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[ "@nadyawilliams Could you please run bazel clean --expunge, forcing bazel to re-download and build each dependency to avoid issues. Also sync the bazel build bazel sync.\r\nThank you!", "Thank you for a suggestion.\r\nI did bazel clean --expunge, before the build and cleaned again when i tried different options. No help.\r\nRunning bazel sync fails with multiple errors starting rom not finding cuda.h (cuda module is loaded and cuda.h is available in \r\ncuda install), followed by not finding python (also is loaded via module and available). Looks like bazel sync is not using any\r\nof the variables set by the modules. For example a python error is\r\nERROR: An error occurred during the fetch of repository 'ubuntu20.04-gcc9_manylinux2014-cuda11.8-cudnn8.6-tensorrt8.4_config_python3.11':\r\n Traceback (most recent call last):\r\n\tFile \"/export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.13.0/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n\t\t_check_python_bin(repository_ctx, python_bin)\r\n\tFile \"/export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.13.0/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\n\t\tauto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\n\tFile \"/export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.13.0/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\n\t\tfail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='/usr/local/bin/python3.11' is not executable. Is it the python binary?\r\n\r\nPer my modules python is\r\nwhich python\r\n/opt/apps/python/3.10.2/bin/python\r\n", "Hello, @nadyawilliams! If you are using TF v2.13 then the supported version for CUDA and cuDNN are 11.8, 8.6 respectively. Could you please verify the tested build configuration as mentioned [here](https://www.tensorflow.org/install/source#gpu) and let us know if that helps?\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.", "I will be able to proceed with the testing of cuda and cudNN 11.8 and 8.6 the earliest next week. both build have to be done according to our build schedule and approach. Please keep the issue open till i try this. Thank you", "Unfortunately at this time we cant upgrade our CUDA installation to 11.8. Please advise which latest version of tensorflow can be compiled with cuda 11.7 and cudNN 8.5. Thank you.", "@nadyawilliams Thanks for your response! We need to use the recommended compatible versions only otherwise the build issues would be hard to resolve. 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.", "Please close the issue. I can compile tensorflow 2.11 with our current cuda install and while it is not the latest it will do for now.", "@nadyawilliams Thank you for the confirmation. \r\nClosing the ticket for now. 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/61978\">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/61978\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/61977/checks?check_run_id=17110097375) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "It is spam, please don't spam. Thank you!" ]

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[ "Hi @innat ,\r\n\r\nI am able to run the code on Colab by following TPU initialization code as per documentation and it executed fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/7165c07c2e944e347ec7f4360045bc57/61976_tpu.ipynb).\r\n\r\nIt seems the issue might be specific to Kaggle environment ?\r\n", "@SuryanarayanaY Thanks for checking. I also check in Colab TPU. For your concern, please check this comment https://github.com/keras-team/tf-keras/issues/655#issuecomment-1733864636 ", "@innat , You mean to say it has problem with TPU VM and on colab as it is TPU node it works fine? I would like to hear from concerned team. Thanks!", "> You mean to say it has problem with TPU VM and on colab as it is TPU node it works fine?\r\n\r\nYes.\r\n\r\n\r\ncc. @@djherbis ", "@SuryanarayanaY \r\n\r\n> I would like to hear from concerned team.\r\n\r\nCould you like to mention the appropriate person (from tf team)?\r\n", "Any update?" ]

