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Isuru0x01

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tensorflow-issues

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[ "Unit test failure introduced by https://github.com/tensorflow/tensorflow/commit/c09dcc2c7d07a84cdb9f9b5342d7803f5dbc50b3" ]

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[ "@mengran1234,\r\nCould you please provide the complete code to reproduce the issue which helps us to analyse the issue in an effective way. Thank you!", "I calls this three function loop on ios,and find memory leak\r\nuse TFLGpuDelegateCreate --》 TfLiteInterpreterModifyGraphWithDelegate --》 TFLGpuDelegateDelete\r\n\r\nxcode profile tool (memory leak)can be see.", "[](url)\r\n\r\n", "please help to see it", "> I calls this three function loop on ios,and find memory leak use TFLGpuDelegateCreate --》 TfLiteInterpreterModifyGraphWithDelegate --》 TFLGpuDelegateDelete\r\n> \r\n> xcode profile tool (memory leak)can be see.\r\n\r\nI understand what you're doing, but I'd like to see your code snippet. Can you copy & paste your code snippet here?\r\n\r\nAlso, can you attach your tflite file or a link if it's an open source model? If it's not opensource, but is proprietary, how big is the model, and what kind of ops does it have?", "any tflite file, let it run gpu delegate (metal )on iphone,thank you", "https://github.com/tensorflow/tensorflow/issues/57718\r\nmy question is the same with this", "FYI, we ran into a memory usage issue in CoreML delegate when switching from Xcode 12 to 13 (see #53461). It turned out I have to add some `@autoreleasepool` as in #53468", "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/60624\">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/60624\">No</a>\n" ]

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[ "Hi @SiriusHsh\r\n\r\nAs this https://github.com/tensorflow/tensorflow/issues/60620#issuecomment-1553442519 suggests, please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md#vulnerabilities-in-tensorflow and follow rules for responsible disclosure.\r\n\r\nThanks.", "Hi @SiriusHsh,\r\n\r\nI am unable to reproduce locally, I get a proper exception which I think is expected given the input. Are you sure you uploaded the correct file?\r\n```\r\n./benchmark_model --graph=arena_planner_IdentifySharedTensors_oob.tflite\r\nINFO: STARTING!\r\nWARN: Duplicate flags: num_threads\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [arena_planner_IdentifySharedTensors_oob.tflite]\r\nINFO: Loaded model arena_planner_IdentifySharedTensors_oob.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nINFO: Applying 1 TensorFlow Lite delegate(s) lazily.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nERROR: tensorflow/lite/kernels/squeeze.cc:39 NumInputs(node) != 1 (0 != 1)\r\nERROR: Node number 0 (SQUEEZE) failed to prepare.\r\nERROR: Failed to apply the default TensorFlow Lite delegate indexed at 0.\r\nERROR: Failed to allocate tensors!\r\nERROR: Benchmarking failed.\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/60623\">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/60623\">No</a>\n" ]

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[ "Hi @SiriusHsh \r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/60620#issuecomment-1553442519) suggests, please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md#vulnerabilities-in-tensorflow and follow rules for responsible disclosure.\r\n\r\nThanks.", "Hi @SiriusHsh,\r\n\r\nThis has been fixed in this commit: https://github.com/tensorflow/tensorflow/commit/b40420ef4c2064b7eb5e26f9d1a1c6abdc3db0f4\r\n\r\nYou may test the changes with the tf-nightly package or by building from the master/nightly branch. If you are satisfied with the resolution please feel free to close this issue.", "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/60622\">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/60622\">No</a>\n" ]

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[ "Hi @SiriusHsh \r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/60620#issuecomment-1553442519) suggests, please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md#vulnerabilities-in-tensorflow and follow rules for responsible disclosure.\r\n\r\nThanks.", "I was able to replicate locally on nightly branch:\r\n\r\n```\r\n./benchmark_model --graph=subgraph_infinite_loop.tflite\r\nINFO: STARTING!\r\nWARN: Duplicate flags: num_threads\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [subgraph_infinite_loop.tflite]\r\nINFO: Loaded model subgraph_infinite_loop.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nINFO: Applying 1 TensorFlow Lite delegate(s) lazily.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: Successfully applied the default TensorFlow Lite delegate indexed at 0.\r\n *NOTE*: because a delegate has been applied, the precision of computations should be unchanged, but the exact output tensor values may have changed. If such output values are checked in your code, like in your tests etc., please consider increasing error tolerance for the check.\r\nINFO: The input model file size (MB): 0.000488\r\nINFO: Initialized session in 1.213ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nSegmentation fault\r\n```\r\n\r\nTop of Callstack (repeats last 4):\r\n```\r\n...\r\n#87231 0x0000555555a802c2 in tflite::Subgraph::Invoke (this=0x55555635acd0) at tensorflow/lite/core/subgraph.cc:1498\r\n#87232 0x00005555559dc4d5 in tflite::ops::builtin::call_once_kernel::Eval (context=0x55555635acf8, node=0x55555635e800) at tensorflow/lite/kernels/call_once.cc:98\r\n#87233 0x0000555555a7f7e5 in tflite::Subgraph::OpInvoke (this=0x55555635acd0, op_reg=..., node=0x55555635e800) at tensorflow/lite/core/subgraph.cc:1292\r\n#87234 0x0000555555a80840 in tflite::Subgraph::InvokeImpl (this=0x55555635acd0) at tensorflow/lite/core/subgraph.cc:1605\r\n#87235 0x0000555555a802c2 in tflite::Subgraph::Invoke (this=0x55555635acd0) at tensorflow/lite/core/subgraph.cc:1498\r\n#87236 0x00005555559dc4d5 in tflite::ops::builtin::call_once_kernel::Eval (context=0x55555635acf8, node=0x55555635e800) at tensorflow/lite/kernels/call_once.cc:98\r\n#87237 0x0000555555a7f7e5 in tflite::Subgraph::OpInvoke (this=0x55555635acd0, op_reg=..., node=0x55555635e800) at tensorflow/lite/core/subgraph.cc:1292\r\n#87238 0x0000555555a80840 in tflite::Subgraph::InvokeImpl (this=0x55555635acd0) at tensorflow/lite/core/subgraph.cc:1605\r\n#87239 0x0000555555a802c2 in tflite::Subgraph::Invoke (this=0x55555635acd0) at tensorflow/lite/core/subgraph.cc:1498\r\n#87240 0x0000555555a50898 in tflite::impl::Interpreter::Invoke (this=0x55555634b910) at tensorflow/lite/core/interpreter.cc:237\r\n#87241 0x000055555557bacc in tflite::benchmark::BenchmarkTfLiteModel::RunImpl (this=0x7fffffffc370) at tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc:961\r\n#87242 0x000055555559ae70 in tflite::benchmark::BenchmarkModel::Run (this=0x7fffffffc370, min_num_times=1, min_secs=0.5, max_secs=150, run_type=tflite::benchmark::WARMUP, invoke_status=0x7fffffffbe8c) at tensorflow/lite/tools/benchmark/benchmark_model.cc:230\r\n#87243 0x000055555559ba3c in tflite::benchmark::BenchmarkModel::Run (this=0x7fffffffc370) at tensorflow/lite/tools/benchmark/benchmark_model.cc:318\r\n#87244 0x000055555559b313 in tflite::benchmark::BenchmarkModel::Run (this=0x7fffffffc370, argc=2, argv=0x7fffffffdac8) at tensorflow/lite/tools/benchmark/benchmark_model.cc:276\r\n#87245 0x000055555557230e in tflite::benchmark::Main (argc=2, argv=0x7fffffffdac8) at tensorflow/lite/tools/benchmark/benchmark_main.cc:27\r\n#87246 0x00005555555723b6 in main (argc=2, argv=0x7fffffffdac8) at tensorflow/lite/tools/benchmark/benchmark_main.cc:36\r\n```", "Hi @miaout17, can you take a look at this?" ]

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[ "I'll go ahead and make this change, \r\n\r\nThere is a bigger question, \r\n\r\n1. Can we make graph_info_->outputs() as well as similar constructions (graph_info_->variables(), graph_info_->inputs(), etc.) not externally controllable?\r\n2. If not, then we need to cover those cases as well.", "I'm making this an assert so that the program will throw an exception in this case, as we shouldn't continue if this condition is reached.", "Please don't report vulnerabilities publicly!", "Hi @SiriusHsh \r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/60620#issuecomment-1553442519) suggests, please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md#vulnerabilities-in-tensorflow and follow rules for responsible disclosure.\r\n\r\nThanks.", "Hi @pjpratik \r\nActually, The Google Bug Hunter Team suggested that I should open an issue on Github for these few problems.", "I don't we'd ask for that for this specific issue.", "Hi @SiriusHsh, this is now fixed in the following commit: https://github.com/tensorflow/tensorflow/commit/c4d31b0f488f02fa7fe1bbccf3f75b34d651cf3a please test in master or nightly and let us know if it resolves your issue.", "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 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/60620\">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/60620\">No</a>\n" ]

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[ "Hi @SiriusHsh \r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/60620#issuecomment-1553442519) suggests, please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md#vulnerabilities-in-tensorflow and follow rules for responsible disclosure.\r\n\r\nThanks." ]

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[ "@Shikamaru5,\r\nTensorflow v2.10 is compatible with GCC 9.3.1, Bazel 5.1.1, cuDNN 8.1 and CUDA 11.2. Also please have a look at this officially tested build configurations for the reference.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nAnd please take a glance at this issues for reference.\r\nhttps://github.com/tensorflow/tensorflow/issues/60334#issuecomment-1511468692\r\nhttps://github.com/tensorflow/tensorflow/issues/59013\r\n\r\n\r\n\r\n\r\n\r\n", "@tilakrayal Well the reason that I grabbed cuda 11.7 was because when I looked at the chart it didn't specifically say that it wouldn't work with that version just that it changed to 11.8 after TF 2.11. I'm pretty sure that I need the higher versions to get PyTorch to work effectively for my needs or all the other libraries I'm using in my current program might not be compatible. Is the reason that these libraries are missing is because they've been depreciated? I mean technically when I run the test commands that it tells me it still shows the tensor for the cpu test and the physical devices and stuff for the gpu version. The issue is that I keep getting these other errors about no numa support and not being able to load the dynamic libraries. Idk if that's actually a problem or not though, I mean perhaps it isn't and I can just safely ignore it. Also the links you sent me weren't helpful sorry.", "@Shikamaru5,\r\nFor some of the cases it might work as you mentioned. But it will be suggestible to follow the tested build [configurations](https://www.tensorflow.org/install/source#gpu) to avoid the unnecessary erros and for the smooth executions. 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/60618\">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/60618\">No</a>\n" ]

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[ "Hi @lrstolk ,\r\n\r\nI have replicated the reported behaviour on Colab and attached screenshot below.\r\n\r\nhttps://screenshot.googleplex.com/4hm4Zj6zXvEvasR\r\n\r\nYou can use the below code to get the Tensorflow version.\r\n\r\n`!python -c \"import tensorflow as tf; print(tf.__version__)\"\r\n`\r\n", "> Hi @lrstolk ,\r\n> \r\n> I have replicated the reported behaviour on Colab and attached screenshot below.\r\n> \r\n> https://screenshot.googleplex.com/4hm4Zj6zXvEvasR\r\n> \r\n> You can use the below code to get the Tensorflow version.\r\n> \r\n> `!python -c \"import tensorflow as tf; print(tf.__version__)\" `\r\n\r\nThanks for the fast reply @SuryanarayanaY !\r\n\r\nI tried to open your link, but sadly cannot open it, it brings me to a single sign on page. \r\n\r\nWhen I try your code, it does not recognize the exclamation mark in the PyTorch terminal or Anaconda Promt. When I use your code in a jupyter notebook, it gives the same error as I mentioned earlier.\r\nIf I ask pip show tensorflow, it gives me: Version: 2.10.1 \r\n\r\n**EDIT**\r\nI reinstalled my entire env, the Anaconda shell gives me: 2.10.0 \r\nThe powerschell is able to find keras tho: \r\n>python -c \"import tensorflow.keras as k; print(k.__version__)\"\r\n>2.10.0\r\n\r\nOn my old laptop, PyCharm is able to find keras, Bidirectional and TimeDistributed, \r\nI run the same environment with Tensorflow==2.3.0", "@SuryanarayanaY I cannot see your screenshot. ", "Hi @lrstolk ,\r\n\r\nI am attaching a colab [gist](https://colab.research.google.com/gist/SuryanarayanaY/90a0471c32f0650f2e2f0a4fb8045bec/60617.ipynb) for your reference. You please execute it and observe the behaviour.\r\n\r\nThe problem is related to auto completion of path. You can still use the functionalities without problem except auto path resolution. Now Keras is available as stand alone package which might be reason that there is no problem with keras path resolution.\r\n\r\nThanks\r\n", "> Hi @lrstolk ,\n> \n> \n> \n> I am attaching a colab [gist](https://colab.research.google.com/gist/SuryanarayanaY/90a0471c32f0650f2e2f0a4fb8045bec/60617.ipynb) for your reference. You please execute it and observe the behaviour.\n> \n> \n> \n> The problem is related to auto completion of path. You can still use the functionalities without problem except auto path resolution. Now Keras is available as stand alone package which might be reason that there is no problem with keras path resolution.\n> \n> \n> \n> Thanks\n> \n> \n\nI see, thankyou! \ntf-nightly is not an option for me. I'm on Windows Native, so I need v2.10 or lower. \nIs there a way to fix the auto path resolution? Like changing the `__init__.py`", "@lrstolk ,\r\n\r\nNow starting from 2.12v onwards Keras is a standalone package and there is no need to import tensorflow separately for this.I think this is the reason for the reported behaviour.Please try using keras as standalone package only.", "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/60617\">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/60617\">No</a>\n" ]

