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[ "TensorFlow example package is not available on PyPI.\r\nWe can clone the TensorFlow example repository from GitHub and run the examples.\r\n\r\nUse this following command in cell\r\n` !git clone https://github.com/tensorflow/examples.git`\r\nIt will download the repo\r\n\r\nThen navigate to following example you want", "I am able to git clone the repo, but in some notebooks the `tensorflow_examples` package is referenced. How to use that? This line fails:\r\n\r\n`from tensorflow_examples.models.pix2pix import pix2pix`\r\n\r\nThis is from segmentation tutorial given [here](https://www.tensorflow.org/tutorials/images/segmentation)", "> I am able to git clone the repo, but in some notebooks the `tensorflow_examples` package is referenced. How to use that? This line fails:\r\n> \r\n> `from tensorflow_examples.models.pix2pix import pix2pix`\r\n> \r\n> This is from segmentation tutorial given [here](https://www.tensorflow.org/tutorials/images/segmentation)\r\n\r\n@kedarps \r\nThanks for reporting this issue. \r\nAs per this [comment](https://github.com/tensorflow/tensorflow/issues/60213#issuecomment-1495942281).\r\nPlease clone the TensorFlow example repository from GitHub. Use `from examples.tensorflow_examples.models.pix2pix import pix2pix` instead of `from tensorflow_examples.models.pix2pix import pix2pix`, Now you can use 'pix2pix' package. please find the gist [here](https://colab.research.google.com/gist/tiruk007/180bc730fb886639f987ba4c993c50ec/segmentation.ipynb#scrollTo=YQX7R4bhZy5h) for reference.\r\n\r\nThank you !\r\n\r\n", "Thank-you. It's working now.", "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/60213\">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/60213\">No</a>\n", "it was solved also for me thanks" ]

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[ "@diwen \r\nThis issue seems to be a keras issue.Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\n\r\nThank you!", "@diwen Thanks for reaching out regarding this issue! We recommend using the newer `.keras` format in TF 2.12. Please install TF 2.12 first and then try this format, as it is may solve many SavedModel related issues:\r\n\r\nmodel_dir = \"./test.keras\"\r\nmodel.save(model_dir, save_format=\"keras_v3\")\r\n\r\nMake sure to use the `.keras` extension when saving and pass the `save_format` flag. Please let me know if this works, thanks!\r\n", "I see. Thank you for the timely reply @nkovela1 and @tiruk007 !", "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/60212\">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/60212\">No</a>\n" ]

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[ "Hi @cemlyn007 ,\r\n\r\nThanks for reporting this. Currently this module is under experimental stage.Could you please confirm whether this API can't handle server side batching. Have you tested this ? I know this is under experimental stage and still many features might not be available right now.\r\n\r\n If you want to contribute you may please raise a Pull request for same and Team may happily review it. Thanks!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "I can confirm that in this [code](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/distribute/experimental/rpc/kernels/rpc_ops.cc) the server does not support server-side batching. I'll have a think about what to do because we are using an older version of TensorFlow (2.8) so we might just decide to implement this as a custom operation." ]

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[ "@L1onKing Thanks for reporting the issue.\r\n\r\nHave you checked with latest stable version TF 2.12 and see if the issue still exists?\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.", "Hello @pjpratik! Thank you for your response.\r\n\r\nI am trying to figure out how to compile it using bazel. Bazel is a new thing to me, so far I don't understand how to work with it. Can you maybe guide me to some tutorial that explains how to build TFLite for WASM using Bazel? Thank you very much!", "Hi @L1onKing \r\n\r\nFor C++ library, the TFLite provides `libtensorflowlite.so` which can be built using `bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so`. I've created a sample gist to build the same using bazel which can be found [here](https://colab.research.google.com/gist/pjpratik/1d1e24c386f42b4f483732dd324d418c/60210.ipynb). Please refer to cross-compilation for ARM with Bazel [here](https://www.tensorflow.org/lite/guide/build_arm#cross-compilation_for_arm_with_bazel).\r\n\r\nAlso, the TFLite supports for Tensorflow.js in which under the hood, the TFLite C++ runtime is packaged in a set of WASM modules with bazel. Please refer to [this](https://github.com/tensorflow/tfjs/tree/master/tfjs-tflite#tflite-support-for-tensorflowjs) documentation for the detailed use and let us know if it helps.\r\n\r\nAnd, please find the relevant thread #46359 \r\n\r\nThanks.", "bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so - so to my understanding, I need to run a command of such format from tensorflow repo directory. I have three questions on which I struggle to find an answer:\r\n\r\n1. What is the `config` value if I want to build libtensorflowlite.a with WASM?\r\n2. How to enable XNNPack during this build?\r\n3. Where to find relevant public headers that I need to include in my project?\r\n\r\n@pjpratik Thank you very much for your help!", "Hi @L1onKing \r\n\r\n>What is the config value if I want to build libtensorflowlite.a with WASM?\r\n\r\nThe `config` value for WASM is not available in `.bazelrc` afaik. We need to build the Bazel Emscripten toolchain manually using the instructions given [here](https://github.com/emscripten-core/emsdk/tree/master/bazel#bazel-emscripten-toolchain) and set `--confg=wasm`. Also, the bazel build generates the `.so` sharable dynamic object file instead of `.a` as CMAKE does.\r\n\r\n>How to enable XNNPack during this build?\r\n\r\nThe XNNPACK can be enable by adding `--define tflite_with_xnnpack=true` during bazel build.\r\n\r\nAs this [comment](https://github.com/tensorflow/tensorflow/issues/46359#issuecomment-1215595828) suggests, try the TFLite with TFjs which uses WASM backend given the bazel support for which comes with pre built binaries.\r\n\r\n\r\nThanks.\r\n\r\n", "Hi @pjpratik ! \r\n\r\nThank you for your recommendations, for the time being I am exploring this - https://github.com/tensorflow/tfjs/tree/master/tfjs-tflite\r\n\r\nSo what I did:\r\n1. Downloaded this repo\r\n2. Navigated to tfjs-tflite folder where BUILD.bazel is located\r\n3. Ran the command `bazel build //tfjs-tflite:tf-tflite`\r\n\r\nBut I am having next output\r\n\r\n```\r\nINFO: Repository npm instantiated at:\r\n /home/nabeel/Documents/algoface/tfjs/WORKSPACE:67:13: in <toplevel>\r\n /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/index.bzl:83:18: in yarn_install\r\nRepository rule yarn_install defined at:\r\n /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/internal/npm_install/npm_install.bzl:1002:31: in <toplevel>\r\nERROR: An error occurred during the fetch of repository 'npm':\r\n Traceback (most recent call last):\r\n\tFile \"/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/internal/npm_install/npm_install.bzl\", line 866, column 40, in _yarn_install_impl\r\n\t\tyarn_version = _detect_yarn_version(repository_ctx, yarn_cmd)\r\n\tFile \"/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/internal/npm_install/npm_install.bzl\", line 844, column 13, in _detect_yarn_version\r\n\t\tfail(\"yarn --version failed: %s (%s)\" % (result.stdout, result.stderr))\r\nError in fail: yarn --version failed: (/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.28' not found (required by /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node)\r\n)\r\nERROR: /home/nabeel/Documents/algoface/tfjs/WORKSPACE:67:13: fetching yarn_install rule //external:npm: Traceback (most recent call last):\r\n\tFile \"/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/internal/npm_install/npm_install.bzl\", line 866, column 40, in _yarn_install_impl\r\n\t\tyarn_version = _detect_yarn_version(repository_ctx, yarn_cmd)\r\n\tFile \"/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/build_bazel_rules_nodejs/internal/npm_install/npm_install.bzl\", line 844, column 13, in _detect_yarn_version\r\n\t\tfail(\"yarn --version failed: %s (%s)\" % (result.stdout, result.stderr))\r\nError in fail: yarn --version failed: (/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.28' not found (required by /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node)\r\n)\r\nERROR: Skipping '//tfjs-tflite:tf-tflite': no such package '@npm//@bazel/concatjs': yarn --version failed: (/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.28' not found (required by /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node)\r\n)\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such package '@npm//@bazel/concatjs': yarn --version failed: (/home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.28' not found (required by /home/nabeel/.cache/bazel/_bazel_nabeel/01ad2d90cd2af4a5ee7bc50cb9e6914b/external/nodejs_linux_amd64/bin/nodejs/bin/node)\r\n)\r\nINFO: Elapsed time: 3.087s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n currently loading: tfjs-tflite\r\n```\r\nIt looks like the issue is in incompatibility of bazel and glibc.\r\n\r\nThe output of `bazel version` on my machine:\r\n\r\n```\r\nBuild label: 5.3.0\r\nBuild target: bazel-out/k8-opt/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar\r\nBuild time: Tue Aug 23 00:45:53 2022 (1661215553)\r\nBuild timestamp: 1661215553\r\nBuild timestamp as int: 1661215553\r\n\r\n```\r\n\r\nCould you please advise what version of bazel should I use in order to make it run? Thank you very much!\r\n", "Hi @L1onKing \r\n\r\nThe Bazel version 5.3.0 should be compatible. Moreover, you can build the `tfjs-tflite` using `yarn build` which inturn uses bazel with wasm backend.\r\n\r\n- Navigate to `tfjs-tflite` repo\r\n- Check for `yarn` installation\r\n- Run `yarn build`\r\n\r\nPlease check this [gist](https://colab.research.google.com/gist/pjpratik/35f081254f9a619cc386b1cd085f059a/60210.ipynb) provided with the `tfjs-tflite` build and can be tested with`yarn test` with successful build.\r\n\r\nThanks.\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 @pjpratik ! Sorry for a long response, I was unavailable last week. Actually I did try `yarn build` command from `tfjs-tflite` folder, but that is the output I got:\r\n\r\ntfjs/tfjs-tflite$ yarn build\r\n00h00m00s 0/0: : ERROR: [Errno 2] No such file or directory: 'build'\r\n\r\nyarn is installed and available in Terminal. Could you advise please?", "**UPDATE**\r\n\r\nI have figured an issue with yarn and now `yarn build` command executes, but I have exactly the same issue:\r\n\r\n`libc.so.6: version `GLIBC_2.28' not found`\r\n\r\n\r\nPlease advise how to fix it", "Hi @L1onKing \r\n\r\nCan you please check if latest `npm` and `nodejs` versions are installed?\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/60210\">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/60210\">No</a>\n" ]

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[ "Quick question, why is there a separate `tensorflow-macos` package? Would it be possible to add ARM64 macOS wheels for the main `tensorflow` package (since it already contains x86 macOS wheels anyway)?", "@cbrnr I know there is `tensorflow-metal` GPU acceleration that is ARM64 macOS specific. I'm not sure if that has anything to do with having a separate `tensorflow-macos` package.", "> @cbrnr I know there is `tensorflow-metal` GPU acceleration that is ARM64 macOS specific. I'm not sure if that has anything to do with having a separate `tensorflow-macos` package.\r\n\r\nI think both `tensorflow-metal` and `tensorflow-macos` are created and maintained by Apple folks.", "Hi @jc-louis ,\r\n\r\nYes, `tensorflow-macos` and `tensorflow-metal` were developed and maintained by Apple. `pip install tensorflow-macos` installs the Apple package and `pip install tensorflow `installs Tensorflow package.\r\n\r\nRecently I observed that pip install tf-nightly installing Arm wheels on Mac and its collecting tf-nightly-macos. There seems some improvements here for which I need more context on this and will confirm.\r\n\r\n```\r\nUsing cached tf_nightly-2.13.0.dev20230417-cp310-cp310-macosx_12_0_arm64.whl (2.1 kB)\r\nCollecting tf-nightly-macos==2.13.0-dev20230417\r\n```\r\nCan you try `pip install tf-nightly` on Mac and check whether arm wheel is installing or not. You can give it a try and let us know if any problems.Though I can't officially confirm any thing on this for now.I will let you know about this once I got more context.\r\n\r\n\r\nThanks!", "Here's the output for `pip install tf-nightly`:\r\n\r\n<details>\r\n\r\n```\r\nCollecting tf-nightly\r\n Downloading tf_nightly-2.13.0.dev20230417-cp310-cp310-macosx_12_0_arm64.whl (2.1 kB)\r\nCollecting tf-nightly-macos==2.13.0-dev20230417 (from tf-nightly)\r\n Downloading tf_nightly_macos-2.13.0.dev20230417-cp310-cp310-macosx_12_0_arm64.whl (188.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 188.2/188.2 MB 4.4 MB/s eta 0:00:00\r\nCollecting absl-py>=1.0.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading absl_py-1.4.0-py3-none-any.whl (126 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 126.5/126.5 kB 18.9 MB/s eta 0:00:00\r\nCollecting astunparse>=1.6.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\r\nCollecting flatbuffers>=23.1.21 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading flatbuffers-23.3.3-py2.py3-none-any.whl (26 kB)\r\nCollecting gast<=0.4.0,>=0.2.1 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading gast-0.4.0-py3-none-any.whl (9.8 kB)\r\nCollecting google-pasta>=0.1.1 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 kB 8.4 MB/s eta 0:00:00\r\nCollecting h5py>=2.9.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading h5py-3.8.0-cp310-cp310-macosx_11_0_arm64.whl (2.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.6/2.6 MB 11.9 MB/s eta 0:00:00\r\nCollecting libclang>=13.0.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading libclang-16.0.0-py2.py3-none-macosx_11_0_arm64.whl (24.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.3/24.3 MB 31.3 MB/s eta 0:00:00\r\nCollecting numpy<1.24,>=1.22 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading numpy-1.23.5-cp310-cp310-macosx_11_0_arm64.whl (13.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.4/13.4 MB 102.6 MB/s eta 0:00:00\r\nCollecting opt-einsum>=2.3.2 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading opt_einsum-3.3.0-py3-none-any.whl (65 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 65.5/65.5 kB 12.7 MB/s eta 0:00:00\r\nCollecting packaging (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached packaging-23.1-py3-none-any.whl (48 kB)\r\nCollecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading protobuf-4.22.3-cp37-abi3-macosx_10_9_universal2.whl (397 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 397.2/397.2 kB 52.5 MB/s eta 0:00:00\r\nRequirement already satisfied: setuptools in ./.direnv/python-3.10.10/lib/python3.10/site-packages (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly) (67.6.1)\r\nCollecting six>=1.12.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)\r\nCollecting termcolor>=1.1.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading termcolor-2.2.0-py3-none-any.whl (6.6 kB)\r\nCollecting typing-extensions>=3.6.6 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading typing_extensions-4.5.0-py3-none-any.whl (27 kB)\r\nCollecting wrapt<1.15,>=1.11.0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading wrapt-1.14.1-cp310-cp310-macosx_11_0_arm64.whl (35 kB)\r\nCollecting grpcio<2.0,>=1.24.3 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading grpcio-1.53.0-cp310-cp310-macosx_12_0_universal2.whl (8.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.4/8.4 MB 98.2 MB/s eta 0:00:00\r\nCollecting tensorboard<2.13,>=2.12 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading tensorboard-2.12.2-py3-none-any.whl (5.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.6/5.6 MB 101.9 MB/s eta 0:00:00\r\nCollecting tensorflow-estimator<2.13,>=2.12.0rc0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading tensorflow_estimator-2.12.0-py2.py3-none-any.whl (440 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 440.7/440.7 kB 55.0 MB/s eta 0:00:00\r\nCollecting keras<2.13,>=2.12.0rc0 (from tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading keras-2.12.0-py2.py3-none-any.whl (1.7 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 90.3 MB/s eta 0:00:00\r\nCollecting wheel<1.0,>=0.23.0 (from astunparse>=1.6.0->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached wheel-0.40.0-py3-none-any.whl (64 kB)\r\nCollecting google-auth<3,>=1.6.3 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading google_auth-2.17.3-py2.py3-none-any.whl (178 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 178.2/178.2 kB 35.1 MB/s eta 0:00:00\r\nCollecting google-auth-oauthlib<1.1,>=0.5 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\r\nCollecting markdown>=2.6.8 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading Markdown-3.4.3-py3-none-any.whl (93 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 93.9/93.9 kB 12.3 MB/s eta 0:00:00\r\nCollecting requests<3,>=2.21.0 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached requests-2.28.2-py3-none-any.whl (62 kB)\r\nCollecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading tensorboard_data_server-0.7.0-py3-none-any.whl (2.4 kB)\r\nCollecting tensorboard-plugin-wit>=1.6.0 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 781.3/781.3 kB 63.5 MB/s eta 0:00:00\r\nCollecting werkzeug>=1.0.1 (from tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading Werkzeug-2.2.3-py3-none-any.whl (233 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 233.6/233.6 kB 35.7 MB/s eta 0:00:00\r\nCollecting cachetools<6.0,>=2.0.0 (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading cachetools-5.3.0-py3-none-any.whl (9.3 kB)\r\nCollecting pyasn1-modules>=0.2.1 (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 155.3/155.3 kB 26.7 MB/s eta 0:00:00\r\nCollecting rsa<5,>=3.1.4 (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading rsa-4.9-py3-none-any.whl (34 kB)\r\nCollecting requests-oauthlib>=0.7.0 (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\r\nCollecting charset-normalizer<4,>=2 (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached charset_normalizer-3.1.0-cp310-cp310-macosx_11_0_arm64.whl (123 kB)\r\nCollecting idna<4,>=2.5 (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached idna-3.4-py3-none-any.whl (61 kB)\r\nCollecting urllib3<1.27,>=1.21.1 (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached urllib3-1.26.15-py2.py3-none-any.whl (140 kB)\r\nCollecting certifi>=2017.4.17 (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached certifi-2022.12.7-py3-none-any.whl (155 kB)\r\nCollecting MarkupSafe>=2.1.1 (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Using cached MarkupSafe-2.1.2-cp310-cp310-macosx_10_9_universal2.whl (17 kB)\r\nCollecting pyasn1<0.5.0,>=0.4.6 (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 77.1/77.1 kB 12.0 MB/s eta 0:00:00\r\nCollecting oauthlib>=3.0.0 (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tf-nightly-macos==2.13.0-dev20230417->tf-nightly)\r\n Downloading oauthlib-3.2.2-py3-none-any.whl (151 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 151.7/151.7 kB 22.9 MB/s eta 0:00:00\r\nInstalling collected packages: tensorboard-plugin-wit, pyasn1, libclang, flatbuffers, wrapt, wheel, urllib3, typing-extensions, termcolor, tensorflow-estimator, tensorboard-data-server, six, rsa, pyasn1-modules, protobuf, packaging, oauthlib, numpy, MarkupSafe, markdown, keras, idna, grpcio, gast, charset-normalizer, certifi, cachetools, absl-py, werkzeug, requests, opt-einsum, h5py, google-pasta, google-auth, astunparse, requests-oauthlib, google-auth-oauthlib, tensorboard, tf-nightly-macos, tf-nightly\r\nSuccessfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.0 certifi-2022.12.7 charset-normalizer-3.1.0 flatbuffers-23.3.3 gast-0.4.0 google-auth-2.17.3 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.53.0 h5py-3.8.0 idna-3.4 keras-2.12.0 libclang-16.0.0 markdown-3.4.3 numpy-1.23.5 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-23.1 protobuf-4.22.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-2.28.2 requests-oauthlib-1.3.1 rsa-4.9 six-1.16.0 tensorboard-2.12.2 tensorboard-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 tensorflow-estimator-2.12.0 termcolor-2.2.0 tf-nightly-2.13.0.dev20230417 tf-nightly-macos-2.13.0.dev20230417 typing-extensions-4.5.0 urllib3-1.26.15 werkzeug-2.2.3 wheel-0.40.0 wrapt-1.14.1\r\n```\r\n</details>\r\n\r\nIndeed, `pip show tf-nightly` says:\r\n\r\n```\r\nName: tf-nightly\r\nVersion: 2.13.0.dev20230417\r\nSummary: TensorFlow is an open source machine learning framework for everyone.\r\nHome-page: https://www.tensorflow.org/\r\nAuthor: Google Inc.\r\nAuthor-email: packages@tensorflow.org\r\nLicense: Apache 2.0\r\nLocation: /Users/clemens/Desktop/temp/.direnv/python-3.10.10/lib/python3.10/site-packages\r\nRequires: tf-nightly-macos\r\nRequired-by: \r\n```\r\n", "Hi @jc-louis ,\r\n\r\nPlease refer the below change in [setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) from TF master branch.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/3ace0c0eb8ef5856acc7a0fe6396b696ff800262/tensorflow/tools/pip_package/setup.py#L155-L159\r\n\r\nThis refers that the command `pip install tf-nightly` on mac-os with Darwin system and Arm64 Architecture (i.e Apple silicon) installs the package built by Apple.\r\n\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Please don't close, it would be nice to get a comment by Apple(?) folks about when Python 3.11 support will be added to `tensorflow-macos`. Would it be possible to update the 2.12 package? Or do we have to wait for 2.13?", "According to the [developer forum](https://developer.apple.com/forums/thread/728962), they're working on providing wheels for 2.12", "Python 3.11 arm64 wheels available for tf-nightly(2.13.0.Dev...) as per above [comment](https://github.com/tensorflow/tensorflow/issues/60209#issuecomment-1533310854) and same can be downloaded on Apple silicon also.Not sure whether Apple has plan to release Python 3.11 arm64 wheels for TF2.12 also.If they release then the same changes can be implemented in TF repo also for 2.12 Version. However the final decision here shall be taken from TF dev team.", "I just tested it and it looks like `pip install tensorflow` on an M1 machine will now install `tensorflow==2.13.0rc0` successfully.", "Python 3.11 wheels for `tensorflow-macos` are now available on PyPI (for both x86_64 and arm64), so I think this issue can be closed.", "> Python 3.11 wheels for `tensorflow-macos` are now available on PyPI (for both x86_64 and arm64), so I think this issue can be closed.\n\nBut there are no 3.11 wheels for the latest stable version, just for an rc version. That's problematic.", "Which latest stable version do you mean? There are wheels for 2.12 now: https://pypi.org/project/tensorflow-macos/#files", "> Which latest stable version do you mean? There are wheels for 2.12 now: https://pypi.org/project/tensorflow-macos/#files\n\nYes, I just saw it was uploaded a couple of hours ago, sorry. I thought you were referring to the rc0 version.", "Closing the issue as it seems resolved now. Please feel free to reopen if still an issue. Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60209\">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/60209\">No</a>\n" ]