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[ "@sermakarevich,\r\nAs mentioned the provided code was executed with the different results in tensorflow [v2.13](https://colab.research.google.com/gist/tilakrayal/8b2044bbe3672aa4c4cca41eb33ae1b9/untitled1377.ipynb). But whereas when I tried to execute the same code on tf-nightly(2.15.0-dev20230926), the **manual cumsum diff is 0.0**. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/df65231d360551bacbc068728fbcd0d1/untitled1378.ipynb). Thank you! ", "@tilakrayal thank you. confirmed that on latest nightly it works well. \r\n\r\nhowever it does not work on nightly of version 2.14.0* even early versions of 2.15.0 still have the same problems\r\nIs this some kind of a known bug and there is a commit with the fix I can check ? Unfortunately bumping tf version is not an option for me atm. ", "@sermakarevich,\r\nAs mentioned, it was the issue/bug with the 2.14 nightly versions which has been resolved with the latest nightly release(2.15.0-dev20230926). You can try to use the latest tf-nightly for immediate use-case and it will be made available from TF 2.15. Thank you!", "@tilakrayal got it. \r\n\r\nIs there a chance to backport the fix into 2.11 and 2.12 tf versions ? ", "@sermakarevich,\r\nIt's unlikely for older versions to receive any bug fixes except when we have security patches. We recommend the community to use the latest versions if the bug is resolved. Thank you!", "@tilakrayal roger. Can you please point me to the commit with the fix ? I tried to scroll down through commits over last three days but did not found anything that looked related to cumsum fix", "@sermakarevich,\r\nI will do a deep dive into the same requirement and try to provide the information if it is available. Meanwhile, you can try to install the tf-nightly from the master branch, where it resolved the issue mentioned above. \r\nhttps://www.tensorflow.org/install\r\nhttps://github.com/tensorflow/tensorflow\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/61975\">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/61975\">No</a>\n" ]

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[ "Hi @PreWisdom ,\r\n\r\nBy looking at attribute error, I would like to suggest to check the issue with TF2.12V and let us know the outcome.It seems there are shiftings in Tf2.12v and Tf2.13v. \r\n\r\nWith Tf2.13V the path `tensorflow.python.distribute.input_lib` don't have the class `DistributedDatasetInterface` but in Tf2.12 it does.\r\n", "> Hi @PreWisdom ,\r\n> \r\n> By looking at attribute error, I would like to suggest to check the issue with TF2.12V and let us know the outcome.It seems there are shiftings in Tf2.12v and Tf2.13v.\r\n> \r\n> With Tf2.13V the path `tensorflow.python.distribute.input_lib` don't have the class `DistributedDatasetInterface` but in Tf2.12 it does.\r\n\r\nthanks for reply, I'm gonna change version to 2.12 and see what happen.", "Hi @PreWisdom ,\r\n\r\nCould you please check the issue with tf-nightly. Actually there has been some changes. Now Keras has became a Multi backend support (for TF,Pytorch and JAX). All the tf.keras module i.e Keras with TF backend has been moved to new repo i.e [tf-keras](https://github.com/keras-team/tf-keras).\r\n\r\nI can see in tf-keras the code is different than in tf.keras module. I want to cross check with you with both tf-nightly and keras-nightly and confirm outcome ?", "> Hi @PreWisdom ,\r\n> \r\n> Could you please check the issue with tf-nightly. Actually there has been some changes. Now Keras has became a Multi backend support (for TF,Pytorch and JAX). All the tf.keras module i.e Keras with TF backend has been moved to new repo i.e [tf-keras](https://github.com/keras-team/tf-keras).\r\n> \r\n> I can see in tf-keras the code is different than in tf.keras module. I want to cross check with you with both tf-nightly and keras-nightly and confirm outcome ?\r\n\r\nsure, there are still has some problems in my code, looks like it's due to a package name change. I'll keep on tracking these bugs.", "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/61974\">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/61974\">No</a>\n" ]

