(tf.keras.callbacks.ModelCheckpoint):\r\n\r\n # define set parameters method \r\n def set_params(self,params:dict)->None:\r\n\r\n ''' make set give parameters dictionary ''' \r\n\r\n if isinstance(params,dict): # make check parameter must be dictionary \r\n for param,value in params.items(): # get parameter from parameter dictionary\r\n if hasattr(self,param):setattr(self,param,value) # if parameter present then sets its given value\r\n else:warnings.warn(f'Invalid paremeter -> {param} !',category=UserWarning)# else raise warning \r\n else:raise ValueError(f'`params` must be dictionary of parameters, with key parameter name and value its value')# raise value error \r\n\r\n \r\n # define method to get all paremeters \r\n def get_params(self)->dict:\r\n\r\n ''' returns dictionary of all parameters '''\r\n\r\n return self.__dict__.copy() # make return all parameters dictionary copy \r\n\r\n\r\n#import tensorflow as tf # load tensorflow \r\nckpt=UpdatedModelCheckpoint('/content/') # make check point with updated class \r\nckpt_params=ckpt.get_params() # get all parameters \r\nprint('Parameters:',ckpt_params) # see parameters dictionary\r\nckpt_params['save_best_only']=True # update parameter \"save_best_only\" to True (default False)\r\nckpt_params['save_weights_only']=True # update parameter \"save_weights_only\" to True (default False)\r\nckpt_params['epochs_since_last_save']=10 # update parameter \"epochs_since_last_save\" to 10 (default 0)\r\nprint('Current parameters:',ckpt.get_params()) # make print orginal paremetrs \r\nckpt.set_params(ckpt_params) # try to make update parameters\r\nprint('Is same as updated?',ckpt_params==ckpt.__dict__) # make check both are same or not \r\nprint('Updated parameters:',ckpt.get_params()) # updated parameters\r\n```\r\n```shell \r\nParameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': False, 'save_weights_only': False, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f597957e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}\r\nCurrent parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': False, 'save_weights_only': False, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f597957e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}\r\nIs same as updated? True\r\nUpdated parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': True, 'save_weights_only': True, 'save_freq': 'epoch', 'epochs_since_last_save': 10, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f597957e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}\r\n```",
"Same updates can be done with `tf.keras.callbacks.EarlyStopping`. \r\n\r\n```python \r\nimport tensorflow as tf # load tensorflow \r\nimport warnings # load warnings \r\nclass UpdatedEarlyStopping(tf.keras.callbacks.EarlyStopping):\r\n\r\n # define set parameters method \r\n def set_params(self,params:dict)->None:\r\n\r\n ''' make set give parameters dictionary ''' \r\n\r\n if isinstance(params,dict): # make check parameter must be dictionary \r\n for param,value in params.items(): # get parameter from parameter dictionary\r\n if hasattr(self,param):setattr(self,param,value) # if parameter present then sets its given value\r\n else:warnings.warn(f'Invalid paremeter -> {param} !',category=UserWarning)# else raise warning \r\n else:raise ValueError(f'`params` must be dictionary of parameters, with key parameter name and value its value')# raise value error \r\n\r\n \r\n # define method to get all paremeters \r\n def get_params(self)->dict:\r\n\r\n ''' returns dictionary of all parameters '''\r\n\r\n return self.__dict__.copy() # make return all parameters dictionary copy \r\n\r\n#import tensorflow as tf # load tensorflow \r\nlstp=UpdatedEarlyStopping() # make early stopping with updated class \r\nlstp_params=lstp.get_params() # get all parameters \r\nprint('Parameters :',lstp_params) # see parameters dictionary\r\nlstp_params['patience']=10 # update parameter \"patience\" to 10 (default 0)\r\nlstp_params['verbose']=1 # update parameter \"verbose\" to 1 (default 0)\r\nprint('Updated parameters:',lstp_params) # see updated parameters \r\nprint('Object\\'s current parameters:',lstp.get_params()) # see parameters dictionary\r\nlstp.set_params(lstp_params) # try to make update parameters\r\nprint('Object\\'s updated parameters:',lstp.get_params()) # see updated parameters dictionary\r\nprint('Is same as updated?',lstp_params==lstp.get_params()) # make check both are same or not \r\n```\r\n```shell\r\nParameters : {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 0, 'verbose': 0, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}\r\nUpdated parameters: {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 10, 'verbose': 1, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}\r\nObject's current parameters: {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 0, 'verbose': 0, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}\r\nObject's updated parameters: {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 10, 'verbose': 1, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}\r\nIs same as updated? True\r\n```",
"Hi **@MegaCreater** \r\nI replicated this issue with Tf 2.14 version. I attached a [gist](https://colab.research.google.com/gist/Venkat6871/5d388972f17a824956fb3e22b8e96778/untitled11.ipynb) for reference.\r\n\r\nThank you!",
"Okay.. I will create pull request. ",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62352\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62352\">No</a>\n"
] | 2023-11-08T02:26:44 | 2023-11-08T12:14:27 | 2023-11-08T12:14:24 |
NONE
| null | null | null |
```
Issue type : Bug
Have you reproduced the bug with TensorFlow Nightly? : No
Source : source
TensorFlow version : v2.14.0-rc1-21-g4dacf3f368e 2.14.0
Custom code : Yes
OS platform and distribution : Google colab
Python version : Python 3.10.12
```
`tf.keras.callbacks.ModelCheckpoint` and `tf.keras.callbacks.EarlyStopping` -> set_params not working as assumed.
```python
import tensorflow as tf # load tensorflow
ckpt=tf.keras.callbacks.ModelCheckpoint('/content/') # make check point
ckpt_params=ckpt.__dict__ # get all parameters
print('Parameters:',ckpt_params) # see parameters dictionary
ckpt_params['save_best_only']=True # update parameter "save_best_only" to True (default False)
ckpt_params['save_weights_only']=True # update parameter "save_weights_only" to True (default False)
ckpt.set_params(ckpt_params) # try to make update parameters
print('Is same as updated?',ckpt_params==ckpt.__dict__) # make check both are same or not
# both look same but if you look carefully `ckpt_params` and `ckpt.__dict__` will contain copy of themself in new key `'params'`
print('Parameters:',ckpt_params) # see parameters dictionary
```
```shell
Parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': False, 'save_weights_only': False, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f5942f5e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}
Is same as updated? True
Parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': True, 'save_weights_only': True, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f5942f5e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>, 'params': {...}}
```
This change can be seen by making a copy of `ckpt_params`.
```python
import tensorflow as tf # load tensorflow
ckpt=tf.keras.callbacks.ModelCheckpoint('/content/') # make check point
ckpt_params=ckpt.__dict__.copy() # get all parameters
print('Parameters:',ckpt_params) # see parameters dictionary
ckpt_params['save_best_only']=True # update parameter "save_best_only" to True (default False)
ckpt_params['save_weights_only']=True # update parameter "save_weights_only" to True (default False)
#ckpt_params_deep_copy={key:value for key,value in ckpt_params.items()} # make deep copy of `ckpt_params`
ckpt.set_params(ckpt_params) # try to make update parameters
print('Is same as updated?',ckpt_params==ckpt.__dict__) # make check both are same or not
# both look same but if you look carefully `ckpt_params` and `ckpt.__dict__` will contain copy of themself in new key `'params'`
print('Uncomman parameters:',[key for key in ckpt.__dict__ if key not in ckpt_params])# see parameters dictionary
print('Uncomman parameters:',ckpt.__dict__['params']) # which is copy of itself only
```
```shell
Parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': False, 'save_weights_only': False, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f5942e3e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}
Is same as updated? False
Uncomman parameters: ['params']
Uncomman parameters: {'validation_data': None, 'model': None, '_chief_worker_only': False, '_supports_tf_logs': True, 'monitor': 'val_loss', 'verbose': 0, 'filepath': '/content/', 'save_best_only': True, 'save_weights_only': True, 'save_freq': 'epoch', 'epochs_since_last_save': 0, '_batches_seen_since_last_saving': 0, '_last_batch_seen': 0, 'best': inf, '_options': <tensorflow.python.saved_model.save_options.SaveOptions object at 0x7a5f5942e3e0>, 'load_weights_on_restart': False, 'period': 1, 'monitor_op': <ufunc 'less'>}
```
Worse is with `tf.keras.callbacks.EarlyStopping`. It not only make a copy but also don't update parameters.
```python
import tensorflow as tf # load tensorflow
lstp=tf.keras.callbacks.EarlyStopping() # make check point
lstp_params=lstp.__dict__.copy() # get all parameters
print('Parameters :',lstp_params) # see parameters dictionary
lstp_params['patience']=10 # update parameter "patience" to 10 (default 0)
lstp_params['verbose']=1 # update parameter "verbose" to 1 (default 0)
print('Updated parameters:',lstp_params) # see updated parameters
lstp.set_params(lstp_params.copy()) # try to make update parameters
print('Is same as updated?',lstp_params==lstp.__dict__) # make check both are same or not
# Even values are not updated from `lstp_params` and `lstp.__dict__` will contain copy of themself in new key `'params'`
print('Object parameters :',lstp.__dict__) # see parameters dictionary
```
```shell
Parameters : {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 0, 'verbose': 0, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}
Updated parameters: {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 10, 'verbose': 1, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}
Is same as updated? False
Object parameters : {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 0, 'verbose': 0, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>, 'params': {'validation_data': None, 'model': None, '_chief_worker_only': None, '_supports_tf_logs': False, 'monitor': 'val_loss', 'patience': 10, 'verbose': 1, 'baseline': None, 'min_delta': 0, 'wait': 0, 'stopped_epoch': 0, 'restore_best_weights': False, 'best_weights': None, 'start_from_epoch': 0, 'monitor_op': <ufunc 'less'>}}
```
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I_kwDOArmXAs52J7xD
| 62,351 |
Python tests fails in GPU VM fails with tensorflow and CUDA execeptions of capable devices are busy or unavailable
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[
"@arielman,\r\nCould you please try [limiting gpu memory growth](https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth) and see if it helps. Before that please make sure you close all python sessions and exit python interpreter, we want to make sure your gpu is not utilized any simulations.\r\n\r\n```\r\n# on the top of your script\r\nimport tensorflow as tf\r\ngpus = tf.config.list_physical_devices('GPU')\r\ntf.config.experimental.set_memory_growth(gpus[0], True)\r\n# Paste rest of your code here\r\n```\r\n\r\nAlso please have a look at this thread from the Nvidia forum for the similar issue.\r\nhttps://forums.developer.nvidia.com/t/nvidia-persistenced-failed-to-initialize-check-syslog-for-more-details/74052/4\r\nhttps://forums.developer.nvidia.com/t/nvidia-persistenced-failed-to-initialize-check-syslog-for-more-details/74052/7\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62351\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62351\">No</a>\n"
] | 2023-11-07T22:06:22 | 2023-11-24T01:48:19 | 2023-11-24T01:48:14 |
NONE
| null | null | null |
In the last days, some python tests started to fail on our GPU VM:
`E tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: all CUDA-capable devices are busy or unavailable`
We check the VM and couldn't find anything suspicious: driver is ok, docker engine is ok, gpu is ok.
this is the output of NVIDIA-SMI from the machine
`NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 CUDA Version: 11.2`
Can someone direct me what else I need to check?
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Update requirements.in and lock files
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Update version numbers for TensorFlow 2.15.0
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 15 -> 15
Patch: 0 -> 0
No lingering old version strings "2.15.0-rc1" found in source directory
"tensorflow/". Good.
No lingering old version strings "2.15.0rc1" found in source directory
"tensorflow/". Good.
```
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Update setup.py with released version of Estimator and Keras
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Use Estimator and Keras 2.15.0
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Guidance on Using libtensorflowlite_flex.so with TensorFlow Lite C++ API
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[
"What compilation errors do you get?",
"In fact, I've figured out how to use libtensorflowlite_flex.so on my own. Here's how I incorporated this library into my work. I used CMake to link the TensorFlow Lite library and compile my code. A simplified example is as follows:\r\n\r\n```CMake\r\ncmake_minimum_required(VERSION 3.11)\r\n\r\nproject(predict)\r\nset(CMAKE_CXX_STANDARD 11)\r\n\r\ninclude_directories(\r\n path/to/tensorflow/include\r\n)\r\n\r\nset(LINK_DIR path/to/tensorflowlite)\r\nadd_executable(predict src/main.cpp )\r\n\r\ntarget_link_libraries(predict ${LINK_DIR}/libflatbuffers.a)\r\ntarget_link_libraries(predict ${LINK_DIR}/libtensorflowlite.so)\r\n```\r\nThis code enables my code to link with TensorFlow Lite. However, it doesn't link with the Flex delegate. To use the Flex delegate, you need to first compile the libtensorflowlite_flex.so library file and place it in an appropriate location. Then, add the following code to the CMake file:\r\n```CMake\r\ntarget_link_libraries(predict\r\n -Wl,--no-as-needed\r\n ${LINK_DIR}/libtensorflowlite_flex.so\r\n -Wl,--as-needed\r\n)\r\n```\r\nI'm not sure if there's a more elegant way to utilize the Flex delegate. If there is, I would be grateful to learn about it.",
"I recently repeated the above on Windwos and noticed a slight difference. The MSVC compiler does not have a compilation option to force links, and can only load the flex dll manually. I was inspired by the [issue](https://github.com/tensorflow/tensorflow/issues/62227)\r\n\r\n```C++\r\n#include \"tensorflow/lite/interpreter.h\"\r\n#include \"tensorflow/lite/kernels/register.h\"\r\n#include \"tensorflow/lite/model.h\"\r\n#include <Windows.h>\r\n\r\n\r\ntflite::ops::builtin::BuiltinOpResolver resolver;\r\nstd::unique_ptr<tflite::Interpreter> interpreter;\r\ntflite::InterpreterBuilder(*model, resolver)(&interpreter);\r\n\r\nauto hdll = LoadLibrary(\"tensorflowlite_flex.dll\");\r\n\r\nauto TF_AcquireFlexDelegate = reinterpret_cast<tflite::Interpreter::TfLiteDelegatePtr (*)()>( GetProcAddress(hdll, \r\n \"TF_AcquireFlexDelegate\"));\r\nif (TF_AcquireFlexDelegate == NULL) {\r\n std::cout << \"TF_AcquireFlexDelegate couldn't be run\" << std::endl;\r\n}\r\n\r\nif (!interpreter) {\r\n std::cerr << \"Failed to construct interpreter.\" << std::endl;\r\n}\r\nauto delegate = TF_AcquireFlexDelegate();\r\nif (interpreter->ModifyGraphWithDelegate(delegate.get()) != kTfLiteOk) {\r\n // error handle\r\n}\r\n```",
"Hi @airchaoz, let's focus on one use case first as I see a lot of information flying around which isn't quite coherent. Do you want to focus on windows or Ubuntu 20.04? Are you using WSL in any way? Can you give us reproducible commands which I presume use the CMake file above to compile/run your project? and then share the errors which these commands produce.\r\n\r\nJust for future reference, all this information is requested in our issues template: https://github.com/tensorflow/tensorflow/blob/master/ISSUE_TEMPLATE.md Please be sure to fill it out when creating an issue.\r\n\r\nOne good thing to try, just to make sure nothing else is going wrong is trying out to see if you can compile and run the minimal example as is and see if that works for you: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62347\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62347\">No</a>\n",
"I have utilized the C API provided in c_api_experimental.h to link the TensorflowLite Flex delegate. [comment](https://github.com/tensorflow/tensorflow/issues/62227#issuecomment-2028599386)"
] | 2023-11-07T13:25:53 | 2024-03-31T08:35:19 | 2023-11-29T01:48:56 |
NONE
| null | null | null |
I'm currently working with the TensorFlow Lite C++ API and have encountered some challenges regarding the usage of the flex delegate. I've successfully built the libtensorflowlite_flex.so library following the instructions provided on the official TensorFlow website. However, I am unclear on how to properly utilize this library, as I couldn't find any documentation on its usage in the guides.
Issue Summary:
After compiling libtensorflowlite_flex.so, I'm unsure how to integrate it into my existing TensorFlow Lite C++ project, which currently works well with the built-in operators. My aim is to leverage additional operators through the flex delegate, but my attempts to replace libtensorflowlite.so with libtensorflowlite_flex.so have led to compilation errors, leaving me uncertain about the correct approach or if there's something I'm missing.
Detailed Description:
I have successfully run TensorFlow Lite models using the C++ API and its built-in operators.
I need to use additional operators, for which I believe the flex delegate is required.
After building libtensorflowlite_flex.so, replacing libtensorflowlite.so with it in my project led to compilation failures.
I'm seeking guidance on the correct process to integrate the flex delegate into my project.
Environment:
OS: Ubuntu 20.04
TensorFlow Version: 2.6.5
I would greatly appreciate any documentation, examples, or guidance on how to properly use the flex delegate with the TensorFlow Lite C++ API. It would be immensely helpful for advancing my project and for others who might face similar challenges.
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[
"To clarify a bit, I disagree with @jelinkovaz and there is no single dist with complete metadata. The manylinux x86_64 would need to also have:\r\n```\r\nRequires-Dist: tensorflow-macos (==2.13.0) ; platform_system == \"Darwin\" and platform_machine == \"arm64\"\r\n```\r\n\r\nTo form a complete set of metadata.\r\n\r\nTo explain the issue here a bit more:\r\n---\r\nAlthough PyPI / PyPA have no strictures against the various distributions for a given project release having different metadata; it is not tooling-friendly in the case of attempting to build multi-platform lock files in particular.\r\n\r\nAlthough PEP-691 is rolling out at PyPI, its not complete and that only covers PyPI; not other indexes or any find links repos at all. As such, to build a multi-platform lock completely correctly every applicable artifact for a given version must be downloaded and its metadata inspected. This is obviously a perf concern in the normal case and pretty much a non-starter in the ML (huge) wheel case. For example, for tensorflow 2.13.0, which is the case that brought this to my attention, that would be 25 files to download and inspect metadata in at ~2.2GB total. This issue was even discussed in the failed attempt to get a Python lock file standard going in PEP-665 (some of the discussion of this can be found by starting here in item 6: https://discuss.python.org/t/pep-665-specifying-installation-requirements-for-python-projects/9911/130)\r\n\r\nPex thus makes the decision to just use the metadata from one of the distributions and not pull down the rest. I'm sure there are more out there, but the two outliers I know of personally are just pytorch (for a while), and now tensorflow. This is after >2 years of production use of Pex multi-platform locking by Pants users in the wild.\r\n\r\nThis may not be a use-case of interest to the tensorflow maintainers, but, afaict, the end result would be compatible with what is released today. I suspect the primary issues making this not attractive might be time & effort to switch + possibly tooling getting in the way. I'm definitely ignorant of tensorflow project details.",
"@jsirois Thank you for your response here!\r\n\r\n@jelinkovaz Could you please try to regenerate the metadata and make sure that the meta file is not corrupted or empty?\r\n\r\nYes, it would be possible to add the dependencies metadata for all of the distributions. However, it would be a significant amount of work. \r\n\r\nThe error \"No interpreter compatible with the requested constraints was found\" can occur when you try to install TensorFlow==2.13 using Pants on your M2 Mac. This is because TensorFlow 2.13 requires a Python interpreter that is compatible with the Apple Silicon architecture. You may try with the different TF.\r\n\r\nThank you!",
"@sushreebarsa Thank you for reply!\r\n\r\nI'm sorry I'm a junior and don't know how it works under the hood exactly. Can you please tell me how you mean to regenerate the metadata ? All the dependencies are being downloaded by pants.\r\nAnd what do you mean by trying to use different TF ? The 2.13 is the earliest to support arm64, right? I've tried with 2.14 with the same result. \r\n\r\nMy colleague has just suggested a solution that actually solved the problem for me. \r\nI've added environment markers to the tensorflow in the pants dependency file like this: \r\n```\r\ntensorflow>=2.10.0; platform_machine == \"x86_64\"\r\ntensorflow-macos>=2.10.0; platform_machine == \"arm64\"\r\n```\r\nand pants downloaded and installed the latest TensorFlow==2.14, tensorflow_macos-2.14.0-cp39-cp39-macosx_12_0_arm64.whl without issue. @jsirois FYI\r\n\r\nWe are now just wondering, shouldn't tensorflow automatically infer the platform and the architecture? We expected it to do that, that's why we didn't try this before. \r\n\r\nThank you so much!\r\n\r\n",
"@jelinkovaz Glad that your issue has been fixed using the following workaround;\r\n```\r\ntensorflow>=2.10.0; platform_machine == \"x86_64\"\r\ntensorflow-macos>=2.10.0; platform_machine == \"arm64\"\r\n```\r\n\r\nYes, TensorFlow should automatically infer the platform and architecture, but if some other software packages would be running on your system it would conflict with TF.\r\n\r\nPlease move this ticket to closed status if the issue has been resolved?\r\nThank you!",
"Hi @sushreebarsa, can you please provide more details on _\"but if some other software packages would be running on your system it would conflict with TF\"_? Regardless whether I am installing `tensorflow-macos` manually or as a part of TF installation, there can be potential conflicts and such conflict should be solved by isolation of runtime environment (virtual envs, containers, etc.). Or am I missing something?\r\n\r\n",
"@jelinkovaz Yes, generally these kinds of conflicts can be avoided using virtual environments or containers.\r\nThe workaround would help as you have mentioned above. For details info on mac build from source installation please have a look at [this](https://www.tensorflow.org/install/source#macos) doc. \r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62346\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62346\">No</a>\n"
] | 2023-11-07T10:53:44 | 2023-12-01T01:52:16 | 2023-12-01T01:52:11 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
MacOS , Ventura 13.1
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hello!
When I try to install `tensorflow==2.13` using `pants` on my M2 mac I run into `No interpreter compatible with the requested constraints was found` issue.
According to the conversation with `pants` guys it's caused by incomplete dependencies metadata for other than `x86_64` distribution, as showed in the https://github.com/pantsbuild/pants/issues/20134
Same issue was someone having with `pytorch` https://github.com/pantsbuild/pants/issues/18936
Would it be please possible to add the dependencies metadata for all of the distributions ?
Thank you very much in advance!
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs52FMZf
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tf.keras.metrics.get return string intead of exception
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[
"Hi **@venergiac-bh** \r\nI replicated this issue with Tf 2.14 ,2.13 and tf-nightly too. I attached a [gist](https://colab.research.google.com/gist/Venkat6871/0ebdf8b84d6ab18e13df7188fc821346/62345_2-14-v.ipynb) here for reference.\r\n\r\nThank you! ",
"Hi @venergiac-bh ,\r\n\r\nI have tested it with keras-nightly(3.0.0.dev2023110803) and it is raising intended exception. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/36958e240b0ecb9583f86d19df361a73/62345.ipynb#scrollTo=41bcOz8UCedO).",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62345\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62345\">No</a>\n"
] | 2023-11-07T10:51:00 | 2023-11-25T01:47:46 | 2023-11-25T01:47:43 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.14.0
### Custom code
No
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Problem on metrics `keras.metrics.get`; it should raise an exception if metric doesn't exist: InvalidIdentifier
### Standalone code to reproduce the issue
```shell
Problem on metrics
import tensorflow as tf
tf.keras.metrics.get('abc')
it returns "abc", but as per documentation it should raise an exception ValueError
```
### Relevant log output
_No response_
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I_kwDOArmXAs52E4i6
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`third_party/xla/third_party/tsl/tsl/platform/denormal.cc` doesn't include <cstdint>
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[
"@tpasternak,\r\nCould you please provide the complete steps and the document you are following to install the tensorflow. It helps to debug the issue in an effective way. Thank you!",
"I just followed this guide https://www.tensorflow.org/install/source on Fedora 38 VM in UTM (fully updated) on Mac.\r\n\r\nPlease not that the problem is relatively simple, you just use symbols from `<cstdint>` but don't include it so you assume it's included indirectly by other headers. Maybe in some setups it just doesn't happen",
"@tpasternak,\r\nThe file which you are referring for the changes to be done are available on the https://github.com/openxla/xla repository where we could not request for the changes. \r\nhttps://github.com/openxla/xla/blob/main/third_party/tsl/tsl/platform/denormal.cc\r\nCould you please raise the issue or PR in the respective repo for the quick resolution. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62344\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62344\">No</a>\n"
] | 2023-11-07T10:07:14 | 2023-11-17T08:19:01 | 2023-11-17T08:18:58 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0-rc1
### Custom code
No
### OS platform and distribution
Fedora aarch64
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
the one from bazelversion
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When building I get
```
ERROR: /home/t/.cache/bazel/_bazel_t/74c2ce4d76380c471da861c13510ec30/external/local_tsl/tsl/platform/BUILD:119:11: Compiling tsl/platform/denormal.cc [for tool] failed: (Exit 1): gcc failed: error executing command (from target @local_tsl//tsl/platform:denormal) /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 71 arguments skipped)
external/local_tsl/tsl/platform/denormal.cc:64:20: error: variable or field 'ArmSetFloatingPointControlRegister' declared void
64 | static inline void ArmSetFloatingPointControlRegister(uint32_t fpcr) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/local_tsl/tsl/platform/denormal.cc:64:55: error: 'uint32_t' was not declared in this scope
64 | static inline void ArmSetFloatingPointControlRegister(uint32_t fpcr) {
| ^~~~~~~~
external/local_tsl/tsl/platform/denormal.cc:20:1: note: 'uint32_t' is defined in header '<cstdint>'; did you forget to '#include <cstdint>'?
19 | #include "tsl/platform/platform.h"
+++ |+#include <cstdint>
20 |
external/local_tsl/tsl/platform/denormal.cc:74:15: error: 'uint32_t' does not name a type
74 | static inline uint32_t ArmGetFloatingPointControlRegister() {
| ^~~~~~~~
external/local_tsl/tsl/platform/denormal.cc:74:15: note: 'uint32_t' is defined in header '<cstdint>'; did you forget to '#include <cstdint>'?
external/local_tsl/tsl/platform/denormal.cc: In function 'bool tsl::port::SetDenormalState(const DenormalState&)':
external/local_tsl/tsl/platform/denormal.cc:106:5: error: 'uint32_t' was not declared in this scope
106 | uint32_t fpcr = ArmGetFloatingPointControlRegister();
| ^~~~~~~~
external/local_tsl/tsl/platform/denormal.cc:106:5: note: 'uint32_t' is defined in header '<cstdint>'; did you forget to '#include <cstdint>'?
external/local_tsl/tsl/platform/denormal.cc:108:7: error: 'fpcr' was not declared in this scope
108 | fpcr |= ARM_FPCR_FZ;
| ^~~~
external/local_tsl/tsl/platform/denormal.cc:110:7: error: 'fpcr' was not declared in this scope
110 | fpcr &= ~ARM_FPCR_FZ;
| ^~~~
external/local_tsl/tsl/platform/denormal.cc:112:40: error: 'fpcr' was not declared in this scope
112 | ArmSetFloatingPointControlRegister(fpcr);
| ^~~~
external/local_tsl/tsl/platform/denormal.cc:112:5: error: 'ArmSetFloatingPointControlRegister' was not declared in this scope
112 | ArmSetFloatingPointControlRegister(fpcr);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/local_tsl/tsl/platform/denormal.cc: In function 'tsl::port::DenormalState tsl::port::GetDenormalState()':
external/local_tsl/tsl/platform/denormal.cc:133:3: error: 'uint32_t' was not declared in this scope
133 | uint32_t fpcr = ArmGetFloatingPointControlRegister();
| ^~~~~~~~
external/local_tsl/tsl/platform/denormal.cc:133:3: note: 'uint32_t' is defined in header '<cstdint>'; did you forget to '#include <cstdint>'?
external/local_tsl/tsl/platform/denormal.cc:134:8: error: 'fpcr' was not declared in this scope
134 | if ((fpcr & ARM_FPCR_FZ) != 0) {
| ^~~~
```
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
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[
"VERSIONS\r\nConda 23.9\r\ntensorflow 2.10.0\r\npython 3.9.18\r\nkeras 1.1.2 by pip install keras\r\n\r\n\r\nCODE\r\n\r\nimport cv2\r\nimport numpy as np\r\n#import tensorflow as tf\r\nimport keras\r\nfrom keras.models import load_model\r\n\r\n\r\nERROR:\r\nC:\\Users\\acer\\fmd_project\\fmd_script>python fmd_backend.py\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 62, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\acer\\fmd_project\\fmd_script\\fmd_backend.py\", line 196, in <module>\r\n import keras\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\keras\\__init__.py\", line 20, in <module>\r\n from keras import distribute\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\keras\\distribute\\__init__.py\", line 18, in <module>\r\n from keras.distribute import sidecar_evaluator\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\keras\\distribute\\sidecar_evaluator.py\", line 17, in <module>\r\n import tensorflow.compat.v2 as tf\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\tensorflow\\__init__.py\", line 37, in <module>\r\n from tensorflow.python.tools import module_util as _module_util\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\tensorflow\\python\\__init__.py\", line 36, in <module>\r\n from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 77, in <module>\r\n raise ImportError(\r\nImportError: Traceback (most recent call last):\r\n File \"C:\\Users\\acer\\anaconda3\\envs\\base4\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 62, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.\r\n",
"Please advise how to resolve this error.\r\n\r\nFirstly should I (1) use keras which is part of tensorflow or (2) installl keras separately and import keras. Thanks\r\n\r\nSecondly I was getting error in the line from keras.models import load_model \r\nI upgraded python and tensorflow but new DLL error came as given in detail.\r\n\r\nThanks. ",
"Hi @janakiashwin ,\r\n\r\nFrom the attached details by you it seems you are using TF2.10v and keras 1.1.2 which is quiet old and incompatible.\r\n\r\nIf you are using tf2.10v just import tensorflow and use tf.keras. Or simply use:\r\n\r\n`from tensorflow import keras\r\n`\r\n\r\nIf you install any latest version of tensorflow it will install keras package also.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62343\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62343\">No</a>\n"
] | 2023-11-07T08:14:02 | 2023-11-25T01:47:48 | 2023-11-25T01:47:45 |
NONE
| null | null | null |
Please go to Stack Overflow for help and support:
https://stackoverflow.com/questions/tagged/tensorflow
If you open a GitHub issue, here is our policy:
1. It must be a bug, a feature request, or a significant problem with the
documentation (for small docs fixes please send a PR instead).