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[ "@sukdamrong Thanks for reporting this issue.\r\n\r\nAs per this [comment](https://github.com/google/XNNPACK/issues/4598#issuecomment-1494631028), the 32 bit builds might not be supported for ios.\r\n\r\nI have tried with `--config=ios_fat` which builts a \"fat\" binary, containing armv7, arm64, and x86_64 and was able to build the framework successfully. Please find the screenshot below.\r\n\r\n<img width=\"565\" alt=\"Screenshot 2023-04-06 at 4 49 55 PM\" src=\"https://user-images.githubusercontent.com/118897289/230377639-3297cb26-7dc4-4160-a5b3-3459b3d3498c.png\">\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/60208\">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/60208\">No</a>\n" ]

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[ "Hi @parismic, to create an op, kindly follow this [guide](https://www.tensorflow.org/guide/create_op). Alternatively, you can also try creating a custom layer which is a common set of useful operations by following the instructions mentioned [here](https://www.tensorflow.org/tutorials/customization/custom_layers#implementing_custom_layers). Thank you!\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/60207\">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/60207\">No</a>\n" ]

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[ "Tried to pip install the nightly pacakge, didn't help.", "Hello @blaze-Youssef !\r\nAs per the documentation, the latest CUDA version 11.2 is supported for TF v2.10.\r\nPlease check this [link](https://www.tensorflow.org/install/source_windows#gpu) for more information.\r\nThank you! ", "@blaze-Youssef -\r\nStarting with TF 2.11 and above, TensorFlow recommends using Windows WSL for GPU Access. See instructions here - https://www.tensorflow.org/install/pip#windows-wsl2\r\nIf you are using Conda, you can create a Conda environment and install inside the conda env.\r\nYou could also give docker containers a try if you prefer that over creating your own conda env (https://www.tensorflow.org/install/docker)", "Thank you so much, I got it working on docker for Windows using the tenserflow jupyer image.", "Hi!\r\n@blaze-Youssef could you please help me solve this issue and give me some link where I can follow what you did to solve the issue? I'm not a programmer, I'm trying to use Tf for behavior analysis in neuroscience research, so I'm a bit of a noob honestly. I have the same issue, driver requiring cuda 12, Tf not supporting cuda 12 and my GPU is not activated during training at all. How did you manage to solve it? Thank you so much in advance! ", "This steps works for me, nvidia RTX4090 (cuda 12) using cuda 11.8 and cuDNN 8.6 .\r\n\r\nhttps://github.com/fdmartins/tensorflow-2-rtx4090\r\n" ]

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[ "@drishtia60,\r\nThere are at least 3 possible scenarios:\r\n\r\n```\r\n1. You need to install the MSVC 2019 redistributable.\r\n2. Your CPU does not support AVX2 instructions.\r\n3. Your CPU/Python is on 32 bits.\r\n4. There is a library that is in a different location/not installed on your system that cannot be loaded.\r\n```\r\nAlso pip install tensorflow will install a version thats compatible with GPU and CPU. So it gives you that warning message.\r\nIf you don't want to see warning messages and want to install the CPU only version, you could - pip install tensorflow-cpu that's a smaller wheel file for the CPU only version.\r\n\r\nTensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install [TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2), or install tensorflow-cpu and, optionally, try the [TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-)\r\nhttps://www.tensorflow.org/install/pip?hl=en#windows-native\r\n\r\nAlso please take a look at this https://github.com/tensorflow/tensorflow/issues/57103 and https://github.com/tensorflow/tensorflow/issues/56976 with a similar error and try to uninstall all the dependencies and recreate a fresh conda env .\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/60205\">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/60205\">No</a>\n" ]

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[ "@rhuddleston \r\nSorry for the late reply, Could you please try with `pip install tensorflow==2.11.*` to install TF 2.11. I was able to install the TF2.11 without any error on the linux virtual machine with python 3.10. Please find the screenshots below:\r\n[Screenshot 1](https://user-images.githubusercontent.com/111861663/229890752-269caf0a-6bc3-4d24-b233-58d4858facd8.png)\r\n[Screenshot 2](https://user-images.githubusercontent.com/111861663/229890807-259d86a8-94ea-4a53-be43-7679f0638e43.png)\r\n\r\n\r\nThank you !\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Also been having issues on a macOS M1 with pipenv.\r\n\r\nRecently, Dependabot wanted to bump `tensorflow` to 2.11.1 but that caused a version mismatch between `tensorflow-cpu-aws` so now `python -c \"import tensorflow\"` fails to find the import and `pipenv graph` has this:\r\n\r\n```sh\r\ntensorflow==2.11.1\r\n - tensorflow-cpu-aws [required: ==2.11.1, installed: ?]\r\n```", "@rhuddleston,\r\nThe package tensorflow-cpu-aws is meant for **Arm/AArch64** processors and it can't be downloaded in `X86_64` architectures through pip. \r\n\r\nPip will try to resolve the wheels suitable for the particular host platform and if it is not found then it raises the error like no matching distribution found.\r\n\r\nThe pre-built pip wheels for X86_64 built and maintained by tensorflow itself which you can install through pip install tensorflow-cpu and this can be installed on x86_64 machines only.\r\n\r\nCould you please refer the documentation [source](https://www.tensorflow.org/install/pip#linux:~:text=Nightly-,Note%3A%20Starting%20with%20TensorFlow%202.10%2C%20Linux%20CPU%2Dbuilds%20for%20Aarch64,package.%20See%20this%20blog%20post%20for%20more%20information%20about%20this%20collaboration.,-conda%20install%20%2D) for more details.\r\n\r\nThis does not look like a problem that is specific to TensorFlow. I think your problem is all about creating a docker container for a foreign architecture from your x86 machine. In order to do that you need to add qemu to the mix so that there is an environment that allows the docker container to execute.\r\nSee https://www.docker.com/blog/multi-platform-docker-builds/\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/60204\">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/60204\">No</a>\n" ]

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[ "Hi @milmor,\r\nAs a workaround, kindly set `interpolation=bilinear` in the **Upsampling2d** layer, which is compatible with GPUs. Kindly find the gist of working code [here](https://colab.sandbox.google.com/gist/synandi/fdd7c053f3d0509a547cbbf390e48ac2/60203_bilinear.ipynb). 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/60203\">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/60203\">No</a>\n" ]

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[ "Hello @PhilippWillms! \r\nCould you try the following and let us know if that helps?\r\n```\r\n$pip install tensorflow-io-gcs-filesystem\r\n```\r\nThank you!", "Hi @sushreebarsa, I am sorry but it is still failing: \r\n\r\n> ERROR: Ignored the following versions that require a different python version: 0.23.0 Requires-Python >=3.6, <3.10\r\nERROR: Could not find a version that satisfies the requirement tensorflow-io-gcs-filesystem==0.32.0 (from versions: 0.23.1, 0.24.0, 0.25.0, 0.26.0, 0.27.0, 0.28.0, 0.29.0, 0.30.0, 0.31.0)\r\nERROR: No matching distribution found for tensorflow-io-gcs-filesystem==0.32.0\r\n\r\nRecall that I need a Windows built.", "Hi @PhilippWillms ,\r\n\r\nYou need to have `tensorflow-io-gcs-filesystem >= 0.23.1` for building pip package. You can proceed with 0.31 version also.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/bc54be865c99c9c8b8174c98bf8665af4ab10949/tensorflow/tools/pip_package/setup.py#L108\r\n\r\nI agree that the latest package for Windows seems to be unavailable for now. I tested in Colab(Linux) and 0.32 version available there. We will note this and inform concerned.\r\n\r\nMeanwhile you can go ahead with the package >=0.23.1 and <0.32 and come back if any problem arises. Thanks!\r\n", "Ok, I followed that proposal and restricted to the mentioned version range in my TOML file.", "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/60202\">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/60202\">No</a>\n", "Imho this is not a proper solution as one has to manually create a top-level dependency to tensorflow-io-gcs-filesystem with the mentioned version restriction. Otherwise e.g. poetry resolves to the newest available version (0.32.0) which fails by default. Please consider to release the Windows wheels for current versions of tensorflow-io-gcs-filesystem again or add the version restriction in the tensorflow package already, so it doesn't have to be restricted at top level." ]

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[ "@shijy16,\r\nI tried to execute the mentioned code on both tensorflow v2.12 [CPU](https://colab.research.google.com/gist/tilakrayal/567f4b305c69b199c9db8448f6763eab/untitled1064.ipynb) and [GPU](https://colab.research.google.com/gist/tilakrayal/047904e5371c1ffd7bdfe92a024b490f/untitled1065.ipynb) & it was executing with the different error where it was mentioned that **Current implementation only supports equal length strides in the row and column dimensions** and also I was not able to get any crash/abort for the mentioned code. Thank you! ", "@tilakrayal \r\nHi, I can still replicate this bug on my local machine. I think this is a **Intel OneDNN library** related bug, and Intel OneDNN is not supported on Colab. \r\nIntel OneDNN is enabled by default on my local machine, thus the crash can be replicated. If I disable Intel OneDNN as the code bellow, the same error in Colab is thrown.\r\n````python\r\nimport os\r\nos.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'\r\nimport tensorflow as tf\r\nprint(tf.__version__)\r\nwith tf.device(\"CPU\"):\r\n strides = [1, 0, 1, 1]\r\n padding = \"VALID\"\r\n explicit_paddings = []\r\n data_format = \"NHWC\"\r\n dilations = [1, 0, 77, 1, 64]\r\n input_sizes = tf.saturate_cast(tf.random.uniform([3], minval=-1024, maxval=1024, dtype=tf.int64), dtype=tf.int32)\r\n filter = tf.random.uniform([16, 3, 3, 5], dtype=tf.bfloat16, minval=-1024, maxval=1024)\r\n out_backprop = tf.random.uniform([1, 0, 0, 1], dtype=tf.bfloat16, minval=-1024, maxval=1024)\r\n res = tf.raw_ops.DepthwiseConv2dNativeBackpropInput(\r\n strides=strides,\r\n padding=padding,\r\n explicit_paddings=explicit_paddings,\r\n data_format=data_format,\r\n dilations=dilations,\r\n input_sizes=input_sizes,\r\n filter=filter,\r\n out_backprop=out_backprop,\r\n )\r\n````", "@shijy16 Please don't file vulnerabilities on GitHub. Please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md and follow rules for responsible disclosure.", "Hi @shijy16 \r\nThis has been fixed. tested with TF2.16v and found raising an exception rather than check fail.Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/09759376b26be8e89764152672ae2eaa/60201_nightly_success.ipynb).\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/60201\">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/60201\">No</a>\n" ]

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[ "@SuryanarayanaY \r\nI was able to reproduce the issue on Coalb using TF v2.12 and tf-nightly. Please find the gist of [2.12](https://colab.research.google.com/gist/tiruk007/44dd95a1bb2324ccc3bc3afbc97838a1/untitled180.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/029818642e75ec9351c6c00c1341403f/untitled180.ipynb) for reference.\r\n\r\nThank you !", "Checked in tf-nightly(2.15.0-dev20231004). Crash due to check fail.\r\n\r\n<img width=\"1509\" alt=\"Screenshot 2023-10-04 at 3 00 34 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/455d7556-4cbf-41ac-805a-982984968e0e\">\r\n" ]

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[ "Hi @shijy16, \r\n\r\nI was able to replicate the issue in Ubuntu using tf-nightly(2.13.0-dev20230331). Please find the log below.\r\n```\r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60199.py\r\n2023-04-03 13:20:37.981051: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-03 13:20:38.789344: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-03 13:20:41.739112: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-03 13:20:41.761387: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 7678600331551628182 with 15, result: -1\r\nAborted (core dumped)\r\n```\r\nThank you!", "@shijy16,\r\nThis indicates the problem is due to a memory issue where the OS crashed in allocating the required memory which is expected.\r\n\r\nAlso please refer to the developer https://github.com/tensorflow/tensorflow/issues/59168#issuecomment-1405633596 related to malloc with high input size which will eventually lead to an OS crash. 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/60199\">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/60199\">No</a>\n" ]

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[ "@sachinprasadhs I was able to replicate the issue on colab using TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/23002b3c2d4cbd311f739dcdd37e2157/60198.ipynb) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/fa9c18ffb6f4829647d36d78942c05af/untitled784.ipynb#scrollTo=hIczSZUMnMVl)(2.13.0-dev20230403), please find the attached gists. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60198\">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/60198\">No</a>\n" ]

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[ "@shijy16,\r\nI can see the **range_max = 3031324185113192368** which you are trying is a very big value. Could you please provide any usecase you are trying the big value. \r\n\r\n\r\nrange_max | An int that is >= 1. The sampler will sample integers from the interval [0, range_max).\r\n-- | --\r\n\r\n\r\n\r\nThank you!", "@tilakrayal \r\nHi, I am doing API testing to find potential bugs/vulnerabilites. \r\nAlthough the error message seems reasonable, the abort/crash is unacceptable. Instead, an exception that can be catched with external code like `try: ... except: ...` should be placed here.", "@shijy16 Please don't file vulnerabilities on GitHub. Please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md and follow rules for responsible disclosure." ]

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[ "@SuryanarayanaY \r\nI was able to reproduce the issue on Coalb using TF v2.12 and tf-nightly. Please find the gist of [tf-nightly](https://colab.research.google.com/gist/tiruk007/4ee179e7a10dc5bbdb4e8df74faf72b4/nightly.ipynb) and [2.12](https://colab.research.google.com/gist/tiruk007/36a4995d912f43acec5185fa2467d781/2-12.ipynb) for reference.\r\n\r\nThank you !", "Checked in tf-nightly(2.15.0-dev20231004).Check fail with crash observed.\r\n\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2023-10-04 at 3 24 58 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/b240b4a7-0953-4165-8fc6-80942504f6a4\">\r\n\r\n\r\n", "Issue persists in current nightly also." ]