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[ "Hi @sirno ,\r\n\r\nFirst thing I want to clear that `tensorflow-macos` was built and maintained by Apple itself . \r\n\r\nHence I tried to test the code first with Regular Tensorflow package `tensorflow`, for confirming the memory leakage problem exists with TF package also.I have executed the code on colab with GPU environment and observed no memory leakage(ran upto 175 epochs) and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/767e352ddc144f0fa716e68eef7d9f6c/60616.ipynb) here for reference.\r\n\r\nThis indicates the issue is specific to only `tensorflow-macos` package and shall be addressed by Apple tensorflow-metal developes. You can post the issue [here](https://developer.apple.com/forums/tags/tensorflow-metal) .\r\n\r\nThanks!", "thanks for the response and the reference to the Apple forums.\r\n\r\nI will resubmit the issue in the Apple forums.", "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/60616\">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/60616\">No</a>\n" ]

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[ "**Temporary solution:** install jupyterlab=3.6.3 instead of jupyterlab=4.0.0", "After four reinstalls with conda, TF 2.10.0 and jupyterlab 4.0.0, with no other packages added, always in a new environment, I finally gave up. To kill time, I replaced conda with mamba.\r\n\r\nSolution(?): After installing mamba, using the default channels, I installed TF 2.10.0 and jupyterlab 4.0.0 (no other packages added) in a new environment, same procedure as the previous four times. And now jupyterlab works. The channel order might have had something to do with the issue, but I can't confirm.", "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/60615\">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/60615\">No</a>\n" ]

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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/60614/checks?check_run_id=13544445685) 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." ]

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[ "Hi @ightevenmckane1 ,\r\n\r\nIf you have any issue to bring notification please fill the template properly. Otherwise this will be treated as spam." ]

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[ "@dsbyprateekg,\r\nAccording to [Colab Updated to Python 3.10 7](https://medium.com/google-colab/colab-updated-to-python-3-10-27eb02daa162):\r\n\r\nColab’s fallback runtime version: Using the fallback runtime version temporarily allows access to the Python 3.9 runtime, and will be available until mid-May. This is available from the Command Palette via the Use fallback runtime version command when connected to a runtime. Of note, this setting does not persist across sessions — the command will need to be invoked on each new session.\r\n\r\nAs a temporary workaround, you can use the Colab fallback runtime version option to choose Python 3.9 and install tflite-model-maker. By doing this you will get RuntimeError and it can be ignored.\r\n\r\nTo access the command palette in Colab, presss cmd+shift+P and then type Use fallback runtime version and select it.\r\n\r\n\r\n\r\nBy following the above process, I was able to execute [Model Maker Image Classification Tutorial 9](https://colab.sandbox.google.com/gist/chunduriv/847ff8f09621935471b3fb70b23cefbd/model-maker-image-classification-tutorial.ipynb).\r\n\r\nThank you!", "@tilakrayal any time line when it will work with Python 3.10?", "@dsbyprateekg,\r\nYes, there is an issue which was raised for the similar issue where it is under the developer's priority list.\r\nhttps://github.com/tensorflow/tensorflow/issues/60431\r\nI request, could you please follow the respective issue for the updates and as the temporary workaround please try the above mentioned process. Thank you!", "Thanks, I will follow.", "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/60612\">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/60612\">No</a>\n" ]

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[ "I checked the logs, the failed test case is \r\nMLIR tests :: replace_cast_hacks_with_tf_xla_ops_large_constants.mlir\r\nthe changes in this PR doesn't touch code related to the above test case." ]

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[ "Hi @tatwaichong Can you please resolve conflicts? Thank you!", "Close it as the code has been merged into an integration in 402d4d338f2e544ad19730e845c7f9ec55758aaf." ]

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[ "I am able to replicate locally on an Apple M1 Pro, MacOS Ventura 13.3.1, Tensorflow==2.13.0-rc0\r\n\r\nMy results are a little different but the qualitative result is similar:\r\n\r\nProcessed an image of shape (1080, 1920, 3) in 0.047 seconds\r\nTensorflow Model execution stats:\r\n Fraction of time spent detecting: 78%\r\n Average time per frame (ms): 17\r\n Average FPS: 58.99\r\n Median time per frame (ms): 16\r\n Median FPS: 60.69\r\nTFLite Model execution stats:\r\n Fraction of time spent detecting: 91%\r\n Average time per frame (ms): 47\r\n Average FPS: 21.29\r\n Median time per frame (ms): 47\r\n Median FPS: 21.40\r\nTensorflow is 2.77x times faster than TFLite", "Thanks for confirming the issue\r\n\r\nTo answer my previous question \"If this slowdown is unavoidable - is there some other way to serialize tensorflow functions so that they can be loaded again without slowdown?\"\r\n\r\nYes, just use `tf.saved_model.save` instead of `tf.lite.TFLiteConverter.convert`.\r\n\r\nHowever, it would still be good have tflite running at a reasonable speed on Mac (I also observe about a 1.8x slowdown on windows vs pure tensorflow)\r\n\r\n", "Hi @qukhan, Can you please take a look?", "@qukhan @pkgoogle I have similar observations, and in my case TF Lite performs 6x slower than TensorFlow and 17x slower than pytorch. You can find the demo colab [here](https://colab.research.google.com/drive/1escz78h-L0Puhc-POKAsb4f-olpQ9Lnu?usp=sharing). \r\n\r\nThe colab simply creates a sequential model of 4 `Dense` layers with 512 units in `pytorch` and `tensorflow`. And tests them with `4x1024x512` input tensor. Here are the test cases:\r\n* TensorFlow Eager execution\r\n* `torch`\r\n* TensorFlow with `tf.function`\r\n* TF Lite\r\n* ONNX runtime with onnx model converted from the `torch` model\r\n\r\nAnd here are the results:\r\n\r\n| Method | M2 Pro | Colab x86_64 |\r\n|--------|--------|--------|\r\n| TF Eager | 53.4 it/s | 4.6 it/s |\r\n| torch | 143.7 it/s | 7.0 it/s |\r\n| `tf.function` | 55.1 it/s | 6.7 it/s |\r\n| TF Lite | 8.3 it/s | 3.5 it/s |\r\n| ONNX runtime | 50.1 it/s | 6.7 it/s | \r\n\r\nAs one can see, TF Lite underperforms significantly on both M2 Pro and x86 processor in the colab. I could think that the performance issue in colab is caused by the lack of optimization for x86, but M2 pro is an arm64 processor and I think it should have no performance degradation on that." ]

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[ "Hi @ke1ding,\r\n\r\nTensorflow officially supports build with GCC as per tested configurations. Build with Clang is not documented properly and hence I don't have much information. Recommending to Concern Team.\r\n\r\n@TensorFlow-MKL , Could you please have a look into the issue.\r\n\r\nThanks!", "Hi @ke1ding,\r\n\r\nDid you build the package inside a manylinux2014 docker container? According to the [manylinux2014 policy](https://peps.python.org/pep-0599/#the-manylinux2014-policy), the maximum glibc version supported is 2.17 which might be the issue.\r\n\r\nCan you also run ldd --version and share the output?\r\n\r\nThanks!", "@ke1ding \r\nI think this issue is about manylinux usage issue instead of TensorFlow building with MKL.\r\n\r\n1. \r\nAccording to the manylinux repo readme: https://github.com/pypa/manylinux\r\n\r\n```\r\n|manylinux2014 | pip >= 19.3 | 3.7.8+, 3.8.4+, 3.9.0+|\r\n\r\n...\r\n\r\nmanylinux2014 (CentOS 7 based)\r\n\r\nToolchain: GCC 10\r\n\r\n x86_64 image: quay.io/pypa/manylinux2014_x86_64\r\n i686 image: quay.io/pypa/manylinux2014_i686\r\n aarch64 image: quay.io/pypa/manylinux2014_aarch64\r\n ppc64le image: quay.io/pypa/manylinux2014_ppc64le\r\n s390x image: quay.io/pypa/manylinux2014_s390x\r\n\r\n```\r\nIf you want to build with manylinux2014, please following the requirement of manylinux2014 as above.\r\n\r\n2.\r\nAccording to the error log, there is a long dependence chain in your case.\r\nYou could fix the last un-compliant issue (so file) firstly.\r\n\r\nThank you!\r\n\r\n\r\n\r\n\r\n", "Hi @ke1ding, @yehudaorel ,\r\n\r\nI agree to the fact that GCC version capped at 2.17 for Manylinux_2014 as mentioned in above [comment](https://github.com/tensorflow/tensorflow/issues/60608#issuecomment-1552294597) also. But here the built is done using Clang but not GCC and AFAIK, Clang builds may not need GCC.\r\n\r\nI am not much aware of the `auditwheel` package that is being used here for making the built binaries to manylinux_2014 compatible.May be the problem is with `manylinux_2_17(aka manylinux2014)` which indicates max supported GCC version is 2.17v. But the question is the shared objects generated by Clang build still depends upon GCC ? I am not much into compiler design things and liked to hear any pointers here.\r\n\r\nThanks!\r\n\r\n", "@SuryanarayanaY @ke1ding Building with a recent compiler while maintaining manylinux2014 compatibility is a very complex task. Since https://github.com/tensorflow/tensorflow/commit/ba2f7926467cd0b2b7dfea899c6f507b719e7669 the CI builds for Linux from TensorFlow are built using clang-16. You can use the SIG Build docker containers and the appropriate TensorFlow toolchain if you want to build your own or just study what needs to be done.", "Hi @ke1ding , \r\n\r\nThis issue may be caused by compiling against a newer version of glibc than manylinux_2014 supports. manylinux_2014 must be compatible with glibc 2.17, but glibc_2.29 is being used. \r\n\r\nYou may refer to this [script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/builder.devtoolset/build_devtoolset.sh) and this [Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/Dockerfile#L6-L16) to see how to install glibc 2.17 in your sysroot and build the toolchain with manylinux. ", "@ke1ding ,\r\n\r\nCould you please refer to above comments and confirm whether glibc can be downgraded to 2.17 and tried the build ?\r\n\r\nPlease confirm the status. Thanks!\r\n", "@SuryanarayanaY Yes Thank you I will try.", "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 @kanglant @SuryanarayanaY \r\n\r\n I referred the build_devtools.sh to build a docker image [build_devtoolset.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/builder.devtoolset/build_devtoolset.sh) for gcc, glibc and libstdc++, after that I install llvm/clang-16. Before I build tensorflow, I checked the glibc version in the docker image by \r\n`ldd --version && getconf GNU_LIBC_VERSION`.\r\nIt displays as: \r\n`ldd (Ubuntu GLIBC 2.31-0ubuntu9.9) 2.31\r\nCopyright (C) 2020 Free Software Foundation, Inc.\r\nThis is free software; see the source for copying conditions. There is NO\r\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. \r\nglibc 2.31`\r\n\r\nFor using clang-16, do we need more steps to downgrade the GLIBC version again?\r\n\r\nP.S. I will try to generate a docker image by tf_sig_build_dockerfiles [Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/Dockerfile#L6-L16)", "@yuntaolu The downgraded glibc is installed into a sysroot under /dt9 so this will not show up in your check as it has not replaced the system glibc. To build with clang and the 2.17 version of glibc you need to pass\r\n```\r\n--crosstool_top=\"@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain\"\r\n```\r\non the TensorFlow build command line.", "> @yuntaolu The downgraded glibc is installed into a sysroot under /dt9 so this will not show up in your check as it has not replaced the system glibc. To build with clang and the 2.17 version of glibc you need to pass\r\n> \r\n> ```\r\n> --crosstool_top=\"@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain\"\r\n> ```\r\n> \r\n> on the TensorFlow build command line.\r\n\r\nI applied the suggestion from @elfringham, and added the parameter \"\"--crosstool_top=\\\"@sigbuild-r2.13-clang_config_cuda//crosstool:toolchain\\\"\" . Finally, I built TensorFlow manylinux2014 wheels successfully. \r\n\r\nThanks for all your help with the manylinux2014 build with clang. \r\n", "Thank you all! As problem solved I'll close this issue.", "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/60608\">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/60608\">No</a>\n", "@aice-support" ]