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[ "Also not sure for `BatchCholesky` whether half dtype should have support? At present as per code `BatchCholesky` supports only float and double and complex dtypes which are supported on Cholesky also excluded.", "I don't think it's as easy as just registering a kernel for the type. These matrix decomposition ops are sensitive to errors, and using `half` directly in the computation will likely lead to horrible accuracy. It's probably best to do the decomposition in higher precision then cast the final result back to half.", "@cantonios ,\r\n\r\nThanks for the explanation.Actually this Op registered with `half` dtype for **XLA** and hence tried adding kernel here. Also when I cross checked with `jit_compile=True` and with `half` dtype its not raising exception but generating few NaNs in output.Refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b4e804b28f1d2119cc123d489c5dea7c/61973_with_jit_compile.ipynb)\r\n\r\nIs this behaviour is due to the reason you mentioned ?\r\n\r\nAs per your suggestion we can do casting to high precision dtype say `float32` then apply cholesky op and convert the output back to `half` dtype. All this pre and post casting be done at Python level only if I am not wrong. The source code link for [cholesky](https://www.tensorflow.org/api_docs/python/tf/linalg/cholesky) is not available in documentation and I couldn't figure out it exactly. Any help is appreciated.", "> @cantonios ,\r\n> \r\n> Thanks for the explanation.Actually this Op registered with `half` dtype for **XLA** and hence tried adding kernel here. Also when I cross checked with `jit_compile=True` and with `half` dtype its not raising exception but generating few NaNs in output.Refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b4e804b28f1d2119cc123d489c5dea7c/61973_with_jit_compile.ipynb)\r\n> \r\n> Is this behaviour is due to the reason you mentioned ?\r\n\r\nProbably. Personally, I don't think any of these matrix decomposition ops _should_ support `half`, but if they must, then it should definitely do the cast before/after.\r\n\r\n> As per your suggestion we can do casting to high precision dtype say `float32` then apply cholesky op and convert the output back to `half` dtype. All this pre and post casting be done at Python level only if I am not wrong.\r\n\r\nYou need to do the casting in C++ if you're registering a `half` C++ kernel. To do the casting, you would need to create a higher-precision copy of the matrix [here](https://github.com/tensorflow/tensorflow/blob/dab293abf1b258358660173965bc955c3884e5c0/tensorflow/core/kernels/linalg/cholesky_op.cc#L49), then also cast the output back during assignment [here](https://github.com/tensorflow/tensorflow/blob/dab293abf1b258358660173965bc955c3884e5c0/tensorflow/core/kernels/linalg/cholesky_op.cc#L64). I don't know about XLA - that probably also needs to be updated.\r\n\r\n\r\n\r\n", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!\r\n ", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Not stale.Will work on it soon.", "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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[ "@RabJon Could you please try to use the latest TF 2.13 as you are using an older version. \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here. Thank you!", "I faced similar but slightly opposite issue once with `tf.saved_model.load` API but worked in ` tf.keras.models.load_model` API. \r\n\r\nhttps://www.kaggle.com/competitions/google-universal-image-embedding/discussion/336534#1882377", "@sushreebarsa unfortunately there is no GPU support after TF 2.10 for Windows Native. Therefore, I tried TF 2.10 instead of the newest version, but the result was the same as for the versions 2.6 and 2.7.\r\n\r\nHowever, I could reproduce the exception on Google Colab with v2.13. Here is the URL for the notebook: [Model_Load_Problem.ipynb](https://colab.research.google.com/drive/1YyyU6kyn947JCuTHOrjRiDGQb2XBOueu?usp=sharing) ", "Thank you @innat for the comment. Unfortunately, in my case both approaches to loading models do not work.", "@RabJon Generally this error is caused because the model you are trying to load was saved using a newer version of TensorFlow than the one you are currently using. To fix this, you will need to upgrade your TensorFlow installation.\r\n\r\nTo upgrade TensorFlow, run the following command:\r\n```\r\n!pip install tensorflow --upgrade\r\n```\r\nOnce you have upgraded TensorFlow, you can reload the model by running the following code:\r\n\r\n``` \r\nmodel = tf.keras.models.load_model(path)\r\n```\r\nPlease let us know if it helps?\r\nThank you!", "@sushreebarsa I tried to run the same code with TF v2.7 and TF v2.10 on Windows Native and I always got the same exception. Since v2.10, which was released in September 2022, is the latest version of TF for Windows Native it would have been impossible for me to save the models with a newer version than that. Furthermore, many of my models are older than the release date of v2.10 so they were created with a lower version for sure.\r\n\r\nI am fine for now, because I managed to load the models through a loophole in my implementation that I actually never thought to be useful: I did not only save the entire model in \"saved_model\" format, but I also saved the model weights alone as h5 files. Given that I didn't change the model architecture I could recreate and compile the models and then just load the corresponding weights from the h5 files. So, personally I don't need a solution anymore. However, I think that this is still a general issue that needs to be solved.", "@RabJon Thank you for your response here!\r\n\r\nYes, you can always use the .h5 extension in case you are using two different versions for training and loading the models. In the HDF5 format with a .h5 extension you can save weights manually. \r\nCould you please move this issue to closed status if it has been resolved from your end? \r\nStarting with TensorFlow 2.11, you will need to install [TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2), or install tensorflow or tensorflow-cpu and, optionally, try the [TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-)\r\nThank you!", "Yes, I will close this issue, because it is solved from my site. However, I want to point out again that this was only possible because I decided to save both the entire TensorFlow model (in \"saved_model\" format) and the weights of the trained model (as .h5 file). So, it was due to my (lucky) design decision that I could solve 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/61972\">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/61972\">No</a>\n" ]