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[ "Hi @shijy16 Thank you for reporting this issue!\r\n\r\nI'm facing a different error when tried to replicate the issue in Ubuntu 20.04 using tf-nightly(2.13.0-dev20230402). Please find the output log below.\r\n\r\nThe issue has been resolved in the lastest tf-nightly version(2.13.0-dev20230402). Thank you!", "@synandi \r\nThanks for your response!\r\nI tried to replicate the issue again on `2.13.0-dev20230402` and can still replicate the crash. \r\nAnd it seems interesting because sometimes an `InvalidArgumentError` was thrown, and sometimes it ended with a crash due to check-fail. This is the output log:\r\n\r\nThis behaviour seems to be caused by using uninitailized values (just a guess).\r\nPlease executed the code for more times or simply add a for loop to replicate the crash as following:\r\n````python\r\nimport tensorflow as tf\r\nwith tf.device(\"CPU\"):\r\n for i in range(100):\r\n strides = [1, 1, 1, 1, 1]\r\n padding = \"VALID\"\r\n data_format = \"NCDHW\"\r\n dilations = [1, 1, 1, 1, 1]\r\n input = tf.random.uniform([1], dtype=tf.bfloat16, minval=-1024, maxval=1024)\r\n filter_sizes = tf.saturate_cast(tf.random.uniform([1], minval=-1024, maxval=1024, dtype=tf.int64), dtype=tf.int32)\r\n out_backprop = tf.random.uniform([2, 12, 3, 5, 10], dtype=tf.bfloat16, minval=-1024, maxval=1024)\r\n try:\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n strides=strides,\r\n padding=padding,\r\n data_format=data_format,\r\n dilations=dilations,\r\n input=input,\r\n filter_sizes=filter_sizes,\r\n out_backprop=out_backprop,\r\n )\r\n except:\r\n pass\r\n````", "@shijy16, I tried executing the code multiple times using tf-nightly(2.13.0-dev20230402). I'm facing `InvalidArgumentError` without crash. Please check the log below \r\n\r\n```\r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:05.654152: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:06.482887: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:09.522641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:09.524565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:14.139250: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:14.970103: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:18.011821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:18.013777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:22.329471: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:23.166682: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:26.442930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:26.444873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:30.577044: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:31.387829: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:34.419442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:34.421327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:37.197768: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:38.015651: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:41.037032: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:41.039006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:43.719981: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:44.536964: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:47.558622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:47.560566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:50.022309: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:50.838318: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:40:53.889195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13492 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:40:53.891310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:40:57.744063: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:40:58.559910: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:41:01.646660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13498 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:41:01.648587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:41:04.205750: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:41:05.019126: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:41:08.034700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13498 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:41:08.036647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ python 60195.py\r\n2023-04-04 09:41:10.367393: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-04-04 09:41:11.197626: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-04-04 09:41:14.278666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13498 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-04-04 09:41:14.280686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13599 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/60195.py\", line 13, in <module>\r\n res = tf.raw_ops.Conv3DBackpropFilterV2(\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py\", line 413, in wrapper\r\n return f(**kwargs)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/gen_nn_ops.py\", line 2209, in conv3d_backprop_filter_v2\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 6653, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__Conv3DBackpropFilterV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Conv3DBackpropFilterOpV2 only supports NDHWC on the CPU. [Op:Conv3DBackpropFilterV2] name: \r\n(tf) ynandi@sindhu-ubuntu-2005:~$ \r\n```\r\n\r\nThank you!", "@synandi \r\nHi, sorry for that.\r\n\r\nI just noticed that the bug can only be replicated when Intel OneDNN is enabled. In the following code, Intel OneDNN is enabled, it ended with a crash.\r\n\r\nIn the following code, Intel OneDNN is disabled by setting environment viriable `TF_ENABLE_ONEDNN_OPTS` as 0, and it ended normally.\r\n\r\nSince your TensorFlow binary seems to be compiled without Intel OneDNN support (there is no output log as below when tensorflow is loaded), the bug cannot be replicated.\r\n````\r\noneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n````", "@shijy16 Please don't file vulnerabilities on GitHub. Please consult https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md and follow rules for responsible disclosure.", "@mihaimaruseac \r\nHi, the bugs I reported here are mostly `check fails`, which are not considered as vulnerabilities according to the rules below. Thus I think reporting them here is okay.\r\n````\r\nIf an assertion failure only leads to program termination and no other exploits, we will no longer consider assertion failures (e.g., CHECK-fails) as vulnerabilities. \r\n````", "Hi @shijy16 ,\r\n\r\nThe issue has been resolved already. Please check the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e536570d5a78a1c9a8facedc7f49aeb0/60195_nightly_success.ipynb) with Tf2.16.", "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/60195\">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/60195\">No</a>\n" ]

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[ "System Information:\r\n\r\nOS Platform and Distribution: Linux Ubuntu 18.04\r\nTensorFlow installed from: TensorFlow binary\r\nTensorFlow version: 2.5.0", "Issue Description:\r\n\r\nWhen trying to convert a TensorFlow model to a TensorFlow Lite model using the tflite_convert command, I am getting an error message saying \"ValueError: The requested operation is not available.\" I am using the following command: \r\n\r\ntflite_convert --saved_model_dir=/path/to/saved_model --output_file=/path/to/output.tflite\r\n", "Output of tflite_convert:\r\n\r\noutput of tflite_convert here", "Standalone Code:\r\n\r\ninclude a minimal reproducible code snippet here that demonstrates the issue", "Additional Information:\r\n\r\nAttached is the saved_model directory I am using for the conversion\r\nThe TensorFlow model was trained using TensorFlow version 2.4.0\r\nI have also tried converting the model using the --enable_v1_converter flag, but I still get the same error message.", "With this information, it would be easier for someone to help diagnose the issue and provide a solution.", "Hi @hartpoli Thanks for reporting the issue.\r\n\r\nCould you please provide a toy model to reproduce the issue, else it is hard to debug from the error.\r\n\r\nAlso, can you check in latest TF 2.12 and see if the issue still exists?\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/60194\">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/60194\">No</a>\n" ]

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[ "Sorry, I am probably not the person to review it", "@gbaned from `git blame tensorflow/lite/tools/evaluation/stages/image_preprocessing_stage.cc`, the file was mainly touched by @srjoglekar246 and @thaink ", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @gbaned, is there anything I can do to move forward?", "> Hi @gbaned, is there anything I can do to move forward?\r\n\r\nHi @freedomtan Sorry for the delay. Nothing pending from your end. ", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!", "Hi @Ferev Can you please review this PR ? Thank you!\r\n\r\n", "Hi @Ferev Can you please review this PR ? Thank you!" ]

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[ "@gbaned Could I humbly ask you to assign some CUDA GPU developer to review this bug? It has been causing silent corruption in masked LSTM and GRU code on GPU since TensorFlow 2.5 (the first TF to use cuDNN 8.1 or newer), and it can be replicated.", "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.11, [v2.12](https://colab.research.google.com/gist/tilakrayal/eaed5692e296bf592bc20505e7d4a031/lstm_gru_cudnn_bug.ipynb) and nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/2fd174e9d58ca52f04b969f29f0014c3/lstm_gru_cudnn_bug_nightly.ipynb).", "Hi, Thanks for your detailed input and pointers, that will certainly help us to investigate further.\r\nBefore moving forward, could you please confirm if you are observing the same behavior with the below configurations using TensorFlow 2.12. \r\nWe had observed the similar behavior with CUDA 11.2 and it was not appearing with the CUDA 11.8 version. Reference issue https://github.com/tensorflow/tensorflow/issues/59671#issuecomment-1471169393\r\n\r\n\r\n<h4 id=\"gpu\" data-text=\"GPU\" role=\"presentation\" style=\"box-sizing: inherit; margin: 32px 0px 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: ; font-stretch: ; font-size: 16px; line-height: ; font-family: Roboto, &quot;Noto Sans&quot;, &quot;Noto Sans JP&quot;, &quot;Noto Sans KR&quot;, &quot;Noto Naskh Arabic&quot;, &quot;Noto Sans Thai&quot;, &quot;Noto Sans Hebrew&quot;, &quot;Noto Sans Bengali&quot;, sans-serif; font-optical-sizing: ; font-kerning: ; font-feature-settings: ; font-variation-settings: ; letter-spacing: normal; overflow: hidden; text-overflow: ellipsis; margin-inline-end: -40px; padding-inline-end: 40px; color: rgb(32, 33, 36); orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"><span class=\"devsite-heading\" role=\"heading\" aria-level=\"4\" style=\"box-sizing: inherit;\">GPU</span><button type=\"button\" class=\"devsite-heading-link button-flat material-icons\" aria-label=\"Copy link to this section: GPU\" data-title=\"Copy link to this section: GPU\" data-id=\"gpu\" style=\"box-sizing: border-box; appearance: none; background: 0px center; border: 0px; border-radius: var(--devsite-button-border-radius,2px); box-shadow: none; color: var(--devsite-icon-color,var(--devsite-secondary-text-color)); cursor: pointer; display: inline-block; font-style: normal; font-variant-ligatures: ; font-variant-caps: ; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: normal; font-stretch: ; font-size: 24px; font-family: &quot;Material Icons&quot;; font-optical-sizing: ; font-kerning: ; font-feature-settings: &quot;liga&quot;; font-variation-settings: ; height: 24px; letter-spacing: normal; line-height: 1; margin: var(--devsite-button-margin,0); margin-inline-end: var(--devsite-button-margin-x-end); max-width: var(--devsite-button-max-width,none); min-width: 36px; outline: 0px; overflow: hidden; padding: 0px 8px; text-align: center; text-decoration: none; text-overflow: ellipsis; text-transform: none; transition: background-color 0.2s ease 0s, border 0.2s ease 0s, box-shadow 0.2s ease 0s; vertical-align: bottom; white-space: nowrap; width: var(--devsite-button-width,auto); overflow-wrap: normal; direction: ltr; -webkit-font-smoothing: antialiased; opacity: 0;\"></button></h4><div class=\"devsite-table-wrapper\" style=\"box-sizing: inherit; margin: var(--devsite-table-margin,16px 0); padding: 0px; overflow: auto; color: rgb(32, 33, 36); font-family: Roboto, &quot;Noto Sans&quot;, &quot;Noto Sans JP&quot;, &quot;Noto Sans KR&quot;, &quot;Noto Naskh Arabic&quot;, &quot;Noto Sans Thai&quot;, &quot;Noto Sans Hebrew&quot;, &quot;Noto Sans Bengali&quot;, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\">\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.12.0 | 3.8-3.11 | GCC 9.3.1 | Bazel 5.3.0 | 8.6 | 11.8\r\n\r\n</div>", "Hi @sachinprasadhs,\r\n\r\nI confirm that I am observing the same behavior (that is, corrupted results, i.e. nonzero batch differences, plus occasional crashes with `CUDA_ERROR_ILLEGAL_ADDRESS`) when using every following configuration:\r\n- TF nightly (2.13.0.dev20230404) from PyPI, cuDNN 8.6, CUDA 11.8\r\n- TF 2.12.0 from PyPI, cuDNN 8.6, CUDA 11.8 (listed on https://www.tensorflow.org/install/source)\r\n- TF 2.11.0 from PyPI, cuDNN 8.1, CUDA 11.2 (listed on https://www.tensorflow.org/install/source)\r\n- TF 2.5.3 from PyPI, cuDNN 8.1, CUDA 11.2 (listed https://www.tensorflow.org/install/source)\r\n\r\nOn the other hand, the following configurations do not exhibit the problem:\r\n- TF 2.4.4 from PyPI, cuDNN 8.0, CUDA 11.0\r\n- custom build of TF 2.11.1, GCC 9, cuDNN 8.0, CUDA 11.1\r\n- custom build of TF 2.8.0, GCC 7, cuDNN 8.0, CUDA 11.1\r\n\r\nNote that there is quite a different codepath for RNNs in cuDNN 8.1 (new set of APIs are used), see https://github.com/tensorflow/tensorflow/blob/4f1dd6d5123f4eb6afc85fac36df09b4a8b49c83/tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc#L1971-L1975 so my guess is that the problem is somewhere there (while it is not in the codepath for cuDNN < 8.1).\r\n\r\nI believe the main problem are actually the corrupted results -- even if there is no crash, the outputs are wrong for some batches. To me, the occasional crash is just an indication that the implementation is actually wrong (and this sometimes manifests in a crash, showing that there is some memory indexing error, which would be consistent with randomly corrupted results).\r\n\r\nIn the issue you mention, no RNNs are being used, so I believe these are two independent issues (because I believe the crash here is caused by the code calling the cuDNN RNN).", "@kaixih can you look into this?", "@kaixih A gentle remainder after two weeks -- I would be great if you could find the time to look into the issue (as the original code author) :pray: ", "Just ran the script on A6000 with the TF2.12 + cuda 12.1 + cudnn 8.9. But I couldn't see the crash even after I use 1000 steps instead of 100 steps. Need to find to try other containers with different settings.", "@kaixih Thanks a lot for looking in to it!\r\n\r\n- Did you observe the changing results (i.e., non-zero max difference)? I believe it is even more serious than the crashes, because for identical batches the results can vary a lot (causing incorrect outputs of CuDNN LSTM/GRU on all PyPI versions of TF 2.5-TF 2.12).\r\n - My guess is that once we find out the reason for the differing results, it will solve the crashing too. My guess is that the differing results are caused by some memory overwriting, because I have observed the different results only when there are at least _two_ RNNs computed in parallel (for example a Bidirectional layer, which runs two RNNs in the same inputs).\r\n- When I was creating the demonstration example, I found out that the larger the dimensionalities of the LSTM/GRU, the larger chance for difference and the larger change for a crash -- so maybe increasing 2048 to 8192 could make the crash happen.\r\n - On GeForce 1080, dimensionalities like 256 were enough to trigger different results; on A40, it had to be increased to thousands (I kept 2048 as it was enough to trigger the problem on Colab).\r\n\r\nCheers!", "Yes, I also observed the changing results. If I use the `CUDA_LAUNCH_BLOCKING=1`, the results will be all the same. I think it is a bug. Let me dig into it to confirm if this is a cuDNN issue or TF issue.", "I just want to add that I can observe similar behavior when using TCN code running under Tensorflow 2.12, CuDNN 8.8, CUDA 11.8 on a GV100 GPU. Our code also uses padding to adjust the sequence length for variable length time series data. Padded sequences are masked out with a masking layer. Our pipeline runs Bayesian optimization with keras tuner to find the optimal hyperparameters. We get the CUDA_ERROR_ILLEGAL_ADDRESS error somewhat randomly, it completes a few trials of BO and then crashes in the middle of an epoch after training for some batches.\r\n\r\nthe same code used to run on the same server with older software, Tensorflow 2.3 and CUDA 10.2 without these crashes.", "A gentle reminder after two weeks -- thanks for looking into this :pray: :+1: ", "I hope this issue will be resolved soon.\r\nThe main reason is that Python 3.8 EOL is coming next year, and when Python 3.8 goes EOL, I will need to move to Python 3.9+, which will also require moving to TF2.5+, which will be affected by this issue.\r\n\r\nDue to the EOL of Python 3.7, I considered moving to Python 3.10+, but this issue forced me to move to Python 3.8. However, only a year later I need to move to Python 3.9+. Therefore, I would appreciate it if you could resolve this issue as soon as possible. Maybe there are others in the same situation as me.\r\n\r\nThank you.", "A gentle reminder after two weeks -- thanks for looking into this :bow: ", "Can you try the latest cuDNN like 8.9.2: https://docs.nvidia.com/deeplearning/cudnn/release-notes/index.html?\r\n\r\nBasically, we guess the root cause is because the TF uses the async memcopy to prepare the `devSeqLengths` and when the copy actually happens, the host side memory might already be freed, causing the corrupted content in `devSeqLengths`. So, we have some workaround from the cuDNN side to not rely on this behavior. So, can you give a shot of this cudnn version and see if the issue is gone?\r\n\r\n", "Hi, thanks a lot for all your work! It is great that just an update of cuDNN will be able to fix this.\r\n\r\nI verified that the example from this issue (forward pass only) no longer produces any differences (and have never crashed so far) when using:\r\n- TF 2.11, CUDA 11.2, cuDNN 8.9.1 and cuDNN 8.9.2\r\n- TF 2.12, CUDA 11.8, cuDNN 8.9.1 and cuDNN 8.9.2\r\n\r\nI used both RTX A4000 with cc 8.6 and Quadro P5000 with cc 6.1 for the tests.\r\n\r\nIn a few days I will test also our initial workload of training a RNN seq2seq model, which consists of a complex computation graph with quite a few RNN layers, and will report here. Leaving open until then.\r\n\r\nBTW, in the release notes for 8.9.1, it is said that\r\n> _Starting in cuDNN 8.9.1, the const int32_t devSeqLengths[] argument in cudnnRNNForward(), cudnnRNNBackwardData_v8(), and cudnnRNNBackwardWeights_v8() APIs will be ignored. All three functions will source variable sequence length arrays from RNN data descriptors, configured through the seqLengthArray parameter of cudnnSetRNNDataDescriptor(). The user does not need to transfer this array to device memory; the operation will be performed automatically by RNN APIs. This refinement simplifies the usage of cuDNN RNN APIs. It is also a workaround for random crashes in multi-GPU RNN training on TensorFlow._\r\n\r\nNote that the problem was not only in multi-GPU training, but also in single-GPU training and also single-GPU inference, and the problem was not just the crashes, but also \"just\" silently worse results (in a few tasks we trained models for ~10 hours and performed inference on million of tokens, and we did not experience any crash, just bad results -- during inference, some of the batches produced corrupted results, while other worked fine).\r\n\r\n:pray: Lastly, out of curiosity, is the TF implementation incorrect and you just decided to fix it on the cuDNN side, or is the problem not as simple as saying \"there is a bug on this line in the TF implementation\"? Was this affecting also for example PyTorch, or TF only?", "Yes, the quoted lines are mainly for resolving another separate issue reported to our cudnn team, which was only reproducible in the multi-GPU settings. I happened to notice it and thought the root cause might be similar to this one. From what we have seen so far, the root cause should be the corrupted `devSeqLengths` on the device side, so depending on its content, it might cause wrong results or even a crash.\r\n\r\nAt this moment, we are not sure if it is TF implementation error or not. I would argue that we need to be very careful to correctly use the cudnn rnn APIs (of course before 8.9.1). For example, currently our guess is that in TF, under some circumstances when we copy the host data to prepare the device array `devSeqLengths`, the host memory might be already freed since it is an async copy. To mitigate such usage overhead, cudnn decides to help users to prepare the `devSeqLengths` so that no explicit `devSeqLengths` is needed anymore. Not very familiar with how the pytorch calls the cudnn. But if it can guarantee the correct devSeqLengths, it should be fine. Hope this explains the backstory.", "Thanks a lot for the detailed response! I initially thought that the fix in 8.9.1 was motivated by this issue, but it seems it was completely independent ;-) I will report back when I try the full training, but I assume it will work fine given that the forward pass seem to be fixed with 8.9.1.", "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.", "Sorry for the delay, something else showed up, I will send the experiment results in a few days.", "I have trained a lemmatizer architecture with a three-layer character LSTM encoder, two-layer sentence-level word LSTM contextualizer, and three-layer character LSTM decoder, and with CUDA 11.8 and cuDNN <8.9.1, it either crashes or gives suboptimal results, and with cuDNN 8.9.2 the results are correct,i.e., they are virtually identical to using cuDNN 8.0.5 (which uses a different code path to cuDNN LSTM).\r\n\r\nSo I think we can close the issue :tada:", "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/60192\">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/60192\">No</a>\n", "Thanks @kaixih for the help :muscle: ", "> On the other hand, the following configurations do not exhibit the problem:\r\n> \r\n> * TF 2.4.4 from PyPI, cuDNN 8.0, CUDA 11.0\r\n\r\nI have used exactly this combination for my gtx1660ti but there werent deterministic results for LSTM training, even used this code:\r\n```\r\nos.environ['TF_DETERMINISTIC_OPS'] = '1'\r\nos.environ['TF_CUDNN_DETERMINISM'] = '1'\r\n```\r\n\r\nNow I put this debugging method:\r\n`os.environ['CUDA_LAUNCH_BLOCKING'] = '1'`\r\nand the results begin deterministic. The only problem is that my gpu usage fall to 30%.... any ideas?", "### Problem solved: Determinism LSTM bidirectional with Tensorflow-GPU:\r\n\r\n**My config:**\r\n\r\n> cuda_11.0.3_451.82_win10\r\n> cudnn-11.0-windows-x64-v8.0.5.39\r\n> tensorflow_gpu-2.4.4-cp38-cp38-win_amd64.whl\r\n> python-3.8.7-amd64\r\n> nvidia driver 536.23\r\n> gpu 1660ti\r\n> i5 3570k (base clock)\r\n\r\n**My code:**\r\n\r\n```\r\nimport os\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport random\r\n\r\nos.environ['TF_DETERMINISTIC_OPS'] = '1'\r\nos.environ['TF_CUDNN_DETERMINISM'] = '1'\r\nos.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\r\ntf.random.set_global_generator(tf.random.Generator.from_seed(1))\r\n\r\nnp.random.seed(1)\r\ntf.random.set_seed(1)\r\nrandom.seed(1)`\r\n```\r\n\r\n**Cublas vs Cuda blocking compared to Non-deterministism standard model:**\r\n\r\nStandard LSTM bidirectional non-deterministic model\r\n\r\n> CPU usage: 15%\r\n> GPU usage: 100%\r\n> Time: 100% (fastest)\r\n\r\n`os.environ['CUDA_LAUNCH_BLOCKING'] = '1'`\r\n\r\n> CPU usage: 50%\r\n> GPU usage: 30%\r\n> Time: 25%\r\n\r\n`os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"`\r\n\r\n> CPU usage: 50%\r\n> GPU usage: 70%\r\n> Time: 80%\r\n\r\n_Interesting: The results using Cuda block or Cublas cfg are the same_\r\n_Important: If you are using a model with Cublas then starting any cpu intensive task will slow down the training and will not return to the original speed._" ]

