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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/60421/checks?check_run_id=13058540072) 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.", "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ " ]

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[ "Hi @gzmkl ,\r\n\r\nAs per Official documentation for building from source on Ubuntu OS, we have to use GCC compiler. Please confirm the GCC compiler version that you are using. Also currently master branch tested for bazel version 5.3.0. Please use this version only and higher versions may have compatibility issues.\r\n\r\nYou can find the tested configurations of TF with GCC, Bazel etc for Linux OS [here](https://www.tensorflow.org/install/source#linux).\r\n\r\nPlease update to tested configurations and let us know if having same problem with all the logs. Thanks!", "Hi @gzmkl ,\r\n\r\nThe issue is probably some kind of build cache corruption that comes from Bazel and can be fixed by changing the build options. Could you please check the cpu/gpu bazelrc files [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools) and try to update the build command for testing? \r\n\r\nPlease let us know if you have any further questions. Thanks! \r\n", "@kanglant Thanks for the pointers. We do not use remote_cache. However, with some experiments the following build command worked for us.\r\n`bazel build --features=-layering_check --copt=-Wno-gnu-offsetof-extensions -c opt //tensorflow/tools/pip_package:build_pip_package`\r\n\r\nCould you please comment on `--features=-layering_check`. Is there any implication in performance in doing so? How to avoid this flag?", "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 @mdfaijul ! Sorry for the delayed response. I was able to fix the same error on Linux by setting the environment variables CC and BAZEL_COMPILER to the actual path to clang-16, e.g., /usr/lib/llvm-16/bin/clang. My build command was:\r\n\r\n`bazel build -s --verbose_failures --copt=-Wno-gnu-offsetof-extensions //tensorflow/tools/pip_package:build_pip_package`\r\n\r\nWith `-s`, you can see the full command line for each command prior to executing it. \r\n\r\nCould you please try this method and see if it fixes the error? Some other optional env variables are `CLANG_CUDA_COMPILER_PATH`, `HOST_CXX_COMPILER`, and `HOST_C_COMPILER`. Bazel does not do well with symbolic links, so please use the actual path to clang. \r\n\r\nGiven a target T with `layering_check` enabled and a source S in T, the build will fail if S #includes a header that is not part of T itself or listed in the hdrs. We should not disable it without a good reason. ", "Hi @gzmkl ,\r\n\r\nApologies for the delayed response. Starting from TF2.13v tensorflow uses Clang16 as compiler for Linux and the instructions are updated in the official [documentation](https://www.tensorflow.org/install/source#install_clang_recommended_linux_only).\r\n\r\nCould you please confirm whether the above instructions can be helpful. Please confirm if this is still an issue. Thanks!", "Hello @SuryanarayanaY @kanglant . I tried building the TF2.13v (Release tag commit) tensorflow from source using the clang-16 compiler and I run into same issue as mentioned in the description of this ticket.\r\n\r\nI see same issue both on Intel ICELAKE (x86) and also on GRAVITON (Arm) with the latest TF.\r\nHowever, we don't face any issues when we use GCC compiler both on x86 and ARM.\r\n\r\nThis looks like a bug with BAZEL + CLANG.\r\n\r\nCan this issue be addressed?\r\n\r\nThis issue is been there for quite a while and I have seen many people complaining about the same issue even with previous clang versions with TF.\r\n\r\n@mdfaijul Hack to use --features=-layering_check flag with the build helps. But is there any implication in using this?\r\nThis could be a temporary workaround.", "> bazel build -s --verbose_failures --copt=-Wno-gnu-offsetof-extensions //tensorflow/tools/pip_package:build_pip_package\r\n\r\n@kanglant This doesn't resolve the issue. I made sure, I set the actual path to clang and also added the config -Wno-gnu-offsetof-extensions", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "@SuryanarayanaY @kanglant \r\nAny further comments from your end will help us to fix this compilation issue. Also let me know, if this is something we have to report it to the LLVM/CLANG community.", "Hello @mraunak, could you please share some details on how you fix this issue? See the above comment. ", "@kanglant With TensorFlow master branch, and on Linux, try \r\n 1. Set up a couple of environment variables\r\n export CC=/usr/lib/llvm-16/bin/clang\r\n export CXX=/usr/lib/llvm-16/bin/clang++\r\n Note: you need adjust RHS with the path where your clang (in my case version 16) is installed\r\n2. Add the following option with your bazel build command\r\n --copt=-Wno-gnu-offsetof-extensions " ]

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[ "Hi @meghendra ,\r\n\r\nThanks for your time in bringing this. Basically `tf.raw_ops` not meant for end users but for library writers and hence documentation is not much exhaustive. There is equivalent high level API for this which is [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather). \r\n\r\nHowever your pointed information is related to Argument description I agree to that it should have some explanation on argument. Hence I can add the same description that is there in `tf.gather` API for the argument `batch_dims` which is mentioned below.I would like to append this line to existing description.\r\n\r\n`The number of batch dimensions. Must be less than or equal to rank(indices)`\r\n\r\nThanks!" ]

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[ "@cantonios This commit has caused a build issue with --config=mkl on avx2 build\r\nhttps://github.com/tensorflow/tensorflow/commit/bc5f83612c6b4b96b652ac60e4138c61cbdb5fc3", "Thanks for letting me know. We don't see the error internally since it only breaks with gcc, not clang.", "Eigen update reverted", "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/60417\">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/60417\">No</a>\n" ]

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[ "Hi @zehao-intel \r\n\r\nThe Value Error occurs if input_map, or return_elements contains names that do not appear in graph_def, or graph_def is not well-formed (e.g. it refers to an unknown tensor).\r\n\r\nAlso, the Saved Model serialisation format is only supported and the TFLite conversion from frozen graph is deprecated.\r\n\r\nCould you please try `from_saved_model` if you are trying convert a saved model from TFLite.\r\n\r\nThanks.\r\n\r\n\r\n\r\n", "Hi pjpratik，\r\n\r\nThanks for your explanation and suggestions. They are very helpful!\r\nIt seems that 'from_saved_model' only convert a saved_model to TFLite model.\r\nI would like to get a correct graph_def from the saved_model. Is that possible to achieve this by using TFLite API?", "Hi @zehao-intel \r\n\r\nAs per [documentation](https://www.tensorflow.org/lite/models/convert/convert_models), the TFLite API supports converting a TensorFlow model to a TensorFlow Lite mode using `saved_model` format which is [recommended](https://www.tensorflow.org/lite/models/convert/convert_models#convert_a_savedmodel_recommended_) by TFLite. \r\n\r\nConverting from `frozen_graph` is no longer supported in TF 2.x but can be accessed using `tf.compat.v1.lite.TFLiteConverter.from_frozen_graph` . \r\n\r\nThanks.\r\n\r\n", "Hi @pjpratik ,\r\n\r\nI have tried ```from_saved_model``` with the following code:\r\n```\r\nimport tensorflow as tf\r\nimport tensorflow_text\r\n\r\nsaved_model_dir = './universal-sentence-encoder-multilingual_3'\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\r\ntflite_model = converter.convert()\r\nwith open('model.tflite', 'wb') as f:\r\n f.write(tflite_model)\r\n```\r\nBut an error occurs:\r\n\r\nIt seems that some binary contents is printed and the execution is stopped at ```converter.convert()```.\r\nCould you please look into it?\r\n\r\nThanks\r\n", "Hi @zehao-intel \r\n\r\nThe TensorFlow Lite builtin operator library only supports a limited number of TensorFlow operators, not every model is convertible. To allow conversion, we can enable the usage of [certain TensorFlow ops](https://www.tensorflow.org/lite/guide/op_select_allowlist) in their TensorFlow Lite model by using following syntax\r\n\r\n```\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n]\r\n```\r\nPlease find the working gist [here](https://colab.research.google.com/gist/pjpratik/173bdfd2355e539ed24c70352e110186/60416.ipynb) and let us know if it helps.\r\n\r\nThanks.", "Hi @pjpratik ,\r\n\r\nMany thanks to your comments.\r\nI have tried your code and inspected the generated model.tflite with netron. I believe the model is correctly converted.\r\nBy the way, I made some investigation. It seems that there is no official way to get graph_def from a TFLite model.\r\nIn fact, I still need to find out how to get graph_def from the original saved_model. I have tried many ways, including the tf lite code I mentioned before, but none of them works.\r\nCould you give me some suggestions about this?\r\n\r\nThank!", "Hi @zehao-intel \r\n\r\nGetting a graph def from tflite is not supported. For getting a frozen graph from checkpoints you can try https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py\r\n\r\nAn example of command-line usage is:\r\n```\r\nbazel build tensorflow/python/tools:freeze_graph && \\\r\nbazel-bin/tensorflow/python/tools/freeze_graph \\\r\n--input_graph=some_graph_def.pb \\\r\n--input_checkpoint=model.ckpt-8361242 \\\r\n--output_graph=/tmp/frozen_graph.pb --output_node_names=softmax\r\n\r\n```\r\nThanks.\r\n\r\n", "Hi @pjpratik ,\r\n\r\nIt's happy to hear that there is a stable method to get a ```frozen graph``` from ```checkpoints```.\r\nBut for this model, it is in ```saved_model``` format.\r\nI saw some arguments related with ```saved_model``` in the script you provided.\r\nIs there a similar usage of getting a ```frozen graph``` from ```saved_model``` with this script?\r\n\r\nThanks", "Hi @zehao-intel \r\n\r\nImporting from tensorflow.python or any other modules not supported, and can break unannounced as anything under tf.python.* is private, intended for development only, rather than for public use.\r\n\r\nAs mentioned earlier, frozen graphs are deprecated and using Saved Model format is recommended.\r\n\r\nIs there any challenge to use Saved Model in your use case?\r\n\r\nThanks.", "Hi @pjpratik,\r\n\r\nThanks for your explanation. So TensorFlow don't recommend to use ```frozen graphs``` in version 2.X.\r\nThe task I am handled is to apply 8bit quantization to the TF model mentioned above. And I need to firstly get the frozen graph from this model so that I can modify the model by change the Op type and fuse Op patterns. I am not able to apply this process to a ```saved_model```.\r\n\r\nThanks.", "Hi @zehao-intel \r\n\r\nSorry for delayed response.\r\n\r\nWe can apply 8 bit quantization while converting the SavedModel to TFLite model by adding these flags:\r\n\r\n```\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\nconverter.inference_input_type = tf.uint8\r\nconverter.inference_output_type = tf.uint8\r\n```\r\nPlease refer to this [documentation](https://www.tensorflow.org/lite/performance/post_training_integer_quant#convert_using_integer-only_quantization_) on converting using integer only quantization 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.", "Hi @pjpratik ，\r\n\r\nSorry for the late reply.\r\nI think this Issue could be closed.\r\nReally appreciate your patient comments!", "Thanks for the confirmation.\r\n\r\nClosing this issue. Please feel free to reopen if you'd like to work on this further." ]

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[ "Hi @ajithkumarmcw \r\n\r\nPython binding actually uses the C++ implementation of Tensorflow Lite. Considering the Python runtime overhead itself, It is expected, under the same running environment, the Python performance should be no better than that of the C++ one.\r\n\r\nBtw, you could use the [TFLite benchmark tool](https://www.tensorflow.org/lite/performance/measurement) to measure the performance of your model. \r\n\r\nAlso, can you test the same in latest 2.12 version?\r\n\r\n\r\nThanks.", "@pjpratik my issue is not with performance its with the output which i am getting \r\n\r\nThis is the code and model files i am using https://github.com/ajithkumarmcw/Yolo_tflite_detection , i am not getting any error but the predictions are almost wrong.\r\nIn python the tflite model files are working fine\r\nBelow are the cpp inference outputs for the tflite models generated from yolov5n.pt/yolov8n.pt files which i took from Yolov5 and Yolov8 repo repectively\r\n\r\nEnvironment: tensorflow==2.4.2, Ubuntu==20.04,bazel==3.7.2, gcc==9.4.0\r\n\r\n- case1: For Yolov5 int 8 tflite model i am getting below output its fine\r\n<img width=\"490\" alt=\"snap_yolov5_int8\" src=\"https://user-images.githubusercontent.com/84714146/235165051-64510b5b-3c6b-4d61-891a-9ae9d8430e6a.png\">\r\n\r\n- case2: For Yolov5 float 16 tflite model i am getting below output and its predictions, labels, bboxes are wrong\r\n<img width=\"494\" alt=\"snap_yolov5_float16\" src=\"https://user-images.githubusercontent.com/84714146/235165249-1476151d-c96c-4468-8059-47e0c6b3cdc6.png\">\r\n\r\n\r\n- case3: For Yolov8 int8 tflite model i am getting below output and its predictions, labels, bboxes are wrong\r\n\r\n<img width=\"488\" alt=\"snap_yolov8_int8\" src=\"https://user-images.githubusercontent.com/84714146/235165341-dd5d2d68-2b0e-470c-9d04-de2a7a99c51e.png\">\r\n\r\n- case4: For Yolov8 float 32 tflite model i am getting below output and its predictions, labels, bboxes are wrong\r\n\r\n<img width=\"488\" alt=\"snap_yolov8_float32\" src=\"https://user-images.githubusercontent.com/84714146/235165381-c658b5e9-3608-4c68-9b72-d0c461a860d1.png\">\r\n\r\nI am not sure why the predictions are correct for Yolov5 uint8 but its failing for other three cases", "Hi @ajithkumarmcw, can you provide the video file that produced the above results? I was able to compile on the latest TF code by d/ling your code and modifying where the TF source directory was coming from but it's now asking me for an input video and output video file, would you mind sharing the input video which produced the above frames? A very short video w/ just the offending frame would be preferred or any other format that is more easily shareable (and short/small):\r\n\r\n```\r\nls\r\nabseil-cpp CMakeFiles debug.sh fft2d FP16-download FXdiv-source ml_dtypes psimd-source release.sh tmp\r\nbuildtests.sh cmake_install.cmake _deps flatbuffers FP16-source gemmlowp neon2sse pthreadpool ruy xnnpack\r\ncheck.sh compile_commands.json eigen flatbuffers-flatc FXdiv main psimd pthreadpool-download src\r\nCMakeCache.txt cpuinfo farmhash FP16 FXdiv-download Makefile psimd-download pthreadpool-source tensorflow-lite\r\n(base) xxxxxxx@pklinuxmaster:~/git/Yolo_tflite_detection/build$ ./main\r\n\r\nError! Usage: <path to tflite model> <path to classes names> <path to input video> <path to output video>\r\n```\r\n\r\nalso what is the \"path to classes names\" referring to.. is that a text file which you have?\r\n\r\nThanks for your help.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "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/60415\">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/60415\">No</a>\n", "Hi, @ajithkumarmcw ,\r\n\r\ncan i check if you were able to solve this issue? I am doing a similar project and experiencing the same problem. Do you mind sharing with me your code?", "@Thunderzen the problem was in the tflite inference code . i was not reshaping the image, was not preprocessing as per yolov8, and i was using the same script for both yolov5 and v8 so the the bounding boxes were not correct i forgot exact reason but it was like this . U can check this code https://github.com/ajithkumarmcw/Yolo_tflite_detection/tree/main/src or any tflite yolov8 code in github.", "And the output of yolov5 and yolov8 models are different so you can check for some inference script specifically written for yolov8", "Hi @ajithkumarmcw, do you have the code for yolov8 instead? I am having troubles with the post-processing of the outputs? Also, I can't seem to find implementations by others on github. Most seemed to be for the Python version and I have completed the Python code but the c++ implementation is quite different. Thanks!", "Sorry i did it for my office so i cant share it but you can check below\r\nhttps://github.com/JustasBart/yolov8_CPP_Inference_OpenCV_ONNX\r\nhttps://github.com/Li-99/yolov8_onnxruntime\r\nhttps://github.com/ultralytics/ultralytics/tree/main/examples/YOLOv8-ONNXRuntime-CPP\r\nhttps://github.com/yide1235/YOLOv8-Inference-TFLite-Openvino/tree/main\r\n\r\nif above didnt help and if u can share u r code. u can share to [email protected] i will look in to it.", "if u r using onnx model script first check what is the difference between the output of onnx model output and tflite model output for the same input (u can give a random numpy array). u can then modify the onnx model script to work for tflite." ]