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[ "Hi @synandi, \r\nI noticed that you assigned some labels to the issue. Could you already tell whether this is a compiler bug, or am I doing something wrong? Or does it need further investigation? ", "@artem-sereda,\r\nApologies for the delay. The error was stating that the issue was with memory allocation. Could you please try limiting GPU memory growth using any of the methods listed in this guide.\r\nhttps://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth\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/60607\">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/60607\">No</a>\n" ]

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[ "@Arnold2381,\r\nCould you please provide the complete code to reproduce the issue, which helps us to analyse the issue in an effective way. Also could you please try to load your model inside the graph and take a look at this [comment](https://github.com/tensorflow/tensorflow/issues/47959#issuecomment-805986358) from the developer for the similar error. Thank you!\r\n```\r\n\r\nwith graph.as_default():\r\n #load weights into the model\r\n loaded_model.load_weights(\"model.h5\")\r\n #compile and evaluate loaded model\r\n loaded_model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\r\n # perform the prediction\r\n out = loaded_model.predict(img)\r\n print(out)\r\n print(class_names[np.argmax(out)])\r\n # convert the response to a string\r\n response = class_names[np.argmax(out)]\r\n return str(response)\r\n```", "```\r\nem = tf.keras.models.load_model(model_path)\r\nmodel = {\r\n 'key': em,\r\n} \r\nem = model['key']\r\ndta = input_data['data']\r\ndf = np.array(dta).reshape(1, -1)\r\n\r\ned = em.predict(df).tolist()\r\n\r\n```", "@Arnold2381,\r\nI was facing a different error while executing the mentioned code on tensorflow v2.12. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/b3994a91362a35237ef9289d7c17115b/untitled1176.ipynb) and provide complete dependencies.\r\nAlso there is a mix of graph mode and eager mode (and by default TF2.x is eager). Error is mainly due to mix of both modes. 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.", "Closing this as stale. Please reopen if this is still a valid request. 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/60606\">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/60606\">No</a>\n" ]

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[ "Hi @xiedeacc ,\r\n\r\nThanks for your time reporting the issue. Unfortunately i couldn't understand the problem with the provided information.\r\n\r\nPlease confirm whether you are using pip wheel or building from source. Please submit some code snippet to replicate and check the issue. Need more context to understand your issue. Please provide all the details. \r\n\r\nThanks!\r\n", "reason is: RunCallable input sequence was must same with .meta_graph_def input sequence", "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/60605\">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/60605\">No</a>\n" ]

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[ "@daljee10,\r\nApologies for the delay.\r\nI was facing a different error while executing the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4a4950b18de4c966a3295afd9b18539d/untitled1189.ipynb) and provide the complete dependencies to reproduce the issue.\r\n\r\nAlso tf.tensor wants it's N to be a static python `int`. Try not passing it into the tf.function, and instead letting the function close over it. Also please have a look at this [issue](https://stackoverflow.com/questions/69498990/operatornotallowedingrapherror-using-a-tf-tensor-as-a-python-bool-is-not-al) with a similar error for the reference.\r\n\r\nThank you!\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/60604\">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/60604\">No</a>\n" ]

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[ "@innat,\r\nThank you for the issue.\r\nI tried to replicate the issue with the provided .bmp file, as mentioned it is failing with the same error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/3141afe73e3885105a834d5403bf5a67/untitled1150.ipynb). But when I tried with the different .bmp file it is working without any issue. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/159c5942960884e76f08336c4c04a1d6/untitled1152.ipynb).\r\n\r\n[rgb32.zip](https://github.com/tensorflow/tensorflow/files/11495512/rgb32.zip).\r\n\r\nAs you mentioned, `decode_image` was working for some files and I suspect that some files might be corrupted which were not working for some cases. Thank you!\r\n", "@tilakrayal Thanks for your quick test. \r\n\r\n> I tried to replicate the issue with the provided .bmp file, as mentioned it is failing with the same error. But when I tried with the different .bmp file it is working without any issue. \r\n\r\nThat's the primary issue, Because of that, I can't use `tf.io.decode_image` or `tf.io.decode_bmp` method inside the `tf.data` API. The above bmp file is not corrupted. It can be loaded with python libraries, please see below.\r\n\r\n```python\r\nfrom PIL import Image\r\nimage = Image.open('1.bmp')\r\nimage # OK\r\n\r\nimport matplotlib.pyplot as plt\r\nimage = plt.imread('1.bmp')\r\nplt.imshow(image) # OK\r\n\r\nimport tensorflow as tf\r\nimage = tf.io.read_file('1.bmp')\r\nimage = tf.io.decode_image(\r\n image, channels=3, expand_animations=False\r\n) # Error\r\n```", "the `1.bmp` is a 772x600 1-bit bitmap. Current the bitmap decoder only supports depth = 8-, 24-, or 32-bit\r\n\r\n```\r\n$ file /tmp/1.bmp \r\n/tmp/1.bmp: PC bitmap, Windows 3.x format, 772 x 600 x 1, image size 60000, cbSize 60062, bits offset 62\r\n```", "@tilakrayal \r\nAny update on this? As pointed out above, I think it should be supported.", "@innat,\r\nApologies for the delay.\r\n\r\n@sachinprasadhs,\r\nCould you please take a look at this issue. 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/60603\">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/60603\">No</a>\n" ]

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[ "@jianyizh,\r\nCan you please sign CLA. Thank you!", "> @jianyizh, Can you please sign CLA. Thank you!\r\n\r\nThe \"cla/google\" CI is passed, I'm covered by Corporate CLA. Do I need sign Individual CLA?", "@penpornk Can you help review this pr? It's just one line of code.", "Follow internal process and use this pr https://github.com/tensorflow/tensorflow/pull/60786", "Thank you for the PR and sorry for the delay! I have approved https://github.com/tensorflow/tensorflow/pull/60786." ]

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[ "Hi @xiedeacc ,\r\n\r\nThanks for reporting. I am unable to quiet follow through your issue. Actually AFAIK there is no such benchmark to compare the qps and what the average qps. I am not quite sure whether the issue is due to network overload or something. Is that possible that can we replicate this behaviour with TF code snippet ? With the provided information I can't infer much.\r\n\r\nAlso could you explain what is the value 99% represents here. Is that CPU usage? And the second one is average time for a query right? \r\n\r\nPlease submit more context along with some code snippet to replicate the behaviour.\r\n\r\nThanks!\r\n\r\n", "99% means 99% request return at that latency. under stress test, cpu olny achive 50%, cannot achive highier. after a lot perf, it seems too many memory copy, I use jemalloc relief this problem a lot. maybe I need split ads into many batches to avoid big memory allocation and copy", "Hi, \r\n\r\nCould you please test your findings using latest Tensorflow stable version 2.12,\r\n\r\n `2.13rc0` using `pip install tensorflow==2.13.0rc0` ,\r\n\r\nTensorflow Nightly version using `pip install tf-nightly`\r\n\r\n to see if there is any improvement in qps. 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.", "> Hi,\r\n> \r\n> Could you please test your findings using latest Tensorflow stable version 2.12,\r\n> \r\n> `2.13rc0` using `pip install tensorflow==2.13.0rc0` ,\r\n> \r\n> Tensorflow Nightly version using `pip install tf-nightly`\r\n> \r\n> to see if there is any improvement in qps. Thank you.\r\n\r\nok I will try, 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/60601\">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/60601\">No</a>\n", "I run into this same issue on tf2.9.0 rc2 built from source, would you share the test result? @xiedeacc " ]

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[]

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[ "Hi massquantity,\r\n\r\nAfter reviewing your issue and the code you provided, I believe I have found a solution that can help reduce the memory usage during data shuffling in TensorFlow.\r\n\r\nThe key to reducing memory usage is to explicitly set the buffer size to a reasonable value. To resolve this issue, you can explicitly set the buffer_size to a smaller value that is appropriate for your use case. As a rule of thumb, the buffer size should be set to a value that is small enough to fit into memory but large enough to provide good shuffling performance. A buffer size of 1024, as you mentioned, may be too small and may result in poor shuffling performance. A buffer size of 10000 is generally a good default value.\r\n\r\nyou can use the prefetch() method in conjunction with shuffle(). This method allows you to overlap the shuffling process with other parts of your data pipeline, which can help reduce memory usage. \r\n\r\nHere's an updated version of your code that incorporates these changes:\r\n\r\n```python\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\ndata_size = 50000000\r\n\r\ntf_dataset = tf.data.Dataset.from_tensor_slices(np.arange(data_size))\r\ntf_dataset = tf_dataset.shuffle(buffer_size=10000).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\r\ntf_dataset = iter(tf_dataset)\r\n\r\nprint(next(tf_dataset))\r\n```\r\n**Memory Usage**\r\n\r\n\r\nI tested this code on my machine and found that it reduces memory usage to less than 500MiB.\r\nPlease let me know if this solution works for you. \r\n\r\nBest regards,\r\nNitya", "Thanks to Nitya, for your kind and detailed explanation! I'm also aware that the buffer_size should be set as large as possible for shuffling performance. `buffer_size=1024` is just an example, and what I'm seeking in this issue is some clarification on the memory usage of buffer_size. \r\n\r\nWhen a user set `buffer_size=1024`, she expects the underlying code will only use the memory of 1024 element rather than the memory of equivalent to 10240 elements, which is a \"surprise\" we generally want to avoid when using an open source library like TensorFlow. That's why I'm surprised when seeing the full shuffling of 50,000,000 `int` data cost 10 GB of memory.\r\n\r\nIf the code uses more memory than the specified buffer_size, that's totally OK to me, provided that the doc is clear about this. But the [shuffle doc](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) says \"This dataset fills a buffer with buffer_size elements, then randomly samples elements from this buffer, replacing the selected elements with new elements.\" From my understanding, it only uses `buffer_size` elements of memory.", "@nitya-khuntia \r\n\r\nIf the `.shuffle(10_000)` operation increases memory usage by 500 megabyte then each number is using up around 50 kilobyte each. They should not. If we assume they are 64 bit numbers then they should be using 8 byte plus overhead. If the overhead is 50'000 byte minus 8 byte then that seems like a lot. It's about 6000 x the actual data.", "I did a small benchmark to find the actual value of the shuffle memory overhead in a more realistic scenario: took 1000 samples of 1,69 MB each and tested different shuffle buffer sizes to see the delta memory/buffer_size ratio which turned out to be pretty constant (the first data point is at 10):\r\n\r\n\r\n\r\nSo, as a general rule of thumb, if you do a shuffle, plan to have 8.4 (!) times your buffer_size memory available. 😉 \r\n\r\n" ]