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[ "@ArttuLinden GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin. Please check the configuration for TF v2.10 as mentioned [here](https://www.tensorflow.org/install/source_windows#gpu). Thank you!", "Hi, I am aware that 2.10 is the latest version for native windows to support tensorflow GPU. I have followed the guideline and my current configuration matches with `tensorflow_gpu-2.10.0 | 3.7-3.10 | MSVC 2019 | Bazel 5.1.1 | 8.1 | 11.2`. As I stated it is a bug in the `cuda_configure.bzl` that python_bin_path can't point to a python executive where the path contains spaces. Function `_check_cuda_libs (line 492)`. Especially these lines in the `cuda_configure.bzl` cause the error:\r\n\r\ncmd += \"system('%s script.py %s');\" % (python_bin, args)\r\nall_paths = [path for path, _ in libs]\r\nchecked_paths = execute(repository_ctx, [python_bin, \"-c\", cmd]).stdout.splitlines()\r\n\r\nPYTHON_BIN_PATH=C:/Python39/python.exe -- This works\r\nPYTHON_BIN_PATH=C:/Program Files/Python39/python.exe -- This fails", "@ArttuLinden, can you pls try PYTHON_BIN_PATH=C:/Program\\ Files/Python39/python.exe\r\nor try C:\\Program Files\\Python39\\python.exe\r\nPlease let us know if you face any issue further or if the error persists", "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/61971\">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/61971\">No</a>\n" ]

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[]

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[ "@GiovanniSmokes,\r\nCould you please elaborate about your Feature. Also, please specify the Use Cases for this feature which helps us to analyse the issue. 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.", "Doesn't belong in the repo, looks more like promo/spam" ]

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[ "I was able to reproduce this in TF 2.14 and nightly as well. Please find this [gist](https://colab.research.google.com/gist/pjpratik/ebeaf229c5c8e161fa24ea6c76b75e4a/61968.ipynb).\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce with the same gist as above, @haozha111, can you please take a look? Thanks." ]

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[ "I was able to reproduce this in TF 2.14 and nightly as well. Please find this [gist](https://colab.research.google.com/gist/pjpratik/3fa5fdf7a56b481f61fd7698e2b6ed50/61967.ipynb).\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.", "I was able to reproduce with the same gist, @abattery, can you please take a look? Thanks." ]

[]

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[ "Hi @A729891 ,\r\n\r\nCould you please file the issue in prescribed format attached [here](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml) with all the relevant details needed?", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@YangChenyuan,\r\nI was able to reproduce the issue on TensorFlow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4a765779a644a2c13cc09d78d81e55fc/untitled1906.ipynb)." ]

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[ "note: this issue picks up where #59762 left off", "@esohns ,\r\n\r\nThanks for your time bringing this.I have gone through the older ticket #59762, understood the context and it seems there might be a fix for this. Would it still exists in latest versions also ? Please confirm meanwhile I will bring this to the attention of concerned team.\r\n\r\nWindows builds are maintained by the intel team, hence I am bringing the issue to their attention to fix it if not fixed already in latest/nightly version.\r\n\r\nCC- @TensorFlow-MKL for comments.\r\n\r\nThank you!", " @TensorFlow-MKL,\r\nthis came from the latest c download (version 2.14) off the project site. After inserting the tf_buffer.h and tsl_status.h header files (both taken from the linux tarball), my project compiles just fine. So at least these two files should be included in the windows release zip file.", "Hi @esohns and @SuryanarayanaY, the issue should be fixed in the upcoming releases. Thank you!", "@esohns Thanks for letting us know! It does seem like we were missing certain headers in the Windows libtensorflow archive. https://github.com/tensorflow/tensorflow/commit/66681ea34bee93fa359486458f92ceea864cc6de should fix it. ", "ok thank you for fixing this issue" ]