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[ "@jyingl3 Can you help to have a look? thanks" ]

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[ "Hi @penpornk Can you please review this PR ? Thank you!", "@penpornk - I don't know what is the failure for Py+CPP job. Looks like there was some timeout, not related to my changes. ", "These 3 CI failures are unrelated\r\n* [Py+CPP Ubuntu CPU](https://source.cloud.google.com/results/invocations/d9fa27f1-ca9a-4b76-ad3b-08851ad96c1f/log): Tests timed out.\r\n* [Py+CPP Ubuntu GPU](https://source.cloud.google.com/results/invocations/5af8fd45-1f55-4a1e-b963-c507082eb812/log): Test timed out.\r\n* [AMD ROCm](http://ml-ci.amd.com:21096/blue/organizations/jenkins/tensorflow%2Fgithub-prs-upstream-master%2FAMD-ROCm-Community-CI-Build/detail/PR-60413/6/pipeline): This PR doesn't modify XLA.\r\n```\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: in deps attribute of cc_library rule //tensorflow/compiler/xla/service/gpu:gpu_executable: Label '//tensorflow/tsl/platform:random' is duplicated\r\n\r\nERROR: /workspace/tensorflow/compiler/xla/service/gpu/BUILD:853:11: Analysis of target '//tensorflow/compiler/xla/service/gpu:gpu_executable' failed\r\n```" ]

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[ "@tpopp @vam-google Would be very cool to have this - is it difficult to do?", "Any updates on this?", "I'm not the appropriate person for this, and I don't know who is." ]

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[ "??", "Hi @IBSApple \r\n\r\nWe will resize and pad the image to keep the aspect ratio and fit the expected size which can vary for different pose estimation algorithms.\r\n\r\nThe tensorflow provides [Movenet](https://www.tensorflow.org/hub/tutorials/movenet) tutorial for human body pose estimation which runs faster than real time (30+ FPS) and tested on most modern desktops, laptops, and phones.\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." ]

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[ "Hi @ezhulenev Can you please review this PR ? Thank you!", "Hi @ezhulenev Can you please review this PR ? Thank you!" ]

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[ "@reedwm this seems to be of high priority given @wenscarl is reporting the perf hit is ~ 10% of the actual matmul call.", "Seems like all the unit tests will have to change to accommodate this change. Wenscarl has only implemented a single unit test to show the shape of things to come and is seeking feedback whether the new paradigm works. @philipphack will also give feedback.", "Version 11 doesn't support non-zero beta. I think that's why we added the version guard [here](https://github.com/tensorflow/tensorflow/blame/0e51c74cd0fa8bef5d9cdbea76f3387a1baae01b/tensorflow/compiler/xla/service/gpu/gemm_rewriter.cc#L728).", "Changes are made with CUDA_VERSION >= 12000 guards removed.", "Can you sync with the master branch to get changes from https://github.com/tensorflow/tensorflow/pull/60502?", "> Can you sync with the master branch to get changes from #60502?\r\n\r\nRebased to current master.", "As part of the OpenXLA PR rotation:\r\nI'll re-trigger the presubmits as soon as I get permissions.", "Since the PR author is OOO, I was going to fix some issues when merging. But it's too hard to test on H100s when merging, so instead I'll commit directly to this PR's branch. So let's not merge this PR yet.", "@philipphack in the FP8 case, when there is no matrix bias, this PR currently sets C to nullptr. But in the FP16 case, when there is no matrix bias, instead C is set to D\r\n\r\nI would prefer that the FP16 and FP8 cases be unified. Is there a fundamental reason why it's important for C to be nullptr in the FP8 case but not in the non-FP8 case? If there is no difference in cublas LT between FP8 and FP16 w.r.t. whether C should be nullptr or equal to D, I will set C to D in this PR. (Since @wenscarl is OOO I am addressing my own comments to this PR before merging it)" ]

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[ "Hi @thorsten-klein Thanks for reporting the issue.\r\n\r\nCould you please mention the steps you have followed to encounter the issue?\r\n\r\nAlso, please provide the error log to better understand the issue.\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/60408\">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/60408\">No</a>\n" ]

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[ "@ioannispan,\r\nCould you you make sure that you're using the latest nightlies - may be clear the gradle cash ?\r\n\r\nWe just cloned tensorflow examples and updated image classification example to include hexagon. I followed the [guide](https://www.tensorflow.org/lite/performance/hexagon_delegate#hexagon_delegate_java_api)\r\n\r\nby adding the gradle dep in build.gradle\r\n\r\nand created directory jniLibs/arm64-v8a and included the files i got from the libhexagon_nn_skel extraction. as explained [here](https://www.tensorflow.org/lite/performance/hexagon_delegate#add_the_shared_library_to_your_app)\r\n\r\nAlso please have a look at this issue https://github.com/tensorflow/tensorflow/issues/55364 and https://github.com/tensorflow/tensorflow/issues/53380 for the reference. Thank you!", "@tilakrayal \r\nI've cleared the gradle cache and done all the steps in the guide, but the error persists. The issues you stated did not help.\r\n\r\n**UPDATE**\r\nI've tried using the TFLite Model Benchmark Tool with Android Apk (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark/android) to see if i'll have the same issue with Hexagon. In order to be able to use the Hexagon delegate i do the following:\r\n\r\n```\r\nadb push libhexagon_interface.so /data/local/tmp\r\nadb push libhexagon_nn_skel.so /data/local/tmp\r\nadb push libhexagon_nn_skel_v65.so /data/local/tmp\r\nadb push libhexagon_nn_skel_v66.so /data/local/tmp\r\n```\r\n\r\nwhere `libhexagon_interface.so` comes from [this link](https://storage.googleapis.com/tensorflow-nightly-public/prod/tensorflow/release/lite/tools/nightly/latest/android_aarch64_libhexagon_interface.so) from [this guide](https://www.tensorflow.org/lite/performance/measurement) and `libhexagon_nn_skel(_v65/6).so` come from [this link](https://storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_skel_v1.20.0.1.run) (v1.20.0.1) from [this guide](https://www.tensorflow.org/lite/android/delegates/hexagon).\r\n\r\n When i set `use_hexagon=true` and **don't** specify the `hexagon_lib_path`, then the path is set to:\r\n\r\n`I tflite : Hexagon lib path: [/data/app/~~K6aeHsxXcgmL2FRThR7mBQ==/org.tensorflow.lite.benchmark-tPmERhUnFAHAenBJZq4OQw==/lib/arm64]`\r\n\r\nand i get the error:\r\n\r\n```\r\nW tflite : Failed to fetch Hexagon NN version. This might be because you're using incompatible versions of libhexagon_interface and libhexagon_nn_skel. You must use compatible versions. Refer to Tensorflow Lite Hexagon Delegate Guide.\r\nI tflite : Hexagon Delegate is not supported.\r\nW tflite : Could not create Hexagon delegate: platform may not support delegate or required libraries are missing\r\n```\r\n\r\nBut when i specify `hexagon_lib_path=/data/local/tmp/`, then the delegate is applied succesfully:\r\n\r\n```\r\nI tflite : Hexagon delegate created.\r\nI tflite : TfLiteHexagonDelegate delegate: 61 nodes delegated out of 61 nodes with 1 partitions.\r\nI tflite : Replacing 61 out of 61 node(s) with delegate (TfLiteHexagonDelegate) node, yielding 1 partitions for the whole graph.\r\n```\r\n\r\nDoes this help with the problem in my Android app in any way?\r\nThanks", "Hi @ioannispan \r\n\r\nAlthough the documentation [suggests](https://www.tensorflow.org/lite/android/delegates/hexagon#example_usage) to use v1.20.0.1 , the [config](https://github.com/tensorflow/tensorflow/blob/master/third_party/hexagon/workspace.bzl) has v1.20.0.9 as latest version to use for nightly snapshots which can be downloaded from [here](https://storage.googleapis.com/mirror.tensorflow.org/storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_headers_v1.20.0.9.tgz).\r\n\r\nCould you please try with `libhexagon_interface.so` using v1.20.0.9 and see if it resolves the issue?\r\n\r\nThanks.", "Hello @pjpratik \r\n\r\nI think i am missing something. The link you provided for v1.20.0.9 does not contain `libhexagon_nn_skel(_v65/6).so` files.\r\nI copy the new `libhexagon_interface.so` to my Android `jniLibs/arm64-v8a/` directory, but the error persists:\r\n\r\n`Failed to load libhexagon_interface.so, Error: dlopen failed: library \"libadsprpc.so\" not found: needed by /data/app/~~3WH2wsowpMNgALxjaSkcCw==/com.example.<project>--XTLMj82qPraRQM25waMKg==/lib/arm64/libhexagon_interface.so in namespace classloader-namespace`\r\n\r\nShould i put `libhexagon_interface.so` somewhere else?\r\nThank you", "Hi @ioannispan \r\n\r\nI have observed that the path for `hexagon_lib_path` has to be `/data/local/tmp/` as per the build file for the benchmark tool.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/785f7f02f78c6af70ee79729ce95724e89a221bd/tensorflow/lite/tools/benchmark/BUILD#L38\r\n\r\nAre per the [hexagon delegate](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/hexagon#hexagon-delegate) documentation,\r\n\r\n>'libhexagon_interface.so' which holds the interface that the delegate uses. It must be available if you linked the hexagon_delegate library to TFLite. You can load it either from shell by overriding LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\"path to the so\", or add it inside your apk in a way it is available.\r\n\r\n>'libhexagon_nn_skel(_v65/_v66).so' which holds the DSP code. Use TfLiteHexagonInitWithPath(..) and provide the path to the directory which holds the shared libraries for the Hexagon NN on device. If you're using TfLiteHexagonInit() then You will need to set environment variable \"ADSP_LIBRARY_PATH\" to \"path_to_the_lib\";/system/lib/rfsa/adsp;/system/vendor/lib/rfsa/adsp;/dsp Note that separator here is ';' not ':' You can push all 3 files, and the library will pick the one needed based on the runtime. Or if you are sure of what you will use on the device then push only one of them.\r\n\r\nCould you please confirm if you are following this instructions to set `.so` files?\r\n\r\nThanks.\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/60407\">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/60407\">No</a>\n" ]

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[ "Hey, anyone check this? I'd appreciate an explanation of why it's written this way and how to avoid security issues.", "Hi,\r\n\r\nFor securities related issues, please use the proper channel.\r\n\r\nFollow [this](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) document for more details. Thanks" ]

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[ "Hi @faguilarc, \r\nThanks for reporting the issue!\r\nCould you please confirm that you installed Tensorflow using pip and kindly share the steps you have followed to install Tensorflow. It is recommended to follow the installation instructions from [here](https://www.tensorflow.org/install/pip#windows-native). Thank you!", "i don't know what is the reason, i had to downgrade, and after uninstalling everything for the fifteenth time it stopped giving me problems. Thanks ", "@faguilarc I'm glad it's working now. If the issue has been resolved, please feel free to close it. 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/60405\">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/60405\">No</a>\n" ]

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[ "Hi @sagi-ezri It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727" ]

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[ "Hi @tilakrayal，\r\n\r\nSorry, I should have noticed that you are the Assignee right now. Please can you shed me some light on this issue?\r\n\r\nThanks~", "Hi @picard314 \r\n\r\nThe `TfLiteGpuDelegateBindGlBufferToTensor` should be able to refer by inclusion of gpu_api_delegate.h\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/41c3f9ed867b91b8922c6f5828ecce63c85188dd/tensorflow/lite/delegates/gpu/cl/gpu_api_delegate.h#L91\r\n\r\nCould you please provide more details or a sample code snippet to reproduce the error?\r\n\r\nThanks", "Hi @pjpratik \r\n\r\nThanks a lot for looking into my issue. I am sorry for replying late since I was swamped in a sudden urgent task.\r\n\r\n[Android.tar.gz](https://github.com/tensorflow/tensorflow/files/11330805/Android.tar.gz)\r\n\r\nHere I paste a demo project which can reveal the error I have encountered. Please unzip this .tar.gz and check \"Android/README.txt\" first. \r\n\r\nBesides, I'd like to ask whether or not your \"texture I/O\" supports TfLiteGpuDelegateV2.\r\n\r\nThanks.\r\n", "Hi, @sachinprasadhs and @impjdi \r\n\r\nMay I lit up this issue again~ Thanks for your attention!\r\n\r\nNow I guess my \"libtensorflowlite_gpu_delegate.so\" (attached) may be incomplete. It was got by running\r\n\r\nbazel build -c opt --config android_arm64 --copt -DCL_DELEGATE_NO_GL --copt -DTFLITE_GPU_BINARY_RELEASE --linkopt -s tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so\r\n\r\n, with logs looking correct. What do you say please~\r\n\r\nI am looking forward to your replies. Thanks again!\r\n\r\nBest,\r\npicard314" ]