2. The form below must be filled out.
3. It shouldn't be a TensorBoard issue. Those go
[here](https://github.com/tensorflow/tensorboard/issues).
**Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
------------------------
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**:
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
- **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue
happens on a mobile device**:
- **TensorFlow installed from (source or binary)**:
- **TensorFlow version (use command below)**:
- **Python version**:
- **Bazel version (if compiling from source)**:
- **GCC/Compiler version (if compiling from source)**:
- **CUDA/cuDNN version**:
- **GPU model and memory**:
- **Exact command to reproduce**:
You can collect some of this information using our environment capture script:
https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
You can obtain the TensorFlow version with:
```bash
python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
```
### Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
### Source code / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
|
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shows illegal instruction (core dumped) while importing tensorflow as tf
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[
"@fti-sfuke Could you please make sure that you are using the latest TF version , if not then please try to reinstall it in your system. Maybe the TensorFlow installation has been corrupted. Another way is to create a virtual environment and test it with the latest TF version. Please let us know if it helps?\r\nThank you!",
"Hi @sushreebarsa \r\nThanks for the response. \r\n\r\nI tried to install the latest version of TensorFlow. Here are the complete installation logs.\r\n\r\n```\r\nroot@d87518e6c38b:/# pip3 install -U --prefer-binary tensorflow\r\nCollecting tensorflow\r\n Downloading tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\r\nCollecting absl-py>=1.0.0 (from tensorflow)\r\n Downloading absl_py-2.0.0-py3-none-any.whl.metadata (2.3 kB)\r\nCollecting astunparse>=1.6.0 (from tensorflow)\r\n Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\r\nCollecting flatbuffers>=23.5.26 (from tensorflow)\r\n Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)\r\nCollecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow)\r\n Downloading gast-0.5.4-py3-none-any.whl (19 kB)\r\nCollecting google-pasta>=0.1.1 (from tensorflow)\r\n Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 kB 3.9 MB/s eta 0:00:00\r\nCollecting h5py>=2.9.0 (from tensorflow)\r\n Downloading h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\r\nCollecting libclang>=13.0.0 (from tensorflow)\r\n Downloading libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl.metadata (5.2 kB)\r\nCollecting ml-dtypes==0.2.0 (from tensorflow)\r\n Downloading ml_dtypes-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\r\nCollecting numpy>=1.23.5 (from tensorflow)\r\n Downloading numpy-1.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.2/61.2 kB 9.8 MB/s eta 0:00:00\r\nCollecting opt-einsum>=2.3.2 (from tensorflow)\r\n Downloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 65.5/65.5 kB 11.2 MB/s eta 0:00:00\r\nCollecting packaging (from tensorflow)\r\n Downloading packaging-23.2-py3-none-any.whl.metadata (3.2 kB)\r\nCollecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow)\r\n Downloading protobuf-4.25.0-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)\r\nRequirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from tensorflow) (59.6.0)\r\nRequirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\r\nCollecting termcolor>=1.1.0 (from tensorflow)\r\n Downloading termcolor-2.3.0-py3-none-any.whl (6.9 kB)\r\nCollecting typing-extensions>=3.6.6 (from tensorflow)\r\n Downloading typing_extensions-4.8.0-py3-none-any.whl.metadata (3.0 kB)\r\nCollecting wrapt<1.15,>=1.11.0 (from tensorflow)\r\n Downloading wrapt-1.14.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (77 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 77.9/77.9 kB 11.3 MB/s eta 0:00:00\r\nCollecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow)\r\n Downloading tensorflow_io_gcs_filesystem-0.34.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (14 kB)\r\nCollecting grpcio<2.0,>=1.24.3 (from tensorflow)\r\n Downloading grpcio-1.59.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)\r\nCollecting tensorboard<2.15,>=2.14 (from tensorflow)\r\n Downloading tensorboard-2.14.1-py3-none-any.whl.metadata (1.7 kB)\r\nCollecting tensorflow-estimator<2.15,>=2.14.0 (from tensorflow)\r\n Downloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl.metadata (1.3 kB)\r\nCollecting keras<2.15,>=2.14.0 (from tensorflow)\r\n Downloading keras-2.14.0-py3-none-any.whl.metadata (2.4 kB)\r\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/lib/python3/dist-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\r\nCollecting google-auth<3,>=1.6.3 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading google_auth-2.23.4-py2.py3-none-any.whl.metadata (4.7 kB)\r\nCollecting google-auth-oauthlib<1.1,>=0.5 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\r\nCollecting markdown>=2.6.8 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading Markdown-3.5.1-py3-none-any.whl.metadata (7.1 kB)\r\nCollecting requests<3,>=2.21.0 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)\r\nCollecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl.metadata (1.1 kB)\r\nCollecting werkzeug>=1.0.1 (from tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading werkzeug-3.0.1-py3-none-any.whl.metadata (4.1 kB)\r\nCollecting cachetools<6.0,>=2.0.0 (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading cachetools-5.3.2-py3-none-any.whl.metadata (5.2 kB)\r\nCollecting pyasn1-modules>=0.2.1 (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading pyasn1_modules-0.3.0-py2.py3-none-any.whl (181 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 181.3/181.3 kB 12.4 MB/s eta 0:00:00\r\nCollecting rsa<5,>=3.1.4 (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading rsa-4.9-py3-none-any.whl (34 kB)\r\nCollecting requests-oauthlib>=0.7.0 (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\r\nCollecting charset-normalizer<4,>=2 (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (33 kB)\r\nCollecting idna<4,>=2.5 (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading idna-3.4-py3-none-any.whl (61 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.5/61.5 kB 10.8 MB/s eta 0:00:00\r\nCollecting urllib3<3,>=1.21.1 (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading urllib3-2.0.7-py3-none-any.whl.metadata (6.6 kB)\r\nCollecting certifi>=2017.4.17 (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading certifi-2023.7.22-py3-none-any.whl.metadata (2.2 kB)\r\nCollecting MarkupSafe>=2.1.1 (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)\r\nCollecting pyasn1<0.6.0,>=0.4.6 (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow)\r\n Downloading pyasn1-0.5.0-py2.py3-none-any.whl (83 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 83.9/83.9 kB 15.4 MB/s eta 0:00:00\r\nRequirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\r\nDownloading tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (489.8 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 489.8/489.8 MB 2.9 MB/s eta 0:00:00\r\nDownloading ml_dtypes-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.0/1.0 MB 12.8 MB/s eta 0:00:00\r\nDownloading absl_py-2.0.0-py3-none-any.whl (130 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 130.2/130.2 kB 14.2 MB/s eta 0:00:00\r\nDownloading flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)\r\nDownloading grpcio-1.59.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.3/5.3 MB 5.6 MB/s eta 0:00:00\r\nDownloading h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.8/4.8 MB 6.6 MB/s eta 0:00:00\r\nDownloading keras-2.14.0-py3-none-any.whl (1.7 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 5.5 MB/s eta 0:00:00\r\nDownloading libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl (22.9 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 22.9/22.9 MB 7.0 MB/s eta 0:00:00\r\nDownloading numpy-1.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.2/18.2 MB 4.6 MB/s eta 0:00:00\r\nDownloading protobuf-4.25.0-cp37-abi3-manylinux2014_x86_64.whl (294 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 294.4/294.4 kB 2.7 MB/s eta 0:00:00\r\nDownloading tensorboard-2.14.1-py3-none-any.whl (5.5 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/5.5 MB 6.5 MB/s eta 0:00:00\r\nDownloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl (440 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 440.7/440.7 kB 5.0 MB/s eta 0:00:00\r\nDownloading tensorflow_io_gcs_filesystem-0.34.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 8.9 MB/s eta 0:00:00\r\nDownloading typing_extensions-4.8.0-py3-none-any.whl (31 kB)\r\nDownloading packaging-23.2-py3-none-any.whl (53 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 53.0/53.0 kB 463.6 kB/s eta 0:00:00\r\nDownloading google_auth-2.23.4-py2.py3-none-any.whl (183 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 183.3/183.3 kB 4.0 MB/s eta 0:00:00\r\nDownloading Markdown-3.5.1-py3-none-any.whl (102 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 102.2/102.2 kB 14.9 MB/s eta 0:00:00\r\nDownloading requests-2.31.0-py3-none-any.whl (62 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.6/62.6 kB 10.5 MB/s eta 0:00:00\r\nDownloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.6/6.6 MB 6.2 MB/s eta 0:00:00\r\nDownloading werkzeug-3.0.1-py3-none-any.whl (226 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 226.7/226.7 kB 1.8 MB/s eta 0:00:00\r\nDownloading cachetools-5.3.2-py3-none-any.whl (9.3 kB)\r\nDownloading certifi-2023.7.22-py3-none-any.whl (158 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 158.3/158.3 kB 5.0 MB/s eta 0:00:00\r\nDownloading charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 142.1/142.1 kB 22.8 MB/s eta 0:00:00\r\nDownloading MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\r\nDownloading urllib3-2.0.7-py3-none-any.whl (124 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.2/124.2 kB 18.5 MB/s eta 0:00:00\r\nInstalling collected packages: libclang, flatbuffers, wrapt, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, packaging, numpy, MarkupSafe, markdown, keras, idna, grpcio, google-pasta, gast, charset-normalizer, certifi, cachetools, astunparse, absl-py, werkzeug, rsa, requests, pyasn1-modules, opt-einsum, ml-dtypes, h5py, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow\r\nSuccessfully installed MarkupSafe-2.1.3 absl-py-2.0.0 astunparse-1.6.3 cachetools-5.3.2 certifi-2023.7.22 charset-normalizer-3.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.23.4 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.59.2 h5py-3.10.0 idna-3.4 keras-2.14.0 libclang-16.0.6 markdown-3.5.1 ml-dtypes-0.2.0 numpy-1.26.1 opt-einsum-3.3.0 packaging-23.2 protobuf-4.25.0 pyasn1-0.5.0 pyasn1-modules-0.3.0 requests-2.31.0 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.14.1 tensorboard-data-server-0.7.2 tensorflow-2.14.0 tensorflow-estimator-2.14.0 tensorflow-io-gcs-filesystem-0.34.0 termcolor-2.3.0 typing-extensions-4.8.0 urllib3-2.0.7 werkzeug-3.0.1 wrapt-1.14.1\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\r\n```\r\nBut still, the TensorFlow installation has some issues.\r\n```\r\nroot@d87518e6c38b:/# python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\nIllegal instruction (core dumped)\r\nroot@d87518e6c38b:/# python3\r\nPython 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\nIllegal instruction (core dumped)\r\nroot@d87518e6c38b:/# \r\n\r\n```\r\nFYI : I'm suspecting this issue might be hardware-specific because on an 11th Gen Intel (R) CoreTM i5-1135G7 @ 2.40GHz machine, it works as expected in the same OS environment.But on the Celeron hardware platform, I'm facing this issue only.\r\n \r\n**CPU Mode :** \r\nIntel(R) Celeron(R) N4505 @ 2.00GHz\r\n\r\n**Kernel Details :** \r\n```\r\nLinux d87518e6c38b 6.5.0-0.deb12.1-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.5.3-1~bpo12+1 (2023-10-08) x86_64 x86_64 x86_64 GNU/Linux\r\nroot@d87518e6c38b:/#\r\n```\r\n**OS Details :** \r\n```\r\nPRETTY_NAME=\"Ubuntu 22.04.3 LTS\"\r\nNAME=\"Ubuntu\"\r\nVERSION_ID=\"22.04\"\r\nVERSION=\"22.04.3 LTS (Jammy Jellyfish)\"\r\nVERSION_CODENAME=jammy\r\nID=ubuntu\r\nID_LIKE=debian\r\nHOME_URL=\"https://www.ubuntu.com/\"\r\nSUPPORT_URL=\"https://help.ubuntu.com/\"\r\nBUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\r\nPRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\r\nUBUNTU_CODENAME=jammy\r\n```",
"Hi, @fti-sfuke!\r\nThank you for your response!\r\nGenerally the \"Illegal instruction (core dumped)\" error caused by a CPU that does not support AVX instructions.\r\nThe Intel(R) Celeron(R) N4505 processor does not support AVX instructions, so you will not be able to use TensorFlow on this CPU without compiling it from the source. The earlier version doesn't require the AVX instructions but the newer version does.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62342\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62342\">No</a>\n",
"i got the same issue . shows illegal instruction(core dump) \r\nhi @sushreebarsa what about Intel(R) Celeron(R) N4020 processors that support AVX instructions?"
] | 2023-11-07T07:30:30 | 2024-05-13T08:05:02 | 2023-11-26T01:49:25 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.12.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
python3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
Intel(R) Celeron(R) N4505 @ 2.00GHz
### Current behavior?
```
===============================
TensorFlow Prereq
===============================
===============================
Python Packages for TensorFlow
===============================
#0300/912-1 | pip_retry install -U --prefer-binary h5py pybind11
----------------------------------------
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: There was an error checking the latest version of pip.
----------------------------------------
[ OK ]
#0300/912-2 | pip_retry install -U --prefer-binary tensorflow==2.12.0
----------------------------------------
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: There was an error checking the latest version of pip.
----------------------------------------
[ OK ]
@@@@ PKG: { elapsed: 43.418, user: 31.256, system: 5.856, percent: 85.47 } @@@@
#0300/912-3 | python3 -c 'import tensorflow as tf
a = tf.random.normal([1000, 1000])
b = tf.reduce_sum(a)
print(float(b))'
----------------------------------------
functions: line 201: 255 Illegal instruction (core dumped) python3 -c 'import tensorflow as tf
a = tf.random.normal([1000, 1000])
b = tf.reduce_sum(a)
print(float(b))'
----------------------------------------
[ ERROR(132) ]
@@@@ TST: { elapsed: 3.641, user: 0.391, system: 0.088, percent: 13.14 } @@@@
```
### Standalone code to reproduce the issue
```shell
# pip install -U --prefer-binary tensorflow==2.12.0
Requirement already satisfied: tensorflow==2.12.0 in /opt/miniconda/lib/python3.10/site-packages (2.12.0)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
#root@buildhost:/mnt# python3
Python 3.10.13 | packaged by conda-forge | (main, Oct 26 2023, 18:07:37) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
Illegal instruction (core dumped)
```
### Relevant log output
_No response_
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I_kwDOArmXAs52Cs7S
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Does tflite support background running on my phone?
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[
"Hi **@TANGATG** \r\nTensorFlow Lite itself doesn't control whether your application runs in the background on your phone. The background execution and multitasking capabilities are managed by the Android system itself, while TensorFlow Lite is responsible for running machine learning models on the device.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"ok，thanks",
"Hi **@TANGATG** ,\r\nThank you for your update, glad it's working fine for you, kindly move this issue to closed status as it is resolved.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62341\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62341\">No</a>\n"
] | 2023-11-07T02:28:37 | 2023-12-04T01:49:10 | 2023-12-04T01:49:06 |
NONE
| null | null | null |
**System information**
- Andriod Platform:
- TensorFlow installed from source:
- TensorFlow version is the latest:
**my issue**
For the tflite image processing application, the Android system usually uses a camera to collect data, for example, a demo of object detection, and uses a mobile phone camera to collect images, and an APP performs recognition. But I want to collect image data from a video on my phone and use my phone for object detection. What should I do? Does tflite support background running on my phone?
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@python_version_repo no longer visible after hermetic python changes
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[
"@oberluz,\r\nCould you please provide the python version and the environment details where you are trying to install the tensorflow. It helps to debug the issue. Thank you!",
"Python 3.11\r\nubuntu 22.04\r\narch amd64 (k8)",
"Here are the logs from my build in case they help:\r\n\r\nNote: these are the logs from building pycoral (not Tensorflow). Pycoral uses the bazel rules from @org_tensorflow... which seem to have been broken.\r\n\r\n'''\r\n$ DOCKER_CPUS=\"k8\" ./scripts/build.sh \r\n+++ dirname ./scripts/build.sh\r\n++ cd ./scripts\r\n++ pwd\r\n+ readonly SCRIPT_DIR=/home/eyeot-demo/src/pycoral/scripts\r\n+ SCRIPT_DIR=/home/eyeot-demo/src/pycoral/scripts\r\n+ readonly MAKEFILE=/home/eyeot-demo/src/pycoral/scripts/../Makefile\r\n+ MAKEFILE=/home/eyeot-demo/src/pycoral/scripts/../Makefile\r\n+ readonly DOCKER_CPUS=k8\r\n+ DOCKER_CPUS=k8\r\n+ readonly DOCKER_IMAGE_OPTIONS=--progress=plain\r\n+ DOCKER_IMAGE_OPTIONS=--progress=plain\r\n+ PYTHON_VERSIONS='37 38 39 310 311'\r\n+ [[ 0 -gt 0 ]]\r\n+ for python_version in ${PYTHON_VERSIONS}\r\n++ docker_image 37\r\n++ case $1 in\r\n++ echo debian:buster\r\n+ make DOCKER_CPUS=k8 DOCKER_IMAGE=debian:buster 'DOCKER_TARGETS=pybind tflite wheel tflite-wheel' -f /home/eyeot-demo/src/pycoral/scripts/../Makefile docker-build\r\ndocker build -t \"coral-edgetpu-debian-buster\" \\\r\n --build-arg IMAGE=debian:buster /home/eyeot-demo/src/pycoral/libcoral/docker\r\n[+] Building 1.9s (18/18) FINISHED \r\n => [internal] load build definition from Dockerfile 0.0s\r\n => => transferring dockerfile: 2.81kB 0.0s\r\n => [internal] load .dockerignore 0.0s\r\n => => transferring context: 2B 0.0s\r\n => [internal] load metadata for docker.io/library/debian:buster 1.8s\r\n => [internal] load build context 0.0s\r\n => => transferring context: 39B 0.0s\r\n => [ 1/13] FROM docker.io/library/debian:buster@sha256:53cf4f4dbe6f827072bde99045671754cca8174d0464d829c194a26e7ba2c134 0.0s\r\n => CACHED [ 2/13] COPY update_sources.sh / 0.0s\r\n => CACHED [ 3/13] RUN /update_sources.sh 0.0s\r\n => CACHED [ 4/13] RUN dpkg --add-architecture armhf 0.0s\r\n => CACHED [ 5/13] RUN dpkg --add-architecture arm64 0.0s\r\n => CACHED [ 6/13] RUN echo 'APT::Immediate-Configure false;' >> /etc/apt/apt.conf 0.0s\r\n => CACHED [ 7/13] RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y libc6-dev:arm64 libc6-dev:armhf 0.0s\r\n => CACHED [ 8/13] RUN DEBIAN_FRONTEND=noninteractive apt-get install -y python3-all bc && PYTHON_VERSION=$(/usr/bin/python3 --version | cut -d '.' -f 2); echo ${PYTHON_VERSION}; if [ $(echo \"${PYTHON_VERSION} < 9\" | bc -l 0.0s\r\n => CACHED [ 9/13] RUN if grep 'Bionic Beaver' /etc/os-release > /dev/null; then add-apt-repository ppa:ubuntu-toolchain-r/test && DEBIAN_FRONTEND=noninteractive apt-get install -y gcc-9 g++-9; fi 0.0s\r\n => CACHED [10/13] RUN mkdir /debs && chmod a=rwx /debs && cd /debs && apt-get update && apt-get download libglib2.0-0 libglib2.0-0:armhf libglib2.0-0:arm64 libglib2.0-dev libglib2.0-dev:armhf libglib2.0-dev:arm64 li 0.0s\r\n => CACHED [11/13] RUN for d in /debs/*.deb; do dpkg -x $d /usr/system_libs; done 0.0s\r\n => CACHED [12/13] RUN git clone https://github.com/raspberrypi/tools.git && cd tools && git reset --hard 4a335520900ce55e251ac4f420f52bf0b2ab6b1f 0.0s\r\n => CACHED [13/13] RUN wget -O /bazel https://github.com/bazelbuild/bazel/releases/download/6.1.0/bazel-6.1.0-installer-linux-x86_64.sh && bash /bazel && rm -f /bazel 0.0s\r\n => exporting to image 0.0s\r\n => => exporting layers 0.0s\r\n => => writing image sha256:a2f7932e00c6ae8dfd13e813d988ba3fe20c6a9c8a8211dc92ca53708925b3ca 0.0s\r\n => => naming to docker.io/library/coral-edgetpu-debian-buster 0.0s\r\ndocker run --rm -i --tty -v /home/eyeot-demo/src/pycoral/:/workspace \\\r\n \"coral-edgetpu-debian-buster\" /bin/bash -c \"chmod a+w /; groupadd --gid 1000 eyeot-demo; useradd -m -e '' -s /bin/bash --gid 1000 --uid 1000 eyeot-demo; echo 'eyeot-demo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers; su eyeot-demo -c 'for cpu in k8; do make CPU=\\${cpu} COMPILATION_MODE=opt -C /workspace/ pybind tflite wheel tflite-wheel || exit 1; done'\"\r\nmake: Entering directory '/workspace'\r\nExtracting Bazel installation...\r\n^C\r\nSession terminated, killing shell...^[[A^[[A ...killed.\r\nmake: *** [/home/eyeot-demo/src/pycoral/libcoral/docker/docker.mk:40: docker-build] Error 130\r\neyeot-demo@eyeotdemo-ThinkPad-X380-Yoga:~/src/pycoral(2_14_0)$ ^C\r\neyeot-demo@eyeotdemo-ThinkPad-X380-Yoga:~/src/pycoral(2_14_0)$ DOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n+++ dirname ./scripts/build.sh\r\n++ cd ./scripts\r\n++ pwd\r\n+ readonly SCRIPT_DIR=/home/eyeot-demo/src/pycoral/scripts\r\n+ SCRIPT_DIR=/home/eyeot-demo/src/pycoral/scripts\r\n+ readonly MAKEFILE=/home/eyeot-demo/src/pycoral/scripts/../Makefile\r\n+ MAKEFILE=/home/eyeot-demo/src/pycoral/scripts/../Makefile\r\n+ readonly DOCKER_CPUS=k8\r\n+ DOCKER_CPUS=k8\r\n+ readonly DOCKER_IMAGE_OPTIONS=--progress=plain\r\n+ DOCKER_IMAGE_OPTIONS=--progress=plain\r\n+ PYTHON_VERSIONS='37 38 39 310 311'\r\n+ [[ 2 -gt 0 ]]\r\n+ case \"$1\" in\r\n+ PYTHON_VERSIONS=310\r\n+ shift\r\n+ shift\r\n+ [[ 0 -gt 0 ]]\r\n+ for python_version in ${PYTHON_VERSIONS}\r\n++ docker_image 310\r\n++ case $1 in\r\n++ echo ubuntu:22.04\r\n+ make DOCKER_CPUS=k8 DOCKER_IMAGE=ubuntu:22.04 'DOCKER_TARGETS=pybind tflite wheel tflite-wheel' -f /home/eyeot-demo/src/pycoral/scripts/../Makefile docker-build\r\ndocker build -t \"coral-edgetpu-ubuntu-22.04\" \\\r\n --build-arg IMAGE=ubuntu:22.04 /home/eyeot-demo/src/pycoral/libcoral/docker\r\n[+] Building 1.3s (18/18) FINISHED \r\n => [internal] load build definition from Dockerfile 0.0s\r\n => => transferring dockerfile: 2.81kB 0.0s\r\n => [internal] load .dockerignore 0.0s\r\n => => transferring context: 2B 0.0s\r\n => [internal] load metadata for docker.io/library/ubuntu:22.04 1.2s\r\n => [internal] load build context 0.0s\r\n => => transferring context: 39B 0.0s\r\n => [ 1/13] FROM docker.io/library/ubuntu:22.04@sha256:2b7412e6465c3c7fc5bb21d3e6f1917c167358449fecac8176c6e496e5c1f05f 0.0s\r\n => CACHED [ 2/13] COPY update_sources.sh / 0.0s\r\n => CACHED [ 3/13] RUN /update_sources.sh 0.0s\r\n => CACHED [ 4/13] RUN dpkg --add-architecture armhf 0.0s\r\n => CACHED [ 5/13] RUN dpkg --add-architecture arm64 0.0s\r\n => CACHED [ 6/13] RUN echo 'APT::Immediate-Configure false;' >> /etc/apt/apt.conf 0.0s\r\n => CACHED [ 7/13] RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y libc6-dev:arm64 libc6-dev:armhf 0.0s\r\n => CACHED [ 8/13] RUN DEBIAN_FRONTEND=noninteractive apt-get install -y python3-all bc && PYTHON_VERSION=$(/usr/bin/python3 --version | cut -d '.' -f 2); echo ${PYTHON_VERSION}; if [ $(echo \"${PYTHON_VERSION} < 9\" | bc -l 0.0s\r\n => CACHED [ 9/13] RUN if grep 'Bionic Beaver' /etc/os-release > /dev/null; then add-apt-repository ppa:ubuntu-toolchain-r/test && DEBIAN_FRONTEND=noninteractive apt-get install -y gcc-9 g++-9; fi 0.0s\r\n => CACHED [10/13] RUN mkdir /debs && chmod a=rwx /debs && cd /debs && apt-get update && apt-get download libglib2.0-0 libglib2.0-0:armhf libglib2.0-0:arm64 libglib2.0-dev libglib2.0-dev:armhf libglib2.0-dev:arm64 li 0.0s\r\n => CACHED [11/13] RUN for d in /debs/*.deb; do dpkg -x $d /usr/system_libs; done 0.0s\r\n => CACHED [12/13] RUN git clone https://github.com/raspberrypi/tools.git && cd tools && git reset --hard 4a335520900ce55e251ac4f420f52bf0b2ab6b1f 0.0s\r\n => CACHED [13/13] RUN wget -O /bazel https://github.com/bazelbuild/bazel/releases/download/6.1.0/bazel-6.1.0-installer-linux-x86_64.sh && bash /bazel && rm -f /bazel 0.0s\r\n => exporting to image 0.0s\r\n => => exporting layers 0.0s\r\n => => writing image sha256:d2a09d4ffef72f6aca1b9e1d7a8501e6b0ba0855bfd4e8fe844aee05ae1da272 0.0s\r\n => => naming to docker.io/library/coral-edgetpu-ubuntu-22.04 0.0s\r\ndocker run --rm -i --tty -v /home/eyeot-demo/src/pycoral/:/workspace \\\r\n \"coral-edgetpu-ubuntu-22.04\" /bin/bash -c \"chmod a+w /; groupadd --gid 1000 eyeot-demo; useradd -m -e '' -s /bin/bash --gid 1000 --uid 1000 eyeot-demo; echo 'eyeot-demo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers; su eyeot-demo -c 'for cpu in k8; do make CPU=\\${cpu} COMPILATION_MODE=opt -C /workspace/ pybind tflite wheel tflite-wheel || exit 1; done'\"\r\nmake: Entering directory '/workspace'\r\nExtracting Bazel installation...\r\nStarting local Bazel server and connecting to it...\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/769f5cc9b8732933140b09e8808d13614182b496.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/668e33c6401abe7844691fb7d47a3cf2d2012dbc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/workspace/WORKSPACE\", line 100, column 49, in <toplevel>\r\n\t\tload(\"@rules_python//python:repositories.bzl\", \"python_register_toolchains\")\r\nError: file '@rules_python//python:repositories.bzl' does not contain symbol 'python_register_toolchains'\r\nERROR: Error computing the main repository mapping: Encountered error while reading extension file 'py_version.bzl': no such package '@python_version_repo//': error loading package 'external': Could not load //external package\r\n checking cached actions\r\nPYTHON_BIN_PATH=/usr/bin/python3 bazel build --sandbox_debug --compilation_mode=opt --copt=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --cpu=k8 --linkopt=-L/workspace/libedgetpu_bin/direct/k8 --linkopt=-l:libedgetpu.so.1 --linkopt=-Wl,--strip-all \\\r\n --embed_label='TENSORFLOW_COMMIT=' \\\r\n --stamp \\\r\n //src:_pywrap_coral\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/668e33c6401abe7844691fb7d47a3cf2d2012dbc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/769f5cc9b8732933140b09e8808d13614182b496.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/workspace/WORKSPACE\", line 100, column 49, in <toplevel>\r\n\t\tload(\"@rules_python//python:repositories.bzl\", \"python_register_toolchains\")\r\nError: file '@rules_python//python:repositories.bzl' does not contain symbol 'python_register_toolchains'\r\nERROR: Error computing the main repository mapping: Encountered error while reading extension file 'py_version.bzl': no such package '@python_version_repo//': error loading package 'external': Could not load //external package\r\nLoading: \r\nmake: *** [Makefile:154: pybind] Error 1\r\nmake: Leaving directory '/workspace'\r\n'''",