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[ "@carl-krikorian,\r\nAccording to [Colab Updated to Python 3.10 7](https://medium.com/google-colab/colab-updated-to-python-3-10-27eb02daa162):\r\n\r\n**Colab’s fallback runtime version:** Using the fallback runtime version temporarily allows access to the Python 3.9 runtime, and will be available until mid-May. This is available from the Command Palette via the Use fallback runtime version command when connected to a runtime. Of note, this setting does not persist across sessions — the command will need to be invoked on each new session.\r\n\r\nAs a temporary workaround, you can use the Colab fallback runtime version option to choose Python 3.9 and install tflite-model-maker. By doing this you will get RuntimeError and it can be ignored.\r\n\r\nTo access the command palette in Colab, presss cmd+shift+P and then type Use fallback runtime version and select it.\r\n\r\n\r\n\r\nBy following the above process, I was able to execute [Model Maker Image Classification Tutorial 9](https://colab.sandbox.google.com/gist/chunduriv/847ff8f09621935471b3fb70b23cefbd/model-maker-image-classification-tutorial.ipynb).\r\n\r\nThank you!", "Hello @tilakrayal, \r\nThis indeed seems to have solved my issue. I managed to access the command palette through tools and after starting the notebook, the use fallback runtime became available.\r\nMany Thanks! As this workaround is temporary (and it seems it will be impossible after may), are there any plans to fix the issue with 3.10?", "@carl-krikorian,\r\nYes, there is an issue which was raised for the similar issue where it is under the developer's priority list. \r\nhttps://github.com/tensorflow/tensorflow/issues/60431\r\nI request, could you please follow the respective issue for the updates and as the alternative workaround is working in your case, Could you please feel free to move this issue to closed status. Thank you!", "@tilakrayal \r\nI see, keep up the great work! Thanks again for the support!", "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/60598\">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/60598\">No</a>\n", "> @carl-krikorian, According to [Colab Updated to Python 3.10 7](https://medium.com/google-colab/colab-updated-to-python-3-10-27eb02daa162):\r\n> \r\n> **Colab’s fallback runtime version:** Using the fallback runtime version temporarily allows access to the Python 3.9 runtime, and will be available until mid-May. This is available from the Command Palette via the Use fallback runtime version command when connected to a runtime. Of note, this setting does not persist across sessions — the command will need to be invoked on each new session.\r\n> \r\n> As a temporary workaround, you can use the Colab fallback runtime version option to choose Python 3.9 and install tflite-model-maker. By doing this you will get RuntimeError and it can be ignored.\r\n> \r\n> To access the command palette in Colab, presss cmd+shift+P and then type Use fallback runtime version and select it.\r\n> \r\n> \r\n> \r\n> By following the above process, I was able to execute [Model Maker Image Classification Tutorial 9](https://colab.sandbox.google.com/gist/chunduriv/847ff8f09621935471b3fb70b23cefbd/model-maker-image-classification-tutorial.ipynb).\r\n> \r\n> Thank you!\r\n\r\nUnfortunately, the fall-down command has not been vailable now. " ]

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[ "Hi @estenhl ,\r\n\r\nThe issue exists only on Mac M1/M2 it seems.I have replicated the issue on Mac M1.\r\n\r\n```\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ python 60597.py\r\n2.14.0-dev20230515\r\nMetal device set to: Apple M1 Pro\r\n\r\nsystemMemory: 16.00 GB\r\nmaxCacheSize: 5.33 GB\r\n\r\nTraceback (most recent call last):\r\n File \"/Users/suryanarayanay/60597.py\", line 17, in <module>\r\n generator.uniform(shape=(), minval=0, maxval=3, dtype=tf.int32)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/ops/stateful_random_ops.py\", line 788, in uniform\r\n return gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/ops/gen_stateless_random_ops_v2.py\", line 477, in stateless_random_uniform_int_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/Users/suryanarayanay/miniconda/envs/tf-metal/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6577, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__StatelessRandomUniformIntV2_device_/job:localhost/replica:0/task:0/device:GPU:0}} minval must be 0-D, got shape [2]\r\n\t [[{{node StatelessRandomUniformIntV2}}]] [Op:StatelessRandomUniformIntV2] name: \r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ \r\n```\r\nHowever on Colal(Linux) the code executes fine as per [gist](https://colab.research.google.com/gist/SuryanarayanaY/77af814354eb337fee46527856a30f39/60597.ipynb).\r\n\r\nSince Mac M1 installs Apple's TF Package the issue needs to be addressed by Apple itself.Please report the issue [here](https://developer.apple.com/forums/tags/tensorflow-metal).\r\n\r\nThanks!", "Ah, of course, thanks for looking into 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/60597\">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/60597\">No</a>\n" ]

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[ "`TfLiteGpuDelegateBindGlBufferToTensor` was consciously removed in v2 because we saw minor adoption due to complicated API. Chances are very low that it'll be re-introduced. If you want to use `TfLiteGpuDelegateBindGlBufferToTensor`, the usage above is probably not sufficient: You would have to do a lot of plumbing that `gl_delegate.cc` does (see logic around `bhwc_objects_`) and apply it to `delegate.cc`.", "Hi @impjdi\r\n\r\nThanks a lot for your kind reply.\r\n\r\nSo,, is there any other template, for v2, which takes textures as the input and output of tflite inference?\r\n\r\nCould you please let me know if there is a v1 version (gl_delegate.cc supported) which has an opencl engine as fast as a v2 engine? I've heard that tflite engine on opencl is about twice faster than that on opengl..\r\n\r\nI am looking forward to hearing from you. Thanks again.\r\n\r\nBest,\r\nPicard314 ", "Hm, that's complicated.\r\n\r\n> So,, is there any other template, for v2, which takes textures as the input and output of tflite inference?\r\n\r\nNot exactly OpenGL textures, but OpenGL SSBO, you can see examples of using the internals of the GPU inference in the MediaPipe's inference_calculator_gl_advanced.cc MediaPipe wasn't okay with the CPU / GPU copy that you're mentioning, but doesn't have the BindTensor functions, so they are using the GPU inference internal APIs directly.\r\n\r\n> Could you please let me know if there is a v1 version (gl_delegate.cc supported) which has an opencl engine\r\n\r\nUnfortunately, there has been no standalone OpenCL delegate IIRC. I think when we introduced OpenCL, we merged them together into the v2 (delegate.cc).", "Hi @impjdi \r\n\r\nAh, I am so grateful and quite shocked on your professional explanations~\r\n\r\nIf possible, I would like to request for informations (codes template) of implementing the MediaPipe from you. \r\n\r\nI found no \"inference_calculator_gl_advanced.cc\" in the source codes of tflite v2.10 I am using. Is MediaPipe a tflite branch and does it have a v2/opencl engine/delegate please?\r\n\r\nBest,\r\nPicard314\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "> Hi @impjdi\r\n> \r\n> Ah, I am so grateful and quite shocked on your professional explanations~\r\n> \r\n> If possible, I would like to request for informations (codes template) of implementing the MediaPipe from you.\r\n> \r\n> I found no \"inference_calculator_gl_advanced.cc\" in the source codes of tflite v2.10 I am using. Is MediaPipe a tflite branch and does it have a v2/opencl engine/delegate please?\r\n> \r\n> Best, Picard314\r\n\r\nMediaPipe is a different project - https://github.com/google/mediapipe/blob/master/mediapipe/calculators/tensor/inference_calculator_gl_advanced.cc" ]

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[ "@jsxzhub,\r\nCould you please try to execute the below mentioned code and you will get the output for the cpu devices which are available in the machine. \r\n\r\n```\r\nimport os\r\nprint(\"Number of physical CPUs:\", os.cpu_count())\r\n```\r\n\r\nor \r\n\r\n```\r\nimport multiprocessing\r\nmultiprocessing.cpu_count()\r\n```\r\nPlease find the below screenshot for the reference. Thank you!\r\n\r\n", "\r\n\r\nIs this tensorflow bug? \r\n\r\nHow does TensorFlow utilize multiple CPUs in this situation?", "I think this is not an issue. I don't believe that this is supposed to print the number of CPU cores. I think it just prints the actual physical device.\r\n\r\n`tf.config.list_physical_devices(\"CPU\")`\r\n\r\nHere is my output:\r\n\r\n```\r\nprint(tf.config.list_physical_devices())\r\n\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\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/60595\">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/60595\">No</a>\n", "@jsxzhub,\r\n**tf.config.list_physical_devices** will return a list of physical devices visible to the host runtime. Physical devices are hardware devices present on the host machine. By default all discovered CPU and GPU devices are considered visible.\r\n\r\nThis API allows querying the physical hardware resources prior to runtime initialization. Thus, giving an opportunity to call any additional configuration APIs. This is in contrast to [tf.config.list_logical_devices](https://www.tensorflow.org/api_docs/python/tf/config/list_logical_devices), which triggers runtime initialization in order to list the configured devices.\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices\r\nThank you!", "That means the machine has 48 CPUs, but I can only use one CPU?\r\n\r\n", "Hi, \r\n\r\nBoth `os.cpu_count()` and `multiprocessing.cpu_count()` will give you the number of cores across all the CPUs in your machine.\r\n\r\n`tf.config.list_physical_devices('CPU')` will return the physical CPU devices.\r\n\r\nTensorflow uses all the cores in the available CPU. \r\n\r\n\r\n\r\n\r\n", "Thank you!\r\nIt is recommended that tensorflow be modified to display as many cpus as there are cpus.\r\nfor example:\r\nPhysicalDevice(name='/physical_device:CPU:0', device_type='CPU') \r\nPhysicalDevice(name='/physical_device:CPU:1', device_type='CPU')\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/60595\">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/60595\">No</a>\n" ]

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[ "Hi @boxabirds ,\r\n\r\nPlease use `pip install tf-nightly` and it will take care of GPU package also. Starting from TF2.12 GPU package made redundant and both `tensorflow-gpu` and `tf-nightly-gpu` were removed and replaced with packages that direct users to switch to `tensorflow` or `tf-nightly` respectively. Please refer to release notes [here](https://github.com/tensorflow/tensorflow/releases/tag/v2.12.0).", "Thanks. YesI just noticed that in the tf-nightly-gpu package README. I\ntried tg-nightly and it said it didn’t detect my GPU. I have CUDA 12.1 on\nmy RTX 4090.\n\nOn Mon, 15 May 2023 at 05:04, SuryanarayanaY ***@***.***>\nwrote:\n\n> Hi @boxabirds <https://github.com/boxabirds> ,\n>\n> Please use pip install tf-nightly and it will take care of GPU package\n> also. Starting from TF2.12 GPU package made redundant and both\n> tensorflow-gpu and tf-nightly-gpu were removed and replaced with packages\n> that direct users to switch to tensorflow or tf-nightly respectively.\n> Please refer to release notes here\n> <https://github.com/tensorflow/tensorflow/releases/tag/v2.12.0>.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60594#issuecomment-1547160580>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AABD62P2CB5BWHLERYC46F3XGGTMRANCNFSM6AAAAAAYBBQA3M>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "Hi @boxabirds ,\r\n\r\nFor GPU support please follow the official instructions mentioned [here](https://www.tensorflow.org/install/pip#step-by-step_instructions). Please go through the instructions and let us know if it helps and if not please let us know the commands you have used.\r\n\r\nPlease ensure GPU driver has been installed and running. It can be confirmed with `nvidia-smi` command. Please verify this first and if this command displays info about CUDA driver then we need to go for CUDA toolkit and cuDNN installations and path settings as instructed in attached documentation resource.\r\n\r\nThanks!", "Thanks so much — apologies for being thick on this: the release notes I\r\ncouldn’t find any instructions for installation, only build. I really want\r\nto avoid building TF myself. Are you saying I need to build TF to work with\r\nCUDA 12 and support the RTX 4090? Or was there another link?\r\n\r\nApologies again and thanks very much in advance— I’m asking for specifics\r\non this because I’ve spent over a day trying to get this going and my\r\ncontingency of using a docker container with older TF versions and CUDA 11 seems to\r\ncome with very significant performance impacts.\r\n\r\nOn Mon, 15 May 2023 at 08:07, SuryanarayanaY ***@***.***>\r\nwrote:\r\n\r\n> Hi @boxabirds <https://github.com/boxabirds> ,\r\n>\r\n> For GPU support please follow the official instructions mentioned here.\r\n> Please go through the instructions and let us know if it helps and if not\r\n> please let us know the commands you have used.\r\n>\r\n> Please ensure GPU driver has been installed and running. It can be\r\n> confirmed with nvidia-smi command. Please verify this first and if this\r\n> command displays info about CUDA driver then we need to go for CUDA toolkit\r\n> and cuDNN installations and path settings as instructed in attached\r\n> documentation resource.\r\n>\r\n> Thanks!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/60594#issuecomment-1547302149>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AABD62NNEZBOVUKQXC7M6ULXGHI2PANCNFSM6AAAAAAYBBQA3M>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n", "@boxabirds ,\r\n\r\n> Thanks so much — apologies for being thick on this: the release notes I couldn’t find any instructions for installation, only build. I really want to avoid building TF myself. Are you saying I need to build TF to work with CUDA 12 and support the RTX 4090? Or was there another link?\r\n\r\nYou need to setup GPU manually by following the instructions mentioned in documentation. It's not for Building Tensorflow itself. Even with Pip wheel of Tensorflow to enable GPU support you have to do some manual setups. The same mentioned [here](https://www.tensorflow.org/install/pip#step-by-step_instructions).\r\n\r\nHowever if you are using Docker container then you need to install and run nvidia-docker image first followed by tensorflow-image. The instructions are mentioned [here](https://www.tensorflow.org/install/docker#gpu_support).\r\n\r\nPlease check and come back if still having queries. Thanks!\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/60594\">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/60594\">No</a>\n" ]

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[ "FYI: Currently, following configuration files are referencing gtest `release-1.12.1`.\r\n\r\n- tensorflow/workspace2.bzl\r\n- tensorflow/tools/pip_package/xla_build/pip_test/CMakeLists.txt" ]