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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/60402/checks?check_run_id=12954689662) 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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[ "@Utkarsha666,\r\nCould 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\nCould you please try using the latest Tensorflow version for windows which can be installed with WSL2.\r\nAlso, from TensorFlow 2.12 CUDA and CuDNN dependency has been upgraded to 11.8 and 8.6 respectively.\r\nRefer the document here for installation instructions https://www.tensorflow.org/install/pip and let us know if you observe the same behavior in the latest Tensorflow version. 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/60401\">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/60401\">No</a>\n" ]

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[ "Hi @foxik\r\nThank you for reporting this issue!\r\n\r\nI was able to replicate the issue in Colab using Tensorflow 2.12 and tf-nightly(2.13.0.dev20230424). Please find the gists - [TF 2.12](https://colab.sandbox.google.com/gist/synandi/3307d76df72bd5181ed5aa4dd639b7ac/60400.ipynb) & [tf-nightly](https://colab.sandbox.google.com/gist/synandi/fef428141788b9021bb529750e6d7f1c/60400_nightly.ipynb). \r\nThank you!\r\n ", "Hi @foxik ,\r\n\r\nThe difference in results are due to 2 reasons.The code `inputs.row_lengths(axis=1)` outputs `KerasTensor` with `type_spec=TensorSpec` whereas inputs.row_lengths(axis=2) outputs` KerasTensor(type_spec=RaggedTensorSpec)`. Please note the difference here `TensorSpec` vs `RaggedTensorSpec`.\r\n\r\nWhen we apply index/slicing operation [:] , internally both calls __getitem__ method and you can find from the colab gist attached in [comment](https://github.com/tensorflow/tensorflow/issues/60400#issuecomment-1520309736), for axis=1 tf.__operators__.getitem' method called and for axis=2 'tf.__operators__.ragged_getitem' called and both are different methods and hence different results.\r\n\r\n```\r\nprint(inputs.row_lengths(axis=1))\r\n#KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.int64, name=None), name='input.row_lengths/sub:0', description=\"created by layer 'input.row_lengths'\")\r\n\r\nprint(inputs.row_lengths(axis=1)[:]) # OK, is the same as above\r\n#KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.int64, name=None), name='tf.__operators__.getitem/strided_slice:0', description=\"created by layer `'tf.__operators__.getitem'\")`\r\n\r\nprint(inputs.row_lengths(axis=2))\r\n#KerasTensor(type_spec=RaggedTensorSpec(TensorShape([None, 64]), tf.int64, 1, tf.int64), description=\"created by layer 'input.row_lengths_2'\")\r\n\r\nprint(inputs.row_lengths(axis=2)[:]) # Not the same as above as it calls different methods\r\n#KerasTensor(type_spec=RaggedTensorSpec(TensorShape([1, 64]), tf.int64, 1, tf.int64), description=\"created by layer 'tf.__operators__.ragged_getitem'\")\r\n```\r\nHere the axis and the type spec making the difference.\r\n\r\nHowever, I have tested the same with actual data and observed same result with same specs and values.Please refer the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/0c013dfafc19261d3dab12d711d21293/60400_r1.ipynb) with the same operations performed on actual ragged tensor.Can you test also with some actual data and confirm if there is any difference.", "Thanks for your answer!\r\n\r\nThe problem is that\r\n```python\r\ninputs.row_lengths(axis=2).shape != inputs.row_lengths(axis=2)[:].shape\r\n```\r\nwhile it should be the same, because for any tensor (or ragged tensor) `a`, it should be the case that `a.shape == (a[:]).shape`.\r\n\r\nI understand the difference between `inputs.row_lengths(axis=1)` and `inputs.row_lengths(axis=2)`; I just included it in the example to show that for `axis=1` it is indeed the case that\r\n```python\r\ninputs.row_lengths(axis=1).shape == inputs.row_lengths(axis=1)[:].shape\r\n```\r\n\r\nThe reported problem concerns only static shape inference; during the graph evaluation, the shape is correct. That means that even if `inputs.row_lengths(axis=2)[:].shape[0]` is `1`, the `tf.shape(inputs.row_lengths(axis=2)[:])[0]` can be something different than 1 (in the gist it is 5), which is incorrect -- such incorrect static shape can cause problems during graph construction.\r\n\r\nWe encountered the problem in a OCR-like task -- given images of fixed height and dynamic width as ragged tensors, we wanted to extract the widths of the images. That can be achieved using for example `inputs.row_lengths(axis=2)[:, 0]` -- but the `.shape` of that is reported as `(1,)`, which is clearly incorrect.\r\n\r\nSo I believe this behavior is indeed a bug." ]

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[ "@hlreicha,\r\nI was facing a different issue while executing the given code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/7eb3058c89de5c1776502b21f3efbabf/untitled1082.ipynb).\r\n\r\nAlso there might be below chances for the low validation accuracy.\r\n\r\n**1.** You are trying to apply some sort of preprocessing (zero meaning, normalizing, etc.) to either your training set or validation set, but not the other.\r\n\r\n**2.** If you built some layers that perform differently during training and inference from scratch, your model might be incorrectly implemented (e.g. are moving mean and moving standard deviation for batch normalization getting updated during training? \r\nIf using dropout, are weights scaled properly during inference?). This might be the case if your code implements these things from scratch and does not use Tensorflow builtin functions.\r\n\r\n**3.** Overfitting. I suspect that the other two options more likely in your specific situation as your validation accuracy might stuck at 50% for some epochs.\r\n\r\nUsing a random sample from your validation set: It means your validation set at each evaluation step is different, so is your validation-loss.\r\nUsing a weighted loss-function(which is used in case of highly imbalanced class-problems). At train step, you weigh your loss function based on class-weights, while at dev step you just calculate the un-weighted loss. In such case, though your network is stepping into convergence, you might see lots of fluctuations in validation loss after each train-step. ", "@tilakrayal \r\nHi, thank you for your response. I can post more functioning code block later if it would help. The problem is that I am not getting low validation (remember, I am in the 90% range), I am getting lower validation score with TensorFlow 2.9 compared to TensorFlow 2.4. The code (loading image, preprocessing, model building, and etc) is all the same, it is the matter of me switching conda enviroments. \r\n\r\n1. I am not doing any preprocessing other than dividing the image by 255.0 and it is applied to both train/val sets. This never caused issues in TensorFlow 2.4\r\n2. I am not using any custom layers, I import my EfficienetNet model, flatten it, and add the final dense layer.\r\n3. I have not experienced this behavior\r\n\r\nI get these results after I ran my training script 5 times for each version.\r\n- TensorFlow 2.4: ~97-98% Accuracy on the validation set.\r\n\r\n- TensorFlow 2.9: ~93-95% Accuracy on the validation set\r\n\r\nI should specify I set my random seed values as well. Did something change between both versions that causes EfficientNet to lose its performance?", "@hlreicha - Would you be able to create a complete script / Colab using standard datasets, for example in `tensorflow_datasets` https://github.com/tensorflow/datasets to replicate this 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 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/60399\">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/60399\">No</a>\n" ]

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[ "It looks like you are encountering a compilation error when building TensorFlow with Bazel on your Debian Bullseye system using CUDA and MKL. The error is related to a missing switch case in the cudnn_frontend library.\r\n\r\nOne possible solution to this issue is to update the cudnn_frontend library to a newer version that has the missing switch case. You can try updating the cudnn_frontend by running the following command:\r\n\r\narduino\r\nCopy code\r\nbazel build --config=cuda --config=mkl --config=monolithic //tensorflow/tools/pip_package:build_pip_package --incompatible_remove_native_http_archive=false\r\nThis command will build TensorFlow with CUDA, MKL, and the latest version of the cudnn_frontend library.\r\n\r\nIf updating the cudnn_frontend library doesn't solve the issue, you can try disabling the --config=mkl option when building TensorFlow with Bazel. You can do this by running the following command:\r\n\r\narduino\r\nCopy code\r\nbazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package\r\nThis command will build TensorFlow with only CUDA and without MKL. This might help you to identify whether the issue is related to the MKL library or not.\r\n\r\nIf neither of these solutions works, you might want to provide more information such as the complete build log or the full error message to help us better understand the issue.", "$ bazel build --config=cuda --config=mkl --config=monolithic //tensorflow/tools/pip_package:build_pip_package --incompatible_remove_native_http_archive=false\r\nERROR: --incompatible_remove_native_http_archive=false :: Unrecognized option: --incompatible_remove_native_http_archive=false\r\n\r\ngives Unrecognized option error\r\n\r\nI also tried removing --config mkl, which I can not get any improvement.", "https://github.com/tensorflow/tensorflow/issues/60362 FYI.\r\n\r\nbtw:\r\ntensorflow V2.12.0 requires 8.6cuDNN and 11.8 CUDA", "Hi @kentosho ,\r\n\r\nPlease look at the tested configurations [here](https://www.tensorflow.org/install/source#gpu). For Tf2.12 version the tested configurations are GCC-9.3.1 , CUDA-11.8,cuDNN-8.6. Can you please cross check with the tested configurations.\r\n\r\nAlso I would like to know the sequence of steps followed for build.Have you installed cuDNN as per instructions ? I am interested to know the sequence of steps followed by you for GPU setup with CUDA,cuDNN toolkit installations etc. Official instructions as per documentation mentioned [here](https://www.tensorflow.org/install/pip#step-by-step_instructions).", "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.", "compile suceeded for gcc-10.2.1 CUDA-11.8 cuDNN-8.9 with following patch\r\nit might be a wired patch\r\n```\r\ndiff --git a/tensorflow/workspace2.bzl b/tensorflow/workspace2.bzl\r\nindex 1261273bc92..0a9d755af0f 100644\r\n--- a/tensorflow/workspace2.bzl\r\n+++ b/tensorflow/workspace2.bzl\r\n@@ -172,9 +172,9 @@ def _tf_repositories():\r\n name = \"cudnn_frontend_archive\",\r\n build_file = \"//third_party:cudnn_frontend.BUILD\",\r\n patch_file = [\"//third_party:cudnn_frontend_header_fix.patch\"],\r\n- sha256 = \"3c7b842cd67989810955b220fa1116e7e2ed10660a8cfb632118146a64992c30\",\r\n- strip_prefix = \"cudnn-frontend-0.7.3\",\r\n- urls = tf_mirror_urls(\"https://github.com/NVIDIA/cudnn-frontend/archive/refs/tags/v0.7.3.zip\"),\r\n+ sha256 = \"d8dba9e2607a0c256aa8eacb45b39986ab6f3f24a4d431d4397047a3cb0cd4fb\",\r\n+ strip_prefix = \"cudnn-frontend-0.9\",\r\n+ urls = tf_mirror_urls(\"https://github.com/NVIDIA/cudnn-frontend/archive/refs/tags/v0.9.zip\"),\r\n )\r\n\r\n```\r\n\r\n", "Hi @kentosho ,\r\n\r\nThis might be due to the fact that TF tested cuDNN version is 8.6 whereas you are trying to use different version 8.9 and hence the change in dependencies might be worked.\r\n\r\nI assume you haven't checked with cuDNN-8.6 which is a tested configuration. If you have tested this but still fails please let us know. have you done the patch suitable for cuDNN-8.9 directly please let us know.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "I try to build with cuDNN 8.6 instead of cuDNN8.9 and sucssess to build with no error.　Also, when using CuDNN 8.9, I had experienced an abnormal termination of python when running model.fit on sensorflow 2.12.0, but this phenomenon was　fixed by using CuDNN 8.6.", "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/60398\">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/60398\">No</a>\n", "> It looks like you are encountering a compilation error when building TensorFlow with Bazel on your Debian Bullseye system using CUDA and MKL. The error is related to a missing switch case in the cudnn_frontend library.\r\n> \r\n> One possible solution to this issue is to update the cudnn_frontend library to a newer version that has the missing switch case. You can try updating the cudnn_frontend by running the following command:\r\n> \r\n> arduino Copy code bazel build --config=cuda --config=mkl --config=monolithic //tensorflow/tools/pip_package:build_pip_package --incompatible_remove_native_http_archive=false This command will build TensorFlow with CUDA, MKL, and the latest version of the cudnn_frontend library.\r\n> \r\n> If updating the cudnn_frontend library doesn't solve the issue, you can try disabling the --config=mkl option when building TensorFlow with Bazel. You can do this by running the following command:\r\n> \r\n> arduino Copy code bazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package This command will build TensorFlow with only CUDA and without MKL. This might help you to identify whether the issue is related to the MKL library or not.\r\n> \r\n> If neither of these solutions works, you might want to provide more information such as the complete build log or the full error message to help us better understand the issue.\r\n\r\nIs this a chatgpt reply?" ]