"I'm getting what looks like a different error when following the instructions:\r\n\r\nthat is after I do this:\r\n```\r\ngit clone --recurse-submodules https://github.com/google-coral/pycoral\r\ncd pycoral\r\ngit submodule init && git submodule update\r\nsudo scripts/build.sh\r\n```\r\n\r\n```\r\nSUBCOMMAND: # @com_google_absl//absl/hash:hash [action 'Compiling absl/hash/internal/hash.cc', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/com_google_absl/absl/hash/_objs/hash/hash.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/com_google_absl/absl/hash/_objs/hash/hash.o' -iquote external/com_google_absl -iquote bazel-out/armv7a-opt/bin/external/com_google_absl '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -Wall -Wextra -Wcast-qual -Wconversion-null -Wmissing-declarations -Woverlength-strings -Wpointer-arith -Wundef -Wunused-local-typedefs -Wunused-result -Wvarargs -Wvla -Wwrite-strings -Wno-missing-field-initializers -Wno-sign-compare -DNOMINMAX -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/com_google_absl/absl/hash/internal/hash.cc -o bazel-out/armv7a-opt/bin/external/com_google_absl/absl/hash/_objs/hash/hash.o)\r\nSUBCOMMAND: # @clog//:clog [action 'Linking external/clog/libclog.a', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-ar @bazel-out/armv7a-opt/bin/external/clog/libclog.a-2.params)\r\nSUBCOMMAND: # @com_google_absl//absl/strings:cord [action 'Compiling absl/strings/cord.cc', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/com_google_absl/absl/strings/_objs/cord/cord.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/com_google_absl/absl/strings/_objs/cord/cord.o' -iquote external/com_google_absl -iquote bazel-out/armv7a-opt/bin/external/com_google_absl '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -Wall -Wextra -Wcast-qual -Wconversion-null -Wmissing-declarations -Woverlength-strings -Wpointer-arith -Wundef -Wunused-local-typedefs -Wunused-result -Wvarargs -Wvla -Wwrite-strings -Wno-missing-field-initializers -Wno-sign-compare -DNOMINMAX -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/com_google_absl/absl/strings/cord.cc -o bazel-out/armv7a-opt/bin/external/com_google_absl/absl/strings/_objs/cord/cord.o)\r\nINFO: From Compiling tensorflow/lite/kernels/local_response_norm.cc:\r\nIn file included from external/ruy/ruy/pack.h:94,\r\n from external/ruy/ruy/create_trmul_params.h:30,\r\n from external/ruy/ruy/frontend.h:30,\r\n from external/ruy/ruy/ruy.h:23,\r\n from external/org_tensorflow/tensorflow/lite/kernels/cpu_backend_gemm_ruy.h:21,\r\n from external/org_tensorflow/tensorflow/lite/kernels/cpu_backend_gemm.h:25,\r\n from external/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:45,\r\n from external/org_tensorflow/tensorflow/lite/kernels/local_response_norm.cc:17:\r\nexternal/ruy/ruy/pack_arm.h:492:9: warning: multi-line comment [-Wcomment]\r\n 492 | #endif // (RUY_PLATFORM_NEON_64 || RUY_PLATFORM_NEON_32) && \\\r\n | ^\r\nIn file included from external/org_tensorflow/tensorflow/lite/kernels/local_response_norm.cc:17:\r\nexternal/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h: In instantiation of 'void tflite::optimized_ops::Transpose3D(const tflite::TransposeParams&, const tflite::RuntimeShape&, const T*, const tflite::RuntimeShape&, T*) [with T = float]':\r\nexternal/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:7376:16: required from 'void tflite::optimized_ops::TransposeImpl(const tflite::TransposeParams&, const tflite::RuntimeShape&, const T*, const tflite::RuntimeShape&, T*) [with T = float; int N = 5]'\r\nexternal/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:7435:26: required from 'void tflite::optimized_ops::Transpose(const tflite::TransposeParams&, const tflite::RuntimeShape&, const T*, const tflite::RuntimeShape&, T*) [with T = float; int N = 5]'\r\nexternal/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:8027:70: required from here\r\nexternal/org_tensorflow/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:7305:7: warning: variable 's1' set but not used [-Wunused-but-set-variable]\r\n 7305 | int s1, s2, s3;\r\n | ^~\r\nSUBCOMMAND: # @org_tensorflow//tensorflow/lite/kernels/internal:quantization_util [action 'Compiling tensorflow/lite/kernels/internal/quantization_util.cc', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/org_tensorflow/tensorflow/lite/kernels/internal/_objs/quantization_util/quantization_util.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/org_tensorflow/tensorflow/lite/kernels/internal/_objs/quantization_util/quantization_util.o' -iquote external/org_tensorflow -iquote bazel-out/armv7a-opt/bin/external/org_tensorflow '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -DFARMHASH_NO_CXX_STRING -Wno-sign-compare -O3 -fno-exceptions -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/org_tensorflow/tensorflow/lite/kernels/internal/quantization_util.cc -o bazel-out/armv7a-opt/bin/external/org_tensorflow/tensorflow/lite/kernels/internal/_objs/quantization_util/quantization_util.o)\r\nSUBCOMMAND: # @flatbuffers//src:flatbuffers [action 'Compiling src/code_generators.cpp', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/code_generators.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/code_generators.o' -iquote external/flatbuffers -iquote bazel-out/armv7a-opt/bin/external/flatbuffers -Ibazel-out/armv7a-opt/bin/external/flatbuffers/src/_virtual_includes/flatbuffers '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/flatbuffers/src/code_generators.cpp -o bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/code_generators.o)\r\nSUBCOMMAND: # @org_tensorflow//tensorflow/lite/kernels/internal:transpose_utils [action 'Linking external/org_tensorflow/tensorflow/lite/kernels/internal/libtranspose_utils.a', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-ar @bazel-out/armv7a-opt/bin/external/org_tensorflow/tensorflow/lite/kernels/internal/libtranspose_utils.a-2.params)\r\nSUBCOMMAND: # @flatbuffers//src:flatbuffers [action 'Compiling src/idl_parser.cpp', configuration: 11cae9684ea823b3911201bf8ead584031fd2c07b224432188557a50a44d29ae, execution platform: @local_execution_config_platform//:platform]\r\n(cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/idl_parser.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/idl_parser.o' -iquote external/flatbuffers -iquote bazel-out/armv7a-opt/bin/external/flatbuffers -Ibazel-out/armv7a-opt/bin/external/flatbuffers/src/_virtual_includes/flatbuffers '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/flatbuffers/src/idl_parser.cpp -o bazel-out/armv7a-opt/bin/external/flatbuffers/src/_objs/flatbuffers/idl_parser.o)\r\nERROR: /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/external/ruy/ruy/BUILD:576:11: Compiling ruy/pack_arm.cc failed: (Exit 1): arm-linux-gnueabihf-gcc failed: error executing command \r\n (cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/sandbox/processwrapper-sandbox/1468/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.o' -iquote external/ruy -iquote bazel-out/armv7a-opt/bin/external/ruy -iquote external/cpuinfo -iquote bazel-out/armv7a-opt/bin/external/cpuinfo -iquote external/clog -iquote bazel-out/armv7a-opt/bin/external/clog -Ibazel-out/armv7a-opt/bin/external/cpuinfo/_virtual_includes/cpuinfo -Ibazel-out/armv7a-opt/bin/external/clog/_virtual_includes/clog '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -Wall -Wextra -Wc++14-compat -Wundef '-mfpu=neon' -O3 -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/ruy/ruy/pack_arm.cc -o bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.o)\r\nExecution platform: @local_execution_config_platform//:platform\r\n\r\nUse --sandbox_debug to see verbose messages from the sandbox arm-linux-gnueabihf-gcc failed: error executing command \r\n (cd /root/.cache/bazel/_bazel_root/eab0d61a99b6696edb3d2aff87b585e8/sandbox/processwrapper-sandbox/1468/execroot/pycoral && \\\r\n exec env - \\\r\n PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PWD=/proc/self/cwd \\\r\n /usr/bin/arm-linux-gnueabihf-gcc -fPIC -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer '-march=armv7-a' '-mfpu=neon-vfpv4' -g0 -O3 -DNDEBUG '-D_FORTIFY_SOURCE=2' -ffunction-sections -fdata-sections -funsafe-math-optimizations -ftree-vectorize '-std=c++14' -MD -MF bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.d '-frandom-seed=bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.o' -iquote external/ruy -iquote bazel-out/armv7a-opt/bin/external/ruy -iquote external/cpuinfo -iquote bazel-out/armv7a-opt/bin/external/cpuinfo -iquote external/clog -iquote bazel-out/armv7a-opt/bin/external/clog -Ibazel-out/armv7a-opt/bin/external/cpuinfo/_virtual_includes/cpuinfo -Ibazel-out/armv7a-opt/bin/external/clog/_virtual_includes/clog '-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION' '-ffp-contract=off' -Wall -Wextra -Wc++14-compat -Wundef '-mfpu=neon' -O3 -no-canonical-prefixes -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__=\"redacted\"' '-D__TIMESTAMP__=\"redacted\"' '-D__TIME__=\"redacted\"' -c external/ruy/ruy/pack_arm.cc -o bazel-out/armv7a-opt/bin/external/ruy/ruy/_objs/pack_arm/pack_arm.o)\r\nExecution platform: @local_execution_config_platform//:platform\r\n\r\nUse --sandbox_debug to see verbose messages from the sandbox\r\nIn file included from external/ruy/ruy/pack_arm.cc:16:\r\nexternal/ruy/ruy/pack_arm.h:492:9: warning: multi-line comment [-Wcomment]\r\n 492 | #endif // (RUY_PLATFORM_NEON_64 || RUY_PLATFORM_NEON_32) && \\\r\n | ^\r\nexternal/ruy/ruy/pack_arm.cc: In function 'void ruy::Pack8bitColMajorForNeon4Cols(const ruy::PackParams8bit&)':\r\nexternal/ruy/ruy/pack_arm.cc:469:72: error: 'asm' operand has impossible constraints\r\n 469 | \"q4\", \"q5\", \"q6\", \"q7\", \"q8\", \"q9\", \"q10\", \"q11\", \"q12\", \"q13\");\r\n | ^\r\nTarget //src:_pywrap_coral failed to build\r\nINFO: Elapsed time: 21.281s, Critical Path: 20.12s\r\nINFO: 445 processes: 185 internal, 260 processwrapper-sandbox.\r\nFAILED: Build did NOT complete successfully\r\nmake: *** [Makefile:152: pybind] Error 1\r\nmake: Leaving directory '/workspace'\r\nmake: *** [/usr/local/google/home/xxxxx/git/pycoral/libcoral/docker/docker.mk:40: docker-build] Error 1\r\n```\r\n\r\nCan you let us know your exact commands? preferably with a fresh repo. I see a lot of cached actions, can you try clearing your docker cache to see if that changes anything?\r\n\r\nThanks for your help.",
"Hi,\r\n\r\nhttps://github.com/google-coral/pycoral is the wrong repo. Work in this repo stopped a couple of years back and it only supports Tensorflow v2.0.0. Note the release page only provides wheels for v2.0.0.\r\n\r\nI forked it to https://github.com/oberluz/pycoral.git and have ported to support Tensorflow v2.7.0 and v2.13.0. Each of these is on a separate branch for now. Support for Python 3.11 was introduced in Tensorflow v2.12.0.\r\n\r\nThis issue relates to branch 2_14_0. So you should clone (recursively)the repo I mentioned with branch 2_14_0.\r\n\r\nI do not work for google. I'm just providing community support after a lot of people complained in the google-coral about lack of progress.\r\n\r\nNote that https://github.com/oberluz/pycoral.git uses libedgepu and libcoral repos which I also forked and modified to support 2.7, 2.13 and 2.14. These are linked as submodules (hence cursive clone)\r\n\r\nalso, don't try to build for armv7a as ruy is broken. I would try:\r\n\r\n$ DOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n\r\nThanks for your help",
"@oberluz,\r\n\r\nThanks for your help, I'm reaching the following error now, hopefully you can help me get past it:\r\n\r\nMy steps:\r\n```\r\ngit clone --recurse-submodules https://github.com/oberluz/pycoral.git\r\ncd pycoral\r\ngit submodule init && git submodule update\r\nDOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n```\r\n\r\n```\r\nDOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n+++ dirname ./scripts/build.sh\r\n++ cd ./scripts\r\n++ pwd\r\n+ readonly SCRIPT_DIR=/usr/local/google/home/xxxxx/git/pycoral/scripts\r\n+ SCRIPT_DIR=/usr/local/google/home/xxxxxx/git/pycoral/scripts\r\n+ readonly MAKEFILE=/usr/local/google/home/xxxxxx/git/pycoral/scripts/../Makefile\r\n+ MAKEFILE=/usr/local/google/home/xxxxxx/git/pycoral/scripts/../Makefile\r\n+ readonly DOCKER_CPUS=k8\r\n+ DOCKER_CPUS=k8\r\n+ PYTHON_VERSIONS='36 37 38 39'\r\n+ [[ 2 -gt 0 ]]\r\n+ case \"$1\" in\r\n+ PYTHON_VERSIONS=310\r\n+ shift\r\n+ shift\r\n+ [[ 0 -gt 0 ]]\r\n+ for python_version in ${PYTHON_VERSIONS}\r\n++ docker_image 310\r\n++ case $1 in\r\n++ echo 'Unsupported python version: 310'\r\nUnsupported python version: 310\r\n++ exit 1\r\n+ make DOCKER_CPUS=k8 DOCKER_IMAGE= 'DOCKER_TARGETS=pybind tflite wheel tflite-wheel' -f /usr/local/google/home/xxxxxx/git/pycoral/scripts/../Makefile docker-build\r\ndocker build -t \"coral-edgetpu-\" \\\r\n --build-arg IMAGE= /usr/local/google/home/xxxxx/git/pycoral/libcoral/docker\r\n[+] Building 0.0s (0/0) \r\nERROR: invalid tag \"coral-edgetpu-\": invalid reference format\r\nmake: *** [/usr/local/google/home/xxxxxx/git/pycoral/libcoral/docker/docker.mk:31: docker-image] Error 1\r\n```\r\n\r\nYou may want to update your compilation instructions on your README so that it points to your new repo rather than the original.",
"Hi,\r\n\r\ntry as follows:\r\n\r\n```\r\n$ git clone https://github.com/google-coral/pycoral\r\n$ cd pycoral\r\n$ git checkout 2_14_0\r\n$ git submodule update --init --recursive\r\n$ DOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n```",
"I was able to reproduce with the following steps:\r\n\r\n```\r\n$ git clone https://github.com/oberluz/pycoral.git\r\n$ cd pycoral\r\n$ git checkout 2_14_0\r\n$ git submodule update --init --recursive\r\n$ DOCKER_CPUS=\"k8\" ./scripts/build.sh --python_versions 310\r\n```\r\n\r\nAfter the docker finishes\r\n```\r\nmake: Entering directory '/workspace'\r\nExtracting Bazel installation...\r\nStarting local Bazel server and connecting to it...\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/769f5cc9b8732933140b09e8808d13614182b496.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/668e33c6401abe7844691fb7d47a3cf2d2012dbc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\n checking cached actions\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/workspace/WORKSPACE\", line 100, column 49, in <toplevel>\r\n\t\tload(\"@rules_python//python:repositories.bzl\", \"python_register_toolchains\")\r\nError: file '@rules_python//python:repositories.bzl' does not contain symbol 'python_register_toolchains'\r\nERROR: Error computing the main repository mapping: Encountered error while reading extension file 'py_version.bzl': no such package '@python_version_repo//': error loading package 'external': Could not load //external package\r\n checking cached actions\r\nPYTHON_BIN_PATH=/usr/bin/python3 bazel build --sandbox_debug --compilation_mode=opt --copt=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --cpu=k8 --linkopt=-L/workspace/libedgetpu_bin/direct/k8 --linkopt=-l:libedgetpu.so.1 --linkopt=-Wl,--strip-all \\\r\n --embed_label='TENSORFLOW_COMMIT=' \\\r\n --stamp \\\r\n //src:_pywrap_coral\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/668e33c6401abe7844691fb7d47a3cf2d2012dbc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/769f5cc9b8732933140b09e8808d13614182b496.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/workspace/WORKSPACE\", line 100, column 49, in <toplevel>\r\n\t\tload(\"@rules_python//python:repositories.bzl\", \"python_register_toolchains\")\r\nError: file '@rules_python//python:repositories.bzl' does not contain symbol 'python_register_toolchains'\r\nERROR: Error computing the main repository mapping: Encountered error while reading extension file 'py_version.bzl': no such package '@python_version_repo//': error loading package 'external': Could not load //external package\r\nLoading: \r\nmake: *** [Makefile:154: pybind] Error 1\r\nmake: Leaving directory '/workspace'\r\nmake: *** [/usr/local/google/home/xxxxxxx/git/pycoral/libcoral/docker/docker.mk:40: docker-build] Error 1\r\n```\r\n\r\nHi @terryheo, can you please take a look? Thanks.",
"@terryheo just to clarify, if I use the previous commit in the 2_14_0 branch which didn't have the hermetic python definitions the behaviour is as follows (the definitions were added in an attempt to fix this error):\r\n\r\n```\r\nLoading: 1 packages loaded\r\nPYTHON_BIN_PATH=/usr/bin/python3 bazel build --sandbox_debug --compilation_mode=opt --copt=-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION --cpu=k8 --linkopt=-L/workspace/libedgetpu_bin/direct/k8 --linkopt=-l:libedgetpu.so.1 --linkopt=-Wl,--strip-all \\\r\n --embed_label='TENSORFLOW_COMMIT=4dacf3f368eb7965e9b5c3bbdd5193986081c3b2' \\\r\n --stamp \\\r\n //src:_pywrap_coral\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/668e33c6401abe7844691fb7d47a3cf2d2012dbc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/769f5cc9b8732933140b09e8808d13614182b496.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://mirror.bazel.build/github.com/bazelbuild/rules_cc/archive/081771d4a0e9d7d3aa0eed2ef389fa4700dfb23e.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Repository go_sdk instantiated at:\r\n /workspace/WORKSPACE:37:14: in <toplevel>\r\n /home/eyeot-demo/.cache/bazel/_bazel_eyeot-demo/eab0d61a99b6696edb3d2aff87b585e8/external/org_tensorflow/tensorflow/workspace0.bzl:135:20: in workspace\r\n /home/eyeot-demo/.cache/bazel/_bazel_eyeot-demo/eab0d61a99b6696edb3d2aff87b585e8/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps\r\n /home/eyeot-demo/.cache/bazel/_bazel_eyeot-demo/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains\r\n /home/eyeot-demo/.cache/bazel/_bazel_eyeot-demo/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk\r\nRepository rule _go_download_sdk defined at:\r\n /home/eyeot-demo/.cache/bazel/_bazel_eyeot-demo/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>\r\nERROR: /workspace/src/BUILD:67:17: While resolving toolchains for target //src:_pywrap_coral.so: invalid registered toolchain '@local_execution_config_python//:py_toolchain': error loading package '@local_execution_config_python//': Unable to find package for @python//:defs.bzl: The repository '@python' could not be resolved: Repository '@python' is not defined.\r\nERROR: Analysis of target '//src:_pywrap_coral' failed; build aborted: \r\nINFO: Elapsed time: 2.569s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (20 packages loaded, 10 targets configured)\r\n currently loading: @local_execution_config_python// ... (15 packages)\r\nmake: *** [Makefile:154: pybind] Error 1\r\nmake: Leaving directory '/workspace'\r\nmake: *** [/home/eyeot-demo/src/pycoral/libcoral/docker/docker.mk:40: docker-build] Error 1\r\n```\r\n",
"@pkgoogle it seems @terryheo is not going to pick this up. Could yo reassign to someone else?",
"Hi @oberluz, there are a lot of simultaneous issues... If you feel you can make progress on a root cause PR please feel free to submit one that we can review.",
"Hi @pkgoogle I understand. I'll wait as I tried fixing it for a few days before raising this issue and didn't get anywhere..."
] | 2023-11-06T19:18:28 | 2023-12-08T09:15:43 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
+load("@python//:defs.bzl", "interpreter")
+load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")
+
+
and the error is:
ERROR: Traceback (most recent call last):
File "/home/eyeot-demo/src/pycoral/WORKSPACE", line 100, column 49, in <toplevel>
load("@rules_python//python:repositories.bzl", "python_register_toolchains")
Error: file '@rules_python//python:repositories.bzl' does not contain symbol 'python_register_toolchains'
ERROR: error loading package '': Encountered error while reading extension file 'py_version.bzl': no such package '@python_version_repo//': error loading package 'external': Could not load //external package
Loading: 0 packages loaded
Could you suggest if I'm missing something?
### Standalone code to reproduce the issue
```shell
https://github.com/oberluz/pycoral/tree/2_14_0
```
### Relevant log output
_No response_
|
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Install error with clang compiler. CUDA device code does not support variadict functions.
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[
"Hi @ujjwalnur,\r\n\r\nCould you please confirm whether you have used the flag `--config=cuda` and set **Y** to the `./configure` step `Do you wish to build TensorFlow with CUDA support?` while building?",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"I've been having this same issue trying to build from source. I did use the `--config=cuda` flag in my build statement and answered **Y** to the CUDA question in the `./configure` step. Here are the details for my system:\r\n\r\n**Source:** latest\r\n**Custom code:** no\r\n**OS platform and distro:** Ubuntu 22.04\r\n**Python version:** 3.10\r\n**Bazel version:** 6.1.0\r\n**Compiler version:** clang-16\r\n**CUDA version:** 12.2.2\r\n**cuDNN version:** 8.9.6\r\n**GPU model:** RTX 3070",
"Hi, There is a similar issue open, till we resolve the issue, could you please follow nvcc instead of clang as per the comment here https://github.com/tensorflow/tensorflow/issues/62459#issuecomment-1830409296",
"Switching to the nvcc compiler worked. Was just able to get a successful build with CUDA. Thanks!",
"Managed to build by adding `-copt=-Xclang --copt=-fcuda-allow-variadic-functions` following conversation in https://github.com/llvm/llvm-project/issues/58410 "
] | 2023-11-06T17:44:49 | 2023-12-09T01:15:43 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.14
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
clang-16
### CUDA/cuDNN version
CUDA 12.2 / CuDNN 8.9
### GPU model and memory
RTX 4090
### Current behavior?
TensorFlow install instructions recommend using Clang as the CUDA compiler. However, the build fails with the error
**CUDA device code does not support variadic functions.**
I have found on this [link](https://discourse.llvm.org/t/cuda-cuda-device-code-does-not-support-variadic-functions-in-clang/60481/2) that at least until 18 months ago, clang did not have general support for variadic functions.
Is that still the case ? I would suspect not because the official tensorflow website recommends using clang as the CUDA compiler.
By the way, I am using clang-16 as shown on the official webpage
### Standalone code to reproduce the issue
```shell
bazel build //tensorflow/tools/pip_package:build_pip_package --verbose_failures
```
### Relevant log output
```shell
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/config.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_arch.cuh:38:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_macro.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/utility:18:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/utility:282:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/concepts:144:
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/boolean_testable.h:45:1: error: CUDA device code does not support variadic functions
_LIBCUDACXX_CONCEPT_FRAGMENT(
^
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/__concept_macros.h:197:8: note: expanded from macro '_LIBCUDACXX_CONCEPT_FRAGMENT'
&_NAME##_LIBCUDACXX_CONCEPT_FRAGMENT_(...))[2] /**/
^
<scratch space>:67:1: note: expanded from here
__boolean_testable__LIBCUDACXX_CONCEPT_FRAGMENT_
^
In file included from tensorflow/core/kernels/sparse_slice_grad_op_gpu.cu.cc:23:
In file included from ./tensorflow/core/kernels/gpu_prim.h:20:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/block/block_load.cuh:39:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/block/../block/block_exchange.cuh:36:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/config.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_arch.cuh:38:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_macro.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/utility:18:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/utility:282:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/concepts:147:
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/common_with.h:53:1: error: CUDA device code does not support variadic functions
_LIBCUDACXX_CONCEPT_FRAGMENT(
^
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/__concept_macros.h:197:8: note: expanded from macro '_LIBCUDACXX_CONCEPT_FRAGMENT'
&_NAME##_LIBCUDACXX_CONCEPT_FRAGMENT_(...))[2] /**/
^
<scratch space>:89:1: note: expanded from here
__common_type_exists__LIBCUDACXX_CONCEPT_FRAGMENT_
^
In file included from tensorflow/core/kernels/sparse_slice_grad_op_gpu.cu.cc:23:
In file included from ./tensorflow/core/kernels/gpu_prim.h:20:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/block/block_load.cuh:39:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/block/../block/block_exchange.cuh:36:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/config.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_arch.cuh:38:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cub/util_macro.cuh:35:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/utility:18:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/utility:282:
In file included from bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/concepts:147:
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/common_with.h:64:1: error: CUDA device code does not support variadic functions
_LIBCUDACXX_CONCEPT_FRAGMENT(
^
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/cuda/std/detail/libcxx/include/__concepts/__concept_macros.h:197:8: note: expanded from macro '_LIBCUDACXX_CONCEPT_FRAGMENT'
&_NAME##_LIBCUDACXX_CONCEPT_FRAGMENT_(...))[2] /**/
^
<scratch space>:17:1: note: expanded from here
__common_type_constructible__LIBCUDACXX_CONCEPT_FRAGMENT_
^
fatal error: too many errors emitted, stopping now [-ferror-limit=]
20 errors generated when compiling for sm_89.
Target //tensorflow/tools/pip_package:build_pip_package failed to build
INFO: Elapsed time: 170.866s, Critical Path: 157.02s
INFO: 1919 processes: 168 internal, 1751 local.
FAILED: Build did NOT complete successfully
```
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PR_kwDOArmXAs5et9tm
| 62,338 |
Fix rpath in pywrap_function_lib.so
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"Made moot by https://github.com/tensorflow/tensorflow/commit/ac91facc65a890dd703367a1fe5a14512de52012"
] | 2023-11-06T17:41:36 | 2023-11-07T09:23:08 | 2023-11-07T09:23:04 |
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The new shared object needs its rpath correcting for installation by pip so add the commands to achieve this to the pip package build script.