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[ "This at most belongs to keras, not 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/61963\">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/61963\">No</a>\n" ]

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[ "This at most belongs to keras, not 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/61962\">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/61962\">No</a>\n" ]

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[ "This at most belongs to keras, not 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/61961\">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/61961\">No</a>\n" ]

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[ "This at most belongs to keras, not TF", "Hi @pzy2000 ,\r\n\r\nWe can't say for sure whether it is related to tensorflow or keras since there is another library i.e `onxx` involved here.\r\n\r\nCould you please submit a code without external libraries dependencies to reproduce the reported issue.We are not sure whether its bug in `onxx` or `TF/keras`.\r\n\r\nThank you!\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61960\">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/61960\">No</a>\n" ]

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[ "This at most belongs to keras, not 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/61959\">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/61959\">No</a>\n" ]

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[ "This at most belongs to keras, not 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/61958\">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/61958\">No</a>\n" ]

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[ "@Aksh-kumar,\r\nApologies for the delay. Thanks for reporting the issue.\r\nSince this request is specific to Keras, could you please close this and open a new issue in Keras repo https://github.com/keras-team/keras/issues\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.", "posted in keras-team github page closing it here.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61957\">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/61957\">No</a>\n" ]

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[ "Hi @logisin ,\r\n\r\nCould you please confirm whether you are trying to build for TF2.13v ? Seems typo mistake in the template.\r\n\r\nPlease confirm the protobuf version you have installed ? You can find the required protobuf versions [here](https://github.com/tensorflow/tensorflow/blob/r2.13/tensorflow/tools/pip_package/setup.py#L96).\r\n\r\nIf using Tf2.13v you need to use bazel 5.3.0v. Other versions may cause inconsistent behaviour. Please refer to attached test [configurations](https://www.tensorflow.org/install/source#gpu) here.\r\n\r\nAfter testing with official configurations and still have any problems please let us know.\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/61956\">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/61956\">No</a>\n" ]

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[ "there are similar issues #8905 #26842", "@sachinprasadhs I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/0782f8b41efbbf02e9b2a45c2b764fa5/61955.ipynb). Thank you!", "As per the comment from one of the linked issues, it looks like an issue from Eigen. \r\n@cantonios , Could you please take a look into this issue. Thanks!", "@yhao-z,\r\nI tried to execute the mentioned code on the tf-nightly and observed that the output of executed code is intended and changed from the earlier output. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/963f9337ef41e6403219cf7bf045eae3/untitled1888.ipynb).\r\n \r\n**Output:**\r\n```python\r\nFalse\r\n[0.01783315 0.00682789 0.00398225 0.00252413 0.0014872 0.00082805\r\n 0.00066117 0.00051815 0.00050564]\r\nFalse\r\nFalse\r\nFalse\r\n[0.01783314 0.00682789 0.00398224 0.00252413 0.0014872 0.00082805\r\n 0.00066117 0.00051815 0.00050564]\r\nFalse\r\nFalse\r\nFalse\r\n```\r\n\r\nThank you!", "Yes, the error will not occur in the nightly version. Perhaps my previous expression in the '**Note**' was not clear enough; I would like to restate the declaration in the '**Note**' here.\r\n- Under the Linux system, both version 2.9 and 2.13 will encounter errors, just as @sushreebarsa has reproduced.\r\n- Under the Linux system, the nightly version will not experience NaN errors.\r\n- Under the Windows system, version 2.9 will also not encounter NaN errors.\r\n- Considering the inconsistencies observed across different versions and systems, I believe it might be a bug.\r\n\r\nTherefore, it might be possible to inspect the differences between the nightly and 2.13 versions to pinpoint the bug, or alternatively, comparing the behavior of version 2.9 under Windows with that under Linux could also help in localization.", "> Yes, the error will not occur in the nightly version. Perhaps my previous expression in the '**Note**' was not clear enough; I would like to restate the declaration in the '**Note**' here.\r\n> \r\n> * Under the Linux system, both version 2.9 and 2.13 will encounter errors, just as @sushreebarsa has reproduced.\r\n> * Under the Linux system, the nightly version will not experience NaN errors.\r\n> * Under the Windows system, version 2.9 will also not encounter NaN errors.\r\n> * Considering the inconsistencies observed across different versions and systems, I believe it might be a bug.\r\n> \r\n> Therefore, it might be possible to inspect the differences between the nightly and 2.13 versions to pinpoint the bug, or alternatively, comparing the behavior of version 2.9 under Windows with that under Linux could also help in localization.\r\n\r\nWe don't backport fixes unless they are high-priority security vulnerabilities. So in this case the bug is now fixed in the latest version.", "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/61955\">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/61955\">No</a>\n" ]