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[ "When Bazel try to compile tensorflow/core/kernels/segment_reduction_ops_gpu_2.cu.cc, no suitable constructor exists to convert error will also happened.\r\n\r\n> .\\tensorflow/core/kernels/segment_reduction_ops_gpu.cu.h(206): error: no suitable constructor exists to convert from \"int\" to \"tensorflow::AlignedVector<int, 4>\"\r\n> detected during:\r\n> instantiation of \"void tensorflow::SegmentOffsetsKernel(Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int, Tsegmentids=int]\"\r\n> (233): here\r\n> instantiation of \"tsl::Status tensorflow::LaunchSegmentOffsetsKernel(const tensorflow::GPUDevice &, Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int\r\n> , Tsegmentids=int]\"\r\n> (570): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUImpl<Treducevec,Tvec,Tindex,Tsegmentids,ReduceOp,Tinit>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmen\r\n> tids, ReduceOp, Tinit, Tinit, __nv_bool, __nv_bool, const Tvec *, const Tsegmentids *, const Tindex *, const Tinit *, Tvec *) [with Treducevec=tensorflow::AlignedVector<int, 4>, Tv\r\n> ec=tensorflow::AlignedVector<int, 4>, Tindex=int, Tsegmentids=int, ReduceOp=tensorflow::functor::Max, Tinit=int]\"\r\n> (617): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUVectorized<Treduce>::Impl<vec_size>::operator()(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, Re\r\n> duceOp, T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, vec_size=4, T=int, Tindex=int, Tsegmentids=int, Reduce\r\n> Op=tensorflow::functor::Max]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(329): here\r\n> instantiation of \"tsl::Status tensorflow::detail::DispatchToVectorizedHelper<VecSize, Functor>::operator()(int64_t, Args &&...) const [with VecSize=4LL, Functor=tensorf\r\n> low::SegmentReduceGPUVectorized<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, int &, int &, int &, tensorflow::functor::Max &, int &, int &, __nv_bool &, __nv_bool &, co\r\n> nst tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(364): here\r\n> instantiation of \"tsl::Status tensorflow::DispatchToVectorized<T,Functor,Args...>(int64_t, Args &&...) [with T=tsl::int32, Functor=tensorflow::SegmentReduceGPUVectorize\r\n> d<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, tsl::int32 &, tsl::int32 &, tsl::int32 &, tensorflow::functor::Max &, tsl::int32 &, tsl::int32 &, __nv_bool &, __nv_bool\r\n> &, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> (647): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPU<Treduce,T,Tindex,Tsegmentids,ReduceOp>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, ReduceOp,\r\n> T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, T=tsl::int32, Tindex=tsl::int32, Tsegmentids=tsl::int32, Reduc\r\n> eOp=tensorflow::functor::Max]\"\r\n> (871): here\r\n> instantiation of \"void tensorflow::functor::UnsortedSegmentFunctor<tensorflow::functor::GPUDevice, T, Index, InitialValueF, ReductionF>::operator()(tensorflow::OpKernel\r\n> Context *, const tensorflow::TensorShape &, tensorflow::TTypes<Index, 1, Eigen::DenseIndex>::ConstFlat, tensorflow::TTypes<T, 2, Eigen::DenseIndex>::ConstTensor, tensorflow::TTypes\r\n> <T, 2, Eigen::DenseIndex>::Tensor) [with T=tsl::int32, Index=tsl::int32, InitialValueF=tensorflow::functor::Lowest<tsl::int32>, ReductionF=tensorflow::functor::Max]\"\r\n> E:\\20220616_1800pm\\_bazel_tensorflow\\jsjos6dw\\execroot\\org_tensorflow\\tensorflow\\core\\kernels\\segment_reduction_ops_gpu_2.cu.cc(45): here\r\n> \r\n> .\\tensorflow/core/kernels/segment_reduction_ops_gpu.cu.h(206): error: no suitable constructor exists to convert from \"int\" to \"tensorflow::AlignedVector<int, 4>\"\r\n> detected during:\r\n> instantiation of \"void tensorflow::SegmentOffsetsKernel(Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int, Tsegmentids=int]\"\r\n> (233): here\r\n> instantiation of \"tsl::Status tensorflow::LaunchSegmentOffsetsKernel(const tensorflow::GPUDevice &, Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int\r\n> , Tsegmentids=int]\"\r\n> (570): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUImpl<Treducevec,Tvec,Tindex,Tsegmentids,ReduceOp,Tinit>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmen\r\n> tids, ReduceOp, Tinit, Tinit, __nv_bool, __nv_bool, const Tvec *, const Tsegmentids *, const Tindex *, const Tinit *, Tvec *) [with Treducevec=tensorflow::AlignedVector<int, 4>, Tv\r\n> ec=tensorflow::AlignedVector<int, 4>, Tindex=int, Tsegmentids=int, ReduceOp=tensorflow::functor::Max, Tinit=int]\"\r\n> (617): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUVectorized<Treduce>::Impl<vec_size>::operator()(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, Re\r\n> duceOp, T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, vec_size=4, T=int, Tindex=int, Tsegmentids=int, Reduce\r\n> Op=tensorflow::functor::Max]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(329): here\r\n> instantiation of \"tsl::Status tensorflow::detail::DispatchToVectorizedHelper<VecSize, Functor>::operator()(int64_t, Args &&...) const [with VecSize=4LL, Functor=tensorf\r\n> low::SegmentReduceGPUVectorized<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, int &, int &, int &, tensorflow::functor::Max &, int &, int &, __nv_bool &, __nv_bool &, co\r\n> nst tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(364): here\r\n> instantiation of \"tsl::Status tensorflow::DispatchToVectorized<T,Functor,Args...>(int64_t, Args &&...) [with T=tsl::int32, Functor=tensorflow::SegmentReduceGPUVectorize\r\n> d<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, tsl::int32 &, tsl::int32 &, tsl::int32 &, tensorflow::functor::Max &, tsl::int32 &, tsl::int32 &, __nv_bool &, __nv_bool\r\n> &, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> (647): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPU<Treduce,T,Tindex,Tsegmentids,ReduceOp>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, ReduceOp,\r\n> T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, T=tsl::int32, Tindex=tsl::int32, Tsegmentids=tsl::int32, Reduc\r\n> eOp=tensorflow::functor::Max]\"\r\n> (871): here\r\n> instantiation of \"void tensorflow::functor::UnsortedSegmentFunctor<tensorflow::functor::GPUDevice, T, Index, InitialValueF, ReductionF>::operator()(tensorflow::OpKernel\r\n> Context *, const tensorflow::TensorShape &, tensorflow::TTypes<Index, 1, Eigen::DenseIndex>::ConstFlat, tensorflow::TTypes<T, 2, Eigen::DenseIndex>::ConstTensor, tensorflow::TTypes\r\n> <T, 2, Eigen::DenseIndex>::Tensor) [with T=tsl::int32, Index=tsl::int32, InitialValueF=tensorflow::functor::Lowest<tsl::int32>, ReductionF=tensorflow::functor::Max]\"\r\n> E:\\20220616_1800pm\\_bazel_tensorflow\\jsjos6dw\\execroot\\org_tensorflow\\tensorflow\\core\\kernels\\segment_reduction_ops_gpu_2.cu.cc(45): here\r\n> \r\n> .\\tensorflow/core/kernels/segment_reduction_ops_gpu.cu.h(206): error: no suitable constructor exists to convert from \"int\" to \"tensorflow::AlignedVector<int, 4>\"\r\n> detected during:\r\n> instantiation of \"void tensorflow::SegmentOffsetsKernel(Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int, Tsegmentids=int]\"\r\n> (233): here\r\n> instantiation of \"tsl::Status tensorflow::LaunchSegmentOffsetsKernel(const tensorflow::GPUDevice &, Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int\r\n> , Tsegmentids=int]\"\r\n> (570): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUImpl<Treducevec,Tvec,Tindex,Tsegmentids,ReduceOp,Tinit>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmen\r\n> tids, ReduceOp, Tinit, Tinit, __nv_bool, __nv_bool, const Tvec *, const Tsegmentids *, const Tindex *, const Tinit *, Tvec *) [with Treducevec=tensorflow::AlignedVector<int, 4>, Tv\r\n> ec=tensorflow::AlignedVector<int, 4>, Tindex=int, Tsegmentids=int, ReduceOp=tensorflow::functor::Max, Tinit=int]\"\r\n> (617): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPUVectorized<Treduce>::Impl<vec_size>::operator()(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, Re\r\n> duceOp, T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, vec_size=4, T=int, Tindex=int, Tsegmentids=int, Reduce\r\n> Op=tensorflow::functor::Max]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(329): here\r\n> instantiation of \"tsl::Status tensorflow::detail::DispatchToVectorizedHelper<VecSize, Functor>::operator()(int64_t, Args &&...) const [with VecSize=4LL, Functor=tensorf\r\n> low::SegmentReduceGPUVectorized<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, int &, int &, int &, tensorflow::functor::Max &, int &, int &, __nv_bool &, __nv_bool &, co\r\n> nst tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> .\\tensorflow/core/util/gpu_kernel_helper.h(364): here\r\n> instantiation of \"tsl::Status tensorflow::DispatchToVectorized<T,Functor,Args...>(int64_t, Args &&...) [with T=tsl::int32, Functor=tensorflow::SegmentReduceGPUVectorize\r\n> d<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, tsl::int32 &, tsl::int32 &, tsl::int32 &, tensorflow::functor::Max &, tsl::int32 &, tsl::int32 &, __nv_bool &, __nv_bool\r\n> &, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n> (647): here\r\n> instantiation of \"tsl::Status tensorflow::SegmentReduceGPU<Treduce,T,Tindex,Tsegmentids,ReduceOp>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, ReduceOp,\r\n> T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, T=tsl::int32, Tindex=tsl::int32, Tsegmentids=tsl::int32, Reduc\r\n> eOp=tensorflow::functor::Max]\"\r\n> (871): here\r\n> instantiation of \"void tensorflow::functor::UnsortedSegmentFunctor<tensorflow::functor::GPUDevice, T, Index, InitialValueF, ReductionF>::operator()(tensorflow::OpKernel\r\n> Context *, const tensorflow::TensorShape &, tensorflow::TTypes<Index, 1, Eigen::DenseIndex>::ConstFlat, tensorflow::TTypes<T, 2, Eigen::DenseIndex>::ConstTensor, tensorflow::TTypes\r\n> <T, 2, Eigen::DenseIndex>::Tensor) [with T=tsl::int32, Index=tsl::int32, InitialValueF=tensorflow::functor::Lowest<tsl::int32>, ReductionF=tensorflow::functor::Max]\"\r\n> E:\\20220616_1800pm\\_bazel_tensorflow\\jsjos6dw\\execroot\\org_tensorflow\\tensorflow\\core\\kernels\\segment_reduction_ops_gpu_2.cu.cc(45): here\r\n", "I have found a way to clear some bugs. Number of errors have reduced to 34 for `segment_reduction_ops_gpu_0.cu.cc`. I need to know for the following CUDA template in `tensorflow/core/util/gpu_kernel_helper.h`:\r\n```\r\ntemplate <typename T>\r\n__host__ __device__ inline const T& tf_max(const T& x, const T& y) {\r\n return x < y ? y : x;\r\n}\r\n```\r\nCan I use it to compare two `tsl::int32` values directly using [`__device__​ int max ( const int a, const int b )`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__INT.html#group__CUDA__MATH__INT_1gcd95edd79e83ba55edb31cce43f4de42)?", "Same errors occur in 2.13 branch. We better resolve it before rc0 release. If you insist to reproduce the errors before answering my question, save the following as a `.sh` script, change paths correspondingly, then run it after bazel linked CUDA headers. The headers will be ready after approximately 5000th-6000th actions.\r\n\r\n```\r\nexport MSYS_NO_PATHCONV=1\r\nexport MSYS2_ARG_CONV_EXCL=\"*\"\r\nexport PATH=\"/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/bin:$PATH\"\r\nexport PATH=\"/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8/extras/CUPTI/lib64:$PATH\"\r\nexport PATH=\"/c/Users/tensorflow/anaconda3:$PATH\"\r\nexport CUDA_TOOLKIT_PATH=\"/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.8\"\r\nexport INCLUDE=\"/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/include;/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/ATLMFC/include;/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Auxiliary/VS/include;/c/Program Files (x86)/Windows Kits/10/include/10.0.22621.0/ucrt;/c/Program Files (x86)/Windows Kits/10/include/10.0.22621.0/um;/c/Program Files (x86)/Windows Kits/10/include/10.0.22621.0/shared;/c/Program Files (x86)/Windows Kits/10/include/10.0.22621.0/winrt;/c/Program Files (x86)/Windows Kits/10/include/10.0.22621.0/cppwinrt\"\r\nexport LIB=\"/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/ATLMFC/lib/x64;/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/lib/x64;/c/Program Files (x86)/Windows Kits/10/lib/10.0.22621.0/ucrt/x64;/c/Program Files (x86)/Windows Kits/10/lib/10.0.22621.0/um/x64\"\r\nexport PATH=\"/c/Program Files/Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/bin/HostX64/x64;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/VCPackages;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TestWindow;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/TeamFoundation/Team Explorer;/c/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/bin/Roslyn;/c/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/Performance Tools/x64;/c/Program Files/Microsoft Visual Studio/2022/Community/Team Tools/Performance Tools;/c/Program Files (x86)/HTML Help Workshop;/c/Program Files (x86)/Windows Kits/10/bin/10.0.22621.0/x64;/c/Program Files (x86)/Windows Kits/10/bin/x64;/c/Program Files/Microsoft Visual Studio/2022/Community/MSBuild/Current/Bin/amd64;/c/Windows/Microsoft.NET/Framework64/v4.0.30319;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/Tools;/c/Windows/system32;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja;/c/Program Files/Microsoft Visual Studio/2022/Community/Common7/IDE/VC/Linux/bin/ConnectionManagerExe:$PATH\"\r\nexport PWD=\"/proc/self/cwd\"\r\nexport PYTHON_BIN_PATH=\"/c/Users/tensorflow/anaconda3/python.exe\"\r\nexport PYTHON_LIB_PATH=\"/c/Users/tensorflow/anaconda3/lib/site-packages\"\r\nexport RUNFILES_MANIFEST_ONLY=1\r\nexport TEMP=\"/c/msys64/tmp\"\r\nexport TF2_BEHAVIOR=1\r\nexport TF_CUDA_COMPUTE_CAPABILITIES=3.5,7.0,8.6\r\nexport TMP=\"/c/msys64/tmp\"\r\ncd E:/_bazel_tensorflow/kxpxxayx/execroot/org_tensorflow\r\n/c/Users/tensorflow/anaconda3/python.exe -B external/local_config_cuda/crosstool/windows/msvc_wrapper_for_nvcc.py /nologo /DCOMPILER_MSVC /DNOMINMAX /D_WIN32_WINNT=0x0600 /D_CRT_SECURE_NO_DEPRECATE /D_CRT_SECURE_NO_WARNINGS /D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS /bigobj /Zm500 /J /Gy /GF /EHsc /wd4351 /wd4291 /wd4250 /wd4996 /I. /Ibazel-out/x64_windows-opt/bin /Iexternal/com_google_absl /Ibazel-out/x64_windows-opt/bin/external/com_google_absl /Iexternal/nsync /Ibazel-out/x64_windows-opt/bin/external/nsync /Iexternal/eigen_archive /Ibazel-out/x64_windows-opt/bin/external/eigen_archive /Iexternal/com_google_protobuf /Ibazel-out/x64_windows-opt/bin/external/com_google_protobuf /Iexternal/gif /Ibazel-out/x64_windows-opt/bin/external/gif /Iexternal/libjpeg_turbo /Ibazel-out/x64_windows-opt/bin/external/libjpeg_turbo /Iexternal/com_googlesource_code_re2 /Ibazel-out/x64_windows-opt/bin/external/com_googlesource_code_re2 /Iexternal/farmhash_archive /Ibazel-out/x64_windows-opt/bin/external/farmhash_archive /Iexternal/fft2d /Ibazel-out/x64_windows-opt/bin/external/fft2d /Iexternal/highwayhash /Ibazel-out/x64_windows-opt/bin/external/highwayhash /Iexternal/zlib /Ibazel-out/x64_windows-opt/bin/external/zlib /Iexternal/double_conversion /Ibazel-out/x64_windows-opt/bin/external/double_conversion /Iexternal/snappy /Ibazel-out/x64_windows-opt/bin/external/snappy /Iexternal/local_config_cuda /Ibazel-out/x64_windows-opt/bin/external/local_config_cuda /Iexternal/local_config_rocm /Ibazel-out/x64_windows-opt/bin/external/local_config_rocm /Iexternal/local_config_tensorrt /Ibazel-out/x64_windows-opt/bin/external/local_config_tensorrt /Iexternal/cudnn_frontend_archive /Ibazel-out/x64_windows-opt/bin/external/cudnn_frontend_archive /Iexternal/llvm-project /Ibazel-out/x64_windows-opt/bin/external/llvm-project /Iexternal/llvm_terminfo /Ibazel-out/x64_windows-opt/bin/external/llvm_terminfo /Iexternal/llvm_zlib /Ibazel-out/x64_windows-opt/bin/external/llvm_zlib /Iexternal/curl /Ibazel-out/x64_windows-opt/bin/external/curl /Iexternal/boringssl /Ibazel-out/x64_windows-opt/bin/external/boringssl /Iexternal/jsoncpp_git /Ibazel-out/x64_windows-opt/bin/external/jsoncpp_git /Ibazel-out/x64_windows-opt/bin/external/local_config_cuda/cuda/_virtual_includes/cuda_headers_virtual /Ibazel-out/x64_windows-opt/bin/external/local_config_tensorrt/_virtual_includes/tensorrt_headers /Ibazel-out/x64_windows-opt/bin/external/local_config_cuda/cuda/_virtual_includes/cudnn_header /Ibazel-out/x64_windows-opt/bin/external/cudnn_frontend_archive/_virtual_includes/cudnn_frontend /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributeInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinDialectIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinLocationAttributesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinOpsIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypeInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/CallOpInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/CastOpInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/FunctionInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/InferTypeOpInterfaceIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/OpAsmInterfaceIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/RegionKindInterfaceIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/SideEffectInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/SymbolInterfacesIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/TensorEncodingIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/ArithBaseIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/ArithCanonicalizationIncGen /Ibazel-out/x64_windows-opt/bin/external/llvm-project/mlir/_virtual_includes/ArithOpsIncGen 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/DLLVM_NATIVE_ARCH=\"X86\" /DLLVM_NATIVE_ASMPARSER=LLVMInitializeX86AsmParser /DLLVM_NATIVE_ASMPRINTER=LLVMInitializeX86AsmPrinter /DLLVM_NATIVE_DISASSEMBLER=LLVMInitializeX86Disassembler /DLLVM_NATIVE_TARGET=LLVMInitializeX86Target /DLLVM_NATIVE_TARGETINFO=LLVMInitializeX86TargetInfo /DLLVM_NATIVE_TARGETMC=LLVMInitializeX86TargetMC /DLLVM_NATIVE_TARGETMCA=LLVMInitializeX86TargetMCA /DLLVM_HOST_TRIPLE=\"x86_64-pc-win32\" /DLLVM_DEFAULT_TARGET_TRIPLE=\"x86_64-pc-win32\" /DLLVM_VERSION_MAJOR=17 /DLLVM_VERSION_MINOR=0 /DLLVM_VERSION_PATCH=0 /DLLVM_VERSION_STRING=\"17.0.0git\" /D__STDC_LIMIT_MACROS /D__STDC_CONSTANT_MACROS /D__STDC_FORMAT_MACROS /DBLAKE3_USE_NEON=0 /DBLAKE3_NO_AVX2 /DBLAKE3_NO_AVX512 /DBLAKE3_NO_SSE2 /DBLAKE3_NO_SSE41 /DCURL_STATICLIB /showIncludes /MD /O2 /DNDEBUG /W0 /Zc:__cplusplus /D_USE_MATH_DEFINES /d2ReducedOptimizeHugeFunctions -DWIN32_LEAN_AND_MEAN -DNOGDI /Zc:preprocessor /arch:AVX2 /std:c++17 -x cuda -DGOOGLE_CUDA=1 --cuda-gpu-arch=sm_35 --cuda-include-ptx=sm_70 --cuda-gpu-arch=sm_70 --cuda-include-ptx=sm_86 --cuda-gpu-arch=sm_86 -Xcuda-fatbinary=--compress-all -DGOOGLE_CUDA=1 -DTENSORFLOW_USE_NVCC=1 -DTENSORFLOW_USE_XLA=1 -DGOOGLE_TENSORRT=1 -DINTEL_MKL -DTENSORFLOW_MONOLITHIC_BUILD /DPLATFORM_WINDOWS /DEIGEN_HAS_C99_MATH /DTENSORFLOW_USE_EIGEN_THREADPOOL /DEIGEN_AVOID_STL_ARRAY /Iexternal/gemmlowp /wd4018 /wd4577 /DNOGDI /DTF_COMPILE_LIBRARY -nvcc_options=relaxed-constexpr -nvcc_options=ftz=true /Fobazel-out/x64_windows-opt/bin/tensorflow/core/kernels/_objs/segment_reduction_ops_gpu/segment_reduction_ops_gpu_0.cu.obj /c tensorflow/core/kernels/segment_reduction_ops_gpu_0.cu.cc\r\n```", "@mraunak, Could you please take a look into this. Thanks", "https://github.com/google/tsl/blob/35680b213852096df805c16cfcc92946c2040f38/tsl/platform/default/integral_types.h#L28 is where the type is documented", "@sachinprasadhs @mraunak @learning-to-play I have found out a way to fix some compilation errors. Screenshots have been uploaded [here](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155144829_link_cuhk_edu_hk/Ej0v7N67oDZAuC6XZuuiiFUBWJbXuYnmDKqTE1adplcIUQ?e=hn0bhx).\r\n\r\nAfter applied the changes, TSL complex operator resolution errors will happened as the log shown. [`DEFINE_BINARY_UPDATE_OPERATOR`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/gpu_kernel_helper.h#L257) cannot choose the Eigen complex operators during compilation. Since the errors are tricky, I am not able to fix. Please also commit the changes to 2.13 and 2.12 branch.", "Hi @learning-to-play, I think TensorFlow GPU or Nvidia Team would be able to resolve this issue as the errors involve CUDA files\r\nHi @johnnkp, thank you for debugging and resolving intermittent errors", "@mraunak I have uploaded patch screenshots [here](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155144829_link_cuhk_edu_hk/Ej0v7N67oDZAuC6XZuuiiFUBWJbXuYnmDKqTE1adplcIUQ?e=hn0bhx). If you are concerned that these changes may break other builds, add macros to make them as Windows specific. ", "Hi @johnnkp, Thank you very much for the solution. could you please create a PR? It would be helpful to review it.", "PR https://github.com/tensorflow/tensorflow/pull/61339 is merged.", "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/60397\">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/60397\">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/60396/checks?check_run_id=12934789977) 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.", "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ " ]