Fixes: https://github.com/tensorflow/tensorflow/issues/62336
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tf.keras.models.load_model can't handle UNC paths
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[
"Hi, @early-stopper!\r\nThis issue generally arises because UNC paths are not supported by the underlying TensorFlow library. TensorFlow uses a local file system to store and load models, and UNC paths are not considered to be local files.\r\nAs you said you can use different saving formats as a workaround for now. \r\nWe are already tracking this issue #62319 here, so please move this issue to closed status?\r\nThank you!",
"Just to be clear: using a different saving format is a workaround for #62319 only. This issue here still occurs, despite me switching to the `.keras` format. The only workaround left is to replace the UNC path with a drive letter path to the same file with the drawback that every machine has to adapt this naming convention. However, this would still be the same path and still not be a local file, but works nevertheless, so I really see no reason why the original UNC path shouldn't work as well.\r\n\r\nHandling UNC paths already worked in an earlier TensorFlow version. If you regard this not a supported feature, hence not a bug, feel free to close the issue. In that case, please consider this a feature request.",
"You may also close this issue, if you consider it a duplicate to #62319. My reasoning here was that the 2 issues, though related, result in different errors and therefore are worth separate tickets.",
"@early-stopper Thanks for the clarification here.\r\n@SuryanarayanaY Could you please have a look at this?\r\nThank you!",
"Hi @early-stopper ,\r\n\r\nAs per [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model#:~:text=Args-,filepath,or%20pathlib.Path%20object%2C%20path%20to%20the%20saved%20model%20file.,-custom_objects) the argument `filepath` should be `str` or `pathlib.Path` object.\r\n\r\nI have changed the path from `pathlib.WindowsPath(path)` to `pathlib.Path(path)` and its working fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/0283b8880b76c39791eb91766e4b9d5a/62337.ipynb) for same.\r\n\r\nCould you please just try with `pathlib.Path(path) `and let us know if it works on WIndows too or not. Thanks!",
"Hi @SuryanarayanaY,\r\nfirst off, since `pathlib.WindowsPath` is a subclass of `pathlib.Path`, you would expect it to work as well in the sence of duck-typing. In fact, by the [documentation](https://docs.python.org/3/library/pathlib.html#pathlib.Path), executing `pathlib.Path(...)` will always create either a `pathlib.WindowsPath` (Windows) or a `pathlib.PosixPath` (Unix) anyway, depending on the underlying OS: on Windows, I get\r\n```python\r\n>> import pathlib\r\n>> pathlib.Path('C:')\r\nWindowsPath('C:')\r\n```\r\nIn other words, replacing `pathlib.WindowsPath` with `pathlib.Path` really should not make a difference on a Windows system. And in fact it doesn't, the resulting error will still be the same.\r\n\r\nThe reason why you can't reproduce the error, I suspect, is that Google Colab is running with an underlying Unix system, where `pathlib.Path` will produce an instance of `pathlib.PosixPath`. Try running `print(type(path))` in the notebook to confirm this. It seems that the problem only affects Windows systems. \r\n",
"> The reason why you can't reproduce the error, I suspect, is that Google Colab is running with an underlying Unix system, where `pathlib.Path` will produce an instance of `pathlib.PosixPath`. Try running `print(type(path))` in the notebook to confirm this. It seems that the problem only affects Windows systems.\r\n\r\nYeah,Because of environment limitations I requested to cross check with Windows OS. Thanks for confirmation. Could you confirm whether older TF versions works fine with this path earlier.Just to check whether it is a regression problem and when it was introduced if so.\r\n\r\nThanks!",
"Also just want to know whether it works with keras-nightly (i.e keras3.0.dev). Could you able to check it if have bandwidth.\r\n\r\nThanks!",
"I know that both this issue and #62319 did not occur when running the same code in TensorFlow 2.11 (and Python 3.10) and I first noted these problems with TensorFlow 2.12 (and Python 3.11).",
"> Also just want to know whether it works with keras-nightly (i.e keras3.0.dev). Could you able to check it if have bandwidth.\r\n\r\nI am a bit confused here, do you mean I should install [this](https://pypi.org/project/keras-nightly/) package in addition to tensorflow? I thought you now were supposed to use `tf.keras` as part of the TensorFlow package rather than `keras` as a standalone package. Or do you mean the [tf-nightly](https://pypi.org/project/tf-nightly/) build? How would I need to adjust the code above to test this?",
"> I am a bit confused here, do you mean I should install [this](https://pypi.org/project/keras-nightly/) package in addition to tensorflow? I thought you now were supposed to use `tf.keras` as part of the TensorFlow package rather than `keras` as a standalone package. Or do you mean the [tf-nightly](https://pypi.org/project/tf-nightly/) build? How would I need to adjust the code above to test this?\r\n\r\nActually current `keras-nightly(Keras 3.0.Dev)` is supporting multi backend now as you may be aware.If your code don't have any TF code then you can test it with `Keras3`. The compatible keras package for tf-nightly is tf-keras-nightly which can be installed from [here](https://pypi.org/project/tf-keras-nightly/) and then import tf-keras and replace tf.keras with tf-keras.\r\n\r\nWith **Keras3** your code can be like below.\r\n\r\n```\r\npip install keras-nightly\r\nimport keras\r\npath = \"\\\\\\\\localhost\\\\d$\"\r\npath = pathlib.WindowsPath(path)\r\npath = path/'test'\r\n\r\npath.mkdir(exist_ok=True, parents=True)\r\n\r\n# Set up and save a dummy model\r\nmodel = keras.Sequential()\r\nmodel.add(keras.Input(shape=(16,)))\r\nmodel.add(keras.layers.Dense(8))\r\nmodel.save(path/'model.keras', save_format='keras')\r\n\r\nmodel = keras.models.load_model(path/'model.keras')\r\n```\r\n\r\nWith **tf-keras** your code can be like this.\r\n\r\n```\r\npip install tf-keras-nightly\r\nimport tf_keras as tfk\r\n\r\npath = \"\\\\\\\\localhost\\\\d$\"\r\npath = pathlib.WindowsPath(path)\r\npath = path/'test'\r\n\r\npath.mkdir(exist_ok=True, parents=True)\r\n\r\n# Set up and save a dummy model\r\nmodel = tfk.Sequential()\r\nmodel.add(keras.Input(shape=(16,)))\r\nmodel.add(keras.layers.Dense(8))\r\nmodel.save(path/'model.keras', save_format='keras')\r\n\r\nmodel = keras.models.load_model(path/'model.keras')\r\n```\r\n\r\n",
"Please also refer to #62332",
"I tested both variants and can confirm that the issue does not occur when using `keras-nightly`, but still occurs with `tf-nightly` + `tf_keras_nightly`.",
"Hi @early-stopper ,\r\n\r\nTo ensure tensorflow uses Keras2 you need to set the environment variable using `os.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"`. Please setup and try again.\r\n\r\nThanks!\r\n\r\n",
"Since some months have passed, it may be worth double checking that we still are on the same page. I set up a new project and used `pip install tensorflow`. This installed `tensorflow==2.15.0` and `keras==2.15.0`. The following code uses a UNC path to first save a model and then load it again:\r\n```python\r\nimport pathlib\r\n\r\nimport tensorflow as tf\r\n\r\ndef main():\r\n path = \"\\\\\\\\localhost\\\\c$\"\r\n path = pathlib.Path(path)\r\n path = path/'test'\r\n\r\n path.mkdir(exist_ok=True, parents=True)\r\n\r\n model = tf.keras.Sequential()\r\n model.add(tf.keras.Input(shape=(16,)))\r\n model.add(tf.keras.layers.Dense(8))\r\n\r\n model.save(path/'model.keras')\r\n\r\n model = tf.keras.models.load_model(path/'model.keras')\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\nSaving the model with the `keras` format works fine, but loading the model raises the error\r\n```python\r\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0xfc in position 89: invalid start byte\r\n```\r\nThis error is seemingly related to the use of a UNC path, because replacing `path = \"\\\\\\\\localhost\\\\c$\"` with its named equivalent `path = \"C:\"` resolves the error.\r\n\r\nWhen modifying the snippet as suggested by adding `os.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"`, I arrive at\r\n```python\r\nimport os\r\nimport pathlib\r\n\r\nos.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"\r\nimport tensorflow as tf\r\n\r\ndef main():\r\n path = \"\\\\\\\\localhost\\\\c$\"\r\n path = pathlib.Path(path)\r\n path = path/'test'\r\n\r\n path.mkdir(exist_ok=True, parents=True)\r\n\r\n model = tf.keras.Sequential()\r\n model.add(tf.keras.Input(shape=(16,)))\r\n model.add(tf.keras.layers.Dense(8))\r\n\r\n model.save(path/'model.keras')\r\n\r\n model = tf.keras.models.load_model(path/'model.keras')\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\nand now get the error\r\n```python\r\nImportError: Keras cannot be imported. Check that it is installed.\r\n```\r\nThis error can be handled by additionally running `pip install tf-keras`. But then, the original error pops up again."
] | 2023-11-06T17:18:10 | 2024-01-22T10:45:53 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I recently issued a bug when saving tf.keras.Models in the `SavedModel` format using a UNC path like
`model.save("\\\\ComputerName\\path\\to\\folder"),`
see #62319. A workaround for the time being is to use the `.keras` save_format instead, so the following works:
`model.save("\\\\ComputerName\\path\\to\\folder\\model.keras", save_format='keras').`
However, loading that model, again using the same UNC path, still is broken: running
`tf.keras.models.load_model("\\\\ComputerName\\path\\to\\folder\\model.keras")`
raises the error shown below.
The problem once again seems to be with the UNC path, because replacing it with a path using the drive's name letter resolves the problem: if `J:` is set as an alias for `\\ComputerName`, then
`tf.keras.models.load_model("J:\\path\\to\\folder\\model.keras")`
works as expected. But doing this is not really a solution for me, because then I would have to rely on others to use the same letter J for the network drive in order to run my code.
### Standalone code to reproduce the issue
```shell
# The issue can be reproduced using, for instance, the path to the root of the D: drive, expressed as UNC path
path = "\\\\localhost\\d$"
path = pathlib.WindowsPath(path)
path = path/'test'
# If we uncomment the following line and replace the path string with one that includes the drive's letter, no error occurs.
#path = pathlib.WindowsPath("D:\\test")
path.mkdir(exist_ok=True, parents=True)
# Set up and save a dummy model
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(8))
model.save(path/'model.keras', save_format='keras')
# Loading the model using the same UNC path raises a UnicodeDecodeError
model = tf.keras.models.load_model(path/'model.keras')
```
### Relevant log output
```shell
Traceback (most recent call last):
File "D:\keras_load_error.py", line 26, in <module>
main()
File "D:\keras_load_error.py", line 23, in main
model = tf.keras.models.load_model(path/'model.keras')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\.venv\Lib\site-packages\keras\src\saving\saving_api.py", line 254, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "D:\.venv\Lib\site-packages\keras\src\saving\saving_lib.py", line 281, in load_model
raise e
File "D:\.venv\Lib\site-packages\keras\src\saving\saving_lib.py", line 234, in load_model
) as gfile_handle, zipfile.ZipFile(gfile_handle, "r") as zf:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\zipfile.py", line 1302, in __init__
self._RealGetContents()
File "C:\Program Files\Python311\Lib\zipfile.py", line 1365, in _RealGetContents
endrec = _EndRecData(fp)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\zipfile.py", line 292, in _EndRecData
fpin.seek(0, 2)
File "D:\.venv\Lib\site-packages\tensorflow\python\util\deprecation.py", line 588, in new_func
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "D:\.venv\Lib\site-packages\tensorflow\python\lib\io\file_io.py", line 139, in seek
self._preread_check()
File "D:\.venv\Lib\site-packages\tensorflow\python\lib\io\file_io.py", line 77, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfc in position 89: invalid start byte
```
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Building pip package broken by new shared object in build
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[
"Fixed by https://github.com/tensorflow/tensorflow/commit/ac91facc65a890dd703367a1fe5a14512de52012",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62336\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62336\">No</a>\n"
] | 2023-11-06T16:44:13 | 2023-11-07T09:23:35 | 2023-11-07T09:23:32 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
git HEAD
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.9.17
### Bazel version
6.1.0
### GCC/compiler version
17.0.0
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current behavior?
Auditwheel used during pip package creation fails due to unable to locate needed shared object.
Failure introduced by https://github.com/tensorflow/tensorflow/commit/96c01ac84cc5befb70d258a2ef06846a71d2bc0c
### Standalone code to reproduce the issue
```shell
$ bazel build --config=mkl_aarch64_threadpool --copt=-flax-vector-conversions --define=tf_api_version=2 --verbose_failures --jobs=100 -- //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package --cpu --project_name tensorflow_aarch64 ./tensorflow-pkg
$ auditwheel repair --plat manylinux2014_aarch64 -w /tensorflow/whl /workspace/tensorflow-pkg/tensorflow_aarch64-2.16.0-cp310-cp310-linux_aarch64.whl
```
### Relevant log output
```shell
INFO:auditwheel.main_repair:Repairing tensorflow_aarch64-2.16.0-cp310-cp310-linux_aarch64.whl
Traceback (most recent call last):
File "/usr/local/bin/auditwheel", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/dist-packages/auditwheel/main.py", line 59, in main
rval = args.func(args, p)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/auditwheel/main_repair.py", line 173, in execute
out_wheel = repair_wheel(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/auditwheel/repair.py", line 80, in repair_wheel
raise ValueError(
ValueError: Cannot repair wheel, because required library "_pywrap_tensorflow_internal.so" could not be located
```
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PR_kwDOArmXAs5es2ti
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Mitigate Image-Scaling Attacks: Implement Secure Scaling Algorithms
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[
"This code will not run. You would need to implement functions for optimized_ops::SecureResizeBilinear, optimized_ops::SecureResizeBilinearInteger, and reference_ops::SecureReiszeBilinear. Right now you are calling functions that do not yet exist. Follow what previous ops have done. You also will need to add some tests if you want to introduce a new op."
] | 2023-11-06T15:02:12 | 2023-11-08T02:26:27 | 2023-11-08T02:26:24 |
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Referencing #42388
This pull request addresses the issue of **image-scaling attacks** by implementing **secure scaling algorithms** in the TensorFlow codebase. Image-scaling attacks pose a security threat to machine learning applications, similar to adversarial examples, as they allow adversaries to manipulate the output of the downscaling operation, impacting the security of machine learning models.
_Changes Made:_
**Secure Scaling Algorithms:** We have introduced secure scaling algorithms in the code to prevent image-scaling attacks. Specifically, we have modified the code to use secure scaling algorithms, which consider all pixels equally during downscaling, making it difficult for adversaries to manipulate specific pixels.
**Parameter Validation:** We have added parameter validation to ensure that critical parameters such as align_corners and half_pixel_centers are used securely. This prevents potential vulnerabilities in the scaling process.
**Input Validation:** We have added input validation to ensure that the up/down sampling size is always positive, addressing a fundamental security concern in the image-scaling process.
_Why These Changes:_
Machine learning models often rely on image downscaling, and ensuring the security of this process is crucial. The changes made in this PR are essential for the following reasons:
**Security:** The modifications aim to enhance the security of machine learning applications by mitigating the risk of **image-scaling attacks**.
**Adversarial Examples:** Image-scaling attacks are analogous to adversarial examples, where slight manipulations can deceive machine learning models. By using **secure scaling algorithms**, we make it harder for attackers to exploit the **downscaling process.**
**Parameter Validation:** Parameter validation ensures that critical parameters are used securely, eliminating potential vulnerabilities.
**Input Validation:** Input validation guarantees that **up/down sampling sizes** are always positive, reducing the **risk of manipulation**.
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[
"I was able to build TF 2.15.0 for ppc64le using \r\n\r\n- https://github.com/pytorch/cpuinfo/pull/170\r\n- https://github.com/tensorflow/tensorflow/pull/62457 ",
"Hi, Thanks for confirming working condition. \r\n\r\nCould you please close this issue, if you don't have any other concerns. \r\n\r\nFor building from source, please refer document here https://www.tensorflow.org/install/source\r\n\r\nFor any new updates, follow our release document https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62333\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62333\">No</a>\n"
] | 2023-11-06T11:01:22 | 2023-11-28T10:39:58 | 2023-11-28T10:39:55 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15.0-rc1
### Custom code
Yes
### OS platform and distribution
RHEL 8
### Mobile device
_No response_
### Python version
3.9
### Bazel version
6.1.0
### GCC/compiler version
GCC 12.3
### CUDA/cuDNN version
Cuda 12.2 , cuDNN 8.8.0
### GPU model and memory
_No response_
### Current behavior?
Building TF 2.15.0-rc1 for x86_64 works fine but for ppc64le it fails with error [a] , looks like sources for `@cpuinfo//:cpuinfo_impl` are not available for ppc64le.
[a]
```
INFO: Found applicable config definition build:short_logs in file build-path/tensorflow-2.15.0/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file build-path/tensorflow-2.15.0/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file build-path/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:opt in file build-path/tensorflow-2.15.0/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:noaws in file build-path/tensorflow-2.15.0/.bazelrc: --define=no_aws_support=true
INFO: Found applicable config definition build:nogcp in file build-path/tensorflow-2.15.0/.bazelrc: --define=no_gcp_support=true
INFO: Found applicable config definition build:nohdfs in file build-path/tensorflow-2.15.0/.bazelrc: --define=no_hdfs_support=true
INFO: Found applicable config definition build:nonccl in file build-path/tensorflow-2.15.0/.bazelrc: --define=no_nccl_support=true
INFO: Found applicable config definition build:linux in file build-path/tensorflow-2.15.0/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file build-path/tensorflow-2.15.0/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
ERROR: build-path/build/6299e46b40df2be0d3560c786022f10c/external/cpuinfo/BUILD.bazel:104:11: configurable attribute "srcs" in @cpuinfo//:cpuinfo_impl doesn't match this configuration. Would a default condition help?
Conditions checked:
@cpuinfo//:linux_x86_64
@cpuinfo//:linux_arm
@cpuinfo//:linux_armhf
@cpuinfo//:linux_armv7a
@cpuinfo//:linux_armeabi
@cpuinfo//:linux_aarch64
@cpuinfo//:linux_mips64
@cpuinfo//:linux_riscv64
@cpuinfo//:linux_s390x
@cpuinfo//:macos_x86_64
@cpuinfo//:macos_x86_64_legacy
@cpuinfo//:macos_arm64
@cpuinfo//:windows_x86_64
@cpuinfo//:android_armv7
@cpuinfo//:android_arm64
@cpuinfo//:android_x86
@cpuinfo//:android_x86_64
@cpuinfo//:ios_x86_64
@cpuinfo//:ios_x86
@cpuinfo//:ios_armv7
@cpuinfo//:ios_arm64
@cpuinfo//:ios_arm64e
@cpuinfo//:ios_sim_arm64
@cpuinfo//:watchos_x86_64
@cpuinfo//:watchos_x86
@cpuinfo//:watchos_armv7k
@cpuinfo//:watchos_arm64_32
@cpuinfo//:tvos_x86_64
@cpuinfo//:tvos_arm64
@cpuinfo//:emscripten_wasm
To see a condition's definition, run: bazel query --output=build <condition label>.
This instance of @cpuinfo//:cpuinfo_impl has configuration identifier 2381005. To inspect its configuration, run: bazel config 2381005.
For more help, see https://bazel.build/docs/configurable-attributes#faq-select-choose-condition.
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted:
```
### Standalone code to reproduce the issue
```shell
it is a build/configure issue
```
### Relevant log output
_No response_
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I_kwDOArmXAs518dSj
| 62,332 |
Status of ragged tensors in tf nightly
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[
"Hi @0x0L ,\r\n\r\nAs Keras now become multi backend to support Pytorch, Jax as backend along with Tensorflow , there has been changes. Keras do have plan to support ragged tensors in future but not sure of exact time line.\r\n\r\nPlease have a look into Keras tickets #[18467](https://github.com/keras-team/keras/issues/18467) and #[18414](https://github.com/keras-team/keras/issues/18414) for more details.\r\n\r\nThanks!",
"Meanwhile, you can still use `tf.keras` or directly the ragged tensors API from within TF though",
"@0x0L ,\r\n\r\nIf you are particularly looking to train a model with ragged input by using tf.keras, AFAIK, you can do it unitil TF2.14 version(Not sure about 2.15V yet). From Keras3(current tf-nightly keras package) onwards the Inputlayer(Input) don't accept `ragged` argument. I tested with a demo and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/4a1cc09b7a92d99679017f5c74ffa3e9/62332.ipynb#scrollTo=-iX3dRvBjgW8) here.",
"@mihaimaruseac Actually no, it does not work in tf-nightly: `tf.keras.Input` does not support the `ragged` kwarg anymore as noted by @SuryanarayanaY (tf keras 2.15rc still has ragged support)\r\n\r\nAs mentioned in https://github.com/keras-team/keras/issues/18467 they (keras) \"may add it back later\"... which does not sound too encouraging",
"Try https://pypi.org/project/tf-keras-nightly/ instead of `keras-nightly`",
"@mihaimaruseac Thank you so much !!\r\n\r\nSo I can expect ragged tensors to stay in tf.keras, is that right ?",
"Hi @0x0L ,\r\n\r\nAFAIK, `tf_keras` will work for replacement of `tf.keras` . To use `tf_keras` with latest `tf-nightly` versions, we need to import `tf_keras` as `keras` and use `keras` instead of `tf.keras` since `tf.keras` still imports `keras-nightly`(i.e. `Keras3`) code instead of `tf_keras` code. \r\n\r\nI tried the same demo above and it works importing `tf_keras` as `keras` and replace `tf.keras` with `keras` and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/4edc38fa6d904988817589d734cefbd3/62332-tf-keras-nightly.ipynb) here for reference.\r\n\r\nThanks to @mihaimaruseac for the inputs.\r\n\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62332\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62332\">No</a>\n"
] | 2023-11-06T10:20:44 | 2023-11-10T11:09:05 | 2023-11-10T11:09:02 |
NONE
| null | null | null |
Hello,
It seems keras 3 (used in tf nightly) has dropped support for ragged tensors.
What are the plans for the future of ragged tensors in keras tf?
Thanks
|
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I_kwDOArmXAs518EGb
| 62,331 |
TF Lite in play services does not support version 1.13.0
|
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[
"@navaronbracke TensorFlow Lite in Google Play services does not support version 1.13.0 because it is still in preview. If you need to use TensorFlow Lite version 1.13.0, you can do so by downloading the TensorFlow Lite SDK directly from the TensorFlow website. You can then add the TensorFlow Lite SDK to your app as a dependency. Hope it helps?\r\nThank you!",
"That explains it, thank you! Not sure if you know if the TensorFlow and the MLKit teams coordinate releases? Otherwise I'll wait for MLKit to update their Tensorflow usage.",
"@navaronbracke Yes, the TensorFlow and ML Kit teams coordinate releases. This helps to ensure that the two products are compatible and that developers can use them together seamlessly. For example, when TensorFlow releases a new version, the ML Kit team will update their product to be compatible with the new version. \r\nThank you!",
"That is enough information for me to inform users of the library. Thanks a lot!"
] | 2023-11-06T09:29:35 | 2023-11-06T13:48:16 | 2023-11-06T13:48:15 |
NONE
| null | null | null |
I received an error report from a user that was using TensorFlow Lite through the `mobile_scanner` library.
As this library uses the most up-to-date version of `mlkit-barcode-scanning`, which has `TensorFlow Lite` as a dependency,
I cannot resolve the root cause of this issue.
From the changelogs I do not directly see which version of TensorFlow Lite supports version `1.13.0` in the Google Play services.
Thus I don't know if I should ask the maintainers of `play-services-mlkit-barcode-scanning` to update to the latest available version with a fix. (the last version of that dependency dates back a few months)
I can ask the user for their device / Google Play services version.
**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
if possible): N/A
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- is a transitive dependency of `com.google.android.gms:play-services-mlkit-barcode-scanning` version 18.3.0
- Google Play Services version
(`Settings` > `Apps` > `Google Play Services` > `App details`): N/A
**Standalone code to reproduce the issue**
```
import com.google.mlkit.vision.barcode.BarcodeScanning
class MobileScanner() {
private var scanner = BarcodeScanning.getClient()
}
```
**Any other info / logs**
```
W/native (30072): W0000 00:00:1698456375.622782 32103 abi_method_util.h:33] not supported: TfLiteInterpreterGetTensor: TF Lite in Google Play services has stable ABI version 1.11.0 which is less than the required version 1.13.0.
```
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PR_kwDOArmXAs5ep5OW
| 62,330 |
Fix build error on TFLite C API for Android with CMake
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Update gemmlowp version that applied patch to avoid unneeded linker flag(`-lpthread` ).
More details are described at #61839
fixes #61839
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I_kwDOArmXAs516tcR
| 62,329 |
Model profile for inference
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[
"Hi **@akote123** \r\nTo get operation-level timings or execution time of specific operations within a graph, you can use the TensorFlow Profiler tool. I attached a [gist](https://colab.research.google.com/gist/Venkat6871/0a522038d4ff7e056d7cdb57cd33d884/62329.ipynb) for your reference.\r\n\r\nThank you!",
"@Venkat6871 , can please provide access to gist\r\n",
"**@akote123** Thanks for your response!\r\nCould you check the gist now. I hope it will run.\r\n\r\nThank you!",
"Hi @Venkat6871 ,\r\n I tired with gist , but I am not able to see data in tensorboard.\r\n\r\n`tf.profiler.experimental.start('logs')\r\n \r\n output = model.predict(encoded_input, verbose=False)[0]\r\n tf.profiler.experimental.stop()`\r\n\r\nThis way I tried. Please let me know if I need to do change ",
"Hi **@akote123** Thanks for your response!\r\nActually I just send enable profiling. Here we need to follow some steps.\r\n1.Enable profiling after this step.\r\n2.Running Tensorboard:\r\nAfter executing your Python script that includes the profiling code, you need to run TensorBoard to visualize the collected profiling data.\r\nOpen your terminal or command prompt and use the following command:\r\n```\r\ntensorboard --logdir=logs\r\n```\r\n3.Accessing Tensorboard:\r\n After you run the command, open a web browser and go to the address shown in the terminal where TensorBoard is running.\r\n",
"@Venkat6871 , I actually followed all the steps and when teansorboard is opened in web , In the profile tab when click for op level , the page is showing empty.",
"@Venkat6871 , is there any other way I can get profile data for inference\r\n",
"@SuryanarayanaY , For the above code I get tensorboard as attached\r\n<img width=\"926\" alt=\"tensorboard\" src=\"https://github.com/tensorflow/tensorflow/assets/133775732/6aaf35fc-206e-43bd-992b-d60ebb44141a\">\r\n",
"Hi @akote123 ,\r\n\r\nPlease refer to this [tutorial](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) for profiling with tensorboard. Also check this tutorial to use [Tesorboard](https://www.tensorflow.org/tensorboard/tensorboard_in_notebooks) in notebokks itself.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"@SuryanarayanaY , @Venkat6871 I able to get tensorboard results when I downgraded protobuf to 3.X but when try to use the same for transfomer model the tensorboard is showing empty again.\r\n`tf.profiler.experimental.start('logs1')\r\nlogits = model(**inputs)\r\ntf.profiler.experimental.stop()`"
] | 2023-11-06T05:30:37 | 2024-02-01T10:35:09 | null |
NONE
| null | null | null |
Hi,
For inference how we can get op level timings in tensorflow
Thanks
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I_kwDOArmXAs515cp_
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TensorFlow doesn't detect cuda drivers
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[
"If someone is solving the same issue, I've installed `cuda toolkit` and `cudnn`, updated `LD_LIBRARY_PATH` and it worked, basically:\r\n\r\n```sh\r\n> conda activate tf-test\r\n> conda install cudatoolkit=11.8\r\n> export LD_LIBRARY_PATH=\"$HOME/miniconda3/envs/tf-test/lib\"\r\n> conda install cudnn=8.9\r\n> python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-11-06 01:43:59.188890: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-06 01:44:00.239682: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-11-06 01:44:01.023567: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 01:44:01.065869: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-06 01:44:01.066223: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62328\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62328\">No</a>\n"
] | 2023-11-05T22:05:28 | 2023-11-05T22:44:49 | 2023-11-05T22:44:47 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.1
### Custom code
No
### OS platform and distribution
Li
### Mobile device
Gentoo Linux 6.1.57-gentoo-x86_64
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8/8.7.0.84
### GPU model and memory
NVIDIA GeForce GTX 1650 Mobile / Max-Q
### Current behavior?
Current behaviour: list of cuda-capable devices is empty
```sh
> python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-11-06 01:01:39.145881: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-11-06 01:01:39.200934: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-11-06 01:01:39.201518: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-11-06 01:01:40.245608: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-11-06 01:01:41.021740: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-11-06 01:01:41.022476: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
```
Expected behaviour: list of coda-capable devices contains one item.
I would be happy to reproduce the bug in `tf-nightly`, but I can't even install it due to broken dependencies with tensorrt:
```sh
> python3 -m pip install 'tf-nightly[and-cuda]'
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.16.0.dev20231103-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Collecting absl-py>=1.0.0 (from tf-nightly[and-cuda])
Using cached absl_py-2.0.0-py3-none-any.whl.metadata (2.3 kB)
Collecting astunparse>=1.6.0 (from tf-nightly[and-cuda])
Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting flatbuffers>=23.5.26 (from tf-nightly[and-cuda])
Using cached flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tf-nightly[and-cuda])
Using cached gast-0.5.4-py3-none-any.whl (19 kB)
Collecting google-pasta>=0.1.1 (from tf-nightly[and-cuda])
Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Collecting h5py>=3.10.0 (from tf-nightly[and-cuda])
Using cached h5py-3.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)
Collecting libclang>=13.0.0 (from tf-nightly[and-cuda])
Using cached libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl.metadata (5.2 kB)
Collecting ml-dtypes~=0.3.1 (from tf-nightly[and-cuda])
Using cached ml_dtypes-0.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
Collecting opt-einsum>=2.3.2 (from tf-nightly[and-cuda])
Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting packaging (from tf-nightly[and-cuda])
Using cached packaging-23.2-py3-none-any.whl.metadata (3.2 kB)
Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tf-nightly[and-cuda])
Using cached protobuf-4.25.0-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)
Requirement already satisfied: setuptools in ./miniconda3/envs/tf-test/lib/python3.11/site-packages (from tf-nightly[and-cuda]) (68.0.0)
Collecting six>=1.12.0 (from tf-nightly[and-cuda])
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting termcolor>=1.1.0 (from tf-nightly[and-cuda])
Using cached termcolor-2.3.0-py3-none-any.whl (6.9 kB)
Collecting typing-extensions>=3.6.6 (from tf-nightly[and-cuda])
Using cached typing_extensions-4.8.0-py3-none-any.whl.metadata (3.0 kB)
Collecting wrapt<1.15,>=1.11.0 (from tf-nightly[and-cuda])
Using cached wrapt-1.14.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
Collecting grpcio<2.0,>=1.24.3 (from tf-nightly[and-cuda])
Using cached grpcio-1.59.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)
Collecting tb-nightly~=2.16.0.a (from tf-nightly[and-cuda])
Using cached tb_nightly-2.16.0a20231105-py3-none-any.whl.metadata (1.7 kB)
Collecting tf-estimator-nightly~=2.14.0.dev (from tf-nightly[and-cuda])
Using cached tf_estimator_nightly-2.14.0.dev2023080308-py2.py3-none-any.whl.metadata (1.3 kB)
Collecting keras-nightly~=3.0.0.dev (from tf-nightly[and-cuda])
Using cached keras_nightly-3.0.0.dev2023110403-py3-none-any.whl.metadata (5.3 kB)
Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tf-nightly[and-cuda])
Using cached tensorflow_io_gcs_filesystem-0.34.0-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (14 kB)
Collecting numpy<2.0.0,>=1.23.5 (from tf-nightly[and-cuda])
Using cached numpy-1.26.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
Collecting nvidia-cublas-cu12==12.2.5.6 (from tf-nightly[and-cuda])
Using cached nvidia_cublas_cu12-12.2.5.6-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-cupti-cu12==12.2.142 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_cupti_cu12-12.2.142-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cuda-nvcc-cu12==12.2.140 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_nvcc_cu12-12.2.140-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-nvrtc-cu12==12.2.140 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_nvrtc_cu12-12.2.140-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-runtime-cu12==12.2.140 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_runtime_cu12-12.2.140-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cudnn-cu12==8.9.4.25 (from tf-nightly[and-cuda])
Using cached nvidia_cudnn_cu12-8.9.4.25-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cufft-cu12==11.0.8.103 (from tf-nightly[and-cuda])
Using cached nvidia_cufft_cu12-11.0.8.103-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-curand-cu12==10.3.3.141 (from tf-nightly[and-cuda])
Using cached nvidia_curand_cu12-10.3.3.141-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cusolver-cu12==11.5.2.141 (from tf-nightly[and-cuda])
Using cached nvidia_cusolver_cu12-11.5.2.141-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.1.2.141 (from tf-nightly[and-cuda])
Using cached nvidia_cusparse_cu12-12.1.2.141-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-nccl-cu12==2.18.3 (from tf-nightly[and-cuda])
Using cached nvidia_nccl_cu12-2.18.3-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-nvjitlink-cu12==12.2.140 (from tf-nightly[and-cuda])
Using cached nvidia_nvjitlink_cu12-12.2.140-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting tensorrt==8.6.1.post1 (from tf-nightly[and-cuda])
Using cached tensorrt-8.6.1.post1.tar.gz (18 kB)
Preparing metadata (setup.py) ... done
Collecting tensorrt-bindings==8.6.1 (from tf-nightly[and-cuda])
Using cached tensorrt_bindings-8.6.1-cp311-none-manylinux_2_17_x86_64.whl (980 kB)
INFO: pip is looking at multiple versions of tf-nightly[and-cuda] to determine which version is compatible with other requirements. This could take a while.