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[ "I volunteer to take this issue", "The error you're encountering is because tf.nest.map_structure requires all elements within the nested structure to be TensorFlow tensors, and MyComplexData is not considered a TensorFlow tensor by default. To make your code compatible with tf.nest.map_structure, you need to ensure that the elements within MyComplexData are TensorFlow tensors.\r\n\r\nYou can do this by adding a method to the MyComplexData class that converts its internal data to TensorFlow tensors. In the updated code, I've included a method called as_tensors that does this conversion for x and y. By calling data.as_tensors(), you can convert your custom data structure into a format compatible with TensorFlow operations. This should allow you to use tf.nest.map_structure with the gather_fn function without encountering the error.", "import tensorflow as tf\r\n\r\nclass MyComplexData(tf.experimental.ExtensionType):\r\n x: tf.Tensor\r\n y: tf.Tensor\r\n\r\n def __init__(self, x, y):\r\n self.x = x\r\n self.y = y\r\n\r\n def as_tensors(self):\r\n return MyComplexData(tf.convert_to_tensor(self.x), tf.convert_to_tensor(self.y))\r\n\r\ndef gather_fn(tensor):\r\n indices = tf.constant([[0], [1]])\r\n return tf.gather_nd(tensor, indices)\r\n\r\ndata = MyComplexData([1, 2, 3], [[1.0, 2.0], [3.0, 4.0]])\r\n\r\n# Convert the data to TensorFlow tensors\r\ndata = data.as_tensors()\r\n\r\ntry:\r\n mapped_data = tf.nest.map_structure(gather_fn, data)\r\n print(mapped_data)\r\nexcept Exception as e:\r\n print(f\"Failed to apply map_structure with gather_fn on MyComplexData: {e}\")\r\n" ]

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[ "Hi @kavjayawardana ,\r\n\r\nBy looking into the error, it appears that the problem might be due to the reason that `SpectralNormalization` layer seems not supported with Graph execution.\r\n\r\nI have set `run_eagerly= True` in `model.compile(run_eagerly= True)` then the error is gone but shape incompatibility error araised. This was corrected by changing the `y` in your code to `y = np.random.rand(batch, height-2, width-2, filters)`.\r\n\r\nWith all these modification it executes fine and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/df980d43493ceeddf0d9b9f4f1242a1d/61953_spectralnormalization.ipynb) for reference.\r\n\r\nPlease cross check and confirm if still have any queries.\r\n\r\nThanks!\r\n\r\n", "Thank you very much @SuryanarayanaY. Setting run_eagerly= True made it trainable. Thank you again for being prompt with your reply and incredibly helpful with your solution\r\n\r\nbest\r\n\r\nkav", "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/61953\">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/61953\">No</a>\n" ]