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[ "solutions that you can try:\r\n\r\nEnsure that you have installed the latest version of TensorFlow for Apple Silicon processors. You can download the latest version from the official TensorFlow website or install it via pip.\r\n\r\nTry running your code with an earlier version of TensorFlow. This issue might be specific to TensorFlow 2.12.0. So, try running your code with TensorFlow 2.11.0 or 2.13.0.\r\n\r\nCheck if there is any issue with your custom code. You can try running the TensorFlow MNIST example code without any modification to check if the issue is with TensorFlow or your code.\r\n\r\nCheck if there are any compatibility issues with the Python version that you have installed. You can try running your code with Python 3.8 or 3.9.\r\n\r\nTry updating your Ventura OS and Xcode version to the latest stable version.\r\n\r\nI hope this helps. If the issue persists, please provide more details about the error, including the full stack trace, the GPU model and memory, and other relevant information.", "@ashok-arora - Can you confirm that you followed the steps here for installing TensorFlow on Mac M1? - https://developer.apple.com/metal/tensorflow-plugin/ . This is the suggested installation from TensorFlow Docs for installing on M1 (https://www.tensorflow.org/install/pip#macos)\r\n\r\nI would also recommend that you create a new Conda environment and install inside there to isolate the issue.", "@sampathweb Yes, I have followed the developer guide for installing TensorFlow on mac M1 and had created a new conda environment. ", "@ashok-arora ,\r\n\r\nSInce you are using M1 apple silicon(arm architecture) you need to follow the metal plugin instructions from Apple which are [here](https://developer.apple.com/metal/tensorflow-plugin/). The instructions in Tensorflow forum can be work for Macos with intel chips(X86-64 architecture).\r\n\r\nCan you please also confirm whether you have installed `tensorflow-macos`(Apple package) or `tensorflow`(TF package)? \r\npip install tensorflow-macos fetches Apple wheel and pip install tensorflow fetches TF wheel but this will work for X86_64 architecture only except for tf-nightly version. You can use `pip install tf-nightly` which can install tf-macos-nightly wheel of Apple based on systems architecture. Please try with tf-nightly version also let us know if same behaviour observed in nightly also.\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.", "@SuryanarayanaY Yes, I have installed it through the given instructions. ", "Hi @ashok-arora , Could you please try to install Tensorflow 2.13 using pip for Apple M1 chips.\r\n\r\n`!pip install tensorflow==2.13.0`\r\n\r\n```\r\nimport tensorflow as tf\r\nprint(tf.config.list_physical_devices())\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n```\r\n\r\nAbove command will install the CPU version, for GPU support use the below command and your system will detect both CPU and GPU.\r\n\r\n`!pip install tesnsorflow-metal `\r\n\r\n```\r\nimport tensorflow as tf\r\nprint(tf.config.list_physical_devices())\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nLet me know if you face any issues. 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/60395\">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/60395\">No</a>\n", "How can I reopen the issue?" ]

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[ "@Erangi2020,\r\nI tried to execute the mentioned code and it was failing due to a different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/9d6225a1748eb9d00b71ec039df2c049/untitled1081.ipynb) and also Have you got the chance to take a look at this PR which was raised for the similar error and it was open and under review with the Developer. \r\n\r\nhttps://github.com/keras-team/keras/pull/16216\r\nhttps://github.com/keras-team/keras/pull/18042\r\nhttps://github.com/keras-team/keras/issues/16202\r\n\r\nRequesting you to follow the same issue for the updates. 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/60394\">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/60394\">No</a>\n" ]

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[ "Hi @sagunb Can you please review this PR ? Thank you!" ]

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[ "Hi @Eva-An, I see you are using --jobs 8, --> This specifies a limit on the number of jobs that should be executed concurrently during the execution phase of the build. You can increase the number of jobs to improve the time taken. However, it also depends upon your CPU specification - no. of cores etc", "Hi @mraunak , I made four modifications and below are the settings and results.\r\n1. set --jobs 16 --local_ram_resources=2048, it took 11 hours\r\n2. set --jobs 8 --local_ram_resources=4096, it took 9 and a half hours\r\n3. set --jobs 8 --local_ram_resources=12288, it took 9 and a half hours\r\n4. set --jobs 16 --local_ram_reources=4069, it took 9 hours and 15 minutes", "Hi @Eva-An, thank you for the update. I ran the build command on my end, it took 30-35 minutes, my CPU specification is Intel Xeon with 24 cores and 56 Logical Processors. command used: \r\nbazel --output_user_root F:\\bazelTemp build --config=opt --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package\r\n\r\nI removed --jobs and --local_ram_resources arguments and gave the machine the flexibility to allocate resources on its own. Could you please try just running the command:\r\nbazel --output_user_root F:\\bazelTemp build --config=opt --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package", "Hi @Eva-An ,\r\n\r\nThe build time depends upon the no of CPU specifications like no of cores, Memory resources etc. Also its hard to compare the build time with different flags `--jobs` and `--local_ram_reources`. If jobs are more it is scalable (if enough RAM) and also more the memory allotted more will be the performance likely.\r\n\r\nWe need comparison without any difference in jobs or local_ram_sources etc. Like mentioned in above [comment](https://github.com/tensorflow/tensorflow/issues/60392#issuecomment-1524275096) please try a simple command for both commits you want to test and let us know the results. Request you to use `bazel clean --expunge` command before second build to clear the cache maintained by bazel.\r\n\r\nThanks!\r\n\r\n\r\n", "Hi All,\r\n\r\n I removed --jobs and --local_ram_resources arguments of my command, the test took 8 hours and 15 minutes. Command: bazel --output_user_root F:\\bazelTemp build --config=opt --subcommands //tensorflow/tools/pip_package:build_pip_package\r\n\r\n@SuryanarayanaY We will only execute the bazel command once on a machine.\r\n\r\nThanks,\r\nEva", "Hi @Eva-An, if the problem still persists, please check CPU utilization, and also make sure the CPU is not overloaded with other tasks while running the build command. Please let us know if you are still facing the issue", "Hi @mraunak, when I start building this project, I will kill other tasks. My machine is a virtual machine with Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz 2.69GHz and Windows specifications are Windows Server 2022 Datacenter.\r\nSo far, it still takes about 9 hours to build it on my machine.", "Hi @Eva-An, thank you for the information. Please run the command:\r\nbazel --output_user_root F:\\bazelTemp build --config=opt --jobs=50 -- subcommands //tensorflow/tools/pip_package:build_pip_package > run.log 2>&1\r\n\r\nI believe Intel(R) Xeon(R) Platinum 8168 processors will have 48 logical processors. --jobs flag will force the system to run 48 jobs in parallel. Please share the log file where we can find the number of processors being utilized. e.g. 64 processors are utilized in the screenshot shared below.\r\n\r\n\r\n\r\n", "Hi @mraunak, I tried to build the project with the command your provided, but it comes up with this issue: fatal error C1002: compiler is out of heap space in pass 2. And I set the --local_ram_resources to 28G, this issue still appears.", "Hi, @Eva-An please try the Bazel cache clean command below and then run the build command shared above. pls check if enough space is left on the disk (F:\\), \r\n\r\nbazel --output_user_root=F:\\bazelTemp clean --expunge\r\n\r\nIf the issue still persists, please share the log" ]