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[ "The issue is related to an unused variable in `external/boringssl/src/crypto/x509/t_x509.c:321:18`. Removing that variable fixes the issue. See attached.\r\n\r\nPatch: [issue_60191_patch.txt](https://github.com/tensorflow/tensorflow/files/11149628/issue_60191_patch.txt)\r\n\r\n", "@feranick \r\nCould you please elaborate more and provide detailed steps to replicate the issue reported here ?\r\n\r\nThank you!", "> Could you please elaborate more and provide detailed steps to replicate the issue reported here ?\r\n\r\n1. Make sure you have `XCode 14.3` installed (earlier versions won't compile TF as per issue: https://github.com/tensorflow/tensorflow/issues/58368 )\r\n2. `git clone` TF and checkout version 2.12.0 (or 2.11.1)\r\n3. `cd` in the folder `tensorflow` run `./configure` with all default options. \r\n4. run compilation: `bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package --verbose_failures`\r\n\r\nAt some point compilation will stop with the error in this issue. \r\n\r\nTo fix it:\r\n1. run your text editor (I use nano) into the external folder with the problematic library boringssl: `nano /private/var/tmp/_bazel_YOU-AS-USER/SOME_ALPHANUMERIC/external/boringssl/src/crypto/x509/t_x509.c`\r\n2. modify the code according to the patch attached (essentially remove all references to the unused variable `l`)\r\n3. restart compilation: `bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package --verbose_failures`\r\n\r\nPatch: \r\n[issue_60191_patch.txt](https://github.com/tensorflow/tensorflow/files/11150058/issue_60191_patch.txt)\r\n\r\n\r\n\r\n", "There might be ways to disable the `-Wunused-but-set-variable` flag, but I prefer to actually fix the code by removing the useless variable in first place. \r\n\r\nRemoving the variable should be applied in the ustream version as well (or make it do something useful, if that was the intent)", "Note: the issue is not present in the master git for boringssl: \r\nhttps://boringssl.googlesource.com/boringssl/\r\n\r\nTh unused variable is simply removed as per my patch above. Therefore TF either needs to resync boringssl for a newer release or apply my patch (attached). \r\nPatch: \r\n[issue_60191_patch.txt](https://github.com/tensorflow/tensorflow/files/11149626/issue_60191_patch.txt)\r\n", "@feranick ,\r\n\r\nThanks for bringing this with the solution. If you are willing to contribute please feel free to raise a PR.\r\n\r\nThanks!", "I would... Unfortunately the library is not included in the main TF tree, as it is pulled from private google servers. It needs to be fixed internally. BTW, TF pulls a specific version (can't tell you which one), but the bug is no longer present in the current master for boringssl (basically it has my patch applied). So bazel or whatever software pulls boringssl from the server needs to be updated to pull a more recent version, something only people with access to Google boringssl private repo can do.\r\nhttps://boringssl.googlesource.com/boringssl/\r\nIt is also fixed in the github repo:\r\nhttps://github.com/google/boringssl\r\nSo all this really needs is to pull a more recent version of boringssl. ", "Also, correct me if I am wrong.The file you mentioned for correction seems to be a temp file generated during bazel build.Not sure we can fix this from TF source tree. May be its related to Bazel.\r\n\r\nI have gone through the bazel build docs in TF repo and found this one have some context for boring SSL. \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/bc54be865c99c9c8b8174c98bf8665af4ab10949/tensorflow/workspace2.bzl#L557-L563\r\n\r\nWhether we can do something here by changing URL or any thing to rectify this problem ?", "> Whether we can do something here by changing URL or any thing to rectify this problem ?\r\n\r\nYes, you are correct. Bazel builds it within a temporary folder.\r\n\r\nAnd yes, I would think changing the URL might do it. However, I am not sure what URL/file to use from git as it probably uses an internal branch that is tar zipped. So the question is whether that package is there exclusively for TF... Maybe one can create a new package branched from main and placed in the same folder and then correct the reference URL in bazel.... ", "OK, on a deeper inspection, it seems that the link has been already fixed in TF master. When looking at `tensorflow/tensorflow/workspace2.bzl`\r\n\r\nTF Master:\r\n```\r\n tf_http_archive(\r\n name = \"boringssl\",\r\n sha256 = \"9dc53f851107eaf87b391136d13b815df97ec8f76dadb487b58b2fc45e624d2c\",\r\n strip_prefix = \"boringssl-c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc\",\r\n system_build_file = \"//third_party/systemlibs:boringssl.BUILD\",\r\n urls = tf_mirror_urls(\"https://github.com/google/boringssl/archive/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc.tar.gz\"),\r\n )\r\n```\r\n\r\nwhile for TF 2.12.0:\r\n```\r\ntf_http_archive(\r\n name = \"boringssl\",\r\n sha256 = \"534fa658bd845fd974b50b10f444d392dfd0d93768c4a51b61263fd37d851c40\",\r\n strip_prefix = \"boringssl-b9232f9e27e5668bc0414879dcdedb2a59ea75f2\",\r\n system_build_file = \"//third_party/systemlibs:boringssl.BUILD\",\r\n urls = tf_mirror_urls(\"https://github.com/google/boringssl/archive/b9232f9e27e5668bc0414879dcdedb2a59ea75f2.tar.gz\"),\r\n )\r\n```\r\nSo, in principle, one would only need to replace the reference links in `workspace2.bzl`to the newer version now in master...", "I am doing a test build where I replaced the strings above from main. Will report shortly.", "So far compilation is proceeding normally, beyond the point where it would crash because of this issue. It seems like the proposed solution (swapping the `tf_http_archive` from master) will fix the issue, possibly also for the 2.12.0 branch.", "I can confirm that compilation proceeds correctly on any platform I tried (MacOSX, linux).", "> Thanks for bringing this with the solution. If you are willing to contribute please feel free to raise a PR.\r\n\r\nPull request is in https://github.com/tensorflow/tensorflow/pull/60259\r\n", "@feranick ,\r\n\r\nThanks for all your effort and time in resolving and raising the PR. Our Team will review and update.\r\n\r\nThanks!", "Hi @feranick ,\r\n\r\nI can see nightly build was updated as required.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/0bc361b51ee4bad40392e77641ab74bd1ec4331a/tensorflow/workspace2.bzl#L569-L575\r\n\r\nCan we mark this as resolved. Please spare some time to verify and close the issue.\r\n\r\nThanks!", "It works now. Thanks for pushing it. Closing.", "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/60191\">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/60191\">No</a>\n" ]

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[ "Hi @terryheo ,\r\n\r\nHope all is well with you and thanks for helping us review the previous TFLite related PRs.\r\n\r\nCould you please review this PR when you have a chance? Thanks again!", "Hi @terryheo ,\r\n\r\nCould you please take a look at this PR when you have some time?\r\n\r\nThank you very much!", "Hi @terryheo Can you please review this PR ? Thank you!", "Hi @terryheo ,\r\n\r\nCould you please review this PR when you have a chance? \r\n\r\nThank you very much!", "Hi @zichuan-wei ,\r\n\r\nThank you so much for reviewing this PR. \r\n\r\nI've made changes as per your review comment. Could you please take a look again? Thanks!", "Hi @zichuan-wei ,\r\n\r\nCould you please review this PR again? Thank you very much!", "Hi @zichuan-wei ,\r\n\r\nCould you please take a look at the code change when you have a chance?\r\n\r\nThanks!", "Hi @zichuan-wei ,\r\n\r\nI think the failures in `Py+CPP Test Suite` check are not related to the code changes in this PR, since the changes are specific to Big-Endian systems and most are enclosed in `#if FLATBUFFERS_LITTLEENDIAN == 0` macros.\r\n\r\nHowever, I'm not sure why the `feedback/copybara - Google internal checks` failed, since I couldn't access the logs.\r\n\r\nCould you please take a look? Thank you very much!", "Hi @gbaned ,\r\n\r\nSince I couldn't access the logs, could you please help check why the `feedback/copybara - Google internal checks` failed? \r\n\r\nThank you very much!", "> Hi @gbaned ,\r\n> \r\n> Since I couldn't access the logs, could you please help check why the `feedback/copybara - Google internal checks` failed?\r\n> \r\n> Thank you very much!\r\n\r\nHi @kun-lu20 Sorry for the delay in response. We will check it. Thank you so much!", "Hi @gbaned ,\r\n\r\nDo you have any updates reg this PR? \r\n\r\nThank you very much!", "> Hi @gbaned ,\r\n> \r\n> Do you have any updates reg this PR?\r\n> \r\n> Thank you very much!\r\n\r\nHi @kun-lu20 Sorry for the delay in response. We are working on this PR internally. Thank you so much!", "@gbaned Thanks for letting me know!" ]

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[ "Thanks @sagunb !", "Looks like the following error shown in the PR checking logs wasn't caused by the code change in this PR:\r\n\r\n```bash\r\nexternal/llvm-project/llvm/include/llvm/IR/Attributes.h:91:14: fatal error: 'llvm/IR/Attributes.inc' file not found\r\n #include \"llvm/IR/Attributes.inc\"\r\n ^~~~~~~~~~~~~~~~~~~~~~~~\r\n1 error generated.\r\n```\r\n\r\n@sagunb @Neilblaze Could you please help me check the status of this PR? Please let me know if there is any change I should make at my end. Thank you very much!", "Hi @gbaned ,\r\n\r\nCould you please help me check the status of this PR?\r\n\r\nThank you very much!", "> Hi @gbaned ,\r\n> \r\n> Could you please help me check the status of this PR?\r\n> \r\n> Thank you very much!\r\n\r\nHi @kun-lu20 This PR is processing internally. Thank you so much!", "@gbaned Thanks for checking!" ]

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[ "Hi @mescoulan-gpsw Thanks for reporting the issue.\r\n\r\nI was able to successfully build the benchmark model with r2.12 version on MacOS with Bazel 5.3.0 as per the instructions given [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#on-android). Please find the screenshot below.\r\n\r\n<img width=\"564\" alt=\"Screenshot 2023-04-03 at 11 20 26 PM\" src=\"https://user-images.githubusercontent.com/118897289/229589801-6d19df8a-7147-4ce2-815a-49bda86f5d45.png\">\r\n\r\n\r\nThe `configure` options are given for your reference.\r\n\r\n\r\n<img width=\"593\" alt=\"Screenshot 2023-04-03 at 11 21 22 PM\" src=\"https://user-images.githubusercontent.com/118897289/229589829-e21515dd-2361-4289-aa46-fb306d83ba99.png\">\r\n\r\n\r\nCan you try you using r2.12 and using [this](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#on-android) and let us know if the issue still exists?\r\n\r\nThanks.\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/60188\">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/60188\">No</a>\n", "Hello @pjpratik , sorry for late reply.\r\nBy using r2.12, bazel 5.3.0 and the exact same configure options as you I was able to build successfully. Thank you !" ]

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[ "Hi @alien2327 ,\r\n\r\nThis is reported by other community member at #60031 and PR has been raised by a contributor for this which is under review. Please go through the issue and confirm if this is different to the other issue mentioned.\r\n\r\nThanks!", "Hi, @SuryanarayanaY !\r\n\r\nThank you for let me know #60031, and yes, this is the same error what I saw.\r\n\r\nAgain, thank you and your team for all the effort to support tf!\r\nBest regards", "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/60187\">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/60187\">No</a>\n" ]

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[ "Hi @terryheo Can you please review this PR ? Thank you!", "Hi @jongkweh Can you please check @terryheo's comments and keep us posted ? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@Weili17,\r\nIn order to expedite the trouble-shooting process, could you please provide a complete code you are using, it helps to analyse the issue in an effective way. Thank you!\r\n", "> @Weili17, In order to expedite the trouble-shooting process, could you please provide a complete code you are using, it helps to analyse the issue in an effective way. Thank you!\r\n\r\nThanks for your reply. I use the trt converter api to optimize my saved model, but when I test the converted model, found out there is no performance(latency) gain when predict. So I print the tf log and found that the conversion process failed with the log “Attribute _tftrt_convert_function was not found”, but I don't know what to do in next step.\r\n\r\nThis is my code.\r\n\r\nfrom __future__ import absolute_import, division, print_function, unicode_literals\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.python.compiler.tensorrt import trt_convert as trt\r\nfrom tensorflow.python.saved_model import tag_constants\r\n\r\nconversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(\r\n precision_mode=trt.TrtPrecisionMode.FP32,\r\n max_workspace_size_bytes=2000000000,\r\n minimum_segment_size= 50)\r\n\r\nconverter = trt.TrtGraphConverterV2(input_saved_model_dir='my_saved_model',\r\n input_saved_model_signature_key='serving_default',\r\n conversion_params=conversion_params)\r\nconverter.convert()\r\nconverter.save(output_saved_model_dir='tftrt_saved_model')\r\nprint('Done Converting to TF-TRT FP32')", "@Weili17,\r\nI was facing a different error while executing the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/e430fae8f49dc675986f8bdeed4cdb68/untitled1076.ipynb) and provide the colab gist with the reported error. \r\n\r\nThe converter takes a SavedModel as input, and processes it:\r\n\r\n1. Load the SavedModel using **tf.saved_model.load**.\r\n2. Extract the default serving signature from the .signatures map.\r\n3. Run a grappler optimization pass on the signature function. This returns a GraphDef.\r\n4. Create a new ConcreteFunction by importing the GraphDef with wrap_function.\r\n6. Save a new SavedModel using tf.saved_model.save, with the converted signature function as the default serving signature.\r\n`A note about the input: `the input SavedModel's default signature function must be labeled with a specific **attribute (_tftrt_convert_function),** otherwise the optimization will not run. \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.", "Closing this as stale. Please reopen if this is still a valid request. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60185\">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/60185\">No</a>\n" ]

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[ "I should mention that I am using my own toolchain, but is it essentially just clang. Code here: https://github.com/p3achyjr/p3achyGo/blob/main/toolchain/BUILD\r\n\r\nI noticed that the generated CUDA toolchain file adds CUDA include paths, whereas I just add c/c++ include paths. Could this have something to do with it?\r\n\r\nAt any rate, is there a principled way to include tensorflow in a bazel project without building the entire thing statically every time? It's very hard to know which target to build just from perusing the source code (do I use `:tensorflow_cc`? `:tensorflow_framework`? Do I define my own `transitive_hdrs` rule?)", "@p3achyjr \r\nSorry for the late reply, As per the documentation, TF v2.11.0 requires 8.1 cuDNN and 11.2 CUDA version.Could you please make sure to follow the instructions mentioned [here](https://www.tensorflow.org/install/source#linux) and check the [tested build configuration](https://www.tensorflow.org/install/source#gpu) as well. Please let us know if it helps?\r\nThank you!", "I tried using gcc 8 and gcc 9 today, and both failed. gcc8 gave the standard \"lld undefined symbol\". gcc failed much more catastrophically--It says the following command failed.\r\n\r\n```\r\ncd /home/axlui/.cache/bazel/_bazel_axlui/f4685961e4a033eb3c5c8f3ed28f41d5/execroot/__main__ && exec env - CUDA_TOOLKIT_PATH=/usr/local/cuda-11.3 CUDNN_INSTALL_PATH=/usr/local/cuda GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-8 LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64 PATH=/home/axlui/.cache/bazelisk/downloads/bazelbuild/bazel-6.1.1-linux-x86_64/bin:/home/axlui/.local/bin:/usr/lib/llvm-12/bin:/home/axlui/.local/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/local/go/bin PWD=/proc/self/cwd PYTHONPATH=/home/axlui/p3achyGo/python:/usr/lib/llvm-12/bin:/home/axlui/.local/bin:/usr/local/cuda/bin:/opt/conda/bin:/opt/conda/condabin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/local/go/bin PYTHON_BIN_PATH=/opt/conda/bin/python3 PYTHON_LIB_PATH=/bin TF_CUDA_COMPUTE_CAPABILITIES=7.5 TF_CUDA_VERSION=11 TF_CUDNN_VERSION=8 external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc @bazel-out/k8-opt/bin/cc/main-2.params\r\n```\r\n\r\nWhen I run this by hand, it spits out tons of linker errors, such as:\r\n\r\n```\r\n/usr/bin/ld: mkl_nn_ops.cc:(.text.startup._Z41__static_initialization_and_destruction_0ii.constprop.218+0x731f): undefined reference to `tensorflow::shape_inference::BatchMatMulV2Shape(tensorflow::shape_inference::InferenceContext*)'\r\n/usr/bin/ld: mkl_nn_ops.cc:(.text.startup._Z41__static_initialization_and_destruction_0ii.constprop.218+0x7372): undefined reference to `tensorflow::register_op::OpDefBuilderWrapper::operator()()'\r\n/usr/bin/ld: mkl_nn_ops.cc:(.text.startup._Z41__static_initialization_and_destruction_0ii.constprop.218+0x7437): undefined reference to `tensorflow::register_op::OpDefBuilderWrapper::operator()()'\r\n/usr/bin/ld: mkl_nn_ops.cc:(.text.startup._Z41__static_initialization_and_destruction_0ii.constprop.218+0x750c): undefined reference to `tensorflow::register_op::OpDefBuilderWrapper::operator()()'\r\n```\r\n\r\nMaybe you could take a look at how I've setup the build? I don't know if I'm using it right. https://github.com/p3achyjr/p3achyGo/blob/main/cc/nn/BUILD", "@p3achyjr ,\r\n\r\nCould you please confirm are you using the TF standard Build instructions mentioned [here](https://www.tensorflow.org/install/source). Are you getting the error during `./configure` ?\r\n\r\nCan you provide exact commands you have used to replicate the reported error ?", "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/60184\">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/60184\">No</a>\n" ]

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[ "editing sh file to new commands proposed by @TomMeowMeow automated the system path", "The issue will move to closed status once the [#2214](https://github.com/tensorflow/docs/pull/2214) is merged. Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60183\">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/60183\">No</a>\n" ]

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[ "Is there anything else to adjust/todo on this one? Seems like the internal CI builds are working at a glance. Thanks!", "@terryheo Just checking if this can be merged. Thanks!", "> @terryheo Just checking if this can be merged. Thanks!\r\n\r\nHi @samypr100 Sorry for the delay. This PR is processing internally, Thank you!", "@gbaned Just checking in if you found any issues during internal testing that the community can help address.\r\n\r\nDo you think that this MR will make it into the 2.13.0 release?\r\n\r\nThanks!", "Good question, I'm curious too. Open to make any other changes needed 💯 to get this 🚢 ", "Hi @terryheo Can you please assist on above comments from @johnthagen, @samypr100. Thank you!", "Hello all,\r\nAnd thank you for all your work.\r\nAny news on this, or should we stay in python 3.10 for tflite-runtime?", "Once this PR is merged, the next TF release (2.14?) will have Python 3.11 releases.", "Thanks @terryheo I've just re-updated the branch w/ master to make sure it's clean", "One of the tests seems to fail in 3.10 and is not failing in master (From what I checked, this is another that is failing).\r\n\r\n```\r\n//bazel_pip/tensorflow/python/data/experimental/kernel_tests/service:local_workers_test TIMEOUT in 1 out of 24 in 900.1s\r\n Stats over 24 runs: max = 900.1s, min = 6.1s, avg = 50.8s, dev = 177.1s\r\n```", "@ShamoX Seems like the [arm-ci.yml](https://github.com/tensorflow/tensorflow/actions/workflows/arm-ci.yml?query=branch%3Amaster) workflow has been failing in master from what I'm seeing at least these past few days" ]