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[ "@yen-dang-ts \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!", "Hi @sushreebarsa ,\r\n\r\n- Load model part:\r\n\r\n```\r\nval options = Interpreter.Options()\r\n val tfLite = Interpreter(\r\n TFLiteUtils.loadModelFile(context.assets, modelFileName),\r\n options\r\n )\r\n```\r\n\r\n- `loadModelFile` function\r\n```\r\nfun loadModelFile(assets: AssetManager, modelFilename: String?): MappedByteBuffer {\r\n val fileDescriptor = assets.openFd(modelFilename!!)\r\n val inputStream = FileInputStream(fileDescriptor.fileDescriptor)\r\n val fileChannel = inputStream.channel\r\n val startOffset = fileDescriptor.startOffset\r\n val declaredLength = fileDescriptor.declaredLength\r\n return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength)\r\n }\r\n```\r\nPls note that this issue just happen on 2.12.0 and 2.13.0 version. Below versions still work fine.\r\nPlease have a look on that.", "Hi @yen-dang-ts \r\n\r\nWhat is the SDK and NDK version that are being used? \r\n\r\nAs per the documentation,\r\n\r\n```\r\nNDK_API_LEVEL=\"26\"\r\nANDROID_BUILD_TOOLS_VERSION=\"30.0.3\"\r\nANDROID_SDK_API_LEVEL=\"30\"\r\n```\r\nare recommended for the latest versions.\r\n\r\nThanks.", "Here is my config:\r\n\r\n```\r\n ndkVersion \"26.0.10792818\"\r\n compileSdkVersion 33\r\n buildToolsVersion \"30.0.3\"\r\n```\r\nAnd issue still happen", "Hi @yen-dang-ts \r\n\r\nThanks for the information. I believe there is a Android version compatibility issue with latest TF versions. \r\n\r\nAre you using tflite-flutter for the task?\r\n\r\nHave you tried compiling the `libtensorflowlite_jni.so` for arm64 with the the latest TF 2.13 and nightly pull and still observe the same error?\r\n\r\nThanks.", "This problem has been reported in May for `tflite-flutter`: https://github.com/tensorflow/flutter-tflite/issues/73\r\n\r\nThe problem happens with Android 7.1.1 too. Not sure if Android 8.x/9.x are impacted (Android 13 is not impacted). (EDIT 2023-10-10: Android >= 8 are not impacted)\r\n\r\nWhere are the instructions to build `libtensorflowlite_jni.so`? I could help investigate this issue. Note that Google ML Kit Image Labeling for Flutter does support Android 5.0+ and includes runtime support for TF Lite.\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.", "> Hi @yen-dang-ts\r\n> \r\n> Thanks for the information. I believe there is a Android version compatibility issue with latest TF versions.\r\n> \r\n> Are you using tflite-flutter for the task?\r\n> \r\n> Have you tried compiling the `libtensorflowlite_jni.so` for amr64 with the the latest TF 2.13 and nightly pull and still observe the same error?\r\n> \r\n> Thanks.\r\n\r\n@pjpratik The problem was introduced in Tensorflow Lite 2.12.0. All versions >= 2.12.0 seem to be [affected](https://github.com/tensorflow/flutter-tflite/issues/73#issuecomment-1754696187). Version 2.11.0 is not affected by this issue. I also tested version 2.10.0, not affected either.\r\n\r\n", "Hi @yen-dang-ts, @andynewman10, can you give me more context to help reproduce your issue. Do you have a toy version of your model that reproduces this issue on load? Does it run into this on any model? If you can export/share a toy project that'll be easiest, or at least tell me your imports that contextualizes your code snippets:\r\n\r\n```\r\nval options = Interpreter.Options()\r\n val tfLite = Interpreter(\r\n TFLiteUtils.loadModelFile(context.assets, modelFileName),\r\n options\r\n )\r\n```\r\n```\r\nfun loadModelFile(assets: AssetManager, modelFilename: String?): MappedByteBuffer {\r\n val fileDescriptor = assets.openFd(modelFilename!!)