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[ "Hi @KaminiSabu, As it is mentioned in the documentation of Tensorflow versions [2.9](https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/DepthwiseConv2D#args), [2.11](https://www.tensorflow.org/versions/r2.11/api_docs/python/tf/keras/layers/DepthwiseConv2D#args) and [2.12](https://www.tensorflow.org/versions/r2.12/api_docs/python/tf/keras/layers/DepthwiseConv2D#args) that the current implementation only supports equal length strides in the row and column dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1. describing the strides argument. This is an expected behavior in all the three versions of Tensorflow. Thank you! ", "Hello @synandi \r\n\r\nBut in the link you shared, against strides, I can see the following entry for 2.9 and 2.11\r\n\r\n\"An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1\"\r\n\r\nThe nonavailability for equal stride support is mentioned only for 2.12\r\n\r\nAnd more importantly, it works in the older installations on a specific machine and not on the new installations I am doing on other machines.\r\n\r\n", "@KaminiSabu Apologies for the confusion. You are right. It is not mentioned in 2.9 and 2.11 doc about the nonavailability for equal stride. Kindly refer to this [issue](https://github.com/keras-team/keras/issues/15667), it is a known issue. The document has been updated in TF v2.12. Thank you! \r\n\r\n ", "Ohh, so it should not work for any previous implementations as well. But it works in an older installation on a specific machine. Any idea how? Was the implementation wrong and hence is not being supported now? I used the same set of commands on that machine and got an output matrix printed:\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\nlayer1 = tf.keras.layers.DepthwiseConv2D(depth_multiplier=2,kernel_size=(1,9),strides=(1,2))\r\nprint(layer1(np.ones((100, 28, 28, 1), dtype=np.float32)))\r\n```", "I'm not sure what has changed since 2.9 in the backend, but the current Argument details is inline with the API behavior.\r\nFor the latest API behavior, please install latest package. 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/60391\">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/60391\">No</a>\n" ]

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[]

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[ "Related to https://github.com/tensorflow/tensorflow/issues/60109\r\nMay be you want to follow-up on that Issue?", "Yeah, it's a duplicate of #60109", "@RickSanchezStoic,\r\nThis is a duplicate of the issue [#60109](https://github.com/tensorflow/tensorflow/issues/60109). Could you please close this issue, since it is already being tracked there? 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 duplicate issue, no need to wait for stale bot", "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/60389\">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/60389\">No</a>\n" ]

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[ "Hi @penpornk,\r\nFor the test failure that https://github.com/tensorflow/tensorflow/pull/60355 introduced, the expected error return that's being tested is unchanged, and the error returned on x86, from `mkl_matmul_op.cc`, is unchanged. So the x86 test should be OK.\r\nThe changes this makes to the error reported by `mkl_batch_matmul_op.cc` (making it consistent with the same error from `mkl_matmul_op.cc`) don't appear to feature in any other tests. So I don't think there are any tests on x86 expecting the old error message from `mkl_batch_matmul_op.cc`.\r\nSo I think there's no need to guard the change.\r\n", "That's great, thanks @penpornk. " ]

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[ "Hi @RameezI\r\nApologies for the delay.\r\nI was able to replicate the issue in Colab using Tensorflow 2.12 and tf-nightly(2.13.0.dev20230424). Please find the gists - [TF v2.12](https://colab.sandbox.google.com/gist/synandi/3182c8600aedacc08ad4f8fa04b27b53/60386.ipynb) & [tf-nightly](https://colab.sandbox.google.com/gist/synandi/a0522fcc8f89b9a254ebc56fbb3dfc2c/60386_nightly.ipynb). It seems like we have to dig deep into the issue, we'll update here soon. Thank you! ", "Hi @RameezI,\r\n\r\nThe two models that you have in your colab are structurally different. Lets say for a `_MAX_RECURSION_DEPTH = N`, \r\nMyModuleMono has ~N objects of type `tf.Module` (`TerminalModule` objects) while in the recursive approach you create double the amount of `tf.Module` objects i.e ~N `MyModule` and ~N `TerminalModule`. The size of the saved model is a result of the inherent difference in the two models. \r\nYour experiment also demonstrates that the iterative approach is better design than recursive for same usecase (which is working as intended in my opinion). \r\nIf you intend to only work with the top level graphs and do not care for saving the full structure, then before saving you could create another module from MyModule that contains only the parts that you intend to save. However, note that it will be a different module than `MyMonoModule` and `MyModule`.\r\nThanks,\r\nHope this helps!\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.", "Hi @silkyarora \r\nThanks a lot for looking into it. Indeed, the structure of the two models are different. The recursive model contains additional symbolic objects that do not enter into computations but makes it easier to define the model in some cases. \r\n\r\nI was basically interested in exporting a new model from recursively constructed model, as you suggested, that does not track/serialize its child models and that how to do it without manually writing the iterative version. It seems like I can unset `_setattr_tracking` attribute of a `tf.Module` to do this? \r\n\r\n" ]

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[ "Hi @sleepingcat4, Thanks for reporting the issue!\r\n\r\nI was able to replicate the issue in Colab using Tensorflow 2.7, please find the gist [here](https://colab.sandbox.google.com/gist/synandi/dbecb1235104ecef478b0c809a58d1f4/60385_error.ipynb). \r\n\r\nI have reshaped `weights` to have shape `(n_layers, n_qubits, 3)` in the quantum_layer. This is because `qml.StronglyEntanglingLayers` expects a tensor of shape `(n_layers, n_qubits, 3)` as input for the weights. Kindly refer to this [doc](https://docs.pennylane.ai/en/stable/code/api/pennylane.StronglyEntanglingLayers.html). Please check the working code [here](https://colab.sandbox.google.com/gist/synandi/34bffecda57235b112c6f2fd86f90efd/60385.ipynb). Thank you!", "Thank you! I followed the pennylane documentation to create VQC, that's why, I missed that reshaping part. Tensorflow is my favourite, looking forward to create more stuff ^^", "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/60385\">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/60385\">No</a>\n" ]

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[ "@pietroorlandi,\r\nThank you for raising the issue. I tried to reproduce the issue on tensorflow v2.12 and facing the issue with the dataset. Could you please provide the TFRECORD_FILE_TRAIN dataset/tfrecord to analyse the issue in an effective way. Also could you please provide the GPU device information where you are trying. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/6c6d16e7b29a057b7c0133d6eb18ea2b/untitled1080.ipynb). Thank you!", "@tilakrayal thanks for your reply!\r\nHow can I upload my tfrecords dataset in the gist? I tried with \"upload in session storage space\", it is a file called _train.tfrecords_.\r\nThe tests that I made to say that prefetch optimization doesn't change the performance were done with two GPU: RTX2080 and RTX2060 Super", "I too have noticed the same problem with tensorflow 2.10 and tensorflow 2.11: I don't see any perfomance gain with prefetch optimization compared to using a parallel version of map function (both setting manually the num_parallel_calls and using AUTOTUNE).\r\n(as GPU I'm using a NVIDIA GeForce GTX 1050Ti)", "@tilakrayal Is there any news?\r\nI saw that, as it mentioned [here](https://www.reddit.com/r/MachineLearning/comments/a08fx6/d_why_tfdata_is_so_much_better_than_feed_dict_and/) it seems that _prefetch_ does not overlap the computation between the CPU and GPU and you don't have the behavior that while GPU train _batch i_ the CPU processes _batch i+1_. It seems that prefetch function overlap data reading and data processing. Is this right?\r\nAlways [here](https://www.reddit.com/r/MachineLearning/comments/a08fx6/d_why_tfdata_is_so_much_better_than_feed_dict_and/) I saw that exist a function called _prefetch_to_device_ that would allow the overlap of computation between CPU and GPU. I tried it but again it does not provide any improvement.\r\nThank you in advance for the answers that will be provided to me" ]

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[ "Hi @cantonios Can you please review this PR ? Thank you!", "This is currently failing the following test internally: `//tensorflow/python/kernel_tests/math_ops:segment_reduction_ops_test_cpu`. It looks like it passes externally though. The error is:\r\n\r\n```\r\n[ RUN ] SparseSegmentReductionOpTest.testGradientV2SegmentsInvalid4\r\nF0607 16:23:53.050092 384 shape_inference.cc:113] Check failed: output(i).IsSet() 1 for {{node SparseSegmentSumGradV2}} = SparseSegmentSumGradV2[T=DT_FLOAT, Tidx=DT_INT32, Tsegmentids=DT_INT32](Const, SparseSegmentSumGradV2/indices, SparseSegmentSumGradV2/segment_ids, SparseSegmentSumGradV2/dense_output_dim0)\r\n```\r\n\r\nPerhaps it only fails internally due to a difference in build flags. Can you see if the following passes, and if not, fix the issue?\r\n\r\n```bash\r\nbazel test -c opt --copt=-UNDEBUG --test_env='TF2_BEHAVIOR=1' //tensorflow/python/kernel_tests/math_ops:segment_reduction_ops_test_cpu\r\n```\r\n\r\nIf this passes for you, we'll debug internally.", "Thanks Reed, I've fixed it." ]

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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/60379/checks?check_run_id=12884492198) 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.", "@pgpetrak, can you update cudnn frontend internally and review this PR?\r\n\r\nCC @kaixih ", "Will do.", "Hi @pgpetrak Any update on this PR? Please. Thank you!" ]

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[ "Hi @penpornk Can you please review this PR ? Thank you!", "Thank you for the review and for the good suggestions. I have addressed the comments. I also included a bug fix in this [line](https://github.com/tensorflow/tensorflow/pull/60378/commits/8ba59847e722c692885ff1eaa49c94cfd01b121e#diff-273ed23e668174f96de6482ae9dc33fa1e59b6c7761690ad00d42dfb90c328ecR486).", "@penpornk I have addressed your review comments. Can you please take a look?", "@penpornk Thanks for approving this PR. Please let me know if you need anything else from my end.", "@bhavani-subramanian Could you please help fix [Windows Bazel failures](https://source.cloud.google.com/results/invocations/eba509ee-fed0-4215-b671-685ec6497b2a/log)? Thank you!\r\n\r\n```\r\n.\\tensorflow/core/kernels/mkl/mkl_matmul_ops_common.h(375): error C2059: syntax error: '#'\r\n.\\tensorflow/core/kernels/mkl/mkl_matmul_ops_common.h(249): note: while compiling class template member function 'void tensorflow::MklDnnMatMulFwdPrimitive<T,T,T,T,T>::Setup(const tensorflow::MklDnnMatMulFwdParams &)'\r\n with\r\n [\r\n T=float\r\n ]\r\n.\\tensorflow/core/kernels/mkl/mkl_matmul_ops_common.h(129): note: see reference to function template instantiation 'void tensorflow::MklDnnMatMulFwdPrimitive<T,T,T,T,T>::Setup(const tensorflow::MklDnnMatMulFwdParams &)' being compiled\r\n with\r\n [\r\n T=float\r\n ]\r\ntensorflow/core/kernels/mkl/mkl_matmul_op_fused.cc(145): note: see reference to class template instantiation 'tensorflow::MklDnnMatMulFwdPrimitive<T,T,T,T,T>' being compiled\r\n with\r\n [\r\n T=float\r\n ]\r\ntensorflow/core/kernels/mkl/mkl_matmul_op_fused.cc(61): note: while compiling class template member function 'void tensorflow::MklFusedMatMulOp<tensorflow::CPUDevice,float,true>::Compute(tensorflow::OpKernelContext *)'\r\ntensorflow/core/kernels/mkl/mkl_matmul_op_fused.cc(341): note: see reference to class template instantiation 'tensorflow::MklFusedMatMulOp<tensorflow::CPUDevice,float,true>' being compiled\r\n[17,997 / 20,875] Compiling tensorflow/compiler/mlir/lite/utils/tftext_utils.cc; 40s local, remote-cache ... (9 actions, 8 running)\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nINFO: Elapsed time: 258.481s, Critical Path: 84.48s\r\nINFO: 17584 processes: 12330 remote cache hit, 4755 internal, 499 local.\r\nFAILED: Build did NOT complete successfully\r\n```" ]

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[ "@reedwm could you please help run Google internal tests?", "Hi @reedwm does this PR pass the internal tests?", "Hi @reedwm Can you please assist on above comments from @Young768. Thank you!", "@Young768, sorry for the delay. This causes compile time regressions, and in some cases compile time increases 300x. I'm worried that if I accept this, it will be rolled back, and rolling back large PRs is messy.\r\n\r\nThe ideal solution would be to improve compile time performance of cuDNN's relu fusions. Can you ask the cuDNN team to see if that is possible? But even if possible, this will take a while. In the meantime, can you guard this feature behind a [debug option flag](https://github.com/tensorflow/tensorflow/blob/9804d0b5eba7a17fce316573be32d2593c8acdd9/tensorflow/compiler/xla/debug_options_flags.cc#L176)? Then after this PR is submitted, we can then try turning the flag on by default, and rolling back that one-line change is easier than rolling back this entire PR. Perhaps the flag can be called `xla_gpu_extra_conv_relu_fusions`, but maybe you can think of a better name.", "Hi @reedwm, what do you mean by `compile time`? Do you mean the cudnn runtime compilation time during the model training/inference? If yes, maybe we can simply use `xla_gpu_use_runtime_fusion`, which was introduced in our first cudnn runtime XLA PR. At that time, it was turned on by default but it seems we now want to turn it off to be more conservative.\r\n\r\nAlso, can you show us in which cases you saw the 300x slowdown? We can use them as the motivation examples to ask our cudnn team to improve the compilation overhead. They are looking for such cases quite enthusiastically.", "For reference, all these 3 extra fusions are handled here: https://github.com/tensorflow/tensorflow/pull/60377/files#diff-0476808ba47a3d580457c1189ffc5677cea8f4fc0fcce600e1f26e342937cc26R82.", "> Hi @reedwm, what do you mean by `compile time`? Do you mean the cudnn runtime compilation time during the model training/inference? If yes, maybe we can simply use `xla_gpu_use_runtime_fusion`, which was introduced in our first cudnn runtime XLA PR. At that time, it was turned on by default but it seems we now want to turn it off to be more conservative.\r\n> \r\n> Also, can you show us in which cases you saw the 300x slowdown? We can use them as the motivation examples to ask our cudnn team to improve the compilation overhead. They are looking for such cases quite enthusiastically.\r\n\r\nI have no details about the compilation regression that @reedwm mentioned, but to me it sounds like using xla_gpu_runtime_fusion flag for this (and disabling it by default) could also be a solution until the problem with compile time is fixed.", "I'm going to try to submit this internally at Google. It will require some minor modifications (that I will take care of).", "This is closed by https://github.com/tensorflow/tensorflow/commit/24f323096dd48615ca59c208708ca2cb8d88685f.\r\n\r\nHope it sticks.", "Unfortunately I have had to disable leaky-relu fusion. On Ampere Waymo hit a convolution that got 0 algorithms during autotuning. Therefore the model fails to run.\r\n\r\nThe failing testcase will be documented in the code so you all can have a look at your leisure.\r\n\r\nrelu6 has not gotten rolled back. Yet. :)", "https://github.com/tensorflow/tensorflow/commit/cb73612a496950fa5ace9ee9967757edb2e5a493 disables leaky-relu and has a testcase that was breaking for us with cudnn 8.9.0.", "And I have now found a case where we have the same behavior with kRelu6: cudnn provides 0 algorithms for running a kRelu6 fusion. I will have to disable that as well. :(", "I entirely disabled cudnn runtime fusion in https://github.com/tensorflow/tensorflow/commit/83bbee775f381a481cbfad4e910ce7fbafa93df1.\r\n\r\nPlease see that commit for testcases that illustrate the problem we're hitting.", "Thanks for the finding. Let me double check it and may need to file a bug to our cudnn team to fix.", "I think the problem may be that we were missing the input/output-channels divisible by 2 check for relu6 and leaky-relu. This check was present for kElu but it looks like it should have been general." ]