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.16.0.dev20231102-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231101-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231031-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231026-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Collecting tb-nightly~=2.15.0.a (from tf-nightly[and-cuda])
Using cached tb_nightly-2.15.0a20231023-py3-none-any.whl.metadata (1.7 kB)
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.16.0.dev20231025-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Collecting nvidia-nccl-cu12==2.16.5 (from tf-nightly[and-cuda])
Using cached nvidia_nccl_cu12-2.16.5-py3-none-manylinux1_x86_64.whl (188.7 MB)
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.16.0.dev20231024-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231022-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
INFO: pip is still looking at multiple versions of tf-nightly[and-cuda] to determine which version is compatible with other requirements. This could take a while.
Using cached tf_nightly-2.16.0.dev20231021-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231020-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)
Using cached tf_nightly-2.16.0.dev20231013-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Collecting ml-dtypes~=0.2.0 (from tf-nightly[and-cuda])
Using cached ml_dtypes-0.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
Collecting keras-nightly~=2.15.0.dev (from tf-nightly[and-cuda])
Using cached keras_nightly-2.15.0.dev2023092207-py3-none-any.whl.metadata (2.5 kB)
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.15.0.dev20231012-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231011-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
Using cached tf_nightly-2.15.0.dev20231010-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231009-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231006-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231005-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231004-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231003-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231002-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20231001-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230930-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230929-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230928-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230927-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230926-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230925-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230924-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230923-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230922-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230921-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230920-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230919-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)
Using cached tf_nightly-2.15.0.dev20230918-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Collecting nvidia-cuda-runtime-cu11==11.8.89 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_runtime_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (875 kB)
Collecting nvidia-cublas-cu11==11.11.3.6 (from tf-nightly[and-cuda])
Using cached nvidia_cublas_cu11-11.11.3.6-py3-none-manylinux1_x86_64.whl (417.9 MB)
Collecting nvidia-cufft-cu11==10.9.0.58 (from tf-nightly[and-cuda])
Using cached nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)
Collecting nvidia-cudnn-cu11==8.7.0.84 (from tf-nightly[and-cuda])
Using cached nvidia_cudnn_cu11-8.7.0.84-py3-none-manylinux1_x86_64.whl (728.5 MB)
Collecting nvidia-curand-cu11==10.3.0.86 (from tf-nightly[and-cuda])
Using cached nvidia_curand_cu11-10.3.0.86-py3-none-manylinux1_x86_64.whl (58.1 MB)
Collecting nvidia-cusolver-cu11==11.4.1.48 (from tf-nightly[and-cuda])
Using cached nvidia_cusolver_cu11-11.4.1.48-py3-none-manylinux1_x86_64.whl (128.2 MB)
Collecting nvidia-cusparse-cu11==11.7.5.86 (from tf-nightly[and-cuda])
Using cached nvidia_cusparse_cu11-11.7.5.86-py3-none-manylinux1_x86_64.whl (204.1 MB)
Collecting nvidia-nccl-cu11==2.16.5 (from tf-nightly[and-cuda])
Using cached nvidia_nccl_cu11-2.16.5-py3-none-manylinux1_x86_64.whl (210.3 MB)
Collecting nvidia-cuda-cupti-cu11==11.8.87 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_cupti_cu11-11.8.87-py3-none-manylinux1_x86_64.whl (13.1 MB)
Collecting nvidia-cuda-nvcc-cu11==11.8.89 (from tf-nightly[and-cuda])
Using cached nvidia_cuda_nvcc_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (19.5 MB)
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.15.0.dev20230917-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230916-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230915-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230914-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230913-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230911-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230910-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230909-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Using cached tf_nightly-2.15.0.dev20230908-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230907-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230906-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230904-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230903-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230902-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230901-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230831-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230830-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230829-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230828-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230827-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230826-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230825-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230824-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230817-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230816-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230815-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230814-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230813-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230812-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230811-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230810-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Collecting tb-nightly~=2.14.0.a (from tf-nightly[and-cuda])
Using cached tb_nightly-2.14.0a20230808-py3-none-any.whl.metadata (1.8 kB)
Collecting keras-nightly~=2.14.0.dev (from tf-nightly[and-cuda])
Using cached keras_nightly-2.14.0.dev2023080207-py3-none-any.whl.metadata (2.5 kB)
Collecting tf-nightly[and-cuda]
Using cached tf_nightly-2.15.0.dev20230809-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230808-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
Using cached tf_nightly-2.15.0.dev20230807-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)
ERROR: Cannot install tf-nightly[and-cuda]==2.15.0.dev20230807, tf-nightly[and-cuda]==2.15.0.dev20230808, tf-nightly[and-cuda]==2.15.0.dev20230809, tf-nightly[and-cuda]==2.15.0.dev20230810, tf-nightly[and-cuda]==2.15.0.dev20230811, tf-nightly[and-cuda]==2.15.0.dev20230812, tf-nightly[and-cuda]==2.15.0.dev20230813, tf-nightly[and-cuda]==2.15.0.dev20230814, tf-nightly[and-cuda]==2.15.0.dev20230815, tf-nightly[and-cuda]==2.15.0.dev20230816, tf-nightly[and-cuda]==2.15.0.dev20230817, tf-nightly[and-cuda]==2.15.0.dev20230824, tf-nightly[and-cuda]==2.15.0.dev20230825, tf-nightly[and-cuda]==2.15.0.dev20230826, tf-nightly[and-cuda]==2.15.0.dev20230827, tf-nightly[and-cuda]==2.15.0.dev20230828, tf-nightly[and-cuda]==2.15.0.dev20230829, tf-nightly[and-cuda]==2.15.0.dev20230830, tf-nightly[and-cuda]==2.15.0.dev20230831, tf-nightly[and-cuda]==2.15.0.dev20230901, tf-nightly[and-cuda]==2.15.0.dev20230902, tf-nightly[and-cuda]==2.15.0.dev20230903, tf-nightly[and-cuda]==2.15.0.dev20230904, tf-nightly[and-cuda]==2.15.0.dev20230906, tf-nightly[and-cuda]==2.15.0.dev20230907, tf-nightly[and-cuda]==2.15.0.dev20230908, tf-nightly[and-cuda]==2.15.0.dev20230909, tf-nightly[and-cuda]==2.15.0.dev20230910, tf-nightly[and-cuda]==2.15.0.dev20230911, tf-nightly[and-cuda]==2.15.0.dev20230913, tf-nightly[and-cuda]==2.15.0.dev20230914, tf-nightly[and-cuda]==2.15.0.dev20230915, tf-nightly[and-cuda]==2.15.0.dev20230916, tf-nightly[and-cuda]==2.15.0.dev20230917, tf-nightly[and-cuda]==2.15.0.dev20230918, tf-nightly[and-cuda]==2.15.0.dev20230919, tf-nightly[and-cuda]==2.15.0.dev20230920, tf-nightly[and-cuda]==2.15.0.dev20230921, tf-nightly[and-cuda]==2.15.0.dev20230922, tf-nightly[and-cuda]==2.15.0.dev20230923, tf-nightly[and-cuda]==2.15.0.dev20230924, tf-nightly[and-cuda]==2.15.0.dev20230925, tf-nightly[and-cuda]==2.15.0.dev20230926, tf-nightly[and-cuda]==2.15.0.dev20230927, tf-nightly[and-cuda]==2.15.0.dev20230928, tf-nightly[and-cuda]==2.15.0.dev20230929, tf-nightly[and-cuda]==2.15.0.dev20230930, tf-nightly[and-cuda]==2.15.0.dev20231001, tf-nightly[and-cuda]==2.15.0.dev20231002, tf-nightly[and-cuda]==2.15.0.dev20231003, tf-nightly[and-cuda]==2.15.0.dev20231004, tf-nightly[and-cuda]==2.15.0.dev20231005, tf-nightly[and-cuda]==2.15.0.dev20231006, tf-nightly[and-cuda]==2.15.0.dev20231009, tf-nightly[and-cuda]==2.15.0.dev20231010, tf-nightly[and-cuda]==2.15.0.dev20231011, tf-nightly[and-cuda]==2.15.0.dev20231012, tf-nightly[and-cuda]==2.16.0.dev20231013, tf-nightly[and-cuda]==2.16.0.dev20231020, tf-nightly[and-cuda]==2.16.0.dev20231021, tf-nightly[and-cuda]==2.16.0.dev20231022, tf-nightly[and-cuda]==2.16.0.dev20231024, tf-nightly[and-cuda]==2.16.0.dev20231025, tf-nightly[and-cuda]==2.16.0.dev20231026, tf-nightly[and-cuda]==2.16.0.dev20231031, tf-nightly[and-cuda]==2.16.0.dev20231101, tf-nightly[and-cuda]==2.16.0.dev20231102 and tf-nightly[and-cuda]==2.16.0.dev20231103 because these package versions have conflicting dependencies.
The conflict is caused by:
tf-nightly[and-cuda] 2.16.0.dev20231103 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231102 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231101 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231031 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231026 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231025 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231024 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231022 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231021 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231020 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.16.0.dev20231013 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231012 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231011 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231010 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231009 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231006 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231005 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231004 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231003 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231002 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20231001 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230930 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230929 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230928 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230927 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230926 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230925 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230924 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230923 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230922 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230921 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230920 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230919 depends on tensorrt-libs==8.6.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230918 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230917 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230916 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230915 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230914 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230913 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230911 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230910 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230909 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230908 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230907 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230906 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230904 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230903 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230902 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230901 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230831 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230830 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230829 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230828 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230827 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230826 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230825 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230824 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230817 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230816 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230815 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230814 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230813 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230812 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230811 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230810 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230809 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230808 depends on tensorrt==8.5.3.1; extra == "and-cuda"
tf-nightly[and-cuda] 2.15.0.dev20230807 depends on tensorrt==8.5.3.1; extra == "and-cuda"
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
```
### Standalone code to reproduce the issue
I've just copied commands from the official website:
```shell
python3 -m pip install 'tensorflow[and-cuda]'
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
Driver:
```sh
> nvidia-smi
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.113.01 Driver Version: 535.113.01 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce GTX 1650 Off | 00000000:01:00.0 On | N/A |
| N/A 43C P8 3W / 50W | 119MiB / 4096MiB | 7% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 15023 C+G ...95206080,4704969098354582108,262144 36MiB |
| 0 N/A N/A 26218 G /usr/bin/X 81MiB |
+---------------------------------------------------------------------------------------+
```
Log of the `tensorflow[and-cuda]` installation:
```shell
Collecting tensorflow[and-cuda]
Downloading tensorflow-2.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Collecting absl-py>=1.0.0 (from tensorflow[and-cuda])
Using cached absl_py-2.0.0-py3-none-any.whl.metadata (2.3 kB)
Collecting astunparse>=1.6.0 (from tensorflow[and-cuda])
Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting flatbuffers>=23.5.26 (from tensorflow[and-cuda])
Using cached flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow[and-cuda])
Using cached gast-0.5.4-py3-none-any.whl (19 kB)
Collecting google-pasta>=0.1.1 (from tensorflow[and-cuda])
Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Collecting h5py>=2.9.0 (from tensorflow[and-cuda])
Using cached h5py-3.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)
Collecting libclang>=13.0.0 (from tensorflow[and-cuda])
Using cached libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl.metadata (5.2 kB)
Collecting ml-dtypes==0.2.0 (from tensorflow[and-cuda])
Using cached ml_dtypes-0.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
Collecting numpy>=1.23.5 (from tensorflow[and-cuda])
Using cached numpy-1.26.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
Collecting opt-einsum>=2.3.2 (from tensorflow[and-cuda])
Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting packaging (from tensorflow[and-cuda])
Using cached packaging-23.2-py3-none-any.whl.metadata (3.2 kB)
Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow[and-cuda])
Using cached protobuf-4.25.0-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)
Requirement already satisfied: setuptools in ./miniconda3/envs/tf-test/lib/python3.11/site-packages (from tensorflow[and-cuda]) (68.0.0)
Collecting six>=1.12.0 (from tensorflow[and-cuda])
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting termcolor>=1.1.0 (from tensorflow[and-cuda])
Using cached termcolor-2.3.0-py3-none-any.whl (6.9 kB)
Collecting typing-extensions>=3.6.6 (from tensorflow[and-cuda])
Using cached typing_extensions-4.8.0-py3-none-any.whl.metadata (3.0 kB)
Collecting wrapt<1.15,>=1.11.0 (from tensorflow[and-cuda])
Using cached wrapt-1.14.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow[and-cuda])
Using cached tensorflow_io_gcs_filesystem-0.34.0-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (14 kB)
Collecting grpcio<2.0,>=1.24.3 (from tensorflow[and-cuda])
Using cached grpcio-1.59.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)
Collecting tensorboard<2.15,>=2.14 (from tensorflow[and-cuda])
Downloading tensorboard-2.14.1-py3-none-any.whl.metadata (1.7 kB)
Collecting tensorflow-estimator<2.15,>=2.14.0 (from tensorflow[and-cuda])
Downloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl.metadata (1.3 kB)
Collecting keras<2.15,>=2.14.0 (from tensorflow[and-cuda])
Downloading keras-2.14.0-py3-none-any.whl.metadata (2.4 kB)
Collecting nvidia-cuda-runtime-cu11==11.8.89 (from tensorflow[and-cuda])
Using cached nvidia_cuda_runtime_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (875 kB)
Collecting nvidia-cublas-cu11==11.11.3.6 (from tensorflow[and-cuda])
Using cached nvidia_cublas_cu11-11.11.3.6-py3-none-manylinux1_x86_64.whl (417.9 MB)
Collecting nvidia-cufft-cu11==10.9.0.58 (from tensorflow[and-cuda])
Using cached nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)
Collecting nvidia-cudnn-cu11==8.7.0.84 (from tensorflow[and-cuda])
Using cached nvidia_cudnn_cu11-8.7.0.84-py3-none-manylinux1_x86_64.whl (728.5 MB)
Collecting nvidia-curand-cu11==10.3.0.86 (from tensorflow[and-cuda])
Using cached nvidia_curand_cu11-10.3.0.86-py3-none-manylinux1_x86_64.whl (58.1 MB)
Collecting nvidia-cusolver-cu11==11.4.1.48 (from tensorflow[and-cuda])
Using cached nvidia_cusolver_cu11-11.4.1.48-py3-none-manylinux1_x86_64.whl (128.2 MB)
Collecting nvidia-cusparse-cu11==11.7.5.86 (from tensorflow[and-cuda])
Using cached nvidia_cusparse_cu11-11.7.5.86-py3-none-manylinux1_x86_64.whl (204.1 MB)
Collecting nvidia-nccl-cu11==2.16.5 (from tensorflow[and-cuda])
Using cached nvidia_nccl_cu11-2.16.5-py3-none-manylinux1_x86_64.whl (210.3 MB)
Collecting nvidia-cuda-cupti-cu11==11.8.87 (from tensorflow[and-cuda])
Using cached nvidia_cuda_cupti_cu11-11.8.87-py3-none-manylinux1_x86_64.whl (13.1 MB)
Collecting nvidia-cuda-nvcc-cu11==11.8.89 (from tensorflow[and-cuda])
Using cached nvidia_cuda_nvcc_cu11-11.8.89-py3-none-manylinux1_x86_64.whl (19.5 MB)
INFO: pip is looking at multiple versions of tensorflow[and-cuda] to determine which version is compatible with other requirements. This could take a while.
Collecting tensorflow[and-cuda]
Downloading tensorflow-2.13.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)
WARNING: tensorflow 2.13.1 does not provide the extra 'and-cuda'
Collecting gast<=0.4.0,>=0.2.1 (from tensorflow[and-cuda])
Downloading gast-0.4.0-py3-none-any.whl (9.8 kB)
Collecting keras<2.14,>=2.13.1 (from tensorflow[and-cuda])
Downloading keras-2.13.1-py3-none-any.whl.metadata (2.4 kB)
Collecting numpy<=1.24.3,>=1.22 (from tensorflow[and-cuda])
Downloading numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 10.9 MB/s eta 0:00:00
Collecting tensorboard<2.14,>=2.13 (from tensorflow[and-cuda])
Downloading tensorboard-2.13.0-py3-none-any.whl (5.6 MB)
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Installing collected packages: libclang, flatbuffers, wrapt, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, six, pyasn1, protobuf, packaging, oauthlib, numpy, MarkupSafe, markdown, keras, idna, grpcio, gast, charset-normalizer, certifi, cachetools, absl-py, werkzeug, rsa, requests, pyasn1-modules, opt-einsum, h5py, google-pasta, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow
Successfully installed MarkupSafe-2.1.3 absl-py-2.0.0 astunparse-1.6.3 cachetools-5.3.2 certifi-2023.7.22 charset-normalizer-3.3.2 flatbuffers-23.5.26 gast-0.4.0 google-auth-2.23.4 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.59.2 h5py-3.10.0 idna-3.4 keras-2.13.1 libclang-16.0.6 markdown-3.5.1 numpy-1.24.3 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-23.2 protobuf-4.25.0 pyasn1-0.5.0 pyasn1-modules-0.3.0 requests-2.31.0 requests-oauthlib-1.3.1 rsa-4.9 six-1.16.0 tensorboard-2.13.0 tensorboard-data-server-0.7.2 tensorflow-2.13.1 tensorflow-estimator-2.13.0 tensorflow-io-gcs-filesystem-0.34.0 termcolor-2.3.0 typing-extensions-4.5.0 urllib3-2.0.7 werkzeug-3.0.1 wrapt-1.15.0
```
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How to solve "DataType error: DataType 0 is not recognized in Java." when using tensorflowlite in android
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[
"I'm sorry for not providing enough information. This is the project exported by Android Studio [onDeviceTrainingEx.zip](https://github.com/tensorflow/tensorflow/files/13278511/onDeviceTrainingEx.zip), it is built on Windows 10(but the model is converted on Linux). ",
"Hi @TiAmoLip \r\n\r\nI have observed that you are using TF 2.7. The Java API's DataType enum are updated with different releases.\r\n\r\nCould you please try with latest version TF2.14 for TFLite conversion as well as in gradle file and let us know if the issue still persist?\r\n\r\nThanks.",
"Thank you for giving me the guidance!\r\nIn fact I have recently tried multiple versions of TF to convert model, including TF2.9, TF2.12, TF2.7 while using TFLite 2.9 in gradle, and they all ended up with this issue.\r\nThis time, I tried to use TF2.14 to convert TFLite model and tensorflow-lite 2.14 in build.gradle, but there is another bug when loading model:\r\n```\r\nval options = Interpreter.Options()\r\nval modelFile = FileUtil.loadMappedFile(context,\"model.tflite\")\r\ninterpreter = Interpreter(modelFile,options)//error occurs\r\n```\r\nI use debug tools in Android studio and find the error occurs in the last line above. The code behind it is read-only which I cannot modify.\r\nThis bug causes the app to crash as soon as it opens, and the error message is:\r\n```\r\nA Fatal signal 11 (SIGSEGV), code 1 (SEGV_MAPERR), fault addr 0xd1240004 in tid 3096 (evicetrainingex), pid 3096 (evicetrainingex)\r\npid: 3096, tid: 3096, name: evicetrainingex >>> com.example.ondevicetrainingex <<<\r\n#00 pc 04b24fa0 /data/app/~~bsUTaPTP2IFBO5JbatUS4Q==/com.example.ondevicetrainingex-N2chS4trwgoA4qPzksoikA==/base.apk!libtensorflowlite_flex_jni.so (offset 0xdfd000)\r\n#01 ......\r\n......\r\n```\r\nThen I tried to downgrade lite in gradle, finding that 2.12 produced the same result, and 2.9 still caused the issue \"Datatype error\". ",
"Hi @TiAmoLip, it looks like your inputs and outputs are not a type supported by the model. You are using MutableMap<String, Any> for both (In TransferLearningHelper.kt:56), I don't know the exact characteristics of your model but typically I've seen people used the right sized ByteBuffers: https://firebase.google.com/docs/ml/android/use-custom-models#run_the_interpreter Here's an example which may help guide you. Let me know if that somehow works for you or if you run into a different problem.\r\n\r\nThanks.",
"Hi @pkgoogle, I'm excited to tell you that this problem has been solved! Thank you for the docs you provided, but the problem may be the cause of the incorrect key of my input hashmap. This doc reminds me to check the signature list of my model, and I found my input keys are \"features\" and \"labels\", which dismatches the output of:\r\n```\r\nsignatures = interpreter.get_signature_list()\r\nprint(signatures) #{'infer': {'inputs': ['x'], 'outputs': ['output']}, 'train': {'inputs': ['x', 'y'], 'outputs': ['loss']}}\r\n```\r\nAfter I modified the key of input hash map, I found that both float and ByteBuffer can successfully run. \r\nDeeply appreciate your help.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62327\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62327\">No</a>\n"
] | 2023-11-05T15:45:55 | 2023-11-10T13:35:45 | 2023-11-10T13:35:42 |
NONE
| null | null | null |
I am trying to perform on device training in kotlin with tensorflowlite in Android Studio. I followed the tutorial from [text](https://tensorflow.google.cn/lite/examples/on_device_training/overview) and the github page on it, but I am trying to train a MLP instead of CNN, and the data is generated by hand. I learnt from the example code from github codes in that web and as long as the training process began, the error occured and the logcat is as follow:
```
FATAL EXCEPTION: pool-2-thread-1
Process: com.example.deeplearningforinfer, PID: 32094
java.lang.IllegalArgumentException: DataType error: DataType 0 is not recognized in Java.
at org.tensorflow.lite.DataTypeUtils.fromC(DataTypeUtils.java:69)
at org.tensorflow.lite.TensorImpl.<init>(TensorImpl.java:479)
at org.tensorflow.lite.TensorImpl.fromSignatureInput(TensorImpl.java:49)
at org.tensorflow.lite.NativeSignatureRunnerWrapper.getInputTensor(NativeSignatureRunnerWrapper.java:49)
at org.tensorflow.lite.NativeInterpreterWrapper.getInputTensor(NativeInterpreterWrapper.java:418)
at org.tensorflow.lite.NativeInterpreterWrapper.runSignature(NativeInterpreterWrapper.java:193)
at org.tensorflow.lite.Interpreter.runSignature(Interpreter.java:261)
at com.example.deeplearningforinfer.TransferLearningHelper.train(TransferLearningHelper.kt:64)
at com.example.deeplearningforinfer.TransferLearningHelper.startTraining$lambda$5(TransferLearningHelper.kt:146)
at com.example.deeplearningforinfer.TransferLearningHelper.$r8$lambda$7My04QIePTcHhSGX3SRRzpn0w1Y(Unknown Source:0)
```
The android device is Pixel 6(API level 30), and I also test it on my vivo neo5(API 33). The version of tensorflow for converting the model is 2.7.0 and on Ubuntu 20.04.
The full description is [stackoverflow](https://stackoverflow.com/questions/77332595/how-to-solve-datatype-error-datatype-0-is-not-recognized-in-java-when-using)
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PR_kwDOArmXAs5enlsl
| 62,326 |
Repair collision between Tuple and typing.Tuple
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[
"Can you please submit this PR to the openXLA repository instead?\r\n\r\nhttps://github.com/openxla/xla/blob/main/xla/python/xla_extension/ops.pyi"
] | 2023-11-05T14:10:28 | 2023-11-06T09:26:57 | 2023-11-06T09:26:54 |
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Truble with building on Arch
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[
"@dani3l125 Could you please make sure that you are using the latest version of the clang-16 compiler?\r\nThank you!",
"Hi @dani3l125 \r\nI was succeeded in building '//tensorflow/tools/pip_package:build_pip_package' right now with the command that you provided: `$ bazel build --compilation_mode=dbg --verbose_failures -j 4 --config=opt //tensorflow/tools/pip_package:build_pip_package` on this commit id: 1c7adbb1e798211fde6f423c70bbc604a3a1e879 \r\n\r\nYou might consider the next helpful steps, [mentioned here](https://www.tensorflow.org/install/source). \r\nPlease note, that before installation I had a previous version of clang (clang-14), installed on my workstation.\r\nI installed clang-16: `sudo apt-get update && sudo apt-get install -y llvm-16 clang-16`\r\nThen during the configuration stage `$ ./configure` I checked that Bazel 6.4.0 is installed (You have bazel 6.4.0 installed.) on my system. As well as clang-16 (You have Clang 16.0.6 installed).\r\nI have used Pyhton3.11\r\nSo, after running the command `$ bazel build --compilation_mode=dbg --verbose_failures -j 4 --config=opt //tensorflow/tools/pip_package:build_pip_package` I've got the expected result:\r\n`INFO: 15025 processes: 493 internal, 14532 local.\r\nINFO: Build completed successfully, 15025 total actions\r\n`\r\nHope it will help.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Thank you for the responses! I have moved to another set up a while after opening the issue and didn't get to close it. Sorry for that :(: \r\nI will let you know if I resolve it.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62325\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62325\">No</a>\n"
] | 2023-11-04T21:47:22 | 2023-11-16T18:27:27 | 2023-11-16T18:27:23 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14
### Custom code
Yes
### OS platform and distribution
Arch Linux
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.4.0
### GCC/compiler version
none (clang-16)
### CUDA/cuDNN version
none
### GPU model and memory
none
### Current behavior?
Hi,
I was trying to build with different flags, as well as using gcc6.4.0 and could not complete the build yet.
Would be happy to hear any advice!
### Standalone code to reproduce the issue
```shell
bazel build --compilation_mode=dbg --verbose_failures -j 4 --config=opt //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
/home/daniel/tensorflow/tensorflow/BUILD:1303:21: Linking tensorflow/libtensorflow_cc.so.2.16.0 failed: (Exit 1): clang-16 failed: error executing command (from target //tensorflow:libtensorflow_cc.so.2.16.0) (cd /home/daniel/.cache/bazel/_bazel_daniel/79db702fc9f94af7d11e11c5d64854d0/execroot/org_tensorflow && \ exec env - \ CLANG_COMPILER_PATH=/usr/bin/clang-16 \ PATH=/usr/local/sbin:/usr/local/bin:/usr/bin:/usr/lib/jvm/default/bin:/usr/bin/site_perl:/usr/bin/vendor_perl:/usr/bin/core_perl \ PWD=/proc/self/cwd \ PYTHON_BIN_PATH=/usr/bin/python3 \ PYTHON_LIB_PATH=/usr/lib/python3.11/site-packages \ TF2_BEHAVIOR=1 \ /usr/bin/clang-16 @bazel-out/k8-dbg/bin/tensorflow/libtensorflow_cc.so.2.16.0-2.params) # Configuration: 739e79f61a896ec2b9b5142b2c84f775153b8318dec3ac78c15572b987f2e3d8 # Execution platform: @local_execution_config_platform//:platform clang-16: error: unable to execute command: Killed clang-16: error: linker command failed due to signal (use -v to see invocation) Target //tensorflow/tools/pip_package:build_pip_package failed to build
```
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TensorflowLite_converter from python to C
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"Hi **@hpssjellis** \r\n\r\nTensorFlow Lite converters are not directly convertible into C/C++ code from Python. And TensorflowJS also not directly convertible into C/C++.\r\n\r\nHowever, you can convert the models (once they're generated) into WebAssembly (WASM) using Emscripten and run them in a browser via JavaScript.\r\n\r\nFor a simple 3 dense layer model, you can create the model using a deep learning library, then convert it to TensorFlowLite or TensorFlow.js using their respective converters. Once you have the model in the appropriate format (.tflite for TensorFlowLite or in the case of TensorFlow.js, as a set of JavaScript files or JSON format), you can then explore the use of Emscripten to compile the model into WebAssembly.\r\n\r\nThank you!\r\n\r\n",
"@Venkat6871 That is really interesting that a tFlite file can be converter to WASM. I will look into that as I have seen it done by [edgeimpulse.com](https://edgeimpulse.com/) . If you have any links that would be useful.\r\n\r\n**What I am trying to do is to simplify the full pipeline for making machine learning models for microcontrollers like Arduinos using client side browser javascript** \r\n\r\nI have already done it using webSerial, an example static webpage is [here](https://hpssjellis.github.io/tinyMLjs/public/acceleration/a00-best-acceleration-nicla.html) to make a tensorflowJS model. Then a [gitpod](https://github.com/hpssjellis/tensorflowjs-to-arduino-for-tinymljs) (if you have a gitpod account just click [here](https://gitpod.io/#github.com/hpssjellis/tensorflowjs-to-arduino-for-tinymljs) ) to convert that model to a tFlite c-header file and then an old [my Tensorflow library](https://github.com/hpssjellis/RocksettaTinyML) I made 3 years ago to help get the Arduino code working.\r\n\r\n\r\nThe above process works except there are two main things I wish to improve:\r\n\r\n1. simplify the conversion without the user having to load a complete python environment. (This might be much easier using ipython notebooks, it still uses python not javascript but at least it would be simple.)\r\n2. Update my [my Tensorflow Library](https://github.com/hpssjellis/RocksettaTinyML) . This will be a fair bit harder and I might need some help.\r\n\r\n\r\n\r\n \r\n",
"Hi @hpssjellis \r\n\r\nThe TFLite converter basically converts the TF/Keras/JAX models to `.tflite` which can be done in either ways, through python API or we can use command line tool to directly convert into `.tflite` without using any python code.\r\n\r\nhttps://www.tensorflow.org/lite/models/convert/convert_models#command_line_tool_\r\n\r\nThe then `.tflite` can be used by TFJS TFLite API which is packaged in a WebAssembly binary that runs in a browser.\r\n\r\nhttps://js.tensorflow.org/api_tflite/0.0.1-alpha.4\r\n\r\nDoes that help your use case?\r\n\r\nThanks.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62324\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62324\">No</a>\n"
] | 2023-11-04T20:03:38 | 2023-11-25T01:47:52 | 2023-11-25T01:47:48 |
NONE
| null | null | null |
Does anyone know if [TensorFlowLite_converter](tensorflow/lite/python/lite.py) and [TensorflowJS converters](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter/python/tensorflowjs/converters) can be converted into C/C++ code from Python?