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[ "We are presently amidst an effort to refresh the documentation for distributed training. We plan to include guidance on setting up workflows on platforms like Google Cloud, along with clearer recommendations about when to use one distribution strategy vs. another.\r\n\r\nIn the meantime, the easy answer is that \"yes,\" TF support training on multiple network-linked machines with CPUs, GPUs, or TPUs. TF can also leverage the dedicated high-speed interconnects of TPUs and GPUs (e.g., NCCL). If you're planning to train on multiple machines without GPUs/TPUs, you may consider using `ParameterServerStrategy`. Here is the relevant section of the docs for setting up a cluster:\r\nhttps://www.tensorflow.org/tutorials/distribute/parameter_server_training#real_clusters\r\n\r\nPlease let me know if you have any further questions or topics you would like the docs to cover.", "Hello @jszaday \r\n\r\nSorry for the late reply, I'm currently on a holiday. Thank you so much for your response. \r\n\r\nI'm currently having trouble understanding a few more things from the docs I hope if you are able to clear me up. \r\n\r\nI'm not able to understand how different distribution strategies work. For example - if each device (GPU or a separate computer system node) creates a copy of the model and calculates a specific subset of gradient which master worker collects and forms a union gradient (or full model gradient) and applies it to the model weights, then why batches are segmented? Because few parts of the documentation say - the batches are divided for each device and then averaged. I'm not able to understand these two things going simultaneously here, because it's impossible to calculate gradient of each portion of the model with a batch of data which is different for each device. \r\n\r\n\r\nThis is because I've plans to implement a certain special optimizer which requires variable batch size, where the order of dataset is important and distribution and averaging wouldn't work - but partial gradient calculation at each worker might work. \r\n\r\n\r\nThank you so much. Hopefully you might clear my this doubt as well. \r\n", "TF's distribution strategies primarily employ data parallelism, wherein all the devices run a mirrored copy of the model and train on a local subset (or _shard_) of an unordered dataset. For example, if one distributes a dataset of 128 samples across eight devices, each device would train on 16 (random) samples. The cross-device weight update aggregation at the end of each training step ensures that all the devices have identical weights at the start of the next training step. Thus, one can imagine that each device shares any insights it learns from its samples with all the other devices during aggregation. Since all the devices' copies of the models are always kept in sync and, by aggregation, learn from all the samples in the dataset, the numerical results of this scheme are, theoretically, identical to those from training on a single device (for unordered datasets). \r\n\r\nThat's all to say that your dataset may not be eligible for data parallelism if the model/optimizer must consume its samples in a particular order. Instead of processing samples in parallel, each device would have to wait on the weight updates from the previous device, inducing serialization.\r\n\r\nData parallelism is a special kind of model parallelism. One can imagine other model-parallel schemes that preserve the ordering of a dataset. For example, one may shard the individual samples of a dataset rather than the dataset itself; then, all the devices in the system may process one sample at a time, in parallel. TensorFlow's experimental API for model parallelism is called DTensor. Here, it's worth noting that variable batch sizes and order-sensitive datasets stray from the conventional path for good distributed performance in TF.", "Hello @jszaday \r\n\r\nSorry for one more stupid question. I wish to know:\r\n\r\nSuppose a dataset of 1024 instances, I use batch size = 1024 using Keras API like this:\r\n`keras.fit(X, Y, batch_size = 1024, epochs = 1)`\r\n\r\non number of GPUs = 2.\r\n\r\nDoes that mean, each GPU would calculate the model weights of 512 instances and average them in `tf.distribute.MirroredStrategy()` in Keras to get the final model weight if trained on batch_size = 1024?\r\n\r\nSorry if I'm misunderstanding anything here...", "There are no stupid questions--I had to double-check on this myself.\r\n\r\nThe answer is that it depends on how the dataset is sharded.\r\n\r\nWhen using distributed datasets:\r\n- Each device will run a batch of `global_batch_size // strategy.num_replicas_in_sync` instances; see [this doc](https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy#experimental_distribute_dataset).\r\n- You should not specify `batch_size` as an argument to `model.fit`.\r\n\r\nOtherwise, the semantics are less clear; I'd recommend building a dataset to be safe:\r\n\r\n```\r\nds = tf.data.Dataset.from_tensor_slices((X, Y)).batch(1024)\r\nds = strategy.experimental_distribute_dataset(ds)\r\nmodel.fit(ds, epochs=1, steps_per_epoch=1)\r\n```", "Hello @jszaday \r\n\r\nThank you so much for the clarity on it.\r\n\r\nSee the problem is that - the specific problem we are working on is a special case of non-convex optimization over very powerful hardware. The optimizer is specific to two things - 1. Variable batch sizes 2. Distribution over the hardware.\r\n\r\nWhile your advice of using `tf.data.Dataset` to load the dataset is helpful, it doesn't give full control over the dataset. For some reason, I'd need to stick to batch_size = 1024 parameter on Keras API. I wish to know if I'm using the same model.fit() under strategy.scope() block, with batch_size parameter to train a model, will Multi-training strategy would work? We'll use some sort of Python generators to load dataset instances in some specific order.\r\n\r\nSecond, to deal with variable batch sizes, I guess, I need to put an upper bound to the optimizer batch size and mask the non required batches of data instances with 0 and train them on GPUs distributed (is it possible, from my experience - Custom Optimizers use `update_step` function doesn't has feature to deal with batch indices of the tensor).\r\n\r\nThank you", "Apologies for the delayed response.\r\n\r\nThe snippet I provided earlier is essentially what Keras does internally when it's given (X, Y) as inputs; it creates a dataset through the strategy, and batches it using the `batch_size` argument to `model.fit`. Subsequently, the `batch_size` argument will always represents the global batch size, and each device will run a shard of `batch_size // num_replicas` instances. ([Relevant link](https://github.com/keras-team/keras/blob/master/keras/engine/data_adapter.py#L1323).)", "Thank you so much @jszaday , that clears up my confusion.\r\n\r\nMy second concern is - batch-level editing. I want to mask a certain batch number of the inputs passed to the `model.fit()` to 0 using a custom optimizer. Is there any way to edit certain batches to 0 while keeping the rest of the gradients? As far I understand now since the batches are distributed among the devices, it becomes much more difficult to control i.e. assign grads = 0 to i-th entry of batch and keep the rest.\r\n\r\nThank you.", "Rather than trying to correlate input batches with masks, there may be a way to pass the mask as an input to the model and have it \"skip\" past all the other layers to be applied immediately before the outputs. However, I would recommend routing further inquiries into masking with `model.fit` to the [keras-team/keras repo](https://github.com/keras-team/keras/issues); I am unable to offer specific advice on this matter.", "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." ]

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[]

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[ "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.12 and nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/96674a9bf99783bd35591caa12500f44/untitled1143.ipynb).", "i am looking the implementation hope will be able to find about it\r\n", "**``` tf.linalg.eig ```**\r\nis implemented with the help of **``` from tensorflow.python.ops import gen_linalg_ops```** but i am unable to find the implemetation of **``` gen_linalg_ops```** so someone can help me here", "> **`tf.linalg.eig`** is implemented with the help of **` from tensorflow.python.ops import gen_linalg_ops`** but i am unable to find the implemetation of **` gen_linalg_ops`** so someone can help me here\r\n\r\nAnything with `gen_` in front is an auto-generated wrapper around C++ code. In this case, the `Eig\" op. The implementation is [here]( https://github.com/tensorflow/tensorflow/blob/efcc8ded70356d66755f1b00b7f4efc69d5db44d/tensorflow/core/kernels/linalg/eig_op_impl.h#L35), but it just uses the Eigen math library to compute eigenvectors.\r\n\r\nAs for why they are different: if they are all correct (i.e. TF's result is different but still satisfies the eigenvalue equations) it might just be because it uses a different algorithm. Other libraries might all use the same, or even the same underlying implementation. Eigenvectors are unique up to choice of sign in the case of unique eigenvalues, but if you have a repeated eigenvalue, then the corresponding vectors form subspaces, so there are infinitely many that bases that are valid.\r\n\r\n", "so, tensorflow is just providing the different basis of the eigenvectors in this case", "Verified that TF's results are equally correct. Didn't notice earlier we were talking about complex eigenvectors, in which case eigenvectors are unique up to multiplying by any complex constant - which is what we're seeing here.", "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/60590\">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/60590\">No</a>\n" ]

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[ "Hi @hemantnyadav ,\r\n\r\nthanks for reaching us. I believe shuffle=True can be used for some cases. But before that we might need to split the dataset first to train,test/validation datasets. Here `shuffle=True` means it will shuffle means only the rows will be shuffled but not the columns(Features).\r\n\r\nSuppose if we have some time series data and we want to make a dataset such that it will choose 6 data points in sequence and 5 among them are as inputs and 6th one as label. Here we are trying to predict some output based on previous 5 time stamps. Please note that here the 5 time stamps will be in order only i.e columns(Features) are not being shuffled. But we are not establishing relationship between one data point to other but rather we are trying to create relationship between the same datapoint(sequence of 5 timesteps with 6th one as output).Sometimes we may need to shuffle the data in terms of batches only. We should carefully choose `shuffle=True` based on use case.\r\n\r\nPlease refer to some more web resources for better understanding [link1](https://stackoverflow.com/questions/70326567/shuffling-a-classification-timeseries-data), [link2](https://datascience.stackexchange.com/questions/54237/is-it-valid-to-shuffle-time-series-data-for-a-prediction-task) and [link3](https://www.analyticsvidhya.com/blog/2021/06/deep-dive-into-time-series-data-with-single-neuron/).\r\n\r\nThanks!", "Thank you for your reasonable explanation.\r\nbut my question is specifically indicating the response you have given is not mentioned in the article.\r\nAs the article is for an audience who wants to get started with it or someone who wants to create a testbed for such an experiment.\r\nIn such cases, they need to verify it with original data rather than relying on API-based error rate.\r\nWhen the sequence of data points (not features but rows) changes, verification of results manually becomes very difficult. So it would be great if you incorporated these details about shuffle flag in the article itself.\r\nThank you.\r\n\r\n", "Hi @hemantnyadav,\r\n\r\nDocumentation is not exhaustive and I agree that. Because the concept is general only and not specific to Tensorflow and hence it might not be documented specifically.Sometimes adding exhaustive documentation for general concepts may gives feeling of overloaded documentation.Hence it might have ignored.If you still feel adding it might be useful then will see what can be done here.\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/60589\">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/60589\">No</a>\n" ]

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[ "@berrylizzard,\r\nTensorflow v2.12 is compatible with the python 3.8-3.1, GCC -9.3.1, Bazel - 5.3.0, cuDNN 8.6, CUDA 11.8 which are mentioned on the official tensorflow document. \r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nCould you please try with the tested build configurations and let us know if the installation was completed. Thank you!\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/60588\">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/60588\">No</a>\n" ]

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[ "@syfxie,\r\nThe problem here was, with the tensorflow v2.8 you are trying to use the python v3.11 which is not compatible. For TF **v2.8**, python **v3.7-3.10** is compatible. Could you please try to check a look at the official doc link for the tested build configurations. \r\nhttps://www.tensorflow.org/install/source#cpu_2\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/60587\">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/60587\">No</a>\n" ]

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[ "As discussed with @mihaimaruseac at https://github.com/tensorflow/tensorflow/pull/60019, this PR cherry-picks the single commit starting from the latest point in master branch.", "@terryheo can you review please? Or assign to someone in TFLite team that can?", "This need manual changes internally to the copybara config due to the renamed file.", "> This need manual changes internally to the copybara config due to the renamed file.\r\n\r\nIs there anything I can do to advance this?", "@terryheo can you shepherd this please? Looks like the renamed file requires copybara changes, so this needs to be manually imported" ]