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[ "Hi @penpornk Can you please review this PR ? Thank you!", "Hi @penpornk Can you please review this PR ? Thank you!", "Thank you for reviewing the PR. I have addressed your comments." ]

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[ "Hi @penpornk Can you please review this PR ? Thank you!", "Hi @penpornk Can you please review this PR ? Thank you!", "@penpornk I have addressed your review comments. Please take a look. Thanks." ]

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[ "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!" ]

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[ "Hi @MichaelHudgins Can you please review this PR ? Thank you!", "@elfringham let me know when you want to get this merged. ", "Hi @elfringham Any update on this CL? Please. Thank you!", "@gbaned The agreement here was to merge after 2.13 release.", "@MichaelHudgins We would like to have this PR merged now please. It adds the toolchain but does not use it by default. This will allow use and testing by other CI systems such as Linaro and ARM.", "Sorry on the delay, was OOO last week. Can you please rebase?", "@MichaelHudgins I cannot see what the failure on the feedback/copybara check is about. Is it something I need to fix?", "Its from a broken windows build from the last LLVM integrate, so not on your end. " ]

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[ "@sumit0190,\r\nThank you for the issue. I tried to execute the mentioned code on MacOS M1 tensorflow v2.11 and tf-nightly and it was executed without any assertion error. Kindly find the screenshot for the reference and also the output for the code was also as expected. Thank you!\r\n\r\n\r\n\r\n\r\n", "@tilakrayal Thanks for trying this out. This only happens on the binaries from `conda-forge` and `anaconda`, and when building from source. I assume the compiler or something else in our setup has something to do with it, but without any debugging information it's hard to understand what. Would you be able to provide us with some instructions on how we can debug this further?", "@sumit0190,\r\nCould you please confirm whether you are following the metal plugin instructions for installing tensorflow? 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/60369\">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/60369\">No</a>\n" ]

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[ "Hi @tomchen1000 ,\r\n\r\nCan you please cross check and confirm the command ? Is it `--config=opt `? Your command getting error below, which is obvious.\r\n\r\n```\r\n(bazel3) suryanarayanay@surya-ubuntu20:~/tensorflow$ bazel build --config opt --define framework_shared_object=false //tensorflow/tools/pip_package:build_pip_package\r\nERROR: Config value 'opt' is not defined in any .rc file\r\n```\r\nWhat are the configurations you have opted in `./configure.py` step if you have used `--config=opt` ?\r\n\r\nI tried building on a Ubuntu VM by omitting `--config=opt` from your command and it seems similar errors with different ops replicated as reported by you. You may please check and confirm.\r\n\r\n```\r\n(bazel3) suryanarayanay@surya-ubuntu20:~/tensorflow$ bazel build --define framework_shared_object=false //tensorflow/tools/pip_package:build_pip_package\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=100\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --define=build_with_onednn_v2=true --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (612 packages loaded, 36319 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /home/suryanarayanay/tensorflow/tensorflow/cc/BUILD:673:22: Linking tensorflow/cc/ops/training_ops_gen_cc [for host] failed: (Exit 1): gcc failed: error executing command /usr/bin/gcc @bazel-out/host/bin/tensorflow/cc/ops/training_ops_gen_cc-2.params\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::TensorDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::TensorDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::TensorDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::ConvolutionDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::TensorDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_TensorDescriptorProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_TensorDescriptorProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_TensorDescriptorProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::ConvolutionDescriptorProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_ConvolutionDescriptorProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::~ConvolutionProto(): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::~TensorDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::~ConvolutionProto(): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::~TensorDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::~ConvolutionProto(): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::~TensorDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::~ConvolutionProto(): error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto::~ConvolutionDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::Clear(): error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto::~ConvolutionDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::Clear(): error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::~TensorDescriptorProto()'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::TensorDescriptorProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:function tensorflow::ConvolutionProto::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::ConvolutionDescriptorProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/core/protobuf/_objs/conv_autotuning_proto_cc_impl/conv_autotuning.pb.o:conv_autotuning.pb.cc:descriptor_table_tensorflow_2fcore_2fprotobuf_2fconv_5fautotuning_2eproto_deps: error: undefined reference to 'descriptor_table_tensorflow_2fcompiler_2fxla_2fstream_5fexecutor_2fdnn_2eproto'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::clear_key(): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::~AlgorithmProto()'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::AlgorithmProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::AlgorithmProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::AlgorithmProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::AlgorithmProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_AlgorithmProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::clear_key(): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::~AlgorithmProto()'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::_InternalParse(char const*, google::protobuf::internal::ParseContext*): error: undefined reference to 'stream_executor::dnn::AlgorithmProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::AlgorithmProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::AlgorithmProto* google::protobuf::Arena::CreateMaybeMessage<stream_executor::dnn::AlgorithmProto>(google::protobuf::Arena*)'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_AlgorithmProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult_FailureResult::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::AlgorithmProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:function tensorflow::AutotuneResult::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::AlgorithmProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/tsl/protobuf/_objs/autotuning_proto_cc_impl/autotuning.pb.o:autotuning.pb.cc:descriptor_table_tensorflow_2ftsl_2fprotobuf_2fautotuning_2eproto_deps: error: undefined reference to 'descriptor_table_tensorflow_2ftsl_2fprotobuf_2fdnn_2eproto'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:function xla::gpu::CudnnConvBackendConfig::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:function xla::gpu::CudnnConvBackendConfig::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&): error: undefined reference to 'stream_executor::dnn::_AlgorithmProto_default_instance_'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:function xla::gpu::CudnnConvBackendConfig::~CudnnConvBackendConfig(): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::~AlgorithmProto()'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:function xla::gpu::CudnnConvBackendConfig::Clear(): error: undefined reference to 'stream_executor::dnn::AlgorithmProto::~AlgorithmProto()'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:function xla::gpu::CudnnConvBackendConfig::ByteSizeLong() const: error: undefined reference to 'stream_executor::dnn::AlgorithmProto::ByteSizeLong() const'\r\nbazel-out/host/bin/tensorflow/compiler/xla/service/gpu/_objs/backend_configs_cc_impl/backend_configs.pb.o:backend_configs.pb.cc:descriptor_table_tensorflow_2fcompiler_2fxla_2fservice_2fgpu_2fbackend_5fconfigs_2eproto_deps: error: undefined reference to 'descriptor_table_tensorflow_2fcompiler_2fxla_2fstream_5fexecutor_2fdnn_2eproto'\r\ncollect2: error: ld returned 1 exit status\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 757.694s, Critical Path: 75.05s\r\nINFO: 769 processes: 68 internal, 701 local.\r\n```\r\n\r\n", "@tomchen1000 ,\r\n\r\n One observation is that even though you are using the flag `--define framework_shared_object=false` its not being considered by bazel as in bazel the default setting is `framework_shared_object=true` and this can be override with `config=monolithic`. Please have a look at source here.\r\nhttps://github.com/tensorflow/tensorflow/blob/f318efaa1175af1e76a3b75f8ebae4f776518cb9/.bazelrc#L218-L222\r\n\r\nFrom the logs attached above you can see `framework_shared_object=true` under `Info` log sets by default and it is not override anywhere. \r\n\r\nWhen I added `config=monolithic` now `framework_shared_object=false` has been set. You can find the command and logs below.\r\n\r\n```\r\n(bazel3) suryanarayanay@surya-ubuntu20:~/tensorflow$ bazel build --config=monolithic //tensorflow/tools/pip_package:build_pip_package\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=171\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:monolithic in file /home/suryanarayanay/tensorflow/.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false\r\nINFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --define=build_with_onednn_v2=true --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Build option --define has changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (0 packages loaded, 36331 targets configured).\r\nINFO: Found 1 target...\r\n[9,722 / 12,411] 8 actions running\r\n Compiling tensorflow/compiler/xla/service/hlo_memory_scheduler.cc; 18s local\r\n Compiling tensorflow/compiler/xla/service/convolution_group_converter.cc; 12s local\r\n Compiling tensorflow/compiler/xla/service/dot_decomposer.cc; 12s local\r\n Compiling tensorflow/compiler/xla/service/layout_assignment.cc; 7s local\r\n Compiling tensorflow/compiler/xla/service/float_normalization.cc; 7s local\r\n Compiling tensorflow/compiler/xla/service/spmd/stateful_rng_spmd_partitioner.cc; 6s local\r\n Compiling tensorflow/compiler/xla/service/spmd/gather_scatter_handler.cc; 5s local\r\n Compiling tensorflow/compiler/xla/service/spmd/dot_handler.cc; 3s local\r\n```\r\n\r\nPlease try the above command and let us if it works. Thanks!", "@SuryanarayanaY \r\n\r\nNo, it doesn't work either. \r\n\r\nGot this error: \r\nImportError: libtensorflow_framework.so.2: cannot open shared object file: No such file or directory.\r\n\r\nSee the command and logs below.\r\n\r\n```\r\nroot@cb0af95e04e4:/tensorflow# bazel build --config=monolithic //tensorflow/tools/pip_package:build_pip_package\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=165\r\nINFO: Reading rc options for 'build' from /tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true\r\n --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=\r\nwith_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link\r\n_static_libraries_once=false\r\nINFO: Reading rc options for 'build' from /tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorf\r\nlow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflo\r\nw/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo\r\n_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensor\r\nflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback\r\n/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow\r\n/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/c\r\npp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/\r\ncore/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:monolithic in file /tensorflow/.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=fals\r\ne --experimental_link_static_libraries_once=false\r\nINFO: Found applicable config definition build:linux in file /tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wn\r\no-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-unknown-warning --copt=-Wno-array-parameter --copt=-Wno-stringop-overflow --copt=-Wno-array-boun\r\nds --copt=-Wunused-result --copt=-Werror=unused-result --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INC\r\nLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_co\r\nncurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (544 packages loaded, 31178 targets configured).\r\nINFO: Found 1 target...\r\n\r\nERROR: /tensorflow/tensorflow/BUILD:1426:19: Executing genrule //tensorflow:tf_python_api_gen_v2 failed: (Exit 1): bash failed: error executing command /bin/bash -c\r\n... (remaining 1 argument skipped)\r\n2023-04-24 21:41:16.396194: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)\r\n to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nTraceback (most recent call last):\r\n File \"/root/.cache/bazel/_bazel_root/68a62076e91007a7908bc42a32e4cff9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/create_tensorflow.python_api_tf_pytho\r\nn_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 62, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: libtensorflow_framework.so.2: cannot open shared object file: No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/root/.cache/bazel/_bazel_root/68a62076e91007a7908bc42a32e4cff9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/create_tensorflow.python_api_tf_pytho\r\nn_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/tools/api/generator/create_python_api.py\", line 22, in <module>\r\n from tensorflow.python.tools.api.generator import doc_srcs\r\n File \"/root/.cache/bazel/_bazel_root/68a62076e91007a7908bc42a32e4cff9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/create_tensorflow.python_api_tf_pytho\r\nn_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/__init__.py\", line 36, in <module>\r\n from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow\r\n File \"/root/.cache/bazel/_bazel_root/68a62076e91007a7908bc42a32e4cff9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/create_tensorflow.python_api_tf_pytho\r\nn_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 77, in <module>\r\n raise ImportError(\r\nImportError: Traceback (most recent call last):\r\n File \"/root/.cache/bazel/_bazel_root/68a62076e91007a7908bc42a32e4cff9/execroot/org_tensorflow/bazel-out/k8-opt/bin/tensorflow/create_tensorflow.python_api_tf_pytho\r\nn_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 62, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: libtensorflow_framework.so.2: cannot open shared object file: No such file or directory\r\n\r\n\r\nFailed to load the native TensorFlow runtime.\r\nSee https://www.tensorflow.org/install/errors for some common causes and solutions.\r\nIf you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nERROR: /tensorflow/tensorflow/lite/python/BUILD:68:10 Middleman _middlemen/tensorflow_Slite_Spython_Stflite_Uconvert-runfiles failed: (Exit 1): bash failed: error ex\r\necuting command /bin/bash -c ... (remaining 1 argument skipped)\r\nINFO: Elapsed time: 5936.681s, Critical Path: 3536.96s\r\nINFO: 13312 processes: 1415 internal, 11897 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\n```\r\n\r\n", "The same error occurs in TensorFlow v2.11.0, v2.12.0 and v2.13.0-rc0.\r\n\r\nAll of the following will produce the same error.\r\n\r\n```bash\r\nbazel build \\\r\n--config=monolithic \\\r\n--config=noaws \\\r\n--config=nohdfs \\\r\n--config=nonccl \\\r\n--config=v2 \\\r\n--define=tflite_pip_with_flex=true \\\r\n--define=tflite_with_xnnpack=true \\\r\n--copt=\"-Wno-stringop-overflow\" \\\r\n--ui_actions_shown=20 \\\r\n//tensorflow/tools/pip_package:build_pip_package\r\n```\r\n```bash\r\nbazel build \\\r\n--config=monolithic \\\r\n--config=noaws \\\r\n--config=nohdfs \\\r\n--config=nonccl \\\r\n--config=v2 \\\r\n--define=tflite_with_xnnpack=true \\\r\n--copt=\"-Wno-stringop-overflow\" \\\r\n--ui_actions_shown=20 \\\r\n//tensorflow/tools/pip_package:build_pip_package\r\n```\r\n```bash\r\nbazel build \\\r\n--config=opt \\\r\n--config=noaws \\\r\n--config=nohdfs \\\r\n--config=nonccl \\\r\n--config=v2 \\\r\n--define=tflite_pip_with_flex=true \\\r\n--define=tflite_with_xnnpack=true \\\r\n--copt=\"-Wno-stringop-overflow\" \\\r\n--ui_actions_shown=20 \\\r\n//tensorflow/tools/pip_package:build_pip_package\r\n```\r\n```bash\r\nbazel build \\\r\n--config=noaws \\\r\n--config=nohdfs \\\r\n--config=nonccl \\\r\n--config=v2 \\\r\n--define=tflite_pip_with_flex=true \\\r\n--define=tflite_with_xnnpack=true \\\r\n--copt=\"-Wno-stringop-overflow\" \\\r\n--ui_actions_shown=20 \\\r\n//tensorflow/tools/pip_package:build_pip_package\r\n```", "Hi, Were you able to find a resolution for? \r\n```\r\nImportError: libtensorflow_framework.so.2: cannot open shared object file: No such file or directory.\r\n```" ]