My end goal is to then use [emscripten.org](https://emscripten.org/) to convert both of them into WASM and try to run them from a browser javascript webpage.
I am only interested in simple ML such as a 3 dense layer model etc.
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[
"Hi @sirakiin, can you please take a look?",
"The visibility of these components has always been \":utils_friends\", hasn't it?\r\n\r\nThe \"schema_utils\" target was only introduced in 2.4 and it had that visibility when first introduced:\r\nhttps://github.com/tensorflow/tensorflow/blob/r2.4/tensorflow/lite/schema/BUILD#L138\r\n\r\nThe \"schema_conversion_utils\" target was only introduced in 2.5 and it had that visibility when first introduced:\r\nhttps://github.com/tensorflow/tensorflow/blob/r2.5/tensorflow/lite/schema/BUILD#L168",
"Yes, it seems it always was. I think I've resolved my issue: I realised I was using bazel 6.1.0 instead of 5.3.0 which is the version used in v2.13.0.\r\n\r\nThank you for looking into this.\r\n \r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62323\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62323\">No</a>\n"
] | 2023-11-04T12:16:35 | 2023-11-11T08:19:09 | 2023-11-11T08:19:06 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13+
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
I am updating https://github.com/google-coral/pycoral to support Tensorflow 2.13+. This repo used to be able to reference and use the following bazel libs:
@org_tensorflow//tensorflow/lite/schema:schema_conversion_utils
@org_tensorflow//tensorflow/lite/schema:schema_utils
But the visibility of these components was changed from v2.2.0 and is now ":utils_friends"
Could these be reverted to public so we can continue to use them or else suggest how I might use them as they currently stand?
Many thanks
### Standalone code to reproduce the issue
```shell
.
```
### Relevant log output
```shell
ERROR: /workspace/coral/learn/BUILD:21:11: in cc_library rule //coral/learn:utils: target '@org_tensorflow//tensorflow/lite/schema:schema_conversion_utils' is not visible from target '//coral/learn:utils'. Check the visibility declaration of the former target if you think the dependency is legitimate
ERROR: /workspace/coral/learn/BUILD:21:11: in cc_library rule //coral/learn:utils: target '@org_tensorflow//tensorflow/lite/schema:schema_utils' is not visible from target '//coral/learn:utils'. Check the visibility declaration of the former target if you think the dependency is legitimate
ERROR: /workspace/coral/learn/BUILD:21:11: Analysis of target '//coral/learn:utils' failed
```
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[
"Hi @dilyar421 ,\r\n\r\nTensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install [TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2). Please refer the WSL2 instructions [here](https://www.tensorflow.org/install/pip#windows-wsl2).\r\n\r\nThanks!",
"I really hope, tensorflow will bring back native GPU support on Windows.",
"@dilyar421 , Do you have cuda showing in the kernel. Sometimes it the kernel which is causing the issue not the module itself\r\n`import torch as pt\r\ntorch.cuda.is_available()`\r\n\r\nif the above shows True then try to check for the CuDnn folder is in the right place ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62322\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62322\">No</a>\n"
] | 2023-11-04T10:32:49 | 2023-12-15T01:49:33 | 2023-12-15T01:49:27 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.12.1
### Custom code
Yes
### OS platform and distribution
Windows 11 23H2
### Mobile device
_No response_
### Python version
3.10.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.7.1
### GPU model and memory
RTX4060
### Current behavior?
I've installed TensorFlow-GPU version 2.12.1 and am trying to run it with a GPU on my Windows system. However, TensorFlow is unable to recognize the GPU. I have CUDA version 11 installed, and I'm unsure if this version is compatible or if there are additional steps required to make TensorFlow detect the GPU.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print("TensorFlow Version:", tf.__version__)
print("Is GPU Available:", tf.config.list_physical_devices('GPU'))
```
### Relevant log output
```shell
TensorFlow Version: 2.12.1
Is GPU Available: []
```
|
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PR_kwDOArmXAs5elyac
| 62,321 |
Documentation of `tf.nn.depthwise_conv2d` Add limitation doc on strides
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[
"The issue mentioned that there is `strides` documentation is not proper",
"> The issue mentioned that there is `strides` documentation is not proper\r\n\r\nAh, I see, the documentation is a bit unclear in the above section. Sure, you can fix that. You'll need to update the PR though.",
"@cantonios, sure I will do some testing for it! and then add it",
"Hi @rajveer43 Any update on this PR? Please. Thank you!",
"Hi @rajveer43 Any update on this PR? Please. Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-11-04T07:50:44 | 2024-01-28T01:48:16 | 2024-01-28T01:48:11 |
NONE
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fixes #62271
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TensorFlow was not built with CUDA kernel binaries compatible with compute capability 9.0
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[
"I found this report. But for them it was not working. For me it is working but takes a long time to start: https://github.com/tensorflow/tensorflow/issues/60739",
"@RocketRider Could you please try to recompile TensorFlow with support for CUDA compute capability 9.0. To do this, you will need to install the CUDA toolkit and the cuDNN library. You can then recompile TensorFlow using the following command:\r\n```\r\npip install tensorflow-gpu --upgrade --user\r\n```\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62320\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62320\">No</a>\n"
] | 2023-11-03T18:11:00 | 2023-11-21T01:50:30 | 2023-11-21T01:50:27 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
TF 2.15.0_rc0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Tested with TF 2.15.0_rc0 on a Machine with 8xH100.
I do get the warning "TensorFlow was not built with CUDA kernel binaries compatible with compute capability 9.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer."
Is it then using the full potential of the GPUs?
It takes really long to start a training.
Could you update the binaries to be compatible with compute capability 9.0?
### Standalone code to reproduce the issue
```shell
start tensorboard
```
### Relevant log output
```shell
TensorFlow was not built with CUDA kernel binaries compatible with compute capability 9.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
```
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tf.keras.Model.save can't handle UNC paths
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[
"You can in fact choose `path=\"\\\\\\\\localhost\\\\c$\"`, the UNC path to the root of the C: drive, to reproduce the error.",
"Hi @**early-stopper** \r\nCan you save the model to a local path and then copy it to the UNC path after saving\r\nOr else use different saving formats\r\nyou can find the documentation on the official website on this.\r\n\r\nThank you!\r\n\r\n",
"I could save the model locally and copy it by hand, which works until I want to implement some sort of automatising the training loop. As I said, using paths with drive letter names also resolves the problem. And, finally, using either the 'keras' or the 'h5' format instead of 'SavedModel' seems to resolve the issue as well, though each of those have shortcomings of their own (e.g. when saving custom objects, according to documentation).\r\n\r\nThose are all possible workarounds and I will settle for one of them for the moment. Nevertheless, I consider `SavedModel` not handling UNC paths correctly a bug, even more so since it already worked in an earlier version of TensorFlow.",
"Hi @early-stopper ,\r\n\r\nAs per [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model#:~:text=Args-,filepath,or%20pathlib.Path%20object%2C%20path%20to%20the%20saved%20model%20file.,-custom_objects) the argument filepath should be `str` or `pathlib.Path` object.\r\n\r\nCould you please just try with pathlib.Path(path) and let us know if it works. Thanks!",
"Hi @SuryanarayanaY,\r\nkindly see my answer in #62337 to this. In short: Running `pathlib.Path(...)` on Windows will produce an instance of `pathlib.WindowsPath` anyway, so replacing the latter with the former doesn't make a difference. Even if this wasn't the case, you would expect `pathlib.WindowsPath` as a subclass of `pathlib.Path` to work anywhere that an instance of `pathlib.Path` is assumed.",
"Also just want to know whether it works with keras-nightly (i.e keras3.0.dev). Could you able to check it if have bandwidth.\r\n\r\nThanks!",
"I cannot reproduce the error with `keras-nightly`, because it does not support saving models in the `SavedModel` format: running\r\n```python\r\nimport pathlib\r\nimport keras\r\n\r\npath = \"\\\\\\\\localhost\\\\c$\"\r\npath = pathlib.Path(path)\r\npath = path/'test'\r\npath.mkdir(exist_ok=True, parents=True)\r\n\r\nmodel = keras.Sequential()\r\nmodel.add(keras.Input(shape=(16,)))\r\nmodel.add(keras.layers.Dense(8))\r\n\r\nmodel.save(path/'model', save_format='tf')\r\n```\r\nwill result in\r\n```\r\nValueError: Invalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `tf.saved_model.save()` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=\\\\localhost\\c$\\test\\model.\r\n```\r\nIt works, of course, when switching to the `keras` saving format, but, as I noted before, so does `tf.saved_model.save()`. The issue only comes up when using the `SavedModel` format.",
"Keras3 won't support 'tf' format. You need to test with .keras only. Its success with Keras3 as per attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/7f2dc1f13c2c451ebc9823393030758d/62319.ipynb#scrollTo=uRgfC8qOh-ng).\r\n\r\nIf you want to test with tf format you need to import tf-keras package and set `os.environ[\"TF_USE_LEGACY_KERAS\"]=\"1\"`.\r\nTeste the code woth tf format after tf-keras import and model saved fine.Please refer attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/93965dd4d3cce98a4085f4838619ace6/62319-tf-keras.ipynb).",
"I think we can close this issue, the solution is to just use the `keras` format for saving models. As far as I understand it now, the `tf` format can be considered deprecated and there is no use in trying to make it work in future code.",
"Hi @early-stopper ,\r\n\r\nThanks for confirmation. Closing the issue as per author confirmation. Please feel reopen if find any problem.\r\n\r\nThanks!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62319\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62319\">No</a>\n"
] | 2023-11-03T16:47:23 | 2024-01-29T06:53:46 | 2024-01-29T06:53:42 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
We use a network drive to share trained model instances among multiple machines. With TensorFlow 2.11, I could use a UNC path to save models like
`model.save("\\\\ComputerName\\path\\to\\folder")`
to save an instance of `tf.keras.Model` to the network drive in the `SavedModel` format. Having updated to TensorFlow 2.14, I now get the following error:
`tensorflow.python.framework.errors_impl.FailedPreconditionError: \\ComputerName\path\to\folder is not a directory.`
A possible workaround is to use the drive letter `J` that I have assigned to `\\ComputerName` on my local machine, so the following works as expected:
`model.save("J:\\path\\to\\folder")`
But that is not really a solution for me, because then I would have to rely on others to use the same letter `J` for the network drive.
### Standalone code to reproduce the issue
```shell
import pathlib
import tensorflow as tf
# Replace dummy with actual UNC path here
path = "\\\\ComputerName\\path\\to\\folder"
path = pathlib.WindowsPath(path)
path.mkdir(exist_ok=True, parents=True)
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(8))
model.save(path)
```
### Relevant log output
_No response_
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ImportError: cannot import name '__version__' from 'tensorflow.keras' \
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[
"@siddharthahiremath,\r\nCould you please try to import the keras directly as **import keras**. I tried and it was able to import the keras on tensorflow v2.14. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/bf75918fdc7c6bc3b707e4b5787018c5/untitled1494.ipynb). Thank you!",
"Hi,\r\n\r\nThanks for reporting the issue.\r\n\r\nSince the migration, there were many changes made, the error you are getting is due to one such change here https://github.com/keras-team/tf-keras/commit/cff6ac903e2b8a0dde2a469d949f0f0ce3b5f282.\r\n\r\nTo get rid of the error, you need to install tf-keras-nightly as well as tf-nightly.\r\n\r\nNote that, tf-keras-nightly is legacy Keras code, to use the Keras 3 with multi-backend support, use keras-nightly and import Keras directly. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62318\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62318\">No</a>\n"
] | 2023-11-03T14:09:06 | 2023-11-22T01:49:29 | 2023-11-22T01:49:26 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
M1 macbook pro macOS 13.4.1 (22F82)
### Mobile device
_No response_
### Python version
3.11.4
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Failing to import DQNAgent
### Standalone code to reproduce the issue
```shell
from rl.agents import DQNAgent
```
### Relevant log output
```shell
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/Users/siddharthahiremath/Ai_myaybe?.ipynb Cell 9 line 1
----> 1 from rl.agents import DQNAgent
2 from rl.policy import BoltzmannQPolicy
3 from rl.memory import SequentialMemory
File /opt/homebrew/lib/python3.11/site-packages/rl/agents/__init__.py:1
----> 1 from .dqn import DQNAgent, NAFAgent, ContinuousDQNAgent
2 from .ddpg import DDPGAgent
3 from .cem import CEMAgent
File /opt/homebrew/lib/python3.11/site-packages/rl/agents/dqn.py:7
4 from tensorflow.keras.models import Model
5 from tensorflow.keras.layers import Lambda, Input, Layer, Dense
----> 7 from rl.core import Agent
8 from rl.policy import EpsGreedyQPolicy, GreedyQPolicy
9 from rl.util import *
File /opt/homebrew/lib/python3.11/site-packages/rl/core.py:7
4 import numpy as np
5 from tensorflow.keras.callbacks import History
----> 7 from rl.callbacks import (
8 CallbackList,
9 TestLogger,
...
----> 8 from tensorflow.keras import __version__ as KERAS_VERSION
9 from tensorflow.python.keras.callbacks import Callback as KerasCallback, CallbackList as KerasCallbackList
10 from tensorflow.python.keras.utils.generic_utils import Progbar
ImportError: cannot import name '__version__' from 'tensorflow.keras' (/opt/homebrew/lib/python3.11/site-packages/keras/api/_v2/keras/__init__.py)
```
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Cannot load_model() for Functional model if compiled with tf.keras.optimizer.get(optimizer_config)
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[
"Hey! I might a little solution for you. Hope it helps There are two ways to solve this error choose which one You like\r\n\r\n1. `\r\ncustom_optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001)\r\n model.compile(optimizer=custom_optimizer, loss='binary_crossentropy', metrics=['accuracy'])\r\n`\r\njust use this code and it will work great. \r\n\r\n2. Another way is to use this \r\n\r\n```\r\n> optimizer_config = {'class_name': 'rmsprop', 'config': {'lr': 0.0001}}\r\n> model.compile(optimizer=tf.keras.optimizers.get(optimizer_config), loss='binary_crossentropy', metrics=['accuracy'])\r\n> \r\n\r\n> model.fit(X_train, y_train, epochs=10)\r\n> \r\n>\r\n> save_path = '/content/'\r\n> model.save(save_path + 'my_model',save_format='tf')\r\n> \r\n> \r\n> loaded_model = tf.keras.models.load_model(save_path + 'my_model')\r\n```\r\n\r\ninstead of using save_format=keras , use tf it will work fine \r\n\r\n\r\nThanks.",
"Thanks a lot! This kind of works for me, but our ideal solution would keep saving the model in the `.keras` format but still being able to define the optimizer from a config file just from a string, without having to manually instantiate the RMSprop class.",
"Hi @alvaro-stylesage ,\r\n\r\nI have replicated the issue with TF2.14 version.\r\n\r\n With latest keras3 nightly version getting the value error as the API parameters got changed.\r\n\r\n`ValueError: Unrecognized keyword arguments passed to Embedding: {'input_length': 5}`\r\n\r\nHowever with tf-keras-nightly which is replacement of tf.keras getting another error like below.\r\n\r\n`ValueError: Could not interpret optimizer identifier: <keras.src.optimizers.rmsprop.RMSprop object at 0x7879100aa440>`\r\n\r\nAttaching [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/01ec12ec3ffbe02b9a61bbd62996ad52/62317.ipynb) for above testings.\r\n\r\nThis needs to be digged more and will get back you once we find out root cause. Thanks!",
"Hi @alvaro-stylesage ,\r\n\r\nThis is not an issue with TF2.15v. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/77e44152e3f0b1c43a3be7e46c8e8378/62317_tf-2-15.ipynb). Could you verify and confirm. Thanks!",
"Even with tf-nightly+keras-nightly also its working fine.\r\n\r\nChanges needed: `'lr' `should be renamed to `learning_rate` as it is deprecated in Keras3.The Embedding layer of Keras3 has no argument `input_length`. It will be automatically inferred from the input or you can also pass it to argument \r\n`input_shape = (batch_size,input_length)`.\r\n\r\nTested with the above changes and it works fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/c681b1fe35d95ea8b85ed469dfe144b8/62317_tf-nightly.ipynb). Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62317\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62317\">No</a>\n"
] | 2023-11-03T11:43:06 | 2024-02-15T01:47:27 | 2024-02-15T01:47:24 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.9.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
We are creating a Functional model object that contains a `TextVectorization()` layer and some `Dense` layers. The layer is adapted with the `.adapt()` method before training, the model is trained correctly and then saved correctly using the `.save(save_format='keras')` method.
For the training the optimizer is passed with `optimizer=tf.keras.optimizers.get(optimizer_config)` . Then when the model is saved and loaded again with the `tf.keras.models.load_model()` method this error raises:
`AttributeError: 'RMSprop' object has no attribute 'build'`
However, if the optimizer is defined as `optimizer='rmsprop' `, the error is not raised.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow import keras
import pandas as pd
from keras.layers import Input, TextVectorization, Embedding, GlobalAveragePooling1D, Dense
from keras.models import Model
train_df = pd.DataFrame({'review':['This is shit', 'I hate this', 'Great I love this!!!'],
'label': [0, 0, 1]})
X_train = train_df['review'].values
y_train = train_df['label'].values
# Define a Functional model:
vectorizer = TextVectorization(max_tokens=20, output_mode='int', output_sequence_length=5, pad_to_max_tokens=True)
vectorizer.adapt(X_train)
text_input = Input(shape=(1,), dtype=tf.string, name='text_input')
vectorizer_text = vectorizer(text_input)
embedding = Embedding(input_dim=20, input_length=5, output_dim=100)(vectorizer_text)
pooled = GlobalAveragePooling1D()(embedding)
output = Dense(1, activation='sigmoid', name='output')(pooled)
# Create the functional model
model = Model(inputs=text_input, outputs=output)
# Compile
optimizer_config = {'class_name': 'rmsprop', 'config': {'lr': 0.0001}}
model.compile(optimizer=tf.keras.optimizers.get(optimizer_config), loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=10)
# Save the model:
save_path = '/content/'
model.save(save_path + 'my_model.keras')
# Load the model:
loaded_model = tf.keras.models.load_model(save_path + 'my_model.keras')
```
### Relevant log output
```shell
AttributeError Traceback (most recent call last)
<ipython-input-61-3abc12e5f733> in <cell line: 2>()
1 # Load the model:
----> 2 loaded_model = tf.keras.models.load_model(save_path + 'my_model.keras')
6 frames
/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/legacy/optimizer_v2.py in __getattribute__(self, name)
985 """Overridden to support hyperparameter access."""
986 try:
--> 987 return super().__getattribute__(name)
988 except AttributeError as e:
989 # Needed to avoid infinite recursion with __setattr__.
AttributeError: 'RMSprop' object has no attribute 'build'
```
|
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PR_kwDOArmXAs5egepO
| 62,316 |
Enhancement: Extend tf.nest to support ExtensionType as a complete nested structure
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/62316/checks?check_run_id=18329404650) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @KartikeyBartwal Can you please sign CLA. Thank you!",
"> Hi @KartikeyBartwal Can you please sign CLA. Thank you!\r\n\r\ndone 🙌",
"Hi @pcish Can you please review this PR ? Thank you!",
"Hi @pcish Can you please review this PR ? Thank you!",
"Hi @pcish Can you please review this PR ? Thank you!",
"Hi @pcish Can you please review this PR ? Thank you!",
"Hi @edloper Can you please review this PR ? Thank you!"
] | 2023-11-03T08:13:40 | 2024-06-07T16:34:52 | null |
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Referencing issue #61954
### What does this PR do ?
The changes made to the **_map_structure** function aim to address the issue of custom types, such as MyComplexData (derived from **tf.experimental.ExtensionType)**, not being recognized as complete nested structures by the **tf.nest** module. To solve this issue, the following modifications were made to the function:
### Custom Type Handling:
A custom type checker, **can_handle_extension_type**, was introduced to identify objects that are instances of custom types based on **tf.experimental.ExtensionType**. This allows the function to determine when to apply custom handling.
### Custom Type Handling Logic:
The **handle_extension_type** function was created to handle custom types based on **tf.experimental.ExtensionType**. In this function, you can define specific logic to process custom types. For example, you can add code to convert the custom data structure into TensorFlow tensors or perform other operations relevant to your use case.
### Registration of Custom Type Handler:
The custom type checker and handler were registered using **tf.nest.register_structure_coder**. This registration ensures that the **tf.nest** module correctly recognizes and processes custom types based on **tf.experimental.ExtensionType.**
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I_kwDOArmXAs51weFt
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SentencepieceOp for TFLite android
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[
"Hi, @istoneyou!\r\n\r\n Please have a look at the following steps to fix such issues;\r\n1. Build TensorFlow Lite with SentencePiece support.\r\n2. Use a TensorFlow Lite binary that was built with SentencePiece support.\r\nCould you try to use the latest TF version and refer to [this]( https://www.tensorflow.org/lite/guide/android) guide as well.\r\nThank you!",
"Hi @sushreebarsa \r\nI used the [prebuilt AAR with TensorFlow ops hosted at MavenCentral](https://search.maven.org/artifact/org.tensorflow/tensorflow-lite-select-tf-ops) like belows, but it does not work.\r\n```\r\n implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\n implementation 'org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly-SNAPSHOT'\r\n implementation 'org.tensorflow:tensorflow-lite-support:0.0.0-nightly-SNAPSHOT'\r\n implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'\r\n```\r\nDoes the prebuilt AAR support the SentencePiece? Do I have to build the AAR that supports the SentencePiece by myself?\r\n\r\n\r\n",
"Hi, @istoneyou!\r\n No, the pre-built AAR does not support the SentencePiece. To use the SentencePiece, you need to build the AAR that supports it.\r\n\r\nTo build the AAR, you will need to install the following:\r\n\r\n1. The Android SDK\r\n2. The Gradle build tool\r\n3. The TensorFlow Lite Java API\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62315\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62315\">No</a>\n"
] | 2023-11-03T08:11:24 | 2023-11-22T01:49:32 | 2023-11-22T01:49:28 |
NONE
| null | null | null |
I convert the [universal-sentence-encoder-multilingual](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3) model to TFLite, and deploy it on Android 13 device with Tensorflow lite.
```
implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'
implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'
```
The below error appears when create Interpreter:
Caused by: java.lang.IllegalArgumentException: Internal error: Cannot create interpreter: Op type not registered 'SentencepieceOp' in binary running on localhost. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib (e.g. `tf.contrib.resampler`), accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
Delegate kernel was not initialized
Node number 301 (TfLiteFlexDelegate) failed to
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I_kwDOArmXAs51wbI_
| 62,314 |
Not supported logical op case.
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[
"Hi @**WenhuiZhao-center** \r\nCan you add templates. And here you are using the old version please upgrade to the latest version Tf 2.14.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62314\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62314\">No</a>\n"
] | 2023-11-03T08:01:46 | 2023-11-21T01:50:33 | 2023-11-21T01:50:30 |
NONE
| null | null | null |
hi,when i use tensorflow lite gpu delegate to invoke a lite model, it warning:
EQUAL: Not supported logical op case.
tensorflow version: 2.6.2
I find this code in tensorflow/lite/gpu/common/model_builder.cc:
if (IsLogicalOp(operation_type_)) {
TensorInfo output_tensor_info;
RETURN_IF_ERROR(GetTensorInfo(context, tflite_node->outputs->data[0],
&output_tensor_info));
if (output_tensor_info.producers.size() != 1 ||
output_tensor_info.consumers.size() != 1) {
return absl::UnavailableError("Not supported logical op case");
}
if (output_tensor_info.consumers[0].second->builtin_code ==
kTfLiteBuiltinCast) {
return absl::OkStatus();
} else {
return absl::UnimplementedError("Not supported logical op case.");
}
}
i don't understand why logicalop not supported on gpu delegate. And what should i do to make this logicalop can execute on gpu delegate?
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I_kwDOArmXAs51v8Cb
| 62,313 |
Addition of `IRFFTN` fft Function to tensorflow
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[
"Kindly share some more details so that I can get better clarity on the feature you are requesting",
"> Kindly share some more details so that I can get better clarity on the feature you are requesting\r\n\r\nI added the necessary description here, kindly acknowledge that",
"@rajveer43,\r\nCould you please have a look at this official document where the **irfft** performs Inverse real-valued fast Fourier transform and let us know if you are looking for this feature.\r\nhttps://www.tensorflow.org/api_docs/python/tf/signal/irfft\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/api_def/base_api/api_def_IRFFT.pbtxt#L22\r\nhttps://github.com/tensorflow/tensorflow/commit/6f08eea0a53ec79717824677c5da915297f0b09c\r\n\r\nThank you! ",
"I already used it, and I found adding `irfftn` adding to tensorflow backend,becuase it provides irfftn for multiple axis, \r\n",
"As per the `pyTorch` implementation also I don't see axis implementation `torch.fft.irfft(input, n=None, dim=-1, norm=None, *, out=None)`.\r\nCould you please clarify with toy example about what different you are expecting other than the existing TensorFlow implementation of `tf.signal.irfft`",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"sure I will do tgat",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-11-03T05:53:31 | 2023-11-30T04:05:04 | 2023-11-30T01:49:15 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 lacks a built-in 𝐑𝐅𝐅𝐓𝐍 function, which is essential for performing real-to-complex Fast Fourier Transforms (FFT) along multiple axes. This means users face limitations when working with real-valued data in the frequency domain.
`irfftn` performs the inverse Fourier transform, which involves reversing the process applied by the forward FFT. It computes the spatial or time-domain representation of the data from its frequency-domain representation. My reuqest is to add this function to the tensorflow backend as it lacks the support for inverse rfftn function.
below is the list of deep learning frameworks already have it this function natively supported.
[torch](https://pytorch.org/docs/stable/generated/torch.fft.irfftn.html)
[JAX](https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.fft.irfftn.html)
[paddlepaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/fft/irfftn_en.html)
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"Hi @never-to-never ,\r\n\r\nYou can use bazel build command some thing like `bazel build //<target_file>` then include it in your project. Please refer bazel [documentation](https://bazel.build/run/build#specifying-build-targets) for more details.\r\n\r\nHope it helps. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62312\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62312\">No</a>\n"
] | 2023-11-03T04:05:03 | 2023-11-25T01:47:54 | 2023-11-25T01:47:50 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.8
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
My program relies on a file such as hlo_pb2, which is imported from tensorflow.compiler.xla.service import hlo_pb2. This brings up a problem, I need to install tensorflow. But I don’t want to install the entire tensorflow when deploying. Is there any way to get hlo_pb2 by compiling certain files in xla?