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[ "it seems that the labels in the validation set are not in the correct format for the model's output. MobileNetV2 outputs a 1001-dimensional vector, where each element represents the probability of the input image belonging to a specific class. The validation set labels are just integers representing the class index.\r\n\r\nTo preprocess the data, you need to convert the labels to one-hot vectors. You can use the tf.one_hot function for this purpose", "> it seems that the labels in the validation set are not in the correct format for the model's output. MobileNetV2 outputs a 1001-dimensional vector, where each element represents the probability of the input image belonging to a specific class. The validation set labels are just integers representing the class index.\r\n> \r\n> To preprocess the data, you need to convert the labels to one-hot vectors. You can use the tf.one_hot function for this purpose\r\n\r\nHi @sunilgiri7 @sushreebarsa \r\nI was missing the one-hot vectors and once I converted the labels to one-hot, the error disappeared. But on evaluation, I was getting really bad accuracy on test data, to the tune of 0.1% accuracy on pretrained mobilenetv2 sourced from tfhub, which is totally incorrect.\r\n\r\n- for test data, I was using imagenet_val dataset consisting of 50000 images. \r\n- val.txt consists of 50000 ground truth labels for the imagenet_val dataset.\r\n- ImageNetLabels.txt was downloaded from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt \r\n\r\non using the below script:\r\n\r\n```\r\nimport tensorflow as tf\r\nimport tensorflow_hub as hub\r\nimport numpy as np\r\nimport os\r\n\r\nm = tf.keras.Sequential([hub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\", output_shape=(1001,))])\r\nm.build([None, 224, 224, 3]) # Batch input shape.\r\n\r\nimages = '/home/Downloads/ILSVRC2012_img_val/'\r\nclasses = '/home/Documents/tflite_models/imagenet_classes.txt'\r\nlabels ='/home/Documents/val.txt'\r\n#labels ={}\r\n\r\nwith open(labels, 'r') as f:\r\n\tlabel_name = [line.strip() for line in f.readlines()]\r\n\r\nclass_map = {}\r\nwith open(classes, 'r') as f:\r\n\tclasses = [line.strip() for line in f]\r\n\tfor i, class_name in enumerate(classes):\r\n\t\tclass_map[class_name] = i\r\n\r\ntest_labels=[]\r\nfor label in label_name:\r\n\tif label in class_map:\r\n\t\ttest_labels.append(class_map[label])\r\n\telse:\r\n\t\tprint(f\"label '{label} not found in class_map\")\r\n\r\n#print(type(test_labels))\r\ntest_labels = tf.one_hot(test_labels, 1001)\r\n\r\nimage_paths = [os.path.join(images, filename) for filename in os.listdir(images)]\r\n\r\ndataset = tf.data.Dataset.from_tensor_slices((image_paths, test_labels))\r\n\r\ndef preprocess_image(image_path):\r\n\timage = tf.io.read_file(image_path)\r\n\timage = tf.image.decode_jpeg(image, channels=3)\r\n\timage = tf.image.resize(image, [224,224])\r\n\t#image = tf.image.convert_image_dtype(image, tf.float32)\r\n\timage = tf.cast(image, tf.float32) / 255.0\r\n\treturn image\r\n\r\ndataset = dataset.map(lambda image_path, label: (preprocess_image(image_path), label))\r\ndataset = dataset.batch(batch_size=32)\r\n\r\nmodel.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\r\nloss, accuracy = model.evaluate(dataset)\r\n\r\nprint('loss: ', loss)\r\nprint('accuracy: ', accuracy)\r\n```\r\n\r\ni get the results as:\r\n\r\n> loss: 10.239829063415527\r\n> accuracy: 0.0009599999757483602\r\n\r\nam i missing something here?\r\nmy val.txt and imagenetlabels.txt file are attached\r\n\r\nthanks\r\n[val.txt](https://github.com/tensorflow/tensorflow/files/11127384/val.txt)\r\n[ImageNetLabels.txt](https://github.com/tensorflow/tensorflow/files/11127385/ImageNetLabels.txt)\r\n\r\n\r\n", "issue might be with the evaluation process or the dataset suyash here is\nsome possibilities>\n- make sure image is in correct format(JPEG)\n- check preprocess function working correctly\n- check if class_map dict map each class name to correct index you could\nprint out dict and compare to ImageNetLabel.text if there any mismatch\n- Try evaluating the model on a smaller subset of the dataset (e.g. 100\nimages) to see if the accuracy improves. If it does, then the issue might\nbe with the size of the dataset or the distribution of the classes\n- Finally, you could try retraining the model on the ImageNet dataset\nhope this helps you suyash\n\nOn Fri, 31 Mar 2023 at 15:24, Suyash ***@***.***> wrote:\n\n> it seems that the labels in the validation set are not in the correct\n> format for the model's output. MobileNetV2 outputs a 1001-dimensional\n> vector, where each element represents the probability of the input image\n> belonging to a specific class. The validation set labels are just integers\n> representing the class index.\n>\n> To preprocess the data, you need to convert the labels to one-hot vectors.\n> You can use the tf.one_hot function for this purpose\n>\n> Hi @sunilgiri7 <https://github.com/sunilgiri7>\n> I was missing the one-hot vectors and once I converted the labels to\n> one-hot, the error disappeared. But on evaluation, I was getting really bad\n> accuracy on test data, to the tune of 0.1% accuracy on pretrained\n> mobilenetv2 sourced from tfhub, which is totally incorrect.\n>\n> - for test data, I was using imagenet_val dataset consisting of 50000\n> images.\n> - val.txt consists of 50000 ground truth labels for the imagenet_val\n> dataset.\n> - ImageNetLabels.txt was downloaded from\n> https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\n>\n> on using the below script:\n>\n> import tensorflow as tf\n> import tensorflow_hub as hub\n> import numpy as np\n> import os\n>\n> m = tf.keras.Sequential([hub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\", output_shape=(1001,))])\n> m.build([None, 224, 224, 3]) # Batch input shape.\n>\n> images = '/home/mtk/Downloads/ILSVRC2012_img_val/'\n> classes = '/home/mtk/Documents/tflite_models/imagenet_classes.txt'\n> labels ='/home/mtk/Documents/val.txt'\n> #labels ={}\n>\n> with open(labels, 'r') as f:\n> \tlabel_name = [line.strip() for line in f.readlines()]\n>\n> class_map = {}\n> with open(classes, 'r') as f:\n> \tclasses = [line.strip() for line in f]\n> \tfor i, class_name in enumerate(classes):\n> \t\tclass_map[class_name] = i\n>\n> test_labels=[]\n> for label in label_name:\n> \tif label in class_map:\n> \t\ttest_labels.append(class_map[label])\n> \telse:\n> \t\tprint(f\"label '{label} not found in class_map\")\n>\n> #print(type(test_labels))\n> test_labels = tf.one_hot(test_labels, 1001)\n>\n> image_paths = [os.path.join(images, filename) for filename in os.listdir(images)]\n>\n> dataset = tf.data.Dataset.from_tensor_slices((image_paths, test_labels))\n>\n> def preprocess_image(image_path):\n> \timage = tf.io.read_file(image_path)\n> \timage = tf.image.decode_jpeg(image, channels=3)\n> \timage = tf.image.resize(image, [224,224])\n> \t#image = tf.image.convert_image_dtype(image, tf.float32)\n> \timage = tf.cast(image, tf.float32) / 255.0\n> \treturn image\n>\n> dataset = dataset.map(lambda image_path, label: (preprocess_image(image_path), label))\n> dataset = dataset.batch(batch_size=32)\n>\n> model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n> loss, accuracy = model.evaluate(dataset)\n>\n> print('loss: ', loss)\n> print('accuracy: ', accuracy)\n>\n> i get the results as:\n>\n> loss: 10.239829063415527\n> accuracy: 0.0009599999757483602\n>\n> am i missing something here?\n> my val.txt and imagenetlabels.txt file are attached\n>\n> thanks\n> val.txt <https://github.com/tensorflow/tensorflow/files/11127384/val.txt>\n> ImageNetLabels.txt\n> <https://github.com/tensorflow/tensorflow/files/11127385/ImageNetLabels.txt>\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60181#issuecomment-1492676976>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AZI5F7EXRWQ6OILLTWHEEF3W65KSXANCNFSM6AAAAAAWN7ZE2M>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "@suyash-narain Could you refer to the comment above and let us know if there is any update?\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.", "> @suyash-narain Could you refer to the comment above and let us know if there is any update? Thank you!\r\n\r\nHi, @sushreebarsa, apologies for the late reply. \r\nIs it possible for you to provide with a sample script on how to preprocess data from a subset of imagenet correctly? My current method took into consideration all the bullet points asked by you and yet my accuracy lies around 0-1%, which is surely incorrect.\r\nthanks ", "@suyash-narain Sorry for the late response!\r\nOn subsets with fewer training images, the model needs to be trained for more epochs and the hyper-parameters need to be adjusted as a requirement. This will give a better performance of the model.\r\nFor preprocessing data from subset of imagenet kindly have a look at [this](https://medium.com/swlh/subsets-of-imagenet-aca5342eb275) article.\r\n\r\nI tried to replicate the issue reported here and faced a different error. Please find the gist and let me know if I am missing something [here](https://colab.research.google.com/gist/sushreebarsa/b0583e20aff5bbf75683cc2aa71a45de/60181.ipynb?pli=1#scrollTo=HtPjd6ONypnv). \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/60181\">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/60181\">No</a>\n" ]

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[ "@mihaimaruseac, can you review?", "@meteorcloudy this is causing the internal build to fail: [cl/520986273](http://cl/520986273). Can you take a look?", "Yes, will do!", "Great to have this merged.\r\nFrom https://github.com/tensorflow/tensorflow/issues/60167#issuecomment-1490907122, this is only a partial fix. How do you plan to finish removing the duplicate compilation?", "Actually the change was automatically rollbacked due to some breakages, see https://github.com/tensorflow/tensorflow/issues/60298. Working on a rollforwad with fix.\r\n\r\nI think the API gen rule is the major one, theoretically we can also fix other similar genrule in a similar way. But in long term, I think there should be a way to tell Bazel that my target and exec are the exact same configuration, so Bazel can avoid building the target twice. /cc @gregestren @aiuto\r\n\r\nOn the other hand, I wonder if there is any way to avoid using those genrule at all? /cc @learning-to-play @mihaimaruseac ", "In the mean time, can we roll back the commit that caused the regression?\r\nI suppose what you propose will take times to get it.", "@nouiz Unfortunately, that will prevent updating Bazel to 6.x for TensorFlow. I think with the fix the performance regression is much less significant, which should be fine?", "The fix is now rolled forward at https://github.com/tensorflow/tensorflow/commit/da439b7d05584f93e745511d66f6d74a58b54b34", "I think getting rid of API generation genrule and requiring the files to be checked in is a long-term solution, needs several changes, including adding CI to test that the API files are up to date." ]

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[ "A recent commit (https://github.com/tensorflow/tensorflow/commit/51a8992bb9daba2364ac4bb931af1010d554f0fa) used non-standard GNU `realpath --relative`. Quick workaround is to use GNU realpath (in coreutils). On my mac, I used macport to install realpath to `/opt/local/libexec/gnubin`, then add `/opt/local/libexec/gnubin` before `/usr/bin` when running bazel.", "> A recent commit ([51a8992](https://github.com/tensorflow/tensorflow/commit/51a8992bb9daba2364ac4bb931af1010d554f0fa)) used non-standard GNU `realpath --relative`. Quick workaround is to use GNU realpath (in coreutils). On my mac, I used macport to install realpath to `/opt/local/libexec/gnubin`, then add `/opt/local/libexec/gnubin` before `/usr/bin` when running bazel.\r\n\r\nCan you give a more specific command to explain how to do it? I don't know how to install GNU realpath. Thanks very much.", "> > A recent commit ([51a8992](https://github.com/tensorflow/tensorflow/commit/51a8992bb9daba2364ac4bb931af1010d554f0fa)) used non-standard GNU `realpath --relative`. Quick workaround is to use GNU realpath (in coreutils). On my mac, I used macport to install realpath to `/opt/local/libexec/gnubin`, then add `/opt/local/libexec/gnubin` before `/usr/bin` when running bazel.\r\n> \r\n> Can you give a more specific command to explain how to do it? I don't know how to install GNU realpath. Thanks very much.\r\n\r\nI am a long time [macports](https://www.macports.org/) user, but I guess most people use [homebrew](https://brew.sh/), which I am not familiar. with For macports, I use `ports install coreutils` to install GNU realpath.\r\n", "> For macports, I use `ports install coreutils` to install GNU realpath.\r\n\r\nFor homebrew is the same command, just do `brew install coreutils` :)", "After install that, I got new error:\r\n\r\n```sh\r\n$ bazel build --verbose_failures //tensorflow/tools/pip_package:build_pip_package \r\nINFO: Options provided by the client: \r\n Inherited 'common' options: --isatty=1 --terminal_columns=156 \r\nINFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/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\r\nINFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/opt/homebrew/opt/python@3.11/bin/python3.11 --action_env PYTHON_LIB_PATH=/opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages --python_path=/opt/homebrew/opt/python@3.11/bin/python3.11\r\nINFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:macos in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (0 packages loaded, 0 targets configured). \r\nINFO: Found 1 target... \r\nERROR: /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/tensorflow/python/BUILD:3740:24: Linking tensorflow/python/_pywrap_tensorflow_internal.so failed: (Aborted): cc_wrapper.sh failed: error executing command \r\n (cd /private/var/tmp/_bazel_ckpn/f1a48392cd9981a14f58ae3ee34a93e9/execroot/org_tensorflow && \\ \r\n exec env - \\ \r\n APPLE_SDK_PLATFORM=MacOSX \\\r\n APPLE_SDK_VERSION_OVERRIDE=13.1 \\\r\n PATH='/opt/homebrew/opt/coreutils/libexec/gnubin:/Applications/CMake.app/Contents/bin:/Users/ckpn/Mine/projects/golang/bin:/opt/java/jdk-18.0.1.1.jdk/Contents/Home/bin:/opt/homebrew/opt/go/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Applications/VMware Fusion Tech Preview.app/Contents/Public:/Library/Apple/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin:/Users/ckpn/.cargo/bin:/Applications/CMake.app/Contents/bin:/Users/ckpn/Mine/projects/golang/bin:/opt/java/jdk-18.0.1.1.jdk/Contents/Home/bin:/opt/homebrew/opt/go/bin:/opt/homebrew/bin:/opt/homebrew/sbin' \\\r\n PYTHON_BIN_PATH=/opt/homebrew/opt/python@3.11/bin/python3.11 \\\r\n PYTHON_LIB_PATH=/opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages \\\r\n TF2_BEHAVIOR=1 \\\r\n XCODE_VERSION_OVERRIDE=14.2.0.14C18 \\\r\n ZERO_AR_DATE=1 \\\r\n external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt/bin/tensorflow/python/_pywrap_tensorflow_internal.so-2.params)\r\n# Configuration: 0d30b901d7e94aed4b42ed061313da68829eea8916fb0996d8f08d6e7e494092\r\n# Execution platform: @local_execution_config_platform//:platform\r\nld: malformed trie, terminalSize extends beyond trie data file 'bazel-out/darwin_arm64-opt/bin/_solib_darwin_arm64/libtensorflow_Slibtensorflow_Ucc.2.13.0.dylib'\r\nclang: error: linker command failed with exit code 1 (use -v to see invocation)\r\nError in child process '/usr/bin/xcrun'. 1\r\nexternal/local_config_cc/cc_wrapper.sh: line 69: 73858 Abort trap: 6 \"$(/usr/bin/dirname \"$0\")\"/wrapped_clang \"$@\"\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build \r\nINFO: Elapsed time: 0.681s, Critical Path: 0.21s \r\nINFO: 12 processes: 12 internal. \r\nFAILED: Build did NOT complete successfully\r\n```", "Well, actually, it's an \"old\" linker error, see, https://github.com/tensorflow/tensorflow/issues/58368\r\n\r\n> After install that, I got new error:\r\n> \r\n> ```shell\r\n> $ bazel build --verbose_failures //tensorflow/tools/pip_package:build_pip_package \r\n> INFO: Options provided by the client: \r\n> Inherited 'common' options: --isatty=1 --terminal_columns=156 \r\n> INFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc:\r\n> Inherited 'common' options: --experimental_repo_remote_exec\r\n> INFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/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\r\n> INFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.tf_configure.bazelrc:\r\n> 'build' options: --action_env PYTHON_BIN_PATH=/opt/homebrew/opt/python@3.11/bin/python3.11 --action_env PYTHON_LIB_PATH=/opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages --python_path=/opt/homebrew/opt/python@3.11/bin/python3.11\r\n> INFO: Reading rc options for 'build' from /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc:\r\n> 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\n> INFO: Found applicable config definition build:short_logs in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\n> INFO: Found applicable config definition build:v2 in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\n> INFO: Found applicable config definition build:macos in file /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\n> INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (0 packages loaded, 0 targets configured). \r\n> INFO: Found 1 target... \r\n> ERROR: /Users/ckpn/Mine/projects/python/bert_tst/tensorflow/tensorflow/python/BUILD:3740:24: Linking tensorflow/python/_pywrap_tensorflow_internal.so failed: (Aborted): cc_wrapper.sh failed: error executing command \r\n> (cd /private/var/tmp/_bazel_ckpn/f1a48392cd9981a14f58ae3ee34a93e9/execroot/org_tensorflow && \\ \r\n> exec env - \\ \r\n> APPLE_SDK_PLATFORM=MacOSX \\\r\n> APPLE_SDK_VERSION_OVERRIDE=13.1 \\\r\n> PATH='/opt/homebrew/opt/coreutils/libexec/gnubin:/Applications/CMake.app/Contents/bin:/Users/ckpn/Mine/projects/golang/bin:/opt/java/jdk-18.0.1.1.jdk/Contents/Home/bin:/opt/homebrew/opt/go/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Applications/VMware Fusion Tech Preview.app/Contents/Public:/Library/Apple/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin:/Users/ckpn/.cargo/bin:/Applications/CMake.app/Contents/bin:/Users/ckpn/Mine/projects/golang/bin:/opt/java/jdk-18.0.1.1.jdk/Contents/Home/bin:/opt/homebrew/opt/go/bin:/opt/homebrew/bin:/opt/homebrew/sbin' \\\r\n> PYTHON_BIN_PATH=/opt/homebrew/opt/python@3.11/bin/python3.11 \\\r\n> PYTHON_LIB_PATH=/opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages \\\r\n> TF2_BEHAVIOR=1 \\\r\n> XCODE_VERSION_OVERRIDE=14.2.0.14C18 \\\r\n> ZERO_AR_DATE=1 \\\r\n> external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt/bin/tensorflow/python/_pywrap_tensorflow_internal.so-2.params)\r\n> # Configuration: 0d30b901d7e94aed4b42ed061313da68829eea8916fb0996d8f08d6e7e494092\r\n> # Execution platform: @local_execution_config_platform//:platform\r\n> ld: malformed trie, terminalSize extends beyond trie data file 'bazel-out/darwin_arm64-opt/bin/_solib_darwin_arm64/libtensorflow_Slibtensorflow_Ucc.2.13.0.dylib'\r\n> clang: error: linker command failed with exit code 1 (use -v to see invocation)\r\n> Error in child process '/usr/bin/xcrun'. 1\r\n> external/local_config_cc/cc_wrapper.sh: line 69: 73858 Abort trap: 6 \"$(/usr/bin/dirname \"$0\")\"/wrapped_clang \"$@\"\r\n> Target //tensorflow/tools/pip_package:build_pip_package failed to build \r\n> INFO: Elapsed time: 0.681s, Critical Path: 0.21s \r\n> INFO: 12 processes: 12 internal. \r\n> FAILED: Build did NOT complete successfully\r\n> ```\r\n\r\n", "> Well, actually, it's an \"old\" linker error, see, #58368\r\n\r\nThe new version of `XCode 14.3` include an updated version of the linker `ld` which fixes the original issue https://github.com/tensorflow/tensorflow/issues/58368. Unless there are other issues unrelated to the linker bug, this issue is resolved as well, and if so, this bug report should be closed.", "Hi @zyxkad ,\r\n\r\nI have replicated the reported error.\r\n\r\n```\r\nsuryanarayanay-macbookpro:tensorflow suryanarayanay$ bazel build --verbose_failures //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=214\r\nINFO: Reading rc options for 'build' from /Users/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/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 /Users/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:macos in file /Users/suryanarayanay/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/0aaa6e679847a4eeb407136e7b0bcef93ec652e6.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/6330447c2509c3669d64ae753c8030be1a38dc72.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/benchmark/archive/f7547e29ccaed7b64ef4f7495ecfff1c9f6f3d03.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/flatbuffers/archive/v23.1.20.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/glennrp/libpng/archive/v1.6.39.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/boringssl/archive/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/openxla/stablehlo/archive/458bdd95771e9861e6488868e315a1b0340058ba.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/7adae8e6ded8fff33d92212ca1028d2419cd34d4.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/pybind/pybind11_abseil/archive/2c4932ed6f6204f1656e245838f4f5eae69d2e29.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (605 packages loaded, 43895 targets configured).\r\nINFO: Found 1 target...\r\nINFO: SystemSuspensionEvent: Computer put to sleep\r\nINFO: SystemSuspensionEvent: Computer woken up\r\nERROR: /Users/suryanarayanay/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/suryanarayanay/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for host] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nINFO: Elapsed time: 1936.248s, Critical Path: 31.46s\r\nINFO: 6866 processes: 2275 internal, 4591 local.\r\nFAILED: Build did NOT complete successfully\r\nsuryanarayanay-macbookpro:tensorflow suryanarayanay$ \r\n```", "The build fails with clang 14.0.0. \r\n\r\n```\r\nsuryanarayanay-macbookpro:tensorflow suryanarayanay$ xcrun ld -v\r\n@(#)PROGRAM:ld PROJECT:ld64-820.1\r\nBUILD 20:07:05 Nov 7 2022\r\nconfigured to support archs: armv6 armv7 armv7s arm64 arm64e arm64_32 i386 x86_64 x86_64h armv6m armv7k armv7m armv7em\r\nLTO support using: LLVM version 14.0.0, (clang-1400.0.29.202) (static support for 29, runtime is 29)\r\nTAPI support using: Apple TAPI version 14.0.0 (tapi-1400.0.11)\r\nsuryanarayanay-macbookpro:tensorflow suryanarayanay$ clang --version\r\nApple clang version 14.0.0 (clang-1400.0.29.202)\r\nTarget: arm64-apple-darwin22.3.0\r\nThread model: posix\r\nInstalledDir: /Library/Developer/CommandLineTools/usr/bin\r\n```\r\n@zyxkad, Could you please try with XCode 14.3 version as mentioned in **[comment-1492839793](https://github.com/tensorflow/tensorflow/issues/60179#issuecomment-1492839793)** and confirm if still a problem with it.", "> The new version of `XCode 14.3` include an updated version of the linker `ld` which fixes the original issue #58368. Unless there are other issues unrelated to the linker bug, this issue is resolved as well, and if so, this bug report should be closed.\r\n\r\nNo, my issue is if I don't install `realpath`, then the build will falid. I'm not sure is that solved?", "I have a event later for an hour, so I will test after that", "Build successed! :)", "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/60179\">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/60179\">No</a>\n", "@zyxkad ,\r\n\r\nThanks for the update and we are glad that your issue resolved. Please confirm whether upgrading XCode version worked for you or any other hacks so that it will help the larger community who visit this ticket. Thanks!", "Solved environment:\r\n\r\nCommit: 55939be39409283c4b35a62049fe548a2de3ce44\r\n\r\n**XCode: 14.3**\r\n**clang**\r\n```\r\nApple clang version 14.0.3 (clang-1403.0.22.14.1)\r\n```\r\n**bazel 5.3.0**", "@zyxkad Hello, building `TensorFlow 2.13` on my Apple M1 also encountered the same issue. Could you confirm if you were able to build it successfully with just the command `brew install coreutils`, without any other modifications?" ]