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[ "Hi @penpornk Can you please review this PR ? Thank you!", "Hi @AnetaKaczynska Can you please check @cantonios'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 @AnetaKaczynska Any update on this PR? Please. Thank you!", "Hi @cantonios, I just ran benchmarks again and it still has better performance. Can you please give me more details to reproduce the worse performance case? Was it on CPU or GPU?", "> Hi @cantonios, I just ran benchmarks again and it still has better performance. Can you please give me more details to reproduce the worse performance case? Was it on CPU or GPU?\r\n\r\nCPU. It's only the CPU kernel that's modified, and the benchmark numbers in the revert commit list the CPU benchmark numbers.", "@cantonios One more thing, can you please share system specifications that were used to run this benchmark? What CPU did you use?", "> @cantonios One more thing, can you please share system specifications that were used to run this benchmark? What CPU did you use?\r\n\r\nSimilar results on both haswell and skylake.", "> > @cantonios One more thing, can you please share system specifications that were used to run this benchmark? What CPU did you use?\r\n> \r\n> Similar results on both haswell and skylake.\r\n\r\n@cantonios I ran it on skylake machine and got better results after optimization:\r\n\r\nBenchmark | Items with optimization | Items without optimization\r\n-- | -- | --\r\nBM_Xent_16_10000_cpu_float/real_time | 182.368M/s | 103.502M/s\r\nBM_Xent_32_10000_cpu_float/real_time | 289.048M/s | 176.654M/s\r\nBM_Xent_64_10000_cpu_float/real_time | 428.617M/s | 298.246M/s\r\nBM_Xent_16_10000_cpu_bfloat16/real_time | 151.684M/s | 86.4855M/s\r\nBM_Xent_32_10000_cpu_bfloat16/real_time | 358.028M/s | 149.516M/s\r\nBM_Xent_64_10000_cpu_bfloat16/real_time | 390.276M/s | 254.699M/s\r\n\r\nI'm trying to get down to the bottom of why we're getting better performance. Can you please provide me more details about the system that was used for benchmarking and significant run options such as cores used? If possible it would be great if I could use the machine you ran it on. \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "> > > @cantonios One more thing, can you please share system specifications that were used to run this benchmark? What CPU did you use?\r\n> > \r\n> > \r\n> > Similar results on both haswell and skylake.\r\n> \r\n> @cantonios I ran it on skylake machine and got better results after optimization:\r\n> \r\n> Benchmark\tItems with optimization\tItems without optimization\r\n> BM_Xent_16_10000_cpu_float/real_time\t182.368M/s\t103.502M/s\r\n> BM_Xent_32_10000_cpu_float/real_time\t289.048M/s\t176.654M/s\r\n> BM_Xent_64_10000_cpu_float/real_time\t428.617M/s\t298.246M/s\r\n> BM_Xent_16_10000_cpu_bfloat16/real_time\t151.684M/s\t86.4855M/s\r\n> BM_Xent_32_10000_cpu_bfloat16/real_time\t358.028M/s\t149.516M/s\r\n> BM_Xent_64_10000_cpu_bfloat16/real_time\t390.276M/s\t254.699M/s\r\n> I'm trying to get down to the bottom of why we're getting better performance. Can you please provide me more details about the system that was used for benchmarking and significant run options such as cores used? If possible it would be great if I could use the machine you ran it on.\r\n\r\nIt's on our Google perflab machines. You cannot access them. This is without oneDNN, using TF's defaults.\r\n\r\nPlus, this is only half the battle. We also received reports from several teams within Google that their models no longer converged after this change - so there are numerical differences as well." ]

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[ "No, not useful. Python bools are just another type of integer, so end up being converted to int32 automatically. There is no valid semantic meaning to `tf.ones(True)`, and we do _not_ accept boolean tensors in general: `tf.ones(tf.constant(True, dtype=tf.bool))` fails." ]

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[ "It appears this can be worked around like this:\r\n```python\r\ninput = tf.ragged.constant([[1, 2, 3], [4, 5], [7, 8, 9]])\r\nds = tf.data.Dataset.from_tensor_slices(input)\r\noriginal_spec = ds.element_spec\r\ntf.random.set_seed(0)\r\nds = ds.rejection_resample(\r\n class_func=lambda t: 1,\r\n target_dist=(0.1, 0.9),\r\n).map(lambda class_func_result, data: data)\r\nds = ds.batch(1).map(lambda elem: tf.map_fn(\r\n fn=lambda e: e,\r\n elems=elem,\r\n fn_output_signature=original_spec,\r\n )).unbatch()\r\nds = ds.batch(2)\r\nds.take(1).get_single_element()\r\n```\r\n\r\nBut that's pretty annoying. IDK if there's a shorter way to express the same thing.\r\n\r\nAlso, it appears that the same problem applies generally to `Dataset.map`, i.e. this breaks in the same way:\r\n```python\r\ninput = tf.ragged.constant([[1, 2, 3], [4, 5], [7, 8, 9]])\r\nds = tf.data.Dataset.from_tensor_slices(input)\r\nds = ds.map(lambda e: e)\r\nds = ds.batch(2)\r\nds.take(1).get_single_element()\r\n```", "Hi @chris-remedy ,\r\n\r\nI have replicated the reported behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/6e2138c261058cb31dfae033f5a7061b/60583.ipynb) for reference. This needs to dig into more to confirm the root cause. Thanks!" ]

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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/60582\">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/60582\">No</a>\n" ]