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[ "Hi @gptshubham595 Thanks for reporting the issue.\r\n\r\nWe see that the colab file you have shared has data access issues. Could you please provide a standalone code or a toy tflite model inorder to reproduce the issue?\r\n\r\nThanks.", "[NoiseSuppressionModel.h5](https://drive.google.com/file/d/1xUQklFyuYFV_ZsuO8Rskc52xsuSPZCip/view?usp=share_link)\r\n[Model.Tflite]( https://drive.google.com/file/d/1xUQklFyuYFV_ZsuO8Rskc52xsuSPZCip/view?usp=sharing)", "Hi @gptshubham595 \r\n\r\nThe input signature of the TFLite model is `'shape_signature': array([ -1, 12000, 1]`. I have tested with random data with shape `[1,1200,1]` and model runs without any error.\r\n\r\nPlease find the colab gist [here](https://colab.research.google.com/gist/pjpratik/bd48804cc8d40239812079b5a249aac3/60367.ipynb) and let us know if it helps.\r\n\r\nThanks.", "I tried this in android but i'm getting this error\r\n\r\n```\r\nprivate fun applyModel() {\r\n val inputFloatArray = Array(1) { Array(inputAudioData.size) { FloatArray(1) } } //1,1200,1\r\n\r\n val outputFloatArray = inputFloatArray //Attempt 1\r\n val outputFloatArray = Array(1) { Array(inputAudioData.size) { FloatArray(1) } } //Attempt 2\r\n\r\n Log.d(\"tflite\", \"Model input data: ${inputFloatArray.toString()}\")\r\n\r\n tflite!!.run(inputFloatArray, outputFloatArray)\r\n\r\n Log.d(\"tflite\", \"Model output data: ${outputFloatArray.toString()}\")\r\n }\r\n```\r\n\r\n\r\n```\r\n java.lang.IllegalStateException: Internal error: Unexpected failure when preparing tensor allocations: tensorflow/lite/kernels/reshape.cc:85 num_input_elements != num_output_elements (1200 != 0)\r\n Node number 6 (RESHAPE) failed to prepare.\r\n \tat org.tensorflow.lite.NativeInterpreterWrapper.allocateTensors(Native Method)\r\n \tat org.tensorflow.lite.NativeInterpreterWrapper.allocateTensorsIfNeeded(NativeInterpreterWrapper.java:308)\r\n \tat org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:248)\r\n \tat org.tensorflow.lite.InterpreterImpl.runForMultipleInputsOutputs(InterpreterImpl.java:101)\r\n \tat org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:77)\r\n \tat org.tensorflow.lite.InterpreterImpl.run(InterpreterImpl.java:94)\r\n \t```", "Hi @gptshubham595 \r\n\r\nIt is hard to say from the code snippet, but as mentioned earlier please check the input tensor shapes and resize before during inference.\r\n\r\nAlso, If the loaded model in TFLite does not have any defined batch size, converter will take the batch size as 1, and when you evaluate it with the different batch size, you are likely to end up with the problem which you are facing.\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/60367\">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/60367\">No</a>\n" ]

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[ "@hujiajie,\r\nThank you for reporting the issue. Have you got the chance to have a look at this open issue https://github.com/tensorflow/tensorflow/issues/57747 https://github.com/tensorflow/tensorflow/issues/57757 where a similar issue has been reported and it is still open.\r\n Also I request to follow the similar feature which has been proposed to have the updates on the similar issue. Thank you!", "I can follow on #57757, thanks!\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/60366\">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/60366\">No</a>\n" ]

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[]

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[ "@dsbyprateekg \r\n\r\nI was trying to check the issue and was able to successfully install in colab. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/6450e69ec889a4090f1b4a1d272508d3/60364.ipynb). We may have downgrade numpy version in colab to solve the dependency issues.\r\n\r\nCan you try giving `--user` access and see if it works?\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/60364\">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/60364\">No</a>\n" ]

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[ "Hi @JustASquid ,\r\n\r\nPlease submit a code snippet to replicate the issue.We can't say whether the issue is related to Distribution strategy bug or bug in code it self. As you mentioned Sage maker as GPU I am assuming you are using AWS Sage maker, and also going through the error log it seems the error might be due to NCCL communication problems. Please find the attached sources [link1](https://github.com/NVIDIA/nccl/issues/352) and [link2](https://github.com/NVIDIA/nccl/blob/master/ext-net/README.md) here and let us know if it helps. ", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60363\">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/60363\">No</a>\n" ]

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[ "@mihaimaruseac, same here. I am trying to build TensorFlow 2.12 in docker:\r\n\r\n```\r\nsudo docker run --gpus all -it -w /tensorflow -v /home/vishnya/tensorflow:/tensorflow -v $PWD:/mnt -e HOST_PERMS=\"\\\\((id -u):\\\\)(id -g)\" tensorflow/tensorflow:devel-gpu bash\r\nbazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package\r\n```", "When building from master, please always give the commit, given TF moves fast forward.\r\n\r\nDoes it reproduce if you switch to a newer commit?", "Sorry, I misread the link to the `.bazelrc` in master branch.\r\n\r\nCan you try a `bazel clean --expunge` and then build again?\r\n\r\nAdding @angerson and @MichaelHudgins for the Docker stuff too.", "> When building from master, please always give the commit, given TF moves fast forward.\r\n> \r\n> Does it reproduce if you switch to a newer commit?\r\n\r\n@mihaimaruseac, TensorFlow fails to build from `r2.12` branch. I haven't tried master, because I try to locally rebuild 2.12 release.\r\n\r\n> Can you try a `bazel clean --expunge` and then build again?\r\n\r\nI'll try it.", "> Can you try a `bazel clean --expunge` and then build again?\r\n\r\n@mihaimaruseac, it didn't help. The same error:\r\n\r\n```\r\nERROR: /tensorflow/tensorflow/compiler/xla/stream_executor/cuda/BUILD:519:11: Compiling tensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/cuda/_objs/cuda_graph/cuda_graph.pic.d ... (remaining 108 arguments skipped)\r\ntensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc: In function ‘tsl::StatusOr<stream_executor::gpu::OwnedCudaGraph> stream_executor::gpu::CaptureCudaGraph(stream_executor::Stream*, absl::lts_20220623::AnyInvocable<tsl::Status()>, cudaStreamCaptureMode)’:\r\ntensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc:135:21: error: ‘cudaGraphDebugDotFlagsVerbose’ was not declared in this scope\r\n 135 | int flags = cudaGraphDebugDotFlagsVerbose;\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc:136:24: error: ‘cudaGraphDebugDotPrint’ was not declared in this scope\r\n 136 | if (auto err = cudaGraphDebugDotPrint(graph, file.c_str(), flags);\r\n | ^~~~~~~~~~~~~~~~~~~~~~\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\n```", "Could it be because 2.12 uses cuDNN 8.6, Cuda 11.8?", "> Could it be because 2.12 uses cuDNN 8.6, Cuda 11.8?\r\n\r\n```\r\nnvcc --version\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2021 NVIDIA Corporation\r\nBuilt on Sun_Feb_14_21:12:58_PST_2021\r\nCuda compilation tools, release 11.2, V11.2.152\r\nBuild cuda_11.2.r11.2/compiler.29618528_0\r\n```\r\n\r\nHmm, maybe I am using the wrong docker container. What's the right docker container with fresh cuda to build 2.12?", "Tagging @angerson ", "Now I am trying the following docker (at least it has correct CUDA version):\r\n\r\n```\r\nsudo docker run --rm --gpus all -it -w /tensorflow -v /home/vishnya/tensorflow:/tensorflow -v $PWD:/mnt -e HOST_PERMS=\"\\\\((id -u):\\\\)(id -g)\" tensorflow/build:2.12-python3.8 bash\r\nbazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package\r\n```", "As for docker containers using the tensorflow/build ones would be the correct ones. See: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles.\r\n\r\nI agree that it likely is the mismatch on the original issue as well. The root level .bazelrc should get an update in the next few days as clang is migrated. Until then https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/gpu_gcc.bazelrc has the bazelrc values being used in TF's CI. \r\n\r\n", "Thanks Michael & Mihai. Those instructions Michael linked should work.\r\n\r\nI'll have to look into updating the https://www.tensorflow.org/install/source#docker_linux_builds docs. I guess they never got changed, as the SIG Build containers were not initially planned to be official.", "I succeeded building TensorFlow 2.12 in `tensorflow/build:2.12-python3.8` docker.", "Thank you all. @SweetVishnya @angerson @MichaelHudgins @mihaimaruseac \r\nThe relevant documents [build from source](https://www.tensorflow.org/install/source) have been updated to the latest version.well done!\r\n\r\n\r\nBTW:\r\nbazel.rc [TensorRT 7 for CUDA 11.1 is compatible with CUDA 11.2](https://github.com/tensorflow/tensorflow/blob/master/.bazelrc#L492)\r\nCUDA 11.2 must be [11.2 update 1](https://developer.nvidia.com/cuda-toolkit-archive),not 11.2 update 2, refer to here [tensorrt-723/support-matrix](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-723/support-matrix/index.html)\r\n\r\nnvrtc is deliver inside the CUDA toolkit, you can also install it separate, I find the links in:\r\n\r\nubuntu1804\r\nhttps://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-nvrtc-11-1_11.1.105-1_amd64.deb\r\nhttps://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-nvrtc-dev-11-1_11.1.105-1_amd64.deb\r\n\r\nubuntu2004\r\nhttps://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2004/x86_64/cuda-nvrtc-11-1_11.1.105-1_amd64.deb\r\nhttps://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2004/x86_64/cuda-nvrtc-dev-11-1_11.1.105-1_amd64.deb\r\n\r\nubuntu2204\r\nhttps://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2204/x86_64/cuda-nvrtc-11-1_11.1.105-1_amd64.deb https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2204/x86_64/cuda-nvrtc-dev-11-1_11.1.105-1_amd64.deb", "In this case, we can close this?", "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/60362\">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/60362\">No</a>\n", "And，I sucessed to build the latest 2.12 version with Cuda11.8+cuDNN8.6+Tensorrt**8.5.2.2**+JAX+Flax under Ubuntu2004 at last.\r\n\r\nyou will continue to meet new \"DataType kFP8\" error as shown in the following information if you use the tensorrt version between [8.5.3](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-853/api/c_api/namespacenvinfer1.html#a83aed11a1c160f30dcd13809678bdd29) and [8.6.0](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-860-ea/api/c_api/namespacenvinfer1.html#a83aed11a1c160f30dcd13809678bdd29) of TensorRT. (API Reference: [8.5.3](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-853/api/c_api/namespacenvinfer1.html#a83aed11a1c160f30dcd13809678bdd29), [8.6.0](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-860-ea/api/c_api/namespacenvinfer1.html#a83aed11a1c160f30dcd13809678bdd29))\r\n\r\nExecution platform: @local_execution_config_platform//:platform\r\ntensorflow/compiler/tf2tensorrt/convert/weights.cc: In member function ‘size_t tensorflow::tensorrt::convert::TRT_ShapedWeights::size_bytes() const’:\r\ntensorflow/compiler/tf2tensorrt/convert/weights.cc:61:10: error: enumeration value ‘kFP8’ not handled in switch [-Werror=switch]\r\n 61 | switch (type_) {\r\n | ^\r\ncc1plus: some warnings being treated as errors\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nINFO: Elapsed time: 1276.053s, Critical Path: 83.16s\r\nINFO: 8084 processes: 3883 internal, 4201 local.\r\nFAILED: Build did NOT complete successfully\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/60362\">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/60362\">No</a>\n" ]

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[ "Thanks for opening the issue, I have created a bug internally to update the document under list of community contributions. ", "@sachinprasadhs \r\nThank you for your help with this! I will continue following this ticket until we get it all updated and the links checked etc.", "Tag @zmughal and @jjn1056 who worked on the Perl code with me", "To get Perl added to the list of supported TensorFlow API languages on the TensorFlow website, you can submit a pull request to the TensorFlow website's GitHub repository. Here are the steps to follow:\r\n\r\nGo to the TensorFlow website's GitHub repository: https://github.com/tensorflow/tensorflow\r\n\r\nClick on the \"Fork\" button on the top-right corner to create a fork of the repository.\r\n\r\nClone the forked repository to your local machine using the following command: git clone https://github.com/<your_username>/tensorflow.git\r\n\r\nCreate a new branch for your changes using the following command: git checkout -b add-perl-api\r\n\r\nNavigate to the _data/api_docs/languages.yml file in the repository and open it in a text editor.\r\n\r\nAdd the following lines to the file:\r\n\r\narduino\r\nCopy code\r\n- title: \"Perl\"\r\n url: \"https://github.com/EntropyOrg/perl-AI-TensorFlow-Libtensorflow\"\r\nSave the file and commit your changes using the following commands:\r\n\r\nsql\r\nCopy code\r\ngit add _data/api_docs/languages.yml\r\ngit commit -m \"Add Perl to supported API languages\"\r\nPush the changes to your forked repository using the following command: git push origin add-perl-api\r\n\r\nGo to the original TensorFlow website's GitHub repository and click on the \"New pull request\" button.\r\n\r\nSelect your forked repository and the add-perl-api branch.\r\n\r\nProvide a brief description of your changes and submit the pull request.\r\n\r\nOnce your pull request is reviewed and accepted by the TensorFlow team, Perl will be added to the list of supported TensorFlow API languages on the website.", "Hi, The change has been made internally last week, It will be visible in the website soon. Thanks", "This got hung up internally, but it looks like it might be moving again.", "@wbraswell , Perl is now listed as community build package in our API document https://www.tensorflow.org/api_docs.\r\n\r\nThanks a lot for contributing to the TensorFlow community. \r\n\r\nCould you please spare some to close the issue as well. Thanks!", "@sachinprasadhs \r\nYes the link looks good, thanks! :-)\r\nShould we be concerned that all the languages in the list are in alphabetical order, except for Perl?\r\n```\r\nC#\r\nHaskell\r\nJulia\r\nMATLAB\r\nR\r\nRuby\r\nRust\r\nScala\r\nPerl\r\n```", "Missed that small detail, you can ignore it for now, will take handle that in future. Thanks!", "Okay thanks @sachinprasadhs !\r\nPlease post a comment back here in this thread when you fix the alphabetical ordering.\r\nI am closing this issue for now! :-)" ]

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[ "@penpornk I have addressed your review comments. Please take a look." ]