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[ "@tracerxia \r\nSorry for the late reply. Could you please make sure to follow the instructions mentioned [here](https://www.tensorflow.org/install/source) and check the [tested build configuration](https://www.tensorflow.org/install/source#gpu) as well. Please let us know if it helps?\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/60178\">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/60178\">No</a>\n" ]

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[ "The **model.evaluat**e method is being called with **train_data** as the input, which suggests that it is being evaluated on the entire training set, whereas **model.fit** is being called with a **train_dataset** that has been batched and shuffled. This difference in the input data may cause the difference in AUC scores.\r\n\r\nThe issue might be with the evaluation process also, When using **model.evaluate()** to compute the AUC, the labels are provided as [**y_train.output_1, y_train.output_2, y_train.output_3]**, but during training, the labels are provided as **(y_train.output_1, y_train.output_2, y_train.output_3)** within the **tf.data.Dataset**.\r\n\r\nPlease check this improved code working: \r\n```\r\neval_dataset = tf.data.Dataset.from_tensor_slices((train_data, (y_train.output_1, y_train.output_2, y_train.output_3))).batch(batch_size)\r\n\r\nauc = model.evaluate(eval_dataset)\r\n\r\n# OR, to get the AUC values separately for each output:\r\noutput1_auc, output2_auc, output3_auc = model.evaluate(eval_dataset)\r\n```", "Hi @jasonxcx, \r\n\r\nThe metrics on the training set are just the mean over all batches during **model.fit**, as the weights are changing with each batch. Using **model.evaluate** will keep the model weights fixed and compute loss/accuracy for the whole data you give in. \r\nKindly refer to the [official documents](https://keras.io/getting_started/faq/#why-is-my-training-loss-much-higher-than-my-testing-loss). Thank you! ", "> The **model.evaluat**e method is being called with **train_data** as the input, which suggests that it is being evaluated on the entire training set, whereas **model.fit** is being called with a **train_dataset** that has been batched and shuffled. This difference in the input data may cause the difference in AUC scores.\r\n> \r\n> The issue might be with the evaluation process also, When using **model.evaluate()** to compute the AUC, the labels are provided as [**y_train.output_1, y_train.output_2, y_train.output_3]**, but during training, the labels are provided as **(y_train.output_1, y_train.output_2, y_train.output_3)** within the **tf.data.Dataset**.\r\n> \r\n> Please check this improved code working:\r\n> \r\n> ```\r\n> eval_dataset = tf.data.Dataset.from_tensor_slices((train_data, (y_train.output_1, y_train.output_2, y_train.output_3))).batch(batch_size)\r\n> \r\n> auc = model.evaluate(eval_dataset)\r\n> \r\n> # OR, to get the AUC values separately for each output:\r\n> output1_auc, output2_auc, output3_auc = model.evaluate(eval_dataset)\r\n> ```\r\n\r\nHi @pat749 \r\nThank you so much, but unluckily it still not work, my training result is 80+%, while my evaluation result is 60+%\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/60177\">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/60177\">No</a>\n", "neg labels and pos label are clustered, and the shuffle() seems not working" ]

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[ "Hello @varunshankar! Could you please have a look at [this](https://www.tensorflow.org/install/source_windows) page which explains well about the tested build configuration for different compatible versions. The compatible python version for TF v2.12. is 3.8 - 3.11 .\r\nThank you!", "2.11.1 contains a critical security update ([CVE-2023-25668](https://github.com/advisories/GHSA-gw97-ff7c-9v96)) that would be nice to have with the same versions of Python which are compatible with 2.11 (i.e., Python 3.7). Can you please distribute 2.11.1 with Python 3.7 support?", "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.", "@sushreebarsa we expect that there would be a build for python 3.7 for TF 2.11.1 especially given the number of vulnerabilities that are resolved in that release. Can you please provide with a tentative timeline on when this will be resolved? ", "Hi @sushreebarsa I wonder if it's intentional to discontinue support for Python 3.7? Is there a technical problem preventing 2.11.1 patch been built on Python 3.7? (we need this patch to deploy on Python 3.7, so want to find a resolution)", "It looks like python 3.7 was removed from some of the automations back in December [here](https://github.com/tensorflow/tensorflow/commit/90d257096c7afcc499ef8a934e78929fb3b7531d). Perhaps this is release to the reason we're not seeing 2.11.1 artifacts for python 3.7?\r\n\r\nFWIW, I was able to compile 2.11.1 with python 3.7 and successfully install the built wheel. This is without thorough testing, though, so I cannot say it's 100% functional.", "Hi @sushreebarsa \r\nI'm also one of developers waiting your reply.\r\nMy team is deploying TF model using python 3.7 and we need to solve vulnerabilities mentioned above(ex. CVE-2023-25668). \r\nIf TF 2.11.1 is not supporting python 3.7 any more, I need a plan B ASAP.\r\n\bI look forward to hearing from you soon\r\nThanks in advance", "@varunshankar @YONGARYZ Sorry for the late response!\r\n\r\nPlease have a look at the documentation [here](https://www.tensorflow.org/install/source#tested_build_configurations) which clearly mentions the build configuration of TF version 2.11. For TF v2.11 , the supported version of python is 3.7- 3.10. That means there is no discontinuation of python 3.7. If you see any compatibility issues then please let us know. Thank you! ", "Hey @sushreebarsa. The issue is that the `tensorflow-cpu` version 2.11.1 for Python 3.7 isn't available on PyPi, which might give the impression that support for 3.7 has been discontinued.\r\n\r\n```shell\r\n$ docker run --platform=linux/amd64 --entrypoint /bin/bash python:3.7-slim -c \"pip install tensorflow-cpu==2.11.1\"\r\nERROR: Could not find a version that satisfies the requirement tensorflow-cpu==2.11.1 (from versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0)\r\nERROR: No matching distribution found for tensorflow-cpu==2.11.1\r\n```", "@sushreebarsa I have a same issue like @alcysec.\r\nAs you mentioned, the documentation from Tensorflow, PyPi[(link)](https://pypi.org/project/tensorflow-cpu/2.11.1/) clearly mentions TF 2.11.1 supports python 3.7. But I can't install using pip. (I'm using an official image python:3.7.13-slim. )\r\nThe reason might be the @camattin's mention above.\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/60176\">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/60176\">No</a>\n", "@sushreebarsa \r\nI'm still looking forward to your reply, but the issue has been closed. Plz reopen this issue.", "@YONGARYZ Reopening the issue as per the request. Thank you!", "@sushreebarsa Thank you for reopening the issue.\r\nCould you please make any comment about the issue?\r\nAs you mentioned in the previous comment (\"For TF v2.11 , the supported version of python is 3.7- 3.10.\"), definitely there is a problem.\r\nI need to make countermeasures for this issue. Therefore please let me know about whether you (or other contributors) are recognizing the issue. if then could you tell me brief plan for solving the issue? I need to make a decision in real quick.", "Hi, \r\n\r\nThanks for reporting the issue, The usual practice in Tensorflow to support any Python version is, during the time of release if that Python version is within the support life cycle as defined here https://devguide.python.org/versions/.\r\nAs per the above link, during the patch release of `2.11.1` Python `3.7` was at `end-of-life `.\r\n\r\nFor any CVEs which needs the specific python version, you can follow out [Patching guidelines](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines).\r\n\r\nWhich basically explains to Build the Tensorflow pip package with the desired python version and cherrypick the chnages you need in the 2.11.0 branch. Thanks!", "I see. \r\nThanks for the comment!", "> Hi,\r\n> \r\n> Thanks for reporting the issue, The usual practice in Tensorflow to support any Python version is, during the time of release if that Python version is within the support life cycle as defined here https://devguide.python.org/versions/. As per the above link, during the patch release of `2.11.1` Python `3.7` was at `end-of-life `.\r\n\r\nThis is false. \r\n\r\n1. 2.11.1 was released on 3/20: https://github.com/tensorflow/tensorflow/releases/tag/v2.11.1\r\n2. The CVE was published 3/24: https://github.com/advisories/GHSA-gw97-ff7c-9v96\r\n3. This issue was reported to you on 3/30. \r\n4. Python 3.7 became EoL on 6/27 (as per your own link).\r\n\r\nFolks were left vulnerable for three months while this issue went unacknowledged *until* Python 3.7 became EoL and that was used as an excuse not to fix it. Fair enough, but no need to retcon when the timeline is public.", "Apologies for the confusion.\r\n\r\nIt is challenging to follow the strict timeline as mentioned in the above comment.\r\n\r\n2.11.1 (3/20) was released in the same timeline as 2.12.0(3/23) and both followed the same python support window as mentioned below for 2.12.0. \r\n\r\n<h2 id=\"tested_build_configurations\" data-text=\"Tested build configurations\" role=\"presentation\" style=\"box-sizing: inherit; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: ; font-stretch: ; font-size: ; line-height: ; font-family: ; font-optical-sizing: ; font-kerning: ; font-feature-settings: ; font-variation-settings: ; letter-spacing: normal; margin: var(--devsite-h2-margin); overflow: hidden; text-overflow: ellipsis; border-bottom: var(--devsite-h2-border,var(--devsite-secondary-border)); padding: var(--devsite-h2-padding); margin-inline-end: -40px; padding-inline-end: 40px; color: rgb(32, 33, 36); orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"><span class=\"devsite-heading\" role=\"heading\" aria-level=\"2\" style=\"box-sizing: inherit;\">Tested build configurations</span><button type=\"button\" class=\"devsite-heading-link button-flat material-icons\" aria-label=\"Copy link to this section: Tested build configurations\" data-title=\"Copy link to this section: Tested build configurations\" data-id=\"tested_build_configurations\" style=\"box-sizing: border-box; appearance: none; background: 0px center; border: 0px; border-radius: var(--devsite-button-border-radius,2px); box-shadow: none; color: var(--devsite-icon-color,var(--devsite-secondary-text-color)); cursor: pointer; display: inline-block; font-style: normal; font-variant-ligatures: ; font-variant-caps: ; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: normal; font-stretch: ; font-size: 24px; font-family: &quot;Material Icons&quot;; font-optical-sizing: ; font-kerning: ; font-feature-settings: &quot;liga&quot;; font-variation-settings: ; height: 24px; letter-spacing: normal; line-height: 1; margin: var(--devsite-button-margin,0); margin-inline-end: var(--devsite-button-margin-x-end); max-width: var(--devsite-button-max-width,none); min-width: 36px; outline: 0px; overflow: hidden; padding: 0px 8px; text-align: center; text-decoration: none; text-overflow: ellipsis; text-transform: none; transition: background-color 0.2s ease 0s, border 0.2s ease 0s, box-shadow 0.2s ease 0s; vertical-align: middle; white-space: nowrap; width: var(--devsite-button-width,auto); overflow-wrap: normal; direction: ltr; -webkit-font-smoothing: antialiased; opacity: 0;\"></button></h2><h3 id=\"cpu\" data-text=\"CPU\" role=\"presentation\" style=\"box-sizing: inherit; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: ; font-stretch: ; font-size: ; line-height: ; font-family: ; font-optical-sizing: ; font-kerning: ; font-feature-settings: ; font-variation-settings: ; letter-spacing: normal; margin-top: 0px; margin-right: ; margin-bottom: ; margin-left: ; overflow: hidden; text-overflow: ellipsis; margin-inline-end: -40px; padding-inline-end: 40px; color: rgb(32, 33, 36); orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"><span class=\"devsite-heading\" role=\"heading\" aria-level=\"3\" style=\"box-sizing: inherit;\">CPU</span><button type=\"button\" class=\"devsite-heading-link button-flat material-icons\" aria-label=\"Copy link to this section: CPU\" data-title=\"Copy link to this section: CPU\" data-id=\"cpu\" style=\"box-sizing: border-box; appearance: none; background: 0px center; border: 0px; border-radius: var(--devsite-button-border-radius,2px); box-shadow: none; color: var(--devsite-icon-color,var(--devsite-secondary-text-color)); cursor: pointer; display: inline-block; font-style: normal; font-variant-ligatures: ; font-variant-caps: ; font-variant-numeric: ; font-variant-east-asian: ; font-variant-alternates: ; font-weight: normal; font-stretch: ; font-size: 24px; font-family: &quot;Material Icons&quot;; font-optical-sizing: ; font-kerning: ; font-feature-settings: &quot;liga&quot;; font-variation-settings: ; height: 24px; letter-spacing: normal; line-height: 1; margin: var(--devsite-button-margin,0); margin-inline-end: var(--devsite-button-margin-x-end); max-width: var(--devsite-button-max-width,none); min-width: 36px; outline: 0px; overflow: hidden; padding: 0px 8px; text-align: center; text-decoration: none; text-overflow: ellipsis; text-transform: none; transition: background-color 0.2s ease 0s, border 0.2s ease 0s, box-shadow 0.2s ease 0s; vertical-align: middle; white-space: nowrap; width: var(--devsite-button-width,auto); overflow-wrap: normal; direction: ltr; -webkit-font-smoothing: antialiased; opacity: 0;\"></button></h3><div class=\"devsite-table-wrapper\" style=\"box-sizing: inherit; margin: var(--devsite-table-margin,16px 0); padding: 0px; overflow: auto; color: rgb(32, 33, 36); font-family: Roboto, &quot;Noto Sans&quot;, &quot;Noto Sans JP&quot;, &quot;Noto Sans KR&quot;, &quot;Noto Naskh Arabic&quot;, &quot;Noto Sans Thai&quot;, &quot;Noto Sans Hebrew&quot;, &quot;Noto Sans Bengali&quot;, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\">\r\n\r\nVersion | Python version | Compiler | Build tools\r\n-- | -- | -- | --\r\ntensorflow-2.12.0 | 3.8-3.11 | MSVC 2019 | Bazel 5.3.0\r\n\r\n</div>\r\n\r\nHowever, as I mentioned above, you can follow [Patching guidelines](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) for any patch specific to the python version you need.\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/60176\">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/60176\">No</a>\n" ]