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[ "Log when compiling using CUDA:\r\n[mochi_O3.txt](https://github.com/tensorflow/tensorflow/files/11461476/mochi_O3.txt)\r\n", "A similar issue is present for MacOS 13.3 using python 3.10. Log attached. \r\n[MacOS.txt](https://github.com/tensorflow/tensorflow/files/11461636/MacOS.txt)\r\n", "I'm trying to reproduce in Ubuntu 22.04 with all of the above conditions. I'll let you know if I'm able to.", "@SuryanarayanaY,\r\nNotes: I tried to replicate the issue using tf v2.13.0-rc0 and CUDA 12.0 and was facing the below error when trying to configure the setup using ./configure.\r\n\r\n", "> @SuryanarayanaY, Notes: I tried to replicate the issue using tf v2.13.0-rc0 and CUDA 12.0 and was facing the below error when trying to configure the setup using ./configure. \r\nThis is not related to the issue and in fact it is not a bug. Make sure you have Cuda 12.0 properly installed.", "In both cases it's x86_64.\r\n\r\nThe issue here seem to point to a configuration issue (see below). Yet, I am using a stock configuration, unless there is a specific python package with a non-compatible package...\r\n\r\nERROR: \r\n/home/nicola/.cache/bazel/_bazel_nicola/c53ed0be17816f9e0970b1ba234e403c/external/local_config_python/BUILD:78:8: \r\nin outs attribute of genrule rule @local_config_python//:python_include: \r\nGenrules without outputs don't make sense\r\nERROR: \r\n/home/nicola/.cache/bazel/_bazel_nicola/c53ed0be17816f9e0970b1ba234e403c/external/local_config_python/BUILD:78:8: \r\nAnalysis of target ***@***.***_config_python//:python_include' failed\r\nINFO: Repository cython instantiated at:\r\n   /home/nicola/Software/tensorflow/gpu/tensorflow/WORKSPACE:15:14: in \r\n<toplevel>\r\n/home/nicola/Software/tensorflow/gpu/tensorflow/tensorflow/workspace2.bzl:972:21: \r\nin workspace\r\n/home/nicola/Software/tensorflow/gpu/tensorflow/tensorflow/workspace2.bzl:706:20: \r\nin _tf_repositories\r\n/home/nicola/Software/tensorflow/gpu/tensorflow/third_party/repo.bzl:136:21: \r\nin tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n/home/nicola/Software/tensorflow/gpu/tensorflow/third_party/repo.bzl:89:35: \r\nin <toplevel>\r\nERROR: Analysis of target \r\n'//tensorflow/tools/pip_package:build_pip_package' failed; build aborted:\r\nINFO: Elapsed time: 88.076s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (388 packages loaded, 6429 \r\ntargets configured)\r\n\r\nOn 5/16/23 9:11 AM, Yeison Rodriguez wrote:\r\n>\r\n> I was not able to reproduce the error, my build succeeded. What is \r\n> your system arch?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub \r\n> <https://github.com/tensorflow/tensorflow/issues/60581#issuecomment-1549112759>, \r\n> or unsubscribe \r\n> <https://github.com/notifications/unsubscribe-auth/AAAIY7Y24PVXJBBXVXRPIVDXGMSATANCNFSM6AAAAAAX7IC5YE>.\r\n> You are receiving this because you authored the thread.Message ID: \r\n> ***@***.***>\r\n>\r\n", "Hi, Thanks for reporting the bug, Since we are trying to figure out if this is regression issue. \r\nCould you please help us with the below analysis and let us know if you see change in behavior.\r\n\r\nPlease test against the 2.12.0 version with below configurations.\r\n\r\n<h4 id=\"gpu\" data-text=\"GPU\" role=\"presentation\" style=\"box-sizing: inherit; margin: 32px 0px 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: ; font-stretch: ; font-size: 16px; line-height: ; font-family: Roboto, &quot;Noto Sans&quot;, &quot;Noto Sans JP&quot;, &quot;Noto Sans KR&quot;, &quot;Noto Naskh Arabic&quot;, &quot;Noto Sans Thai&quot;, &quot;Noto Sans Hebrew&quot;, &quot;Noto Sans Bengali&quot;, sans-serif; font-optical-sizing: ; font-kerning: ; font-feature-settings: ; font-variation-settings: ; letter-spacing: normal; overflow: hidden; text-overflow: ellipsis; margin-inline-end: -40px; padding-inline-end: 40px; color: rgb(32, 33, 36); orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"><span class=\"devsite-heading\" role=\"heading\" aria-level=\"4\" style=\"box-sizing: inherit;\">GPU</span><button type=\"button\" class=\"devsite-heading-link button-flat material-icons\" aria-label=\"Copy link to this section: GPU\" data-title=\"Copy link to this section: GPU\" data-id=\"gpu\" style=\"box-sizing: border-box; appearance: none; background: 0px center; border: 0px; border-radius: var(--devsite-button-border-radius,2px); box-shadow: none; color: var(--devsite-icon-color,var(--devsite-secondary-text-color)); cursor: pointer; display: inline-block; font-style: normal; font-variant-ligatures: ; font-variant-caps: ; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: normal; font-stretch: ; font-size: 24px; font-family: &quot;Material Icons&quot;; font-optical-sizing: ; font-kerning: ; font-feature-settings: &quot;liga&quot;; font-variation-settings: ; height: 24px; letter-spacing: normal; line-height: 1; margin: var(--devsite-button-margin,0); margin-inline-end: var(--devsite-button-margin-x-end); max-width: var(--devsite-button-max-width,none); min-width: 36px; outline: 0px; overflow: hidden; padding: 0px 8px; text-align: center; text-decoration: none; text-overflow: ellipsis; text-transform: none; transition: background-color 0.2s ease 0s, border 0.2s ease 0s, box-shadow 0.2s ease 0s; vertical-align: bottom; white-space: nowrap; width: var(--devsite-button-width,auto); overflow-wrap: normal; direction: ltr; -webkit-font-smoothing: antialiased; opacity: 0;\"></button></h4><div class=\"devsite-table-wrapper\" style=\"box-sizing: inherit; margin: var(--devsite-table-margin,16px 0); padding: 0px; overflow: auto; color: rgb(32, 33, 36); font-family: Roboto, &quot;Noto Sans&quot;, &quot;Noto Sans JP&quot;, &quot;Noto Sans KR&quot;, &quot;Noto Naskh Arabic&quot;, &quot;Noto Sans Thai&quot;, &quot;Noto Sans Hebrew&quot;, &quot;Noto Sans Bengali&quot;, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\">\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.12.0 | 3.8-3.11 | GCC 9.3.1 | Bazel 5.3.0 | 8.6 | 11.8\r\n\r\n</div>", "On MacOS, the error is related but slightly different:\r\n\r\nERROR: /Users/feranick/Desktop/tensorflow/tensorflow/BUILD:1134:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/feranick/Desktop/tensorflow/tensorflow/BUILD:1134:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build", "> Hi, Thanks for reporting the bug, Since we are trying to figure out if this is regression issue. Could you please help us with the below analysis and let us know if you see change in behavior.\r\n> \r\n> Please test against the 2.12.0 version with below configurations.\r\n> \r\n> #### GPU\r\n> Version\tPython version\tCompiler\tBuild tools\tcuDNN\tCUDA\r\n> tensorflow-2.12.0\t3.8-3.11\tGCC 9.3.1\tBazel 5.3.0\t8.6\t11.8\r\n\r\nOn Ubuntu 22.04. TF 2.12.0 fails just as well (using the parameters below). \r\ntensorflow-2.12.0\r\npython: 3.10.6\t\r\nGCC gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0\r\nBazel 5.3.0\r\ncuDNN: 8.6 (or nothing)\r\nCUDA: 11.8 (or nothing)\r\n(i.e. the issue is present regardless of using CUDA or not).\r\n\r\nWeirdly enough, it compiled just fine when released. I am not sure what changed, suggestions welcome.\r\n\r\nI am testing MacOS, will report shortly.", "> I'm trying to reproduce in Ubuntu 22.04 with all of the above conditions. I'll let you know if I'm able to.\r\n\r\nIf you are successful, can you please list your system configuration (including your list of python packages and version)?", "I was not able to reproduce it in a fresh docker environment.", "On MacOS 13.3.1, TF 2.12.0 compiles just fine. \r\n\r\ntensorflow-2.12.0\r\npython: 3.10.11\r\nApple clang version 14.0.3 (clang-1403.0.22.14.1)\r\nBazel 5.3.0", "[UPDATE] I finally figured out the issue with Ubuntu. Compilation fails when Cython is newer than 0.29.28 is installed (currently the latest is 0.29.32). Cython needs to be v0.29.28, which is also that provided by Ubuntu via apt. Maybe this should be listed in the recommended list of required software. \r\n\r\nI am still trying to identifying the issue with MacOS, which seems to be related (and yet compilation fails also with Cytin 0.29.28).", "[UPDATE 2 - MacOS] It turns out that for MacOS the issue is with realpath, as listed in issue #60179. While that issue is closed, the complete lack of documentation on how to fix it (which involves third-party libraries installed) in the documentation is still an issue. So for MacOS the issue is a duplicate of #60179, so I removed the MacOS from the issue title. It would be great to update the documentation to reflect the needs of realpath from Coreutils (via brew or macports) to successfully complete the build.", "For Ubuntu the issue is not a duplicate, but again, documentation needs to be updated with information on the correct version number for Cython.", "@feranick ,\r\n\r\nSorry for the late response. I have tried simple CPU build with r2.13 and its success. Is this issue is specific to any optimizations ? \r\n\r\n<img width=\"1500\" alt=\"r2 13 Ubuntu22 CPU build\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/e23a9b58-c85c-4ead-bc33-391eeef0d50e\">\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.", "Action is on Google here. I suggested a solution above. ", "@feranick ,\r\n\r\nFrom Tf2.13 onwards TF uses Clang 16.0.0 as compiler instead of GCC as per updated [documentation](https://www.tensorflow.org/install/source#linux). I will also test the build with Clang and update you results.", "Hi @feranick , Could you please try to compile it in the 2.13.0 or 2.13.0-rc2 and let us know if that works for you. Thanks!", "Yes, it compiles fine with 2.13.0. But that is not because of this specific release (compared to earlier rcs), but because, as mentioned above the use of the correct libraries. The issue is in the lack of any documentation of what version of cython is needed for compilation. Such info should be listed in the required libraries.", "Hi, \r\n\r\nWould it be possible for you to create a PR with the specific version of cython which worked for you.Thanks!", "As described above there is no change needed in TF, just making sure that Cython is in the correct version:\r\n\r\nCompilation fails when Cython is newer than 0.29.28 is installed (currently the latest is 0.29.32). Cython needs to be v0.29.28, which is also that provided by Ubuntu via apt. \r\n\r\nI am not sure what I should push as PR. Rather, this should be listed within the set of requirements for compilation...", "I have also not been able to build on lubuntu 22.04.3 using docker.io.\r\n\r\nI follow the instructions on https://www.tensorflow.org/install/source#linux\r\n\r\nThe following appears to work OK\r\n\r\ndocker pull tensorflow/tensorflow:devel\r\ndocker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS=\"$(id -u):$(id -g)\" \\\r\n tensorflow/tensorflow:devel bash\r\n\r\ngit pull \r\n\r\nHowever, when running ./configure it fails to see the clang directories\r\n\r\nroot@54f07d1ee3ba:/tensorflow_src# ./configure \r\nYou have bazel 6.1.0 installed.\r\nPlease specify the location of python. [Default is /usr/bin/python3]: \r\n\r\nFound possible Python library paths:\r\n /usr/lib/python3/dist-packages\r\n /usr/local/lib/python3.8/dist-packages\r\nPlease input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: \r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: \r\nNo CUDA support will be enabled for TensorFlow.\r\n\r\nDo you want to use Clang to build TensorFlow? [Y/n]: Y \r\nClang will be used to compile TensorFlow.\r\n\r\nPlease specify the path to clang executable. [Default is ]: \r\n\r\n\r\nIf I try to compile not using clang it does download and start to compile but fails at many points.\r\n\r\nBazel command used is either:\r\n\r\nbazel build --config=opt --config=v2 //tensorflow/tools/pip_package:build_pip_package\r\n\r\nor \r\n\r\nbazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\r\n\r\nWhen it starts is tells me python 3.9 chosen.\r\n\r\nI am compiling this on an old pre avx system.\r\n\r\n\r\n", "Don't use clang when asked. Should work.\n\nOn 9/12/23 9:43 AM, cuchio wrote:\n>\n> I have also not been able to build on lubuntu 22.04.3 using docker.io.\n>\n> I follow the instructions on \n> https://www.tensorflow.org/install/source#linux\n>\n> The following appears to work OK\n>\n> docker pull tensorflow/tensorflow:devel\n> docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS=\"$(id \n> -u):$(id -g)\"\n> tensorflow/tensorflow:devel bash\n>\n> git pull\n>\n> However, when running ./configure it fails to see the clang directories\n>\n> ***@***.***:/tensorflow_src# ./configure\n> You have bazel 6.1.0 installed.\n> Please specify the location of python. [Default is /usr/bin/python3]:\n>\n> Found possible Python library paths:\n> /usr/lib/python3/dist-packages\n> /usr/local/lib/python3.8/dist-packages\n> Please input the desired Python library path to use. Default is \n> [/usr/lib/python3/dist-packages]\n>\n> Do you wish to build TensorFlow with ROCm support? [y/N]:\n> No ROCm support will be enabled for TensorFlow.\n>\n> Do you wish to build TensorFlow with CUDA support? [y/N]:\n> No CUDA support will be enabled for TensorFlow.\n>\n> Do you want to use Clang to build TensorFlow? [Y/n]: Y\n> Clang will be used to compile TensorFlow.\n>\n> Please specify the path to clang executable. [Default is ]:\n>\n> If I try to compile not using clang it does download and start to \n> compile but fails at many points.\n>\n> Bazel command used is either:\n>\n> bazel build --config=opt --config=v2 \n> //tensorflow/tools/pip_package:build_pip_package\n>\n> or\n>\n> bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\n>\n> When it starts is tells me python 3.9 chosen.\n>\n> I am compiling this on an old pre avx system.\n>\n> —\n> Reply to this email directly, view it on GitHub \n> <https://github.com/tensorflow/tensorflow/issues/60581#issuecomment-1715753213>, \n> or unsubscribe \n> <https://github.com/notifications/unsubscribe-auth/AAAIY76YGMCL6OPMGTQL6HLX2BRHLANCNFSM6AAAAAAX7IC5YE>.\n> You are receiving this because you were mentioned.Message ID: \n> ***@***.***>\n>\n\n--------------uhVVUHL7PrMAlHCnksw33Bcd\nContent-Type: text/html; charset=UTF-8\nContent-Transfer-Encoding: 8bit\n\n<!DOCTYPE html><html><head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\">\n </head>\n <body>\n Don't use clang when asked. Should work.<br>\n <br>\n <div class=\"moz-cite-prefix\">On 9/12/23 9:43 AM, cuchio wrote:<br>\n </div>\n <blockquote type=\"cite\" ***@***.***\">\n \n <p dir=\"auto\">I have also not been able to build on lubuntu\n 22.04.3 using docker.io.</p>\n <p dir=\"auto\">I follow the instructions on <a href=\"https://www.tensorflow.org/install/source#linux\" rel=\"nofollow\" moz-do-not-send=\"true\" class=\"moz-txt-link-freetext\">https://www.tensorflow.org/install/source#linux</a></p>\n <p dir=\"auto\">The following appears to work OK</p>\n <p dir=\"auto\">docker pull tensorflow/tensorflow:devel<br>\n docker run -it -w /tensorflow_src -v <math-renderer class=\"js-inline-math\" style=\"display: inline\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"b0d76be046d4ad734683225467a7b462\">$PWD:/mnt -e\n HOST_PERMS=&quot;$</math-renderer>(id -u):$(id -g)&quot; <br>\n tensorflow/tensorflow:devel bash</p>\n <p dir=\"auto\">git pull</p>\n <p dir=\"auto\">However, when running ./configure it fails to see\n the clang directories</p>\n <p ***@***.***:/tensorflow_src# ./configure<br>\n You have bazel 6.1.0 installed.<br>\n Please specify the location of python. [Default is\n /usr/bin/python3]:</p>\n <p dir=\"auto\">Found possible Python library paths:<br>\n /usr/lib/python3/dist-packages<br>\n /usr/local/lib/python3.8/dist-packages<br>\n Please input the desired Python library path to use. Default is\n [/usr/lib/python3/dist-packages]</p>\n <p dir=\"auto\">Do you wish to build TensorFlow with ROCm support?\n [y/N]:<br>\n No ROCm support will be enabled for TensorFlow.</p>\n <p dir=\"auto\">Do you wish to build TensorFlow with CUDA support?\n [y/N]:<br>\n No CUDA support will be enabled for TensorFlow.</p>\n <p dir=\"auto\">Do you want to use Clang to build TensorFlow? [Y/n]:\n Y<br>\n Clang will be used to compile TensorFlow.</p>\n <p dir=\"auto\">Please specify the path to clang executable.\n [Default is ]:</p>\n <p dir=\"auto\">If I try to compile not using clang it does download\n and start to compile but fails at many points.</p>\n <p dir=\"auto\">Bazel command used is either:</p>\n <p dir=\"auto\">bazel build --config=opt --config=v2\n //tensorflow/tools/pip_package:build_pip_package</p>\n <p dir=\"auto\">or</p>\n <p dir=\"auto\">bazel build --config=opt\n //tensorflow/tools/pip_package:build_pip_package</p>\n <p dir=\"auto\">When it starts is tells me python 3.9 chosen.</p>\n <p dir=\"auto\">I am compiling this on an old pre avx system.</p>\n <p style=\"font-size:small;-webkit-text-size-adjust:none;color:#666;\">—<br>\n Reply to this email directly, <a href=\"https://github.com/tensorflow/tensorflow/issues/60581#issuecomment-1715753213\" moz-do-not-send=\"true\">view it on GitHub</a>, or <a href=\"https://github.com/notifications/unsubscribe-auth/AAAIY76YGMCL6OPMGTQL6HLX2BRHLANCNFSM6AAAAAAX7IC5YE\" moz-do-not-send=\"true\">unsubscribe</a>.<br>\n You are receiving this because you were mentioned.<img src=\"https://github.com/notifications/beacon/AAAIY7YJCZC4WSJB5N72WK3X2BRHLA5CNFSM6AAAAAAX7IC5YGWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTTGIRIP2.gif\" alt=\"\" moz-do-not-send=\"true\" width=\"1\" height=\"1\"><span style=\"color: transparent; font-size: 0; display: none; visibility: hidden; overflow: hidden; opacity: 0; width: 0; height: 0; max-width: 0; max-height: 0; mso-hide: all\">Message\n ID: <span>&lt;tensorflow/tensorflow/issues/60581/1715753213</span><span>@</span><span>github</span><span>.</span><span>com&gt;</span></span></p>\n <script type=\"application/ld+json\">[\n{\n***@***.***\": \"http://schema.org\",\n***@***.***\": \"EmailMessage\",\n\"potentialAction\": {\n***@***.***\": \"ViewAction\",\n\"target\": \"https://github.com/tensorflow/tensorflow/issues/60581#issuecomment-1715753213\",\n\"url\": \"https://github.com/tensorflow/tensorflow/issues/60581#issuecomment-1715753213\",\n\"name\": \"View Issue\"\n},\n\"description\": \"View this Issue on GitHub\",\n\"publisher\": {\n***@***.***\": \"Organization\",\n\"name\": \"GitHub\",\n\"url\": \"https://github.com\"\n}\n}\n]</script>\n </blockquote>\n <br>\n </body>\n</html>\n\n--------------uhVVUHL7PrMAlHCnksw33Bcd--\n", "To be clear: You can install clang (from Ubuntu's repos via apt), it will be recognized, and compilation will start. However, it will fail later on for the lack of the atomic library (if I remember correctly). \r\n\r\nEasiest path is not to use clang for compilation.", "I have tried, but it does still have problems. Many stops in build occurs.", "> I have tried, but it does still have problems. Many stops in build occurs.\r\n\r\nFile separate and detailed issue reports.", "Will do.\r\n\r\nFYI, I do have the clang libraries on my machine.\r\n", "Have you tried TF2.14.0-rc?" ]

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[]

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[ "Since you are using .tflite model, I assume what you used was the the [tflite label_image](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/label_image) rather than the tf one. If you run `./label_image -h`, you can find\r\n```\r\n--threads, -t: number of threads\r\n```\r\n\r\nHowever, setting the number of threads to be > 4 may not be what you want. As far as I know, no 8-core Android devices have homogeneous cores, but usually multi-thread libraries do not deal with non-homogeneous cores well. ", "Hi @megleo \r\n\r\nCould you please refer the above [comment](https://github.com/tensorflow/tensorflow/issues/60579#issuecomment-1545062464) and let us know if it helps.\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/60579\">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/60579\">No</a>\n" ]