### Standalone code to reproduce the issue
```shell
I don't know how to do it, please help
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs51twKN
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Enable to build TF Lite for armv7-a , vfpv3
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[
"@ghchams Yes, TensorFlow Lite officially supports the VFPv3 architecture. TensorFlow Lite supports a wide range of devices, including smartphones, tablets, wearables, and embedded devices with ARM, NEON, and VFPv3. \r\nPlease have a look at this for more information; https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite\r\n\r\nThank you!",
"Thank you for your response : \r\ncross compiling Tflite with bazel gives the following Lib file : \r\ni used this command bazel build --config=elinux_armhf -c opt //tensorflow/lite:libtensorflowlite.so \r\n\r\n readelf -A bazel-bin/tensorflow/lite/libtensorflowlite.so\r\nAttribute Section: aeabi\r\nFile Attributes\r\n Tag_CPU_name: \"8.2-A\"\r\n Tag_CPU_arch: v8\r\n Tag_CPU_arch_profile: Application\r\n Tag_ARM_ISA_use: Yes\r\n Tag_THUMB_ISA_use: Thumb-2\r\n Tag_FP_arch: FP for ARMv8\r\n Tag_Advanced_SIMD_arch: NEON for ARMv8.1\r\n Tag_ABI_PCS_wchar_t: 4\r\n Tag_ABI_FP_denormal: Needed\r\n Tag_ABI_FP_exceptions: Needed\r\n Tag_ABI_FP_number_model: IEEE 754\r\n Tag_ABI_align_needed: 8-byte\r\n Tag_ABI_enum_size: int\r\n Tag_ABI_VFP_args: VFP registers\r\n Tag_CPU_unaligned_access: v6\r\n Tag_FP_HP_extension: Allowed\r\n Tag_ABI_FP_16bit_format: IEEE 754\r\n Tag_MPextension_use: Allowed\r\n Tag_Virtualization_use: TrustZone and Virtualization Extensions\r\n\r\nmy target architecture is armv7 , could please explain? (i'm NOT using a raspberry PI) \r\nThank you ",
"Hi @ghchams \r\n\r\nDid you try CMAKE compilation for ARM ? Please check these [examples](https://www.tensorflow.org/lite/guide/build_cmake_pip#build_examples) for cmake build for various targets.\r\n\r\nIf the generated binaries are not compatible with your target, you need to use your own toolchain or provide custom build flags. For example, you can try with ARMCC flag `-march=armv7-a -mfpu=neon-vfpv3` as mentioned [here](https://www.tensorflow.org/lite/guide/build_cmake_pip#how_to_use_a_custom_toolchain).\r\n\r\nThanks. ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62311\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62311\">No</a>\n"
] | 2023-11-02T19:42:30 | 2023-11-25T01:47:57 | 2023-11-25T01:47:52 |
NONE
| null | null | null |
### System information
- **Target architecture**: armv7 with vfpv3
- **Target OS**: Debian 10 or Debian 12
- **OS Platform and Distribution (e.g., Linux Ubuntu 22.04 LTS)**:
- **TensorFlow installed from (source or binary)**: source
- **Python version**: trying to build for 3.7 or 3.11
- **Bazel version (if compiling from source)**: 6.1.0
- **GCC/Compiler version (if compiling from source)**: 2.35
- **CUDA/cuDNN version**: N/A (Building for ARM)
- **GPU model and memory**: N/A (Building for ARM)
I'm trying to build TensorFlow Lite for an ARMv7 architecture with vfpv3 support, but I've encountered multiple issues during the build process:
build/xnnpack/src/amalgam/gen/armsimd32.c:1832:48: error: expected ';' before '__sel'
const int16x2_t vmultiplier02 = (int16x2_t) __sel((uint8x4_t) vnegative_multiplier, (uint8x4_t) vpositive_multiplier);
When attempting to cross-compile using the command:
bazel build --config=elinux_armhf -c opt //tensorflow/lite:libtensorflowlite.so
the resultant library is tailored for the armv8 ABI with vfpv4, even though I specified flags for vfpv3.
Attempting to build a TensorFlow Lite wheel with the following command:
CI_DOCKER_EXTRA_PARAMS="-e CUSTOM_BAZEL_FLAGS=--copt=-mfpu=neon-vfpv3 -e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.6" \
tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 tensorflow/lite/tools/pip_package/build_pip_package_with_bazel.sh armhf
resulted in multiple errors.
Furthermore, referencing a previous issue (#39957) did not offer a solution, as it pointed towards older packages and was based on an older version of Ubuntu. Additionally, the PI-Python3 file mentioned in the issue seems to be missing from the TensorFlow repository.
Does TensorFlow Lite officially support the vfpv3 architecture?
Is it feasible to build a TensorFlow Lite Python wheel that's compatible with armv7 with vfpv3?
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PR_kwDOArmXAs5edDTM
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[tosa] legalize tfl.fully_connected to tosa.conv2d
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[
"> This patch changes legalization of tfl.fully_connected to tosa.conv2d instead of tosa.fully_connected\r\n\r\nCan you explain why this is preferred over matmul? From a computational standpoint a matmul and bias add seem more correct.",
"> > This patch changes legalization of tfl.fully_connected to tosa.conv2d instead of tosa.fully_connected\r\n> \r\n> Can you explain why this is preferred over matmul? From a computational standpoint a matmul and bias add seem more correct.\r\n\r\nMatmul + add would have following shortcomings:\r\n- To use MATMUL we would need to transpose the second matrix\r\n- MATMUL does not support asymmetric types (like int16 x int8) allowed by FULLY_CONNECTED\r\n- Bias addition for 48 bit accumulator (eg 16x8 operation) would require a 48-bit ADD which would be a new operation",
"Hi @rdzhabarov Can you please review this PR ? Thank you!",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!\r\n",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"> This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.\r\n\r\nresolved conflicts",
"@NatashaKnk would you help review this?",
"The code itself looks good, but I'm a bit concerned about this approach. From a clarity standpoint this seems confusing -- The first point of conversion between tfl and tosa should probably be as close of a 1:1 mapping we can get. Happy to review it as a later-on pass though.",
"the motivation for this PR is that we want to deprecate tosa.fully_connected operator.\r\nbefore we can do that, we wanted to remove its use in tf/tfl loweing to tosa, so that we wont break tf/tfl\r\nwhen we removed tosa.fully_connected as an op. Therefore this PR.",
"I think if fully_connected is being depricated, it's reasonable to expect that matmul would be able to pick up everything it was able to do before deprecation. It can later be converted into conv2d for optimization purposes, but mapping ftl directly to conv2d seems a bit hacky and confusing to me.",
"The reasons we did not lower to Matmul (followed by an Add), are:\r\n\r\n- To use MATMUL we would need to transpose the second matrix\r\n- MATMUL does not support asymmetric types (like int16 x int8) allowed by FULLY_CONNECTED\r\n- Bias addition for 48 bit accumulator (eg 16x8 operation) would require a 48-bit ADD which would be a new TOSA operation\r\n\r\nthats why we chose the implementation in this PR",
"I understand why this is the best approach with the existing set-up, I'm just not sure that looking at this PR in isolation is the best way to go about this. If there is no way to support existing fully_connected behavior through an op that is a reasonable default mapping of that op (which I don't consider Conv2D to be), the reasons for/approach to deprecation should be looked at.",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!",
"> Hi @Tai78641 Can you please resolve conflicts? Thank you!\r\n\r\ndone"
] | 2023-11-02T16:37:41 | 2024-06-07T16:33:50 | null |
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This patch changes legalization of tfl.fully_connected to tosa.conv2d instead of tosa.fully_connected
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Improvement of the regular expression
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/62309/checks?check_run_id=18296443495) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"I've sent Google CLA",
"Hi @kanglant Can you please review this PR ? Thank you!",
"Hi @kanglant Can you please review this PR ? Thank you!",
"Hi @kanglant Can you please review this PR ? Thank you!",
"Hi @kanglant Can you please review this PR ? Thank you!",
"Hi @kanglant Can you please review this PR ? Thank you!"
] | 2023-11-02T10:51:51 | 2024-06-10T04:33:03 | 2024-06-10T04:33:03 |
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The improvement of the regular expression
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r2.15 cherry-pick: e44f8a08051 "check hasattr on the type, not the instance."
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Incorrect/outdated link in Beginner Quickstart guide.
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"Hi @sunny0531,\r\n\r\nI have verified the reported problem and agree that the mnist link now asking for credentials to join. Will look into it and take appropriate action. Thanks!",
"Hi @sunny0531,\r\nI believe we need to use this link https://web.archive.org/web/20220331130319/https://yann.lecun.com/exdb/mnist/ instead of that link which requires credentials. I have verified the issue from my end.",
"Thanks for the link! @Antonyj12 \r\nAnd it seems to work on my device, so that might be a suitable replacement\r\nEdit: Or you can use the mirror that tensorflow download the file \r\n2nd Edit: What if the server just down for a while? According to https://web.archive.org/web/20231031180229/https://yann.lecun.com/exdb/mnist/, the link still work at 31 Oct 2023. Should we just wait and see if it resolve itself?",
"Hi @sunny0531 ,\r\n\r\nThe hyperlink to mnist dataset is removed with the above PR because it is external dependency and it won't affect the guide anyways.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Seeing how the pull request is merged, I will close this now. Thanks for your help, especially @SuryanarayanaY",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62306\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62306\">No</a>\n"
] | 2023-11-01T22:25:24 | 2023-11-23T07:57:22 | 2023-11-23T07:56:31 |
NONE
| null | null | null |
https://www.tensorflow.org/tutorials/quickstart/beginner
It has a link to the MNIST dataset and it leads to http://yann.lecun.com/exdb/mnist/
I assume that used to be the right link but now it require login to visit? So should it be removed or is there a new website?
Thanks!
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[
"Hello, @rajveer43!\r\nThank you for raising this feature request!\r\n\r\nThe addition of the rfftn function to the TF backend would be beneficial for a number of reasons. Once this is implemented, then it would be optimized for performance. \r\n\r\n@sachinprasadhs Could you please have a look at this?\r\nThank you! ",
"Its been in the other frameworks already! e.g. `pytorch`, `JAX`, `numpy`, `paddlepaddle`. so it does make sense to add this in TF too! would like to create PR for the same.\r\n\r\n> Hello, @rajveer43! Thank you for raising this feature request!\r\n> \r\n> The addition of the rfftn function to the TF backend would be beneficial for a number of reasons. Once this is implemented, then it would be optimized for performance.\r\n> \r\n> @sachinprasadhs Could you please have a look at this? Thank you!\r\n\r\n"
] | 2023-11-01T13:14:46 | 2023-11-06T23:24:56 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to add a `rfftn` function to tensorflow as it does not have that
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
_No response_
|
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`rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])
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[
"As per my knowledge you are encountering this error because you are not providing a scalar value, instead you are providing a list of values and also note that the rate value you provide must be between 0 and 1 , so in this line either change\r\nlayer = keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6]) into this layer = keras.layers.SpatialDropout2D(rate=a value b/w 0 and 1) or apply the dropout for each feature map seperately",
"Hi @j**ikechao** ,\r\n\r\nThe issue you're facing arises from the way you're defining the SpatialDropout2D layer. The error specifically mentions that the \"rate\" must be a scalar or scalar tensor, yet you're passing a list of values as the rate argument when initializing the SpatialDropout2D layer. This error is due to the invalid specification of the \"rate\" parameter. And you should take the rate(values between 0 to 1). Here I attached a [gist](https://colab.research.google.com/drive/1NnUptIW0t_ApHo2IJQDouAfOtxcGNGjI?authuser=0#scrollTo=4XIXUVodyPI9) for your reference.\r\n\r\nThank you!",
"@Venkat6871 @KrishChalana Thanks for your detailed explanation. Indeed, this crash was caused by the wrong usage. \r\nWould you add an assertion to reject calls to this API?\r\nBTW, Would you add an assertion to reject such unreasonable API calls when executing the statements `keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6])`? Honestly, it is also acceptable to check the validity when saving the model.",
"Hope it is what you are looking for @jikechao \r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow import keras as keras\r\nfrom tensorflow.keras import layers, models\r\nimport numpy as np\r\n\r\ndef create_spatial_dropout_layer(rate):\r\n # Check if rate is a list of valid dropout rates\r\n if isinstance(rate, list) and all(isinstance(r, (int, float)) and 0 <= r <= 1 for r in rate):\r\n return keras.layers.SpatialDropout2D(rate=rate)\r\n else:\r\n raise ValueError(\"Invalid dropout rate. The rate should be a list of float values between 0 and 1.\")\r\n\r\ninput_shape = [12, 10, 6, 18]\r\ninput_data = np.random.random(input_shape)\r\n\r\n# Create the spatial dropout layer with the specified rates\r\ndropout_layer = create_spatial_dropout_layer(rate=[0.3, 0.4, 0.5, 0.6])\r\n\r\nx = layers.Input(shape=input_shape[1:], dtype=\"float32\") \r\ny = dropout_layer(x)\r\nmodel = models.Model(x, y)\r\nmodel.summary()\r\nres_keras = model(input_data)\r\ntf.saved_model.save(model, \"tf_model\")\r\n\r\n```",
"Thanks.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62304\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62304\">No</a>\n"
] | 2023-11-01T11:52:01 | 2023-11-04T13:27:59 | 2023-11-04T13:27:56 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.saved_model.save` API can not convert the model with `SpatialDropout2D` operator and threw a crash message: "
ValueError: `rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])"
However, for models containing other operators, The script can run well.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow import keras as keras
from tensorflow.keras import layers, models
import numpy as np
layer = keras.layers.SpatialDropout2D(rate=[3, 4, 5, 6])
input_shape = [12, 10, 6, 18]
input_data = np.random.random(input_shape)
weights = layer.get_weights()
layer.set_weights(weights)
x = layers.Input(shape=input_shape[1:], dtype="float32")
y = layer(x)
model = models.Model(x, y)
model.summary()
res_keras = model(input_data)
tf.saved_model.save(model, "tf_model")
```
```
### Relevant log output
```shell
Traceback (most recent call last):
File "test.py", line 17, in <module>
tf.saved_model.save(model, "tf_model")
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1331, in save
save_and_return_nodes(obj, export_dir, signatures, options)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1366, in save_and_return_nodes
_build_meta_graph(obj, signatures, options, meta_graph_def))
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1578, in _build_meta_graph
return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 1490, in _build_meta_graph_impl
signatures = signature_serialization.find_function_to_export(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\signature_serialization.py", line 109, in find_function_to_export
for name, child in children:
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\saved_model\save.py", line 189, in list_children
for name, child in super(_AugmentedGraphView, self).list_children(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 75, in list_children
for name, ref in super(ObjectGraphView,
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\checkpoint\trackable_view.py", line 84, in children
for name, ref in obj._trackable_children(save_type, **kwargs).items():
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\functional.py", line 460, in _trackable_children
dependencies.update(super()._trackable_children(save_type, **kwargs))
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\training.py", line 4002, in _trackable_children
children = super()._trackable_children(save_type, **kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\engine\base_layer.py", line 3470, in _trackable_children
children = self._trackable_saved_model_saver.trackable_children(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\base_serialization.py", line 61, in trackable_children
children = self.objects_to_serialize(serialization_cache)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 79, in objects_to_serialize
return self._get_serialized_attributes(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 106, in _get_serialized_attributes
object_dict, function_dict = self._get_serialized_attributes_internal(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\model_serialization.py", line 57, in _get_serialized_attributes_internal
objects, functions = super()._get_serialized_attributes_internal(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 117, in _get_serialized_attributes_internal
functions = save_impl.wrap_layer_functions(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 168, in wrap_layer_functions
original_fns = _replace_child_layer_functions(layer, serialization_cache)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 305, in _replace_child_layer_functions
serialized_functions = child_layer._trackable_saved_model_saver._get_serialized_attributes( # noqa: E501
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 106, in _get_serialized_attributes
object_dict, function_dict = self._get_serialized_attributes_internal(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\layer_serialization.py", line 117, in _get_serialized_attributes_internal
functions = save_impl.wrap_layer_functions(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 223, in wrap_layer_functions
fn.get_concrete_function()
File "C:\software\conda\envs\torch\lib\contextlib.py", line 126, in __exit__
next(self.gen)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 390, in tracing_scope
fn.get_concrete_function(*args, **kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 1222, in get_concrete_function
concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 1192, in _get_concrete_function_garbage_collected
self._initialize(args, kwargs, add_initializers_to=initializers)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 694, in _initialize
self._concrete_variable_creation_fn = tracing_compilation.trace_function(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 178, in trace_function
concrete_function = _maybe_define_function(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 284, in _maybe_define_function
concrete_function = _create_concrete_function(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compilation.py", line 308, in _create_concrete_function
traced_func_graph = func_graph_module.func_graph_from_py_func(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1059, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 597, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 632, in wrapper
ret = method(*args, **kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 190, in wrap_with_training_arg
return control_flow_util.smart_cond(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\utils\control_flow_util.py", line 108, in smart_cond
return tf.__internal__.smart_cond.smart_cond(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 53, in smart_cond
return true_fn()
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 192, in <lambda>
lambda: replace_training_and_call(True),
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py", line 188, in replace_training_and_call
return wrapped_call(*new_args, **new_kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\saving\legacy\saved_model\save_impl.py", line 698, in call_and_return_conditional_losses
call_output = layer_call(*args, **kwargs)
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\layers\regularization\dropout.py", line 120, in call
output = control_flow_util.smart_cond(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\utils\control_flow_util.py", line 108, in smart_cond
return tf.__internal__.smart_cond.smart_cond(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\framework\smart_cond.py", line 53, in smart_cond
return true_fn()
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\layers\regularization\dropout.py", line 116, in dropped_inputs
return self._random_generator.dropout(
File "C:\software\conda\envs\torch\lib\site-packages\keras\src\backend.py", line 2172, in dropout
return tf.nn.dropout(
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\software\conda\envs\torch\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 5786, in _dropout
raise ValueError(
ValueError: `rate` must be a scalar or scalar tensor. Received: rate=ListWrapper([3, 4, 5, 6])
```
|
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I_kwDOArmXAs51jLQt
| 62,303 |
Remove resizeInput interface to TFLite Interpreter
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[
"Hi @Burton2000, apologies for all the whiplash but TF and TFL are a living codebases so everything is quite dynamic still. With 2.15 we are currently supporting dynamic input shapes: https://www.tensorflow.org/lite/guide/inference#run_inference_with_dynamic_shape_model That being said, please try your previous test to see if the original issue, is still an issue, and then we can take it from there.",
"Hey @pkgoogle not a problem. There is no .aar of 2.15.0 of TFLite available on maven to try, I updated to 2.14.0 which is the latest available there but no difference to previous issue reported.",
"Hi @LakshmiKalaKadali, can you please take a look? Thanks.",
"As this issue is no longer valid and there is a workaround for the original issue which does not affect performance we will close this issue as invalid."
] | 2023-11-01T11:04:39 | 2024-05-30T22:58:10 | 2024-05-30T22:58:10 |
NONE
| null | null | null |
As per the issue linked below, it appears that resizeInput is no longer supported due to performance issues so should be removed as an interface from the TFLite Interpreter.
> @Burton2000 So we essentially dropped support of dynamic input shapes generally speaking due to performance considerations and the difficulty of supporting it while maintaining good performance. But of course since this issue is old, maybe this wasn't true at the time, and I think resizeInput is a legacy interface that we should probably remove. In order for us to properly prioritize, can you perhaps create a new issue to remove that interface so that we can remain consistent in our design philosophy? Thanks for your help.
_Originally posted by @pkgoogle in https://github.com/tensorflow/tensorflow/issues/44016#issuecomment-1787818211_
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I_kwDOArmXAs51ikym
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How to Test all OPS/KERNELS Accuracy and Performance in my one device?
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[
"Hi, \r\n\r\nFor platform level test, there is a test file here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/platform/test.py.\r\n\r\nFor OP specific, there will be separate test files, which you can find it similar to this file here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/eager/ops_test.py\r\n\r\nTensorFlow has majority of test cases implemented in both `.py and .cc`.\r\nFor any specific file, the relevant test file will be `filename_test.py/.cc` similar to the example here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/conv3d_test.cc",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62302\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62302\">No</a>\n"
] | 2023-11-01T09:18:35 | 2023-11-30T01:49:24 | 2023-11-30T01:49:17 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
1.15
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have a device X
I am adapting TensorFlow on X
I want to know if my kernel are **Correct** and it's **Performance**，so I'll test nearly **ALL ops** both on CPU/GPU/TPN and X, and comparison results
if there is a **.py** to test all ops? or I need to write every case maself?
### Standalone code to reproduce the issue
```shell
nothing
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5eSQCL
| 62,301 |
[TF2XLA] Support ResizeNearestNeighborGrad
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[
"Thanks a lot! @changm could you take a look?",
"Hi @Princejain1101, Can you please take a look on this PR? Thank you!",
"Hi @Princejain1101, Can you please take a look on this PR? Thank you!",
"Sorry for the long delay here, this mostly looks OK. Please fix the other reviewers comments and ensure the tests pass. Thanks!",
"Hi @lgeiger Can you please check @changm's comments and keep us posted ? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"> Hi @lgeiger Can you please check @changm's comments and keep us posted ? Thank you!\r\n\r\nI think all comments have been addressed from my side, just awaiting approval from @changm\r\n\r\n",
"Sorry for the delay, I'm trying to run this internally to ensure tests pass however a lot of tests fail. I'm not exactly sure how external PRs work versus internal CI/CD runs, but can you get external tests to pass?\r\n\r\nI'm also getting errors such as :\r\n\r\n```\r\n10135 xla_op_registry.cc:105] Registrations of ResizeNearestNeighborGrad have incompatible compile time constant inputs.\r\nxla_op_registry.cc:606] XLA op registration ResizeNearestNeighborGrad is incompatible with existing registration of the same name.\r\n*** Check failure stack trace: ***\r\n\r\nabsl::log_internal::LogMessageFatal::~LogMessageFatal()\r\ntensorflow::XlaOpRegistrar::XlaOpRegistrar()\r\n _GLOBAL__sub_I_image_resize_ops.cc\r\n __libc_csu_init\r\n__libc_start_main\r\n _start\r\n``` \r\n\r\nWhich sounds like you need to mark the op as having compile time constant inputs.",
"Hi @lgeiger Can you please check @changm's [comments](https://github.com/tensorflow/tensorflow/pull/62301#issuecomment-1964439591) and keep us posted? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-10-31T23:41:26 | 2024-04-05T01:47:32 | 2024-04-05T01:47:25 |
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This PR adds support for `ResizeNearestNeighborGrad` similar to how `ResizeBilinearGrad` is implemented.
Fixes #57575
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PR_kwDOArmXAs5eRsS_
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Update version numbers for TensorFlow 2.15.0-rc1
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 15 -> 15
Patch: 0 -> 0
No lingering old version strings "2.15.0-rc0" found in source directory
"tensorflow/". Good.
WARNING: Below are potentially instances of lingering old version string
"2.15.0rc0" in source directory "tensorflow/" that are not updated by this
script. Please check them manually!
tensorflow/tools/pip_package/setup.py:120:2.15.0rc0
tensorflow/tools/pip_package/setup.py:121:2.15.0rc0
```
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PR_kwDOArmXAs5eRjsn
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[XLA:CPU][oneDNN] Disable oneDNN rewrite for small Matmul.
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[
"@penpornk Any update on this PR?",
"@penpornk Thanks for approving this PR. Let me know anything needed from my side.",
"Hi @penpornk Can you please help on import code failure. Thank you!"
] | 2023-10-31T20:21:55 | 2023-11-30T12:32:16 | 2023-11-30T12:32:16 |
CONTRIBUTOR
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This PR addresses the following issue https://github.com/openxla/xla/issues/5875. Here, we do not rewrite XLA HLO dot instruction for oneDNN matmul primitive when matrix-multiplication is small in terms of MAC (multiply-accumulate) ops. A threshold for rewrite is empirically determined using jax benchmarks (https://github.com/google/jax/blob/main/benchmarks/math_benchmark.py)
The following are performance data on matmul benchmarks ([jax-benchmarks](https://github.com/google/jax/blob/main/benchmarks/math_benchmark.py)) on Intel Xeon CPUs.
Problem Size [MxKxN] | oneDNN Perf ratio w and w/o
-- | --
16x16x16_float32 | 1.039257981
32x32x32_float32 | 1.036032261
64x64x64_float32 | 1.001214975
128x128x128_float32 | 1.050383228
256x256x256_float32 | 1.215597972
512x512x512_float32 | 1.266954652
1024x1024x1024_float32 | 2.886963409
1x2x256_float32 | 0.98122133
1x8x256_float32 | 0.984010011
1x18x300_float32 | 1.002542827
1x37x256_float32 | 1.010663823
1x91x256_float32 | 0.99543998
1x111x256_float32 | 1.011275892
1x192x192_float32 | 1.041369639
1x226x256_float32 | 1.011219147
1x256x192_float32 | 1.000978686
1x256x256_float32 | 1.048574995
1x512x512_float32 | 1.168676295
1x300x18_float32 | 0.98463839
21x24x1_float32 | 1.013919095
21x120x1_float32 | 1.002718558
10x10x10_float32 | 1.033871514
100x100x100_float32 | 1.008651831
18x1x300_float32 | 1.023119851
18x300x1_float32 | 0.999035281
300x1x18_float32 | 1.004889669
300x18x1_float32 | 1.033368366
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schema_generated.h checked into source instead of generated
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[
"Hi, @aws-taylor! Could you please follow the steps below ;\r\n1. Remove the checked-in schema_generated.h file from the source tree\r\n2. Regenerate the schema_generated.h file using flatc\r\n3. Add the generated schema_generated.h file to the project's build configuration.\r\n4. Update the file to check the changes \r\nPlease let us know which TF version you are using?\r\n\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @sushreebarsa,\r\n\r\nI think I was unclear - the issue isn't that the file is out of date - the issue is that the file shouldn't be checked into the source tree it all - it should be generated as part of the build. I can take the steps you mention, but that just pushes the problem down the road. \r\n\r\n> Please let us know which TF version you are using?\r\nThis issue exists on the top of tree\r\n\r\n-Taylor\r\n",
"Hi @aws-taylor, thanks for raising this issue.\r\n\r\nI was able to regenerate the file after deleting it but I feel like it was likely checked-in for a reason.\r\n\r\n@qukhan, Can you please take a look? Perhaps you have better insight here, thanks.",
"also running into this at alpine linux via chromium. `schema_generated.h` is actually just one of them\r\n```sh\r\n$ rg -l '\"Non-compatible flatbuffers version included\"'\r\ntensorflow/lite/acceleration/configuration/configuration_generated.h\r\ntensorflow/lite/experimental/acceleration/configuration/configuration_generated.h\r\ntensorflow/lite/delegates/gpu/common/gpu_model_generated.h\r\ntensorflow/lite/delegates/gpu/common/task/serialization_base_generated.h\r\ntensorflow/lite/delegates/xnnpack/weight_cache_schema_generated.h\r\ntensorflow/lite/delegates/gpu/cl/serialization_generated.h\r\ntensorflow/lite/delegates/gpu/cl/compiled_program_cache_generated.h\r\ntensorflow/compiler/mlir/lite/schema/schema_generated.h\r\ntensorflow/lite/schema/conversion_metadata_generated.h\r\ntensorflow/lite/schema/schema_generated.h\r\n```"
] | 2023-10-31T20:15:14 | 2024-05-25T20:33:06 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
master
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The [schema_generated.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_generated.h) file is checked into the source tree, as opposed to being generated by flatc. This in turn induces a dependency on a very specific version of flatc:
```
static_assert(FLATBUFFERS_VERSION_MAJOR == 23 &&
FLATBUFFERS_VERSION_MINOR == 5 &&
FLATBUFFERS_VERSION_REVISION == 26,
"Non-compatible flatbuffers version included");
```
There are other files that are generated by flatc in the [BUILD](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/BUILD). What is special about this file that requires it to be checked into the source tree? There are some references to `schema_generated.h.oss` that suggest that there's some Google specific stuff that's being omitted, but that's just a guess.
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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[
"Checkout this benchmark shows count is faster than find - https://quick-bench.com/q/F_JiLDSu-7tQVgb-pHqPSoDxQsc",
"That's for a container of a smaller size, I don't think that is that relevant here.\r\n\r\nIf you have a user-level macro benchmark that proves this is an optimization I'll reopen, but just fudging with a micro benchmark to change numbers doesn't work",
"Also, your benchmark seems influenced by noise, rerunning gives https://quick-bench.com/q/F_JiLDSu-7tQVgb-pHqPSoDxQsc",
"That is, clearing the cache and rerunning generates new results. Your link now proves the contrary of what you've said."
] | 2023-10-31T18:26:21 | 2023-11-02T00:05:53 | 2023-10-31T19:24:42 |
CONTRIBUTOR
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Instead of find and comparing to end(), using count to check for membership in the std::unordered_set. which is more efficient.
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I_kwDOArmXAs51beDw
| 62,296 |
In GCP Airflow - tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you'll have numpy 1.24.4 which is incompatible
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[
"Hi @**Aravinviju** ,\r\nCould you try to downgrade NumPy to a version that is compatible with TensorFlow 2.12.0 by running the following command:\r\n```\r\npip install numpy==1.23.4 \r\n```\r\nThis will install NumPy version 1.23.4, which is compatible with TensorFlow 2.12.0.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62296\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62296\">No</a>\n"
] | 2023-10-31T10:26:27 | 2023-11-18T01:48:23 | 2023-11-18T01:48:20 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.12.0
### Custom code
Yes
### OS platform and distribution
GCP - Airflow Environment - Ubuntu 16.04
### Mobile device
Ubuntu 16.04
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
GCP airflow composer-2.3.1
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to install python packages in the airflow pypi console in GCP. While installing a package that requires numpy>=1.24, this gives a conflict with the tensorflow as it requires <1.24 for tensorflow. If I reduce the tf version then that becomes incompatible with my required package (reliability). I don't understand how to resolve this.
**ERROR**
> ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.
### Standalone code to reproduce the issue
```shell
All I tried to do was to, install the required package (reliability - https://pypi.org/project/reliability/#:~:text=reliability%20is%20a%20Python%20library,only%20available%20in%20proprietary%20software.) in the GCP airflow environment(GCP airflow composer-2.3.1) through the PYPI packages GCP airflow environment console. The python version used in this build is Python 3.8.
Everytime I try to install the package, the environment build fails with this conflict with TF and numpy.
```
### Relevant log output
```shell
tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you'll have numpy 1.24.4 which is incompatible.