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[ "@freddan80,\r\nCould you please confirm whether you are facing the issue with protobuf 3.19.4 or protobuf 3.20.3? Also there is an issue which was already raised for the similar issue #59931 which was already assigned to the developer for the investigation. Thank you!", "Hi @tilakrayal \r\nprotobuf 3.19.4 => doesn't work\r\nprotobuf 3.20.3 => works\r\n", "Hi @freddan80 ,\r\n\r\nThe requirements for building the pip-package are listed in setup.py under `REQUIRED_PACKAGES` which is located [here](https://github.com/tensorflow/tensorflow/blob/r2.11/tensorflow/tools/pip_package/setup.py#L83).\r\n\r\nEven documentation also having a note mentioned. Please refer below snapshot of same.\r\n\r\n<img width=\"911\" alt=\"Screenshot 2023-04-06 at 3 34 22 PM\" src=\"https://user-images.githubusercontent.com/116063290/230345460-9348a949-f926-41b4-8f74-918426846123.png\">\r\n\r\nThanks!", "Thx. I was hoping for a `requirements.txt` with all must-have dependencies listed, it's just simple and unambiguous. Anyways, `setup.py` has the protobuf issues listed, so they're being worked on. I'll check back next time I need to build from source to see if I run into an issue. Feel free to close this ticket in the meanwhile.", "@freddan80 ,\r\n\r\nClosing the issue as per confirmation. Please feel free to open new ticket if you have any problems in your future builds.\r\n\r\nThanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60175\">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/60175\">No</a>\n" ]

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[ "The behavior you are observing is expected, and it is not a logging issue. When we cache a dataset to disk with **dataset.cache(\"some_folder\")**, TensorFlow only partially caches the dataset to disk. The caching mechanism works by storing the elements of the dataset in memory as they are consumed, and then periodically flushing these elements to disk to avoid consuming too much memory.\r\n\r\nIf you want to ensure that the dataset is fully cached to disk, you can add a call to **dataset.prefetch()** after the call to **dataset.cache()**. This will cause TensorFlow to fully cache the dataset to disk before starting to consume it.\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\ndataset = tf.data.Dataset.range(1_000_000, dtype=tf.float32)\r\ndataset = dataset.cache(\"cached\")\r\ndataset = dataset.prefetch(tf.data.AUTOTUNE)\r\nfor _ in dataset:\r\n pass # Exhaust the dataset to force it to cache\r\n\r\n```\r\n\r\n", "Sorry, even with the addition of `prefetch` I am still seeing this log message.", "Try `dataset = dataset.take(5000).cache(\"cached\")` #Use take() before cache(). the take() function is used to create a new dataset that contains only the first 500 elements of the original dataset. The cache() function is then called on this new dataset to ensure that only the first 500 elements are cached. Finally, the dataset is iterated over to force it to cache.\r\n\r\nBy using take() before cache(), the warning message should be resolved and the dataset should be properly cached. https://github.com/tensorflow/tensorflow/issues/42773\r\n", "I have not tested this solution, however I will not use it because I want to iterate over the entire dataset and not just part of it. Additionally, I do not use `take` or any other function that limits the number of elements used from the dataset.", "@Thf772 , I would second to the suggestion given by @bhagirath20. \r\n`Cache` should be used only when the dataset is small and if the dataset fits into your memory. \r\nThe existing behavior is expected.\r\nIn general, cache should be used after loading and preprocessing the data but before `shuffling, repeating, batching, and prefetching` so that each instance will only be read and preprocessed once instead of every epoch in your model.", "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/60174\">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/60174\">No</a>\n" ]

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[]

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[ "```\r\nimport tensorflow as tf\r\nprint(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\r\ntf.config.list_physical_devices('GPU')\r\ntf.config.list_physical_devices()\r\n\r\n```\r\nOutput :\r\n```\r\nNum GPUs Available: 1\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\r\n PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n\r\n```\r\nWhen I running this code I was getting true output . Please check if It is possible that another process is already using the GPU, preventing TensorFlow from accessing it. Check if any other GPU-intensive programs are running on your system and close them.", "Hi @NoteDancing, \r\nAs per the official Tensorflow documenation,\r\n> TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install [TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2), or install tensorflow-cpu and, optionally, try the [TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-)\r\n\r\nAlso note that,\r\n> TensorFlow with GPU access is supported for WSL2 on Windows 10 19044 or higher.\r\n\r\n Kindly refer to the [official documentation](https://www.tensorflow.org/install/pip#windows-wsl2) for more information. Thank you!", "@synandi Tensorflow 2.12.0 Can't use GPU on Windows 11?", "\r\n> @synandi Tensorflow 2.12.0 Can't use GPU on Windows 11?\r\n\r\n@NoteDancing Yes. In order to leverage GPU with TensorFlow 2.12, you must install TensorFlow in WSL2 or you can also try the [TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-). Thank you!\r\n", "@synandi Does tensorflow-directml-plugin make tensorflow 2.12.0 use GPU on windows 11?Thank you!\r\n", "> @synandi Does tensorflow-directml-plugin make tensorflow 2.12.0 use GPU on windows 11?Thank you!\r\n\r\n@NoteDancing Yes. Kindly refer to the [TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-). Thank you!", "@synandi I try pip install tensorflow-directml-plugin, but I get error.\r\nERROR: Could not find a version that satisfies the requirement tensorflow-directml-plugin (from versions: none)\r\nERROR: No matching distribution found for tensorflow-directml-plugin", "@NoteDancing Apologies for the inconvenence. We are working on it. As a workaround, kindly install Tensorflow in WSL2. Refer to this [document](https://www.tensorflow.org/install/pip#windows-wsl2) on how to install Tensorflow in WSL2. 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/60172\">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/60172\">No</a>\n" ]

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[ "Thanks for opening the issue, Tensorflow 2.9 was release in May 2022, which is within the 1 year release window.\r\n@learning-to-play , Do we have any plan to release 2.9.4 for the CVEs listed in the issue? Thanks!", "There aren't any plans to release TensorFlow 2.9.4. Please see [RELEASE.md](https://github.com/tensorflow/tensorflow/blob/r2.11/RELEASE.md): \"Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself steps. You can refer to the release notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.\"\r\n", "Thanks for the clarification, @learning-to-play . I'm sorry to hear that, as it negatively affects the use of Tensorflow in production environments, where you can't easily jump versions, or want to spend the effort re-building and patching TF. ", "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/60171\">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/60171\">No</a>\n", "@alexlang74 Thank you very much for letting us know!\r\n@rishikasinha-tf FYI", "I have been saying that this would be a negative of dropping support for older patch releases for almost half a year now.\r\n\r\nHowever, the cost of doing these patches is expensive, it doesn't scale given the ratio between number of users of patch release and number of users of latest `.0` release." ]

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[ "@ftesser,\r\nI was facing a different assertion error while executing the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/740c36db1b70b0946d60e7984a61f254/untitled1059.ipynb). Thank you!", "> @ftesser, I was facing a different assertion error while executing the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/740c36db1b70b0946d60e7984a61f254/untitled1059.ipynb). Thank you!\r\n\r\nThe assertion \"assert history.history['loss'] == history_0.history['loss']\" is precisely the assertion that I inserted to verify the determinism, i.e. I check that the history of the new training is equal to the history of training \"0\".\r\n\r\nSo the behavior I described in the issue was reproduced.\r\nIf you try to set recurrent_dropout=0 the assertion does not fails.\r\n\r\n\r\n", "@ftesser,\r\nThank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n", "> @ftesser, Thank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues).\r\n> \r\n> Could you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues). To know more please refer: https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999 Thank you!\r\n\r\nthanks @tilakrayal (I opened the issue https://github.com/keras-team/tf-keras/issues/49)", "@ftesser,\r\nCould you please feel free to move this issue to closed status, so that we can track in Keras repo. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60170\">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/60170\">No</a>\n" ]

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[ "Hi @lu-wang-g Can you please review this PR ? Thank you!" ]

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[ "One additional thing I noticed when debugging: If I am to multiply the quantization or dequantization scale by a constant, the issue goes away and the engine can be built. For example, changing [these lines](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc#L69-L77) to this: \r\n```cpp\r\n if (scale_from_min_side < scale_from_max_side) {\r\n scales.quantize_scale[0] = scale_from_min_side;\r\n scales.dequantize_scale[0] = *min_range / min_quantized;\r\n *max_range = max_quantized * scales.dequantize_scale[0] * 2.0f;\r\n } else {\r\n scales.quantize_scale[0] = scale_from_max_side;\r\n scales.dequantize_scale[0] = *max_range / max_quantized;\r\n *min_range = min_quantized * scales.dequantize_scale[0] * 2.0f;\r\n }\r\n```\r\nI suspect it's some sort of overflow problem when the scale factors are actually correct. Will keep digging.", "Second update, it still works when I update the scaling factors to a very small adjustment, like `1.00001` or `0.99999`, but not when we keep the original value (no adjustment). ", "Third update: The issue was resolved by ensuring that `narrow_range=False` on the tensorflow side, and retraining the QAT model with that setting. The warning should be an error on the TF-TRT side. I will close this issue as I no longer need support. ", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60168\">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/60168\">No</a>\n" ]

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[ "Related issue is https://github.com/bazelbuild/bazel/issues/14848#issuecomment-1183500102", "I'll send a fix for this soon", "@nluehr Can you check if https://github.com/tensorflow/tensorflow/pull/60180 fixes the performance regression for you?", "I confirmed that with the fix from https://github.com/tensorflow/tensorflow/pull/60180, building takes 70 minutes, compared to 114 minutes without the fix and 62 minutes with distinct_host_configuration=false.", "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/60167\">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/60167\">No</a>\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/60167\">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/60167\">No</a>\n" ]

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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60166\">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/60166\">No</a>\n", "@johnypark \r\nI tried to reproduce the issue on Colab using TF v2.12 but facing a different error. Could you please refer to the gist [here](https://colab.research.google.com/gist/tiruk007/872b0bc68dcffe20b7351a5eb9eeafdb/untitled177.ipynb) for reference. Could you please provide detailed steps to replicate the issue reported here.\r\n\r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you." ]

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[ "Hi, Estimator `tf.estimator.BestExporter` is deprecated from Tensorflow 2.12 since it uses Tensorflow V1 behavior.\r\nCould you please migrate from Estimator to Keras API which will enable you to perform the tasks such as [model building](https://www.tensorflow.org/guide/keras/custom_layers_and_models), gradient application, [training](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit), evaluation, and prediction.\r\nRefer Migration guide here https://www.tensorflow.org/guide/migrate/migrating_estimator for more details. Thanks!", "The issue persists with the tf.io.gfile.rename function. We use this function for various tasks. It worked before Tensorflow 2.6. According to the documentation, it should still work. However it is not working with S3 folders, as you can see in the example code above", "Thanks for pointing out, \r\n`tf.compat.v1.gfile` is uses Tensorflow 1 behavior and usually we do not make any changes to the legacy code.\r\nAlternative to that from Tensorflow 2 is `tf.io.gfile.rename` https://www.tensorflow.org/api_docs/python/tf/io/gfile/rename.\r\nCould you please use the above API and let us know if you still face an error. Thanks!", "The issue still exists for tf.io.gfile.rename:\r\n\r\n```\r\nprint(tf.io.gfile.exists(SOURCE_DIR))\r\ntf.io.gfile.rename(SOURCE_DIR, DEST_DIR)\r\n```\r\n\r\nWhich outputs:\r\n```\r\nTrue\r\nTraceback (most recent call last):\r\n File \"/home/j99ca/.config/JetBrains/PyCharm2023.1/scratches/tf2_s3_rename_test.py\", line 9, in <module>\r\n tf.io.gfile.rename(SOURCE_DIR, DEST_DIR)\r\n File \"/home/j99ca/venvs/eigen-ml-tf2-12/lib/python3.10/site-packages/tensorflow/python/lib/io/file_io.py\", line 622, in rename_v2\r\n _pywrap_file_io.RenameFile(\r\ntensorflow.python.framework.errors_impl.FailedPreconditionError: Source is a directory or empty file\r\n```", "Any resolution on this issue? The documentation states that tf.io.gfile.rename should work with directories. Again, this issue seems to be for S3 even with importing tensorflow_io. Local files seem to work. I haven't tried gcs or any other cloud provider. Other operations seem to work fine like checking if the folder exists in S3 with tf.io.gfile.exists", "@sachinprasadhs is there any progress for this issue?", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60165\">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/60165\">No</a>\n" ]

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[ "When using **tf.autodiff.ForwardAccumulator** with **@tf.function**, the forward-pass is executed during tracing, which means that the **ForwardAccumulator** instance is not being executed during the graph execution. To fix this, you can wrap the **ForwardAccumulator** instance with **tf.custom_gradient**.\r\n```\r\n@tf.function\r\ndef train(self, data):\r\n with tf.GradientTape() as upper_tape:\r\n loss1= self.loss1(data)\r\n grad1 = upper_tape.gradient(loss1, self.net1.variables)\r\n\r\n def jvp_with_acc(tangents):\r\n with tf.autodiff.ForwardAccumulator(primals=self.net1.variables, tangents=tangents) as acc:\r\n with tf.GradientTape() as lower_tape:\r\n loss2 = self.loss2(data)\r\n grad2 = lower_tape.gradient(loss2, self.net2.variables)\r\n\r\n final_grad = acc.jvp(grad2)\r\n return final_grad, None\r\n\r\n final_grad = jvp_with_acc(grad1)\r\n self.optimizer.apply_gradients(zip(final_grad, self.net2.variables))\r\n```\r\n", "> When using **tf.autodiff.ForwardAccumulator** with **@tf.function**, the forward-pass is executed during tracing, which means that the **ForwardAccumulator** instance is not being executed during the graph execution. To fix this, you can wrap the **ForwardAccumulator** instance with **tf.custom_gradient**.\r\n> \r\n> ```\r\n> @tf.function\r\n> def train(self, data):\r\n> with tf.GradientTape() as upper_tape:\r\n> loss1= self.loss1(data)\r\n> grad1 = upper_tape.gradient(loss1, self.net1.variables)\r\n> \r\n> def jvp_with_acc(tangents):\r\n> with tf.autodiff.ForwardAccumulator(primals=self.net1.variables, tangents=tangents) as acc:\r\n> with tf.GradientTape() as lower_tape:\r\n> loss2 = self.loss2(data)\r\n> grad2 = lower_tape.gradient(loss2, self.net2.variables)\r\n> \r\n> final_grad = acc.jvp(grad2)\r\n> return final_grad, None\r\n> \r\n> final_grad = jvp_with_acc(grad1)\r\n> self.optimizer.apply_gradients(zip(final_grad, self.net2.variables))\r\n> ```\r\n\r\nThank you so much for replying. I managed to get the code to work under eager mode using the following code, yet when I turn on graph mode using @tf.function I got \"ValueError: Tried to convert 'input' to a tensor and failed. Error: None values not supported.\". I guess it is because \"grad1\" is None when first building the graph. Is there something I can do?\r\n\r\n```\r\ndef train(self, data):\r\n with tf.GradientTape() as upper_tape:\r\n loss1= self.loss1(data)\r\n grad1 = upper_tape.gradient(loss1, self.net1.variables)\r\n \r\n @tf.custom_gradient\r\n def jvp_with_acc(tangents):\r\n with tf.autodiff.ForwardAccumulator(primals=self.net1.variables, tangents=tangents) as acc:\r\n with tf.GradientTape() as lower_tape:\r\n loss2 = self.loss2(data)\r\n grad2 = lower_tape.gradient(loss2, self.net2.variables)\r\n\r\n final_grad = acc.jvp(grad2)\r\n def grad(variables):\r\n return None\r\n return final_grad, grad\r\n\r\n final_grad = jvp_with_acc(grad1)\r\n self.optimizer.apply_gradients(zip(final_grad, self.net2.variables))\r\n\r\n```", " you can try explicitly converting your input to a **tf.Tensor** before computing the gradients. You can also try setting a default value for **grad1** in case it is not being computed properly", "> \r\n\r\nTurns out the error comes from somewhere else in my loss function. The code is all good now. Much thanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60164\">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/60164\">No</a>\n" ]