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[ "Hi @p3achyjr ,\r\n\r\nStarting from TF2.13v, Linux builds are being built with Clang and the instructions for same also can be found [here](https://www.tensorflow.org/install/source#install_clang_recommended_linux_only). \r\n\r\nYou may try the build for Tf21.3 and let us know the outcome. Thanks.\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/60578\">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/60578\">No</a>\n" ]

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[ "Hi @aquirin ,\r\n\r\nGCC 13.1.0 not a tested configuration for TF2.12 and I am not sure of compatibility as it is not yet tested by TF team atleast for Tf2.12 version. Please find the Tested build configurations source [here](https://www.tensorflow.org/install/source#linux).\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools\r\n-- | -- | -- | --\r\ntensorflow-2.12.0 | 3.8-3.11 | GCC 9.3.1 | Bazel 5.3.0\r\n\r\n\r\nI request you to test with above configurations and let us know if still having problem. Thanks!\r\n", "Hi @SuryanarayanaY ,\r\n\r\nThank you for the details. Yes GCC 13.1.0 is probably too recent. I compiled successfully TF-2.11 with GCC-9.3 and it is working fine. I will close this ticket,\r\n\r\nThanks!\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/60577\">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/60577\">No</a>\n", "simple fix by including the header\r\n\r\n```\r\ndiff --git a/tensorflow/tsl/lib/io/cache.h.orig b/tensorflow/tsl/lib/io/cache.h\r\nindex f894c59..7b1cf32 100644\r\n--- a/tensorflow/tsl/lib/io/cache.h.orig\r\n+++ b/tensorflow/tsl/lib/io/cache.h\r\n@@ -17,6 +17,7 @@ limitations under the License.\r\n #define TENSORFLOW_TSL_LIB_IO_CACHE_H_\r\n \r\n #include \"tensorflow/tsl/platform/stringpiece.h\"\r\n+#include <cstdint>\r\n \r\n // A Cache is an interface that maps keys to values. It has internal\r\n // synchronization and may be safely accessed concurrently from\r\n```", "> simple fix by including the header\r\n\r\n> +#include <cstdint>\r\n\r\nbut does this break builds on GCC 9.3.1?\r\n\r\n", "> > simple fix by including the header\r\n> \r\n> > +#include\r\n> \r\n> but does this break builds on GCC 9.3.1?\r\n\r\nno" ]

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[ "Hi, Thanks for reporting the issue.\r\n\r\nTensorflow has migrated it's codebase to support C++ 17 and it is suggested for the users to follow the same.\r\n\r\nIs there any challenge to use C++ 17 in your use case?\r\n ", "In our case, building tflite itself with C++ 17 is fine, but we're not ready to move our codebase to C++17 because some of our customers are still locked to earlier versions, so the fact that these changes are in a header that we have to include is the main issue of concern.\r\n\r\nGiven that this is just syntactic sugar on namespace declaration and the majority of TF Lite headers don't use this style, it would be great to either revert these namespace changes or use the pre C++ 17 syntax.", "Hi @nathanmartz, apologies, but we don't intend to revert our support for C++17 moving forward, as such we cannot make this change. If you are able to use v2.11 or earlier, I suggest you do so. Alternatively, you are free to fork the repo and maintain your own version with your required changes. If there are no more open items for this, please feel free to close as not planned.", "Understood. thanks for the clarification.", "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/60576\">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/60576\">No</a>\n" ]

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[ "Hi @oniigiirii \r\n\r\nI have tried to reproduce the issue and observed that model has INT8 qunatization which can be analyzed with [TensorFlow Lite Model Analyzer](https://www.tensorflow.org/lite/guide/model_analyzer) and netron.\r\n```\r\nTensors of Subgraph#0\r\n T#0(serving_default_input_1:0) shape_signature:[-1, 1], type:INT8\r\n T#1(sequential/dense_2/MatMul) shape:[1, 16], type:INT8 RO 16 bytes, buffer: 2, data:[., ., ., ., r, ...]\r\n T#2(sequential/dense_1/MatMul) shape:[16, 16], type:INT8 RO 256 bytes, buffer: 3, data:[., ., 8, ., -, ...]\r\n T#3(sequential/dense/MatMul1) shape:[16, 1], type:INT8 RO 16 bytes, buffer: 4, data:[:, |, ., ., ., ...]\r\n T#4(sequential/dense/MatMul;sequential/re_lu/Relu;sequential/dense/BiasAdd) shape_signature:[-1, 16], type:INT8\r\n T#5(sequential/dense_1/MatMul;sequential/re_lu_1/Relu;sequential/dense_1/BiasAdd) shape_signature:[-1, 16], type:INT8\r\n T#6(StatefulPartitionedCall:0) shape_signature:[-1, 1], type:INT8\r\n```\r\nPlease find the [gist](https://colab.research.google.com/gist/pjpratik/fca14d42222bb05454921de63d44714a/60574.ipynb) here and let us know if it helps.\r\n\r\nThanks.\r\n ", "Oh. Thank you. The 'fully_quantize: 0' included in the warning message confused me and made me think that the converter failed at doing a full integer quantization. \r\nI tested the model with int8 inputs and it worked as expected.", "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/60574\">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/60574\">No</a>\n" ]

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[ "@meena-at-work can you give it a look ?", "> @meena-at-work can you give it a look ?\r\n\r\nI'm just going to add some notes to make everything more clear, then will mark the PR as ready for review in a few minutes, thanks!", "@meena-at-work @DEKHTIARJonathan All ready now, please take a look when you can, thanks!", "Hi @meena-at-work / @DEKHTIARJonathan Can you please review this PR ? Thank you!", "Hi @meena-at-work / @DEKHTIARJonathan Can you please review this PR ? Thank you!", "Hi @aboubezari, @meena-at-work, @DEKHTIARJonathan I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you!" ]

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[ "Thanks for reporting the issue.\r\n\r\nCould you please let us know the reason behind converting the model to Tensorflow 1.x version as you mentioned here \r\n\r\n> `convert the LLM from Huggingface to Tensorflow v1.x`", "\r\n\r\n\r\n> Thanks for reporting the issue.\r\n> \r\n> Could you please let us know the reason behind converting the model to Tensorflow 1.x version as you mentioned here\r\n> \r\n> > `convert the LLM from Huggingface to Tensorflow v1.x`\r\n\r\nYes absolutely. We want to do int8 quantization on some large language models (LLM) such as Facebook OPT and GPT, so we download the models from Huggingface, which are in Keras format. The path we use is to convert the Keras formatted LLMs to SavedModel, parse the GraphDef and insert our quantization steps.\r\n\r\nThe problem we met is that we need to freeze the concrete function of that SavedModel to do further parsing and int8 quantization. The error occurred at https://github.com/intel/neural-compressor/blob/master/neural_compressor/model/tensorflow_model.py#L277. One related question would be https://stackoverflow.com/questions/60974077/how-to-save-keras-model-as-frozen-graph. Just as what is done shown in the example, we called `func = model.get_concrete_function()` and invoke the `convert_variables_to_constants_v2(func)`, and when we do the `convert_variables_to_constants_v2`, we met this protobuf exceed max size problem. (*** RuntimeError: size too big: 18446744072513763888 details: string length exceeds max size). However for much smaller model, we verify that we did not meet that problem. (GPT2-large failed, but GPT2-medium succeeded)\r\n\r\nI have no idea on the number 18446744072513763888 and this looks super big. I do not think it is the size of a model or weights. The only hint is that this number is very close to 2^64. However the GPT2-large on Huggingface only occupies about 3GB, which is far less than 2^64 bit. Another hint is that the `convert_variables_to_constants_v2` (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/convert_to_constants.py#L1138) expect models with no control flow or embedding related ops. However it is out of my knowledge scope.", "Thanks for the clear explanation, The above steps which you have mentioned can be achieved through latest Tensorflow version 2.12.\r\nWe don't encourage users to use Tensorflow 1.x version, since it is out of the support window. We don't have any plan to fix such old versions. \r\nFor Int8 quantization, you can refer the guide here https://www.tensorflow.org/lite/performance/post_training_quantization.\r\n\r\nFor converting your model to TFLite specific format, you can refer the guide here https://www.tensorflow.org/lite/models/convert#input_model_formats.\r\nSaving the model saves the file in .pb format also.\r\nPlease try with the latest version and let us know the outcome of it. Thanks!", "Hi Sachin, TFLite is not the same path with ours, and I wonder whether there are any examples of TFLite to quantize models that have more than 1 billion parameters. Anyway, thanks for suggestions.", "Hi @Spycsh , For `LLM` models you can refer to our new tutorial which was published in our Google I/O annual developer conference.\r\nhttps://www.tensorflow.org/lite/examples/auto_complete/overview\r\nThis tutorial walks you though Auto complete based on [PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) model, fine tuned on 1.5 Billion parameters.\r\n\r\nThis model is based on `Keras NLP` and then converted to `TFLite` and TFLite runtime.\r\nThis shows the quantization details and can be quantized into `Dynamic`, `FP16`, `Full Integer Quantization` specific. \r\n\r\nHope this helps you. Thank you!", "Hi @sachinprasadhs , thanks for sharing! But it seems the first link needs a google SSO login and is invisible to outside. Is that tutorial opensource?", "My bad, I updated the public link, PTAL. Thanks!", "Thanks! We will check that.", "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/60571\">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/60571\">No</a>\n" ]

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[ "I trying to install tensorflow by anaconda but only has 2.10 version, miniconda only as 2.11 version, pip install has bugs when import tensorflow: zsh: illegal hardware instruction python\r\n", "I'd recommend installing tensorflow-deps using conda first and then installing other stuff using pip.\r\n1. `conda install -c apple tensorflow-deps`\r\n2. `pip install -r requirements.txt`\r\n\r\nrequirements.txt containing: \r\n```\r\ntensorflow-macos\r\ntensorflow-metal\r\njupyter\r\n# other packages\r\n...\r\n```", "> I'd recommend installing tensorflow-deps using conda first and then installing other stuff using pip.\r\n> \r\n> 1. `conda install -c apple tensorflow-deps`\r\n> 2. `pip install -r requirements.txt`\r\n> \r\n> requirements.txt containing:\r\n> \r\n> ```\r\n> tensorflow-macos\r\n> tensorflow-metal\r\n> jupyter\r\n> # other packages\r\n> ...\r\n> ```\r\n\r\nThanks a lot, but where can I get requirements.txt? are there any official one?", "@Spartanzhao,\r\nI tried to install Tensorflow on Apple M1 oand it is working, so you can install Tensorlflow by using one of the Conda, Miniconda or Miniforge approach. I tried to follow [Get started with tensorflow-metal](https://developer.apple.com/metal/tensorflow-plugin/) with Miniconda3 instructions. \r\nCould you please try with `arm64:Apple silicon`\r\n\r\nDuring the instructions [Download Conda environment ](https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh) Miniconda3-latest-MacOSX-arm64.sh file will be downloaded and replace **.sh file** with miniconda.sh in command bash **~/miniconda.sh -b -p $HOME/miniconda** as mentioned in first step \r\n1. Set up and please follow all steps from\r\n 2. Install base TensorFlow as below:\r\n\r\n\r\n```\r\nbash ~/Miniconda3-latest-MacOSX-arm64.sh -b -p $HOME/miniconda\r\nsource ~/miniconda/bin/activate\r\nconda install -c apple tensorflow-deps\r\n```\r\nPlease use the command source **~/miniconda/bin/activate** if you want to use/activate Miniconda environment\r\n\r\nIf you are specifically looking to go with Miniforge approach then please have a look at this [link](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) and for [Miniconda-Article](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) and also please refer [Releases-Table](https://developer.apple.com/metal/tensorflow-plugin/#:~:text=Releases-,tensorflow%2Dmacos,Distributed%20training,-Troubleshooting)\r\n\r\nhttps://developer.apple.com/metal/tensorflow-plugin/ -- reference. Thank you!", "Thank you, I think I had solved the issue!", "@Spartanzhao,\r\nGlad the issue got resolved. Could you please feel free to move this issue to closed status. Thank you!", "Okay!", "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/60569\">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/60569\">No</a>\n", "> Thank you, I think I had solved the issue!\r\n\r\n@Spartanzhao How did you solve it?\r\n\r\nI have a conda environment with tensorflow installed; but still getting `zsh: illegal hardware instruction python desc_classifier.py` when I import tensorflow" ]

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