Successfully installed autograd-1.6.2 autograd-gamma-0.5.0 contourpy-1.1.1 cycler-0.12.1 fonttools-4.43.1 kiwisolver-1.4.5 matplotlib-3.7.3 mplcursors-0.5.2 numpy-1.24.4 pandas-2.0.3 pillow-10.1.0 reliability-0.8.14 tzdata-2023.3
+ python3 -m pipdeptree --warn
Warning!!! Possibly conflicting dependencies found:
* tensorflow==2.12.0
- numpy [required: >=1.22,<1.24, installed: 1.24.4]
------------------------------------------------------------------------
Warning!! Cyclic dependencies found:
* apache-airflow => apache-airflow => apache-airflow
* apache-airflow-providers-ssh => apache-airflow => apache-airflow-providers-ssh
* apache-airflow-providers-sqlite => apache-airflow => apache-airflow-providers-sqlite
* apache-airflow-providers-sendgrid => apache-airflow => apache-airflow-providers-sendgrid
* apache-airflow-providers-postgres => apache-airflow => apache-airflow-providers-postgres
* apache-airflow-providers-mysql => apache-airflow => apache-airflow-providers-mysql
* apache-airflow-providers-imap => apache-airflow => apache-airflow-providers-imap
* apache-airflow-providers-http => apache-airflow => apache-airflow-providers-http
* apache-airflow-providers-hashicorp => apache-airflow => apache-airflow-providers-hashicorp
* apache-airflow-providers-google => apache-airflow => apache-airflow-providers-google
* apache-airflow-providers-ftp => apache-airflow => apache-airflow-providers-ftp
* apache-airflow-providers-dbt-cloud => apache-airflow => apache-airflow-providers-dbt-cloud
* apache-airflow-providers-common-sql => apache-airflow => apache-airflow-providers-common-sql
* apache-airflow-providers-cncf-kubernetes => apache-airflow => apache-airflow-providers-cncf-kubernetes
* apache-airflow-providers-apache-beam => apache-airflow => apache-airflow-providers-apache-beam
* apache-airflow => apache-airflow-providers-apache-beam => apache-airflow
* apache-airflow => apache-airflow-providers-cncf-kubernetes => apache-airflow
* apache-airflow => apache-airflow-providers-common-sql => apache-airflow
* apache-airflow => apache-airflow-providers-common-sql => apache-airflow
* apache-airflow => apache-airflow-providers-dbt-cloud => apache-airflow
* apache-airflow => apache-airflow-providers-ftp => apache-airflow
* apache-airflow => apache-airflow-providers-google => apache-airflow
* apache-airflow => apache-airflow-providers-hashicorp => apache-airflow
* apache-airflow => apache-airflow-providers-http => apache-airflow
* apache-airflow => apache-airflow-providers-imap => apache-airflow
* apache-airflow => apache-airflow-providers-mysql => apache-airflow
* apache-airflow => apache-airflow-providers-postgres => apache-airflow
* apache-airflow => apache-airflow-providers-sendgrid => apache-airflow
* apache-airflow => apache-airflow-providers-sqlite => apache-airflow
* apache-airflow => apache-airflow-providers-ssh => apache-airflow
------------------------------------------------------------------------
appdirs==1.4.4
google-cloud-datacatalog-lineage-producer-client==0.0.9
├── cachetools [required: >=5.0.0, installed: 5.2.1]
└── google-cloud-datacatalog-lineage [required: ==0.1.6, installed: 0.1.6]
├── google-api-core [required: >=1.28.0,<3.0.0dev, installed: 2.11.0]
│ ├── google-auth [required: >=2.14.1,<3.0dev, installed: 2.16.0]
│ │ ├── cachetools [required: >=2.0.0,<6.0, installed: 5.2.1]
│ │ ├── pyasn1-modules [required: >=0.2.1, installed: 0.2.8]
│ │ │ └── pyasn1 [required: >=0.4.6,<0.5.0, installed: 0.4.8]
│ │ ├── rsa [required: >=3.1.4,<5, installed: 4.9]
│ │ │ └── pyasn1 [required: >=0.1.3, installed: 0.4.8]
│ │ └── six [required: >=1.9.0, installed: 1.16.0]
│ ├── googleapis-common-protos [required: >=1.56.2,<2.0dev, installed: 1.59.0]
│ │ └── protobuf [required: >=3.19.5,<5.0.0dev,!=4.21.5,!=4.21.4,!=4.21.3,!=4.21.2,!=4.21.1,!=3.20.1,!=3.20.0, installed: 4.22.5]
│ ├── protobuf [required: >=3.19.5,<5.0.0dev,!=4.21.5,!=4.21.4,!=4.21.3,!=4.21.2,!=4.21.1,!=4.21.0,!=3.20.1,!=3.20.0, installed: 4.22.5]
│ └── requests [required: >=2.18.0,<3.0.0dev, installed: 2.28.2]
│ ├── certifi [required: >=2017.4.17, installed: 2022.12.7]
│ ├── charset-normalizer [required: >=2,<4, installed: 2.1.1]
│ ├── idna [required: >=2.5,<4, installed: 3.4]
│ └── urllib3 [required: >=1.21.1,<1.27, installed: 1.26.14]
├── googleapis-common-protos [required: >=1.55.0,<2.0.0dev, installed: 1.59.0]
│ └── protobuf [required: >=3.19.5,<5.0.0dev,!=4.21.5,!=4.21.4,!=4.21.3,!=4.21.2,!=4.21.1,!=3.20.1,!=3.20.0, installed: 4.22.5]
├── libcst [required: >=0.2.5, installed: 1.0.0]
│ ├── pyyaml [required: >=5.2, installed: 6.0]
│ ├── typing-extensions [required: >=3.7.4.2, installed: 4.4.0]
│ └── typing-inspect [required: >=0.4.0, installed: 0.9.0]
│ ├── mypy-extensions [required: >=0.3.0, installed: 1.0.0]
│ └── typing-extensions [required: >=3.7.4, installed: 4.4.0]
└── proto-plus [required: >=1.19.6, installed: 1.22.2]
└── protobuf [required: >=3.19.0,<5.0.0dev, installed: 4.22.5]
reliability==0.8.14
├── autograd [required: >=1.5, installed: 1.6.2]
│ ├── future [required: >=0.15.2, installed: 0.18.3]
│ └── numpy [required: >=1.12, installed: 1.24.4]
├── autograd-gamma [required: >=0.5.0, installed: 0.5.0]
│ ├── autograd [required: >=1.2.0, installed: 1.6.2]
│ │ ├── future [required: >=0.15.2, installed: 0.18.3]
│ │ └── numpy [required: >=1.12, installed: 1.24.4]
│ └── scipy [required: >=1.2.0, installed: 1.10.1]
│ └── numpy [required: >=1.19.5,<1.27.0, installed: 1.24.4]
├── matplotlib [required: >=3.7.1, installed: 3.7.3]
│ ├── contourpy [required: >=1.0.1, installed: 1.1.1]
│ │ └── numpy [required: >=1.16,<2.0, installed: 1.24.4]
│ ├── cycler [required: >=0.10, installed: 0.12.1]
│ ├── fonttools [required: >=4.22.0, installed: 4.43.1]
│ ├── importlib-resources [required: >=3.2.0, installed: 5.10.2]
│ │ └── zipp [required: >=3.1.0, installed: 3.11.0]
│ ├── kiwisolver [required: >=1.0.1, installed: 1.4.5]
│ ├── numpy [required: >=1.20,<2, installed: 1.24.4]
│ ├── packaging [required: >=20.0, installed: 23.0]
│ ├── pillow [required: >=6.2.0, installed: 10.1.0]
│ ├── pyparsing [required: >=2.3.1, installed: 3.0.9]
│ └── python-dateutil [required: >=2.7, installed: 2.8.2]
│ └── six [required: >=1.5, installed: 1.16.0]
├── mplcursors [required: >=0.5.2, installed: 0.5.2]
│ └── matplotlib [required: >=3.1, installed: 3.7.3]
│ ├── contourpy [required: >=1.0.1, installed: 1.1.1]
│ │ └── numpy [required: >=1.16,<2.0, installed: 1.24.4]
│ ├── cycler [required: >=0.10, installed: 0.12.1]
│ ├── fonttools [required: >=4.22.0, installed: 4.43.1]
│ ├── importlib-resources [required: >=3.2.0, installed: 5.10.2]
│ │ └── zipp [required: >=3.1.0, installed: 3.11.0]
│ ├── kiwisolver [required: >=1.0.1, installed: 1.4.5]
│ ├── numpy [required: >=1.20,<2, installed: 1.24.4]
│ ├── packaging [required: >=20.0, installed: 23.0]
│ ├── pillow [required: >=6.2.0, installed: 10.1.0]
│ ├── pyparsing [required: >=2.3.1, installed: 3.0.9]
│ └── python-dateutil [required: >=2.7, installed: 2.8.2]
│ └── six [required: >=1.5, installed: 1.16.0]
├── numpy [required: >=1.24.2, installed: 1.24.4]
├── pandas [required: >=2.0.1, installed: 2.0.3]
│ ├── numpy [required: >=1.20.3, installed: 1.24.4]
│ ├── python-dateutil [required: >=2.8.2, installed: 2.8.2]
│ │ └── six [required: >=1.5, installed: 1.16.0]
│ ├── pytz [required: >=2020.1, installed: 2022.7.1]
│ └── tzdata [required: >=2022.1, installed: 2023.3]
└── scipy [required: >=1.10.1, installed: 1.10.1]
└── numpy [required: >=1.19.5,<1.27.0, installed: 1.24.4]
+ [[ -z fail ]]
+ python3 -m pip check
tensorflow 2.12.0 has requirement numpy<1.24,>=1.22, but you have numpy 1.24.4.
The command '/bin/sh -c bash installer.sh $COMPOSER_PYTHON_VERSION fail' returned a non-zero code: 1
ERROR
ERROR: build step 0 "gcr.io/cloud-builders/docker" failed: step exited with non-zero status: 1
```
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Different Behavior of tf.raw_ops.RGBToHSV with jit_compile=True
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[
"This is likely due to fusion, where the intermediate result may be computed and kept in float32 in the case of jit-compilation, whereas without fusion it would cast to bfloat16 between the ops and produce a less precise answer. Still, both are correct. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62295\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62295\">No</a>\n",
"I see it fails in both jit_compile = True and jit_compile = False on GPU, since it invloves random values as an input, results are not guaranteed to match in both the scenarios.\r\nHere is the attached [Gist](https://colab.sandbox.google.com/gist/sachinprasadhs/babe79d71ca277d4a949e7327d78b2ba/tf-raw_ops-xloggy-tf-raw_ops-lgamma.ipynb) for reference.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @tilakrayal ,thanks for your information. It seems that I failed to recognize that this input included a random value. Please feel free to close this issue.",
"As per the above user comments, closing this issue as it is resolved. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62295\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62295\">No</a>\n"
] | 2023-10-31T07:42:27 | 2024-02-05T11:12:53 | 2024-02-05T11:12:50 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.RGBToHSV** operation is invoked within a tf.function with JIT compilation enabled **(jit_compile=True),** it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.RGBToHSV(images=x, )
return x
m = Network()
inp = {
"x": tf.random.normal([9, 8, 6, 3], dtype=tf.bfloat16),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 26, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(9, 8, 6, 3) dtype=float64) = '
0.6328125, 1.59375, 0.765625, ...
b'y (shape=(9, 8, 6, 3) dtype=float64) = '
0.6328125, 1.59375, 0.765625, ...
```
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I_kwDOArmXAs51aSNv
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Different Behavior of tf.raw_ops.Pow with jit_compile=True
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[
"Hi @zoux1a ,\r\n\r\nI have replicated the reported behaviour with` jit_compile=True`.One interesting observation is that if I rerun the same code multiple times some times the assertion is success. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1712342e4e59190ba824ea5968593e86/62294_gpu.ipynb) for reference. ",
"@zoux1a ,\r\n\r\nCould you please verify the behaviour I mentioned above?",
"Hi @SuryanarayanaY Su\r\n\r\nI have attempted to run this program a total of 15 times, and on each occasion, I encountered the same bug.",
"Hi @zoux1a ,\r\n\r\nI noticed two issues here. \r\n\r\n1. You are taking power to large number which eventually overflows since the output is not representable in int32 range\r\n2. Inside the call function you are generating the value for y, which in each calls generates different values which makes the output different for obvious reason.\r\n\r\nI have done the changes the dtype to int64 and also shifted y to outside the call function to ensure same inputs. I ran 10 experiments and printed the reduce_sum difference of outputs using:\r\n\r\n `print(tf.reduce_sum(no_op_res)-tf.reduce_sum(op_res))` \r\n\r\nwhich outputs `0` in every run. This indicates this is not an issue with any TF code.\r\n\r\nPlease refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e429d7b42b664cc4d5788a5ebe06355c/62294_final.ipynb).\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62294\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62294\">No</a>\n"
] | 2023-10-31T07:34:40 | 2023-12-29T01:46:11 | 2023-12-29T01:46:08 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.Pow** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **GPU** device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.Pow(x=x, y=tf.random.uniform([4, 1], minval=0, maxval=1000000, dtype=tf.int32))
return x
m = Network()
inp = {
"x": tf.random.uniform([], minval=-1000000, maxval=1000000, dtype=tf.int32),
}
with tf.device('/GPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/GPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 26, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(4, 1) dtype=float64) = '
-249281983.0, 1935278665.0, -1676834287.0, ...
b'y (shape=(4, 1) dtype=float64) = '
-1839635735.0, -50435503.0, 1024727513.0, ...
```
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I_kwDOArmXAs51aOqA
| 62,293 |
Different Behavior of tf.raw_ops.BatchMatMulV2+tf.raw_ops.Cos with jit_compile=True
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[
"Hello, @zoux1a! \r\nI was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/3de02546d56ee135a3083b7ae0e83493/62293.ipynb#scrollTo=FZK9eLBV9D6p).\r\nThank you!",
"Hi,\r\n\r\nI see it fails in both` jit_compile = True` and `jit_compile = False` on GPU, since it invloves random values as an input, results are not guaranteed to match in both the scenarios.\r\nHere is the attached Gist for reference https://gist.github.com/sachinprasadhs/f0d7227796f7dcb74fb5ca735b84f06c",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62293\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62293\">No</a>\n"
] | 2023-10-31T07:24:30 | 2023-11-29T01:49:02 | 2023-11-29T01:48:58 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.BatchMatMulV2+tf.raw_ops.Cos** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **GPU** device.
The problem occurs when input Tensors pass through tf.raw_ops.BatchMatMulV2+tf.raw_ops.Cos. With individual Ops there is no issue.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.BatchMatMulV2(y=x, adj_x=False,adj_y=False,x=tf.random.normal([8, 3], dtype=tf.float32))
x = tf.raw_ops.Cos(x=x, )
return x
m = Network()
inp = {
"x": tf.random.normal([8, 3, 8, 7, 3, 4], dtype=tf.float32),
}
with tf.device('/GPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/GPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5eLEQO
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Add NDK r26 support
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[
"Is there anything I can do for this PR? I don't think that ROCm build failure is related to my implementation. ",
"Hi @angerson Can you please review this PR ? Thank you!",
"Hi @angerson Can you please review this PR ? Thank you!",
"Hi @angerson Can you please review this PR ? Thank you!",
"@gbaned @angerson This is a quite small PR. And it would be nice to merge this to allow people to use up to date NDK.",
"Hi @angerson Can you please review this PR ? Thank you!",
"Hi @angerson Can you please review this PR ? Thank you!"
] | 2023-10-30T22:46:22 | 2024-06-07T16:32:26 | null |
NONE
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* Update NDK build rule version
* Fix NDK API level check: I used `min` and `max` values in `platforms.json` as it is proper way to determine compatibility. However, it must be noted that some android targets (such as `benchmark_tool`) cannot be built even if the API level is greater than the one NDK supports, as they may need higher API versions (`gpu` delegate requires `>=26`).
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PR_kwDOArmXAs5eKx00
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Microfrontend warnings
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/62291/checks?check_run_id=18202117349) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!"
] | 2023-10-30T21:30:33 | 2024-06-07T16:31:38 | null |
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Fix for #62290
Fixes compile issues in microfrontend when -Werror -Wsigned-compare -Wdouble-promotion are enabled.
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Build Microfrontend -Wsigned-compare and -Wdouble-promotion
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[
"Attached a PR #62291 "
] | 2023-10-30T21:29:07 | 2023-10-31T10:15:06 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
master
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Apple Clang 14 and GNU Arm Embedded Toolchain 10.3-2021.07
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Building the tensorflow/lite/microfrontend sources with -Werror -Wsigned-compare -Wdouble-promotion results in compiler errors when warnings as errors are enabled.
### Standalone code to reproduce the issue
```shell
Compile tensorflow with the flags: -Werror -Wsigned-compare -Wdouble-promotion
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5eJruL
| 62,289 |
Update version numbers for TensorFlow 2.14.1
|
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 14 -> 14
Patch: 0 -> 1
WARNING: Below are potentially instances of lingering old version string
"2.14.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/tools/versioning/runtime_version.cc:113:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:251:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:252:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:280:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:368:2.14.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:33:2.14.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:35:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:29:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:30:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:31:2.14.0
tensorflow/tools/pip_package/setup.py:118:2.14.0
tensorflow/tools/pip_package/setup.py:121:2.14.0
tensorflow/tools/pip_package/setup.py:125:2.14.0
Binary file
tensorflow/cc/saved_model/testdata/chunked_saved_model/non_chunked_model/saved_m
odel.pb matches
tensorflow/cc/saved_model/testdata/chunked_saved_model/chunked_model/saved_model
.pbtxt:342:2.14.0
WARNING: Below are potentially instances of lingering old version string
"2.14.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/tools/versioning/runtime_version.cc:113:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:251:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:252:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:280:2.14.0
tensorflow/lite/tools/versioning/runtime_version.cc:368:2.14.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:33:2.14.0
tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt:35:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:29:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:30:2.14.0
tensorflow/tools/ci_build/release/requirements_common.txt:31:2.14.0
tensorflow/tools/pip_package/setup.py:118:2.14.0
tensorflow/tools/pip_package/setup.py:121:2.14.0
tensorflow/tools/pip_package/setup.py:125:2.14.0
Binary file
tensorflow/cc/saved_model/testdata/chunked_saved_model/non_chunked_model/saved_m
odel.pb matches
tensorflow/cc/saved_model/testdata/chunked_saved_model/chunked_model/saved_model
.pbtxt:342:2.14.0
```
|
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PR_kwDOArmXAs5eJHFo
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Update release notes for TensorFlow 2.14.1
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This PR is intentionally incomplete. One of the Release Owners for 2.14.1
needs to fill in the internal release notes for this version before the PR gets
submitted. Click on the :pencil2: icon in the header for `RELEASE.md` under
"Files Changed" above.
|
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Different Behavior of tf.raw_ops.Cos+tf.raw_ops.Erfc with jit_compile=True
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[
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.14 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0dbc72508a2a07c52f7eeed00f25cf4c/untitled1504.ipynb).",
"I was able to replicate the reported behavior when `jit_compile` is set to `True` and `False`.\r\n\r\nWhen the `jit_compile` is set to `True`, I see error close to `0.3` which is bigger than the `atol` and `rtol` value `0.001`\r\n\r\nHere is the Gist for reference https://gist.github.com/sachinprasadhs/64ec59dc673f360c840d19b3f6b8e7a5",
"A \"difference\" is not an \"error\". This is likely due to fusion, where the intermediate result may be computed and kept in float32 in the case of jit-compilation, whereas without fusion it would cast to bfloat16 between the ops and produce a less precise answer. Still, both are correct.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62287\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62287\">No</a>\n"
] | 2023-10-30T13:41:05 | 2023-11-13T20:33:57 | 2023-11-13T20:33:32 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.Cos+tf.raw_ops.Erfc** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.
The problem occurs when input Tensors pass through **tf.raw_ops.Cos+tf.raw_ops.Erfc** and raw_ops.Sin. With individual Ops there is no issue.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.Cos(x=x, )
x = tf.raw_ops.Erfc(x=x, )
return x
m = Network()
inp = {
"x": tf.random.normal([10, 9, 8], dtype=tf.bfloat16),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 27, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(10, 9, 8) dtype=float64) = '
0.17578125, 1.1484375, 0.267578125, ...
b'y (shape=(10, 9, 8) dtype=float64) = '
0.1767578125, 1.1484375, 0.267578125, ...
```
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Different Behavior of tf.raw_ops.RandomGammaGrad with jit_compile=True
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[
"Hi,\r\n\r\nI was trying to simplify the code for debug purpose.\r\n\r\nI observed that the code you have provided yields NaN most of the times.\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\[email protected](jit_compile=True)\r\ndef result(x):\r\n x = tf.raw_ops.RandomGammaGrad(sample=x, alpha=tf.random.normal([9, 8, 8, 8, 1, 7, 1], dtype=tf.float32))\r\n return x\r\n\r\n\r\ninp = {\r\n \"x\": tf.random.normal([1, 1, 1], dtype=tf.float32),\r\n}\r\nprint(result(**inp))\r\n```",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62286\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62286\">No</a>\n"
] | 2023-10-30T13:34:59 | 2023-11-21T01:50:39 | 2023-11-21T01:50:35 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.RandomGammaGrad** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
def replace_special_values(tensor):
# Convert tensor to tf.float32 if it's not a supported dtype
supported_dtypes = [tf.float16, tf.float32, tf.float64, tf.bfloat16]
if tensor.dtype not in supported_dtypes:
original_dtype = tensor.dtype
tensor = tf.cast(tensor, tf.float32)
else :
original_dtype = None
# Replace NaNs with zeros
tensor = tf.where(tf.math.is_nan(tensor), tf.zeros_like(tensor), tensor)
# Replace positive infinities with a large number (e.g., 1e30)
tensor = tf.where(tf.math.is_inf(tensor), 100, tensor)
# Replace negative infinities with a small number (e.g., -1e30)
tensor = tf.where(tf.math.is_inf(tensor) & tf.math.less(tensor, 0), -100, tensor)
# Convert tensor back to its original dtype
if original_dtype is not None :
tensor = tf.cast(tensor, original_dtype)
return tensor
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.RandomGammaGrad(sample=x, alpha=tf.random.normal([9, 8, 8, 8, 1, 7, 1], dtype=tf.float32))
return x
m = Network()
inp = {
"x": tf.random.normal([1, 1, 1], dtype=tf.float32),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
no_op_res = replace_special_values(no_op_res)
op_res = replace_special_values(op_res)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 77, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(9, 8, 8, 8, 1, 7, 1) dtype=float64) = '
0.826047420501709, 0.0, 0.6310948133468628, ...
b'y (shape=(9, 8, 8, 8, 1, 7, 1) dtype=float64) = '
0.0, 0.0, 1.6988459825515747, ...
```
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I_kwDOArmXAs51Uoxe
| 62,285 |
Different Behavior of tf.raw_ops.TruncateMod with jit_compile=True
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[
"Hi, @zoux1a!\r\nI was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/d6b11a409c657c99235a7365595af84d/62285.ipynb). Thank you!",
"I observe similar result in both `jit_compile = True` and `jit_compile = False`, since `y` arg in `tf.raw_ops.TruncateMod ` involves random number input, it can not guarantee the same outcome for both the scenarios. \r\nAttaching the Gist [here](https://colab.sandbox.google.com/gist/sachinprasadhs/1849bf4e52087ddbf5ef175d0339c9d4/tf-raw_ops-truncatemod.ipynb) for reference, which fails the condition `atol` and `rtol` = 0.001",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62285\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62285\">No</a>\n"
] | 2023-10-30T13:20:43 | 2023-11-29T01:49:04 | 2023-11-29T01:49:00 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.TruncateMod** operation is invoked within a tf.function with JIT compilation enabled (jit_compile=True), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.TruncateMod(x=x, y=tf.random.normal([8, 8, 8], dtype=tf.float32))
return x
m = Network()
inp = {
"x": tf.random.normal([8, 8, 8], dtype=tf.float32),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 27, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(8, 8, 8) dtype=float64) = '
0.005041487514972687, -1.6233494281768799, -0.45818427205085754, ...
b'y (shape=(8, 8, 8) dtype=float64) = '
0.5953081846237183, -0.28119754791259766, -0.05403769016265869, ...
```
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I_kwDOArmXAs51UoA5
| 62,284 |
Different Behavior of tf.raw_ops.AdjustContrastV2 with jit_compile=True
|
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[
"Hi @**zoux1a** ,\r\n\r\nI have replicated the reported behaviour with jit_compile=True and with jit_compile = False as well. It seems not related to the jit_compile issue. I attached a [gist](https://colab.research.google.com/gist/Venkat6871/0392c20a4f3fde08005a3b3e730895d9/62284_cpu-2-14-v.ipynb) for your reference.\r\n\r\nThank you!",
"Hi @zoux1a ,\r\n\r\nIt seems there is precision related errors as you are casting float32 to float64. If I print overall result like converting the array to a number using `tf.reduce_sum()` then `tf.reduce_sum(tf.cast(no_op_res, tf.float64))` is equal to `tf.reduce_sum(tf.cast(op_res, tf.float64)` in the tolerance level of 0.00001.\r\n\r\nI have checked this assertion and its success.\r\n\r\n```\r\ntf.debugging.assert_near(\r\n tf.reduce_sum(tf.cast(no_op_res, tf.float64)), \r\n tf.reduce_sum(tf.cast(op_res, tf.float64)), \r\n atol=0.00001, \r\n rtol=0.00001\r\n)\r\n```\r\n\r\nThis means the difference in results are just precision related or the order of elements might be different.But overall the result is same as described in the attached [gist-r1](https://colab.sandbox.google.com/gist/SuryanarayanaY/9116b42c582035936a6e4714feddd270/62284_r1.ipynb#scrollTo=lXqqISCFic5Y).\r\n\r\nThanks!\r\n",
"Hi @zoux1a ,\r\nI have debugged more into the issue and the root cause is the `contrast_factor` which you are using as random initializer and this value is different for both eager and graph execution. I have printed the same and you can observe both `contrast_factor` values are different in both cases which is causing the difference results but keeping the overall sum same.\r\n\r\nIf I pass a fixed `contrast_factor` say `0.2 `, the results are same with and without jit_compile. \r\n\r\nPlease find the attached [gist-r2](https://colab.sandbox.google.com/gist/SuryanarayanaY/27fafcb8ef71957f59a9afc61446ef84/62284_r2.ipynb) for same excercise.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62284\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62284\">No</a>\n"
] | 2023-10-30T13:19:07 | 2023-12-21T01:48:49 | 2023-12-21T01:48:46 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the tf.raw_ops.AdjustContrastv2 operation is invoked within a tf.function with JIT compilation enabled (jit_compile=True), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a CPU device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.AdjustContrastv2(images=x, contrast_factor=tf.random.normal([], dtype=tf.float32))
return x
m = Network()
inp = {
"x": tf.random.normal([8, 8, 8], dtype=tf.float32),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 27, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(8, 8, 8) dtype=float64) = '
0.09821675717830658, -0.057733699679374695, 0.08248079568147659, ...
b'y (shape=(8, 8, 8) dtype=float64) = '
0.17769138514995575, -0.02411397360265255, 0.15723268687725067, ...
```
|
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I_kwDOArmXAs51UktN
| 62,283 |
Different Behavior of tf.raw_ops.MatMul+tf.raw_ops.Sin with jit_compile=True
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[
"@zoux1a ,\r\n This is likely due to fusion, where the intermediate result may be computed and kept in float32 in the case of jit-compilation, whereas without fusion it would cast to bfloat16 between the ops and produce a less precise answer. Still, both are correct. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62283\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62283\">No</a>\n"
] | 2023-10-30T13:12:13 | 2023-12-01T01:52:18 | 2023-12-01T01:52:14 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
GPU 0: NVIDIA GeForce RTX 2070 GPU 1: NVIDIA GeForce RTX 2070 GPU 2: NVIDIA GeForce RTX 2070 GPU 3: NVIDIA GeForce RTX 2070
### Current behavior?
When the **tf.raw_ops.MatMul** operation is invoked within a tf.function with JIT compilation enabled (**jit_compile=True**), it produces different results compared to the same operation called without JIT compilation. This inconsistency is observed when the code is executed on a **CPU** device.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import traceback
class Network(tf.Module):
def __init__(self):
super().__init__()
@tf.function(jit_compile=True)
def __call__(self, x):
x = tf.raw_ops.MatMul(b=x, a=tf.random.normal([10, 8], dtype=tf.float16), transpose_a=convert(False),transpose_b=convert(False))
return x
m = Network()
inp = {
"x": tf.random.normal([8, 10], dtype=tf.float16),
}
with tf.device('/CPU:0'):
tf.config.run_functions_eagerly(True)
no_op_res = m(**inp)
tf.config.run_functions_eagerly(False)
with tf.device('/CPU:0'):
op_res = m(**inp)
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
```
### Relevant log output
```shell
File "/home/guihuan/LLM/results/tf-2/2023-10-22-20-21/test.py", line 76, in <module>
tf.debugging.assert_near(tf.cast(no_op_res, tf.float64), tf.cast(op_res, tf.float64), atol=0.001, rtol=0.001)
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/guihuan/.conda/envs/night/lib/python3.9/site-packages/tensorflow/python/ops/control_flow_assert.py", line 102, in Assert
raise errors.InvalidArgumentError(
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''
b'x and y not equal to tolerance rtol = tf.Tensor(0.001, shape=(), dtype=float64), atol = tf.Tensor(0.001, shape=(), dtype=float64)'
b'x (shape=(10, 10) dtype=float64) = '
0.5849609375, 2.87890625, 4.51171875, ...
b'y (shape=(10, 10) dtype=float64) = '
2.326171875, 7.35546875, 1.875, ...
```
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