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[ "Hi @Evan-0715, the code provided above does not raise any error. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/03334bfe2386f6dc345e3e063a7d7d57/60163.ipynb). Kindly share complete reproducible code so that we can replicate the issue from our end. Thank you! ", "```\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import layers, models, Model, Sequential\r\nfrom keras.layers import concatenate\r\nfrom tensorflow.keras.layers import Conv2D, Flatten, Dense\r\nfrom tensorflow import matmul, reshape, reduce_sum, transpose\r\n\r\ndef hilbert_transform(x, N=None, axis=-1):\r\n if x.dtype == tf.complex64 or x.dtype == tf.complex128:\r\n raise ValueError(\"x must be real.\")\r\n if N is None:\r\n N = x.shape[axis]\r\n if N <= 0:\r\n raise ValueError(\"N must be positive.\")\r\n x_br = tf.signal.rfft3d(x[:, :, :1])\r\n x_bi = tf.signal.rfft3d(x[:, :, 1:])\r\n x_complex = concatenate([x_br, x_bi], axis=-1)\r\n\r\n h = np.zeros(N)\r\n if N % 2 == 0:\r\n h[0] = h[N // 2] = 1\r\n h[1:N // 2] = 2\r\n else:\r\n h[0] = 1\r\n h[1:(N + 1) // 2] = 2\r\n if x.shape.ndims > 1:\r\n ind = [tf.newaxis] * x.shape.ndims\r\n ind[axis] = slice(None)\r\n h = h[tuple(ind)]\r\n x_complex_h = x_complex * h\r\n x_bj = tf.signal.ifft3d(x_complex_h[:, :, :1])\r\n x_bk = tf.signal.ifft3d(x_complex_h[:, :, 1:])\r\n x_hilbert = concatenate([tf.math.imag(x_bj), tf.math.imag(x_bk)], axis=-1)\r\n return x_hilbert\r\nclass ImageUpgradingBlock(tf.keras.layers.Layer):\r\n def __init__(self, **kwargs):\r\n super().__init__(**kwargs)\r\n self.conv1 = tf.keras.layers.Conv2D(filters=3, kernel_size=64)\r\n self.conv2 = tf.keras.layers.Conv2D(filters=3, kernel_size=64)\r\n\r\n def call(self, inputs, *args, **kwargs):\r\n maps_x = inputs[:, :, :, :3]\r\n v1 = self.conv1(maps_x)\r\n v2 = self.conv2(maps_x)\r\n v1 = reshape(v1, [-1, 3, 1]) # basis vector with shape(-1,3,1)\r\n v2 = reshape(v2, [-1, 3, 1])\r\n V = concatenate([v1, v2], axis=-1) # the 2-D column subspace with shape(_,3,2)\r\n V = V / (1e-6 + reduce_sum(abs(V), axis=1, keepdims=True))\r\n b_, h_, w_, c = maps_x.shape\r\n maps_x_t = transpose(reshape(maps_x, [-1, h_ * w_, c]), perm=[0, 2, 1])\r\n V_t = transpose(V, perm=[0, 2, 1]) \r\n mat = matmul(V_t, V)\r\n mat_inv = tf.linalg.inv(mat)\r\n # Projection ---> Y = V×(V_t×V)_(-1)×V_t×(X)\r\n reconstruct_x_t = matmul(V, matmul(matmul(mat_inv, V_t), maps_x_t))\r\n if b_ is None: \r\n reconstruct_x = tf.linalg.lstsq(tf.squeeze(V, axis=[0]), tf.squeeze(reconstruct_x_t, axis=[0]))\r\n reconstruct_x = tf.expand_dims(reconstruct_x, axis=0) # shape(1,c,h*w) why can‘t(None,c,h*w)\r\n else:\r\n reconstruct_x = tf.linalg.lstsq(V, reconstruct_x_t)\r\n reconstruct_x = transpose(reconstruct_x, perm=[0, 2, 1]) # shape(-1,h*w,c)\r\n reconstruct_x_h = hilbert_transform(reconstruct_x, axis=1)\r\n reconstruct_x = reshape(concatenate([reconstruct_x, reconstruct_x_h], axis=-1), [-1, h_, w_, c * 2 - 2])\r\n return reconstruct_x\r\n\r\n def get_config(self):\r\n config = super().get_config()\r\n return config\r\ndef RQIUNet():\r\n input_image = layers.Input(shape=(64, 64, 3), dtype=\"float32\")\r\n x_4d = ImageUpgradingBlock()(input_image)\r\n result = Conv2D(3, 3, padding=\"same\")(x_4d)\r\n model = models.Model(inputs=input_image, outputs=result)\r\n return model\r\n\r\nloss_fn = tf.keras.losses.MeanSquaredError()\r\noptimizer = tf.keras.optimizers.Adam()\r\n\r\n\r\n@tf.function\r\ndef train_step(inputs, targets):\r\n with tf.GradientTape() as tape:\r\n predictions = RQIUNet()(inputs)\r\n loss = loss_fn(targets, predictions)\r\n gradients = tape.gradient(loss, RQIUNet().trainable_variables)\r\n optimizer.apply_gradients(zip(gradients, RQIUNet().trainable_variables))\r\n return loss\r\n\r\n\r\nx = tf.random.normal((1, 64, 64, 3))\r\ny = tf.random.normal((1, 64, 64, 3))\r\n\r\nfor i in range(10):\r\n loss = train_step(x, y)\r\n print(f\"Step {i}, Loss: {loss:.4f}\")\r\n```\r\nHi, synandi. As the original project was quite large, I converted it into a small instance, which took a little time, sorry for that. If you run this line of code, you will get the error:\r\n LookupError: gradient registry has no entry for: RFFT3D\r\nWaiting for your reply, it will help me a lot in my experiment.", "@Evan-0715 Thank you for sharing the complete reproducible code. I was able to replicate the error in Colab using TF v2.12 and tf-nightly(v2.13.0a20230330). Kindly refer to this [gist-2.12](https://colab.sandbox.google.com/gist/synandi/57f3d2e6c05981c76d519d44f334ef59/60163_3.ipynb) and [tf-nightly](https://colab.sandbox.google.com/gist/synandi/f9aa87c44146cdaee92ccf7e739fab0a/60163_nightly.ipynb). Thank you!\r\n\r\n", "@Evan-0715 You are right that TensorFlow might not have implemented gradients for tf.signal.rfft3d. One way to resolve this is to replace the rfft3d and ifft3d functions with their 1D counterparts, rfft and irfft, which have gradients defined.\r\n\r\nHere's the modified hilbert_transform function using tf.signal.rfft and tf.signal.irfft: \r\n\r\ndef hilbert_transform(x, N=None, axis=-1):\r\n if x.dtype == tf.complex64 or x.dtype == tf.complex128:\r\n raise ValueError(\"x must be real.\")\r\n if N is None:\r\n N = x.shape[axis]\r\n if N <= 0:\r\n raise ValueError(\"N must be positive.\")\r\n x_br = tf.signal.rfft(x[:, :, :1])\r\n x_bi = tf.signal.rfft(x[:, :, 1:])\r\n x_complex = tf.concat([x_br, x_bi], axis=-1)\r\n\r\n h = np.zeros(N)\r\n if N % 2 == 0:\r\n h[0] = h[N // 2] = 1\r\n h[1:N // 2] = 2\r\n else:\r\n h[0] = 1\r\n h[1:(N + 1) // 2] = 2\r\n if x.shape.ndims > 1:\r\n ind = [tf.newaxis] * x.shape.ndims\r\n ind[axis] = slice(None)\r\n h = h[tuple(ind)]\r\n x_complex_h = x_complex * h\r\n x_bj = tf.signal.irfft(x_complex_h[:, :, :1])\r\n x_bk = tf.signal.irfft(x_complex_h[:, :, 1:])\r\n x_hilbert = tf.concat([tf.math.imag(x_bj), tf.math.imag(x_bk)], axis=-1)\r\n return x_hilbert\r\n\r\nHowever, there is another issue in your code. You are creating a new instance of the model at every training step, which will reset the model's weights and gradients. You should create an instance of the model and pass it as an argument to the train_step function.\r\n\r\nHere's the modified train_step function:\r\n\r\n@tf.function\r\ndef train_step(model, inputs, targets):\r\n with tf.GradientTape() as tape:\r\n predictions = model(inputs)\r\n loss = loss_fn(targets, predictions)\r\n gradients = tape.gradient(loss, model.trainable_variables)\r\n optimizer.apply_gradients(zip(gradients, model.trainable_variables))\r\n return loss\r\n\r\nAnd you should create the model outside the loop:\r\nx = tf.random.normal((1, 64, 64, 3))\r\ny = tf.random.normal((1, 64, 64, 3))\r\n\r\nmodel = RQIUNet()\r\n\r\nfor i in range(10):\r\n loss = train_step(model, x, y)\r\n print(f\"Step {i}, Loss: {loss:.4f}\")\r\n\r\n\r\nWith these changes, the code should run without the LookupError and should train the model correctly", "Thanks a lot! @synandi @Adesoji1 It really helped me to implement the Hilbert transform. If a input has tensor shape (b,h,w,2), it can be considered as a complex signal. Tensorflow's Hilbert transform code for its implementation is:\r\n```\r\ndef hilbert_transform(x, N=None, axis=-1):\r\n ##Constructing complex number\r\n x_complex = tf.complex(x[:, :, :1], x[:, :, 1:])\r\n if x_complex.dtype != tf.complex64 and x_complex.dtype != tf.complex128:\r\n raise ValueError(\"x must be complex.\")\r\n if N is None:\r\n N = x.shape[axis]\r\n if N <= 0:\r\n raise ValueError(\"N must be positive.\")\r\n x_t = transpose(x_complex, perm=[0, 2, 1])\r\n x_f = tf.signal.fft(x_t)\r\n x_f = transpose(x_f, perm=[0, 2, 1])\r\n\r\n h = np.zeros(N)\r\n if N % 2 == 0:\r\n h[0] = h[N // 2] = 1\r\n h[1:N // 2] = 2\r\n else:\r\n h[0] = 1\r\n h[1:(N + 1) // 2] = 2\r\n if x.shape.ndims > 1:\r\n ind = [tf.newaxis] * x.shape.ndims\r\n ind[axis] = slice(None)\r\n h = h[tuple(ind)]\r\n x_f_h = x_f * h\r\n x_f = transpose(x_f_h, perm=[0, 2, 1])\r\n x_hilbert = tf.signal.ifft(x_f)\r\n x_hilbert = transpose(x_hilbert, perm=[0, 2, 1])\r\n x_hilbert = concatenate([tf.math.imag(x_hilbert), tf.math.real(x_hilbert)], axis=-1)\r\n return x_hilbert\r\n```\r\nIt will return the output of a complex linear time-invariant system which has the shape (b,h,w,2). Thanks again!", "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/60163\">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/60163\">No</a>\n", "In addition, this can be added to the network for forward and backward propagation." ]

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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60162\">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/60162\">No</a>\n", "tested" ]

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[ "@tpopp could you take a look?", "Checking, should I review this again or not (it is marked as draft)?", "Hi @tpopp! @RoboTux is ooo until April 11th. He asked me to let you know he wanted to have a look at the CI failures before taking it out of draft. Cheers and have a nice weekend!", "> Hi @tpopp! @RoboTux is ooo until April 11th. He asked me to let you know he wanted to have a look at the CI failures before taking it out of draft. Cheers and have a nice weekend!\r\n\r\nI'm slowly catching up but the problem seems to be that tensorflow/python/tools/saved_model_cli_test.py assumes both x86 and aarch64 are always available. I'll see how to remove that expectation on Monday, any suggestion welcome though.", "I'll share it with others in the US in hopes that someone else has thoughts but won't be able to look earlier than Monday or Tuesday myself. ", "FYI: I have a revised patch that I'm going to push shortly (probably tomorrow). That should fix the python test failures observed with the current patch.", "Hey, sorry, I just came back from vacation. I'm going to see if I can find a more appropriate person to review this and will try to, in parallel, make sure everything passes, so there are no unexpected surprises later.", "To me it seems like @jakeharmon8 and @MichaelHudgins might be the ones who should look at this?", "Failure seems unrelated:\r\n\"ModuleNotFoundError: No module named 'tensorflow.compiler.mlir.stablehlo'\"\r\n\r\nIt also passed CI before this last merge.", "> Failure seems unrelated: \"ModuleNotFoundError: No module named 'tensorflow.compiler.mlir.stablehlo'\"\r\n> \r\n> It also passed CI before this last merge.\r\n\r\nSame error on master as well: https://github.com/tensorflow/tensorflow/actions/runs/5010911951/jobs/8981212479", "@RoboTux That failure was introduced with https://github.com/tensorflow/tensorflow/commit/c09dcc2c7d07a84cdb9f9b5342d7803f5dbc50b3", "@vam-google or @jakeharmon8 could you PTAL?", "Ping?", "@RoboTux can you please take a look at the errors in \"Py+CPP Test Suite - Ubuntu CPU, Python 3.9\". They do look related to the PR ", "I'm working on fixing remaining issues.", "The latest push should fix the API and codestyle issues. Note: this does indeed introduce a new function in the test API and follows a similar pattern to ROCm and cuda. Let me know if it isn't fine.", "All remaining issues seem unrelated to this change:\r\n- Arm CI fails to find the pip package it created (ls: cannot access '/tensorflow/whl/-*310*310*.whl': No such file or directory)\r\n- AMD ROCm fails to find clang\r\n- MacOS has 16 tests timing out", "@jakeharmon8 @MichaelHudgins ", "Ping?", "@jakeharmon8 can you rereview? \r\n@RoboTux can you please rebase?", "> @jakeharmon8 can you rereview? @RoboTux can you please rebase?\r\n\r\nSure thing, done.", "Windows build failure looks unrelated:\r\n\r\n```\r\nexternal/llvm-project/mlir/lib/Interfaces/ViewLikeInterface.cpp(118): error C2065: 'not': undeclared identifier\r\nexternal/llvm-project/mlir/lib/Interfaces/ViewLikeInterface.cpp(118): error C2146: syntax error: missing ')' before identifier 'scalables'\r\nexternal/llvm-project/mlir/lib/Interfaces/ViewLikeInterface.cpp(118): error C2059: syntax error: ')'\r\nexternal/llvm-project/mlir/lib/Interfaces/ViewLikeInterface.cpp(119): error C2146: syntax error: missing ';' before identifier 'printer'\r\n```", "MacOS build seems to fail for performance reasons if I read this correctly:\r\n```\r\n//tensorflow/python/kernel_tests/linalg/sparse:csr_sparse_matrix_sparse_mat_mul_grad_test_cpu FAILED in 150 out of 150 in 1.2s\r\n Stats over 150 runs: max = 1.2s, min = 0.5s, avg = 0.8s, dev = 0.1s\r\n```\r\n\r\nSince this code only changes the build system that must be unrelated.\r\n\r\nAs to ARM CI there is one test where TF crashes which I'm not sure about and one x86 dialect loading failure which is most likely related to this diff. I'll have a look now\r\n\r\n```\r\nFAIL: //tensorflow/compiler/xla/mlir/math/transforms/tests:math_optimization.mlir.test\r\n(...)\r\nLLVM ERROR: Building op `x86vector.avx.rsqrt` but it isn't registered in this MLIRContext: the dialect may not be loaded or this operation isn't registered by the dialect.\r\n```", "//tensorflow/dtensor/mlir/tests:spmd_expansion.mlir.test is known as flaky, see https://github.com/tensorflow/tensorflow/issues/61164", "Hi @RoboTux Can you please resolve conflicts? Thank you!", "fyi - @RoboTux is on leave until 26th.", "I can confirm that this PR works for Windows CUDA build. The remaining are resolving conflicts and merge quickly before new merge conflicts happens.", "Ping?", "There are two more blockers that I'm working on:\r\n\r\n1) Internal technical issues that I should have fixed soon\r\n2) Changes to the API normally require an RFC+Review (see [here](https://github.com/tensorflow/community/blob/master/rfcs/yyyymmdd-rfc-template.md)). I'm looking into what the process is for external contributors, and whether or not there's a shorter process for small changes like this. If you'd like to avoid that red tape, you could refactor this to remove the API changes.", "> There are two more blockers that I'm working on:\r\n> \r\n> 1. Internal technical issues that I should have fixed soon\r\n> \r\n> 2. Changes to the API normally require an RFC+Review (see [here](https://github.com/tensorflow/community/blob/master/rfcs/yyyymmdd-rfc-template.md)). I'm looking into what the process is for external contributors, and whether or not there's a shorter process for small changes like this. If you'd like to avoid that red tape, you could refactor this to remove the API changes.\r\n\r\nI'm not sure how I could remove the API change. I need a way to skip a target CPU specific test if the support for that target is not built in so need a way to query that." ]

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[ "cc: @penpornk @cantonios " ]

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[ "@Ruparani777,\r\ntensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import of `tensorflow.python.keras` and use the public API with from `tensorflow import keras or import tensorflow as tf`; **tf.keras**\r\n\r\nAlso I tried to execute the given code in tensorflow v2.12 and it was executed without any errors/issues. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/3a7b99a5268fd3f2659c139fc97dc180/untitled1057.ipynb). Thank you!", "Do This below.\r\n\r\nfrom tensorflow.keras.layers import Input, Dense\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.optimizers import Nadam\r\nimport numpy as np\r\n\r\nipt = Input(shape=(4,))\r\nout = Dense(1, activation='sigmoid')(ipt)\r\n\r\nmodel = Model(ipt, out)\r\nmodel.compile(optimizer=Nadam(lr=1e-4), loss='binary_crossentropy')\r\n\r\nX = np.random.randn(32, 4)\r\nY = np.random.randint(0, 2, (32, 1))\r\nmodel.train_on_batch(X, Y) \r\n\r\nLog output result is \r\n\r\n2023-03-31 22:52:20.351042: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): NVIDIA GeForce RTX 2060, Compute Capability 7.5\r\n2023-03-31 22:52:20.421809: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-03-31 22:52:20.926921: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n2023-03-31 22:52:21.114032: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process\r\n0.9700681567192078..\r\n\r\nOS\r\nWSL\r\nPython 3.8\r\nTensorflow 2.10", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60159\">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/60159\">No</a>\n" ]

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[ "* Additional Info.\r\nI am using TensorFlow Lite C API with podspec dependency 'TensorFlowLiteC', 'TensorFlowLiteC/Metal' (version is 2.11.0)\r\nThe interpreter option is default and delegate option is as followed.\r\n - allow_precision_loss = true\r\n - wait_type = TFLGpuDelegateWaitTypePassive\r\n - enable_quantization = true\r\n\r\ntflite with other target_spec.supported_types(fp16, fp32) has same problem.\r\n(current is default convert option, it has int8 type inside)\r\nAnd when I running on Android device with same tflite file, it is working well.", "Hi @gyujin-kim Thanks for reporting this issue.\r\n\r\nCan you please provide the steps you have followed in order to reproduce the issue?\r\n\r\nAre you using the [ESRGAN model](https://tfhub.dev/captain-pool/lite-model/esrgan-tf2/1) from TF Hub? \r\n\r\nThanks.", "Hi @pjpratik \r\nI am using [ECBSR](https://github.com/xindongzhang/ECBSR).\r\nAs mentioned above, i'am using TensorFow C API(not C++).\r\nCurrent test sequence is as followed.\r\n 1. Input image is RGB and it is resized to model input size & converted to YUV420. \r\n 2. Only Y-channel data is served to model input as float.\r\n ( just cast to float, (float)input )\r\nI think, pre-process step can be skipped.\r\nBecause this issue is the image offset(shape) problem, 1 channel data of input(ex. R channel data) is served to model, same issue will be produced.\r\nAnd i can't share current test code cause the company policy.\r\nI will try to attach SR model to another sample app.\r\n\r\nThanks.\r\n", "@gyujin-kim Thanks for the information.\r\n\r\n@sachinprasadhs Could you please look into this issue. Thanks.", "Hi @sachinprasadhs @impjdi \r\nI applied super-resolution to [tensorflow lite segmentation example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/ios).\r\n(https://drive.google.com/file/d/1PoGE9gd1X8HODtl5TvNBQc5aQQWsTEjE/view?usp=share_link)\r\nAlthough it is different from the environment I actually use (C API vs Swift API), the test result using random input produced the same issue.\r\n\r\nCreate an interpreter: ImageSegmentationHelper.swift line 64-81\r\ninvoke : ImageSegmentationHelper.swift line 161~211\r\nYou can test 2x and 4x by modifying ImageSegmentationHelper.swift line 367.\r\n2x works fine, 4x is giving 'GPU address fault'.\r\n\r\nPlease review.\r\nthank you", "https://github.com/tensorflow/tensorflow/commit/c6250a0571668e920989f9506c2fbdfc53baff6d\r\nDepth to space fixed in this commit", "@roserg \r\nI confirmed that the problem was resolved in the version 'TensorFlowLiteC 0.0.1-nightly.20230406'.\r\nthank you for your hard work.\r\n\r\nAlso, thank you for all the members who cared about this issue(@tiruk007, @pjpratik , @sachinprasadhs, @impjdi ).\r\nThanks.", "@gyujin-kim , Thanks for confirming, could you please close the issue as well, Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60158\">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/60158\">No</a>\n" ]

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[ "Hi @sssssshift, We see that the issue [template]( https://github.com/tensorflow/tensorflow/issues/new/choose) has not been filled, could you please do so as it helps us analyze the issue [tf version, steps followed before you ran into this error or stand alone code/colab gist to reproduce the issue faced]. Thank you!\t" ]

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[ "@bluesteven \r\nBeginning with Tensorflow **2.11**, support for GPU on native windows has changed. You will need to install TensorFlow in **WSL2** or install tensorflow-cpu within windows machines or try the **TensorFlow-DirectML-Plugin**. \r\nhttps://www.tensorflow.org/install/pip#windows-native\r\nhttps://www.tensorflow.org/install/pip#windows-wsl2\r\n\r\nGoing forward, Tensorflow support will be developed and maintained by Tensorflow Official build collaborators (Intel, AWS, ARM, linaro etc.). For more details please take a look at this [link].(https://blog.tensorflow.org/2022/09/announcing-tensorflow-official-build-collaborators.html)\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/60156\">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/60156\">No</a>\n" ]