\r\n val inputStream = FileInputStream(fileDescriptor.fileDescriptor)\r\n val fileChannel = inputStream.channel\r\n val startOffset = fileDescriptor.startOffset\r\n val declaredLength = fileDescriptor.declaredLength\r\n return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength)\r\n }\r\n```\r\nI should note that currently we only support NDK up to 25b and not 26, you might want to see if that resolves your issues.", "https://github.com/tensorflow/tensorflow/blob/1bd1b7f4b0311575ead19f14e74488a97b524555/tensorflow/lite/g3doc/android/lite_build.md?plain=1#L126\r\nApparently , the minimum supported version of the official build is Android 8.0", "> Hi @yen-dang-ts, @andynewman10, can you give me more context to help reproduce your issue. Do you have a toy version of your model that reproduces this issue on load? Does it run into this on any model? If you can export/share a toy project that'll be easiest, or at least tell me your imports that contextualizes your code snippets:\r\n\r\n@pkgoogle You can use any model, for instance, this one (that I confirm works flawlessly):\r\n\r\nhttps://tfhub.dev/tensorflow/lite-model/efficientnet/lite4/uint8/2\r\n\r\nClick on the Download link on the right (download size: 14.34MB), and load this model in your code.\r\n\r\nYour code does not have to do anything meaningful. Just import the TF Lite dependencies (>= 2.12.0) in your Gradle file, and make sure that in your program, you execute the lines of code you mentionned in your post above\r\n\r\nThen run your the code on Android 7.1.1 (real device or emulator) and this will clearly replicate the issue (libtensorflowlite_jni.so issue with 'strtod_l')\r\n\r\nNow switch to Tensorflow Lite 2.11.0 dependencies in your Gradle file : the issue will not show up.\r\n\r\n\r\n", "> https://github.com/tensorflow/tensorflow/blob/1bd1b7f4b0311575ead19f14e74488a97b524555/tensorflow/lite/g3doc/android/lite_build.md?plain=1#L126\r\n> \r\n> \r\n> Apparently , the minimum supported version of the official build is Android 8.0\r\n\r\nNice find. Apparently, this commit (dated August, 7) is the one changing API level from 21 to 26:\r\n\r\nhttps://github.com/tensorflow/tensorflow/commit/6d53a5aefdecaba99e93c0933d6cc32050c108d7\r\n\r\nAs I am writing these lines, this is the latest commit affecting this file.\r\n", "@fiberflow, my bad I thought the previous comment referred to the line above that\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/1bd1b7f4b0311575ead19f14e74488a97b524555/tensorflow/lite/g3doc/android/lite_build.md?plain=1#L125C1-L125C74\r\n```\r\nbuild --action_env ANDROID_NDK_HOME=\"/usr/local/android/android-ndk-r21e\"\r\n```\r\nwhich is the NDK version not the NDK_API_LEVEL.", "Hi @yen-dang-ts, @andynewman10 I was able to replicate here (with SDK API level = 22, i.e. Android 5.1): [Test61951_min22.zip](https://github.com/tensorflow/tensorflow/files/12875033/Test61951_min22.zip). I was able to get around the error by using a higher SDK level, I tried with SDK API 34. If you can update your Android version and thus SDK API level you can possibly get around this.\r\n\r\nHi @miaout17, can you please take a look? Thanks.\r\n", "@pkgoogle Here is what I did:\r\n\r\n- I reinstalled Android Studio Giraffe from scratch using latest version\r\n- imported your project (Test61951_min22.zip) that has `minSdkVersion 22` specified in build.gradle, as well as `org.tensorflow:tensorflow-lite:2.13.0`\r\n- ran the program on my Android 7.1.1 (API level 25) device\r\n\r\nAs expected, I am getting the exception:\r\n\r\n```\r\nProcess: com.example.test61951_min22, PID: 17748\r\njava.lang.UnsatisfiedLinkError: Failed to load native TensorFlow Lite methods. Check that the correct native libraries are present, and, if using a custom native library, have been properly loaded via System.loadLibrary():\r\n java.lang.UnsatisfiedLinkError: dlopen failed: cannot locate symbol \"strtod_l\" referenced by \"/data/app/com.example.test61951_min22-1/lib/arm64/libtensorflowlite_jni.so\"...\r\n\tat org.tensorflow.lite.TensorFlowLite.init(TensorFlowLite.java:137)\r\n\tat org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:62)\r\n\tat org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:36)\r\n\tat org.tensorflow.lite.Interpreter.<init>(Interpreter.java:232)\r\n```\r\nAfter that, I switched to `org.tensorflow:tensorflow-lite:2.11.0`: the exception disappears.\r\n\r\n**Conclusion**: Android < 8 compatibility was suddenly lost with Tensorflow Lite >= 2.12.0.\r\n\r\nIs that expected?\r\n", "Hi @pkgoogle I have tried [Test61951_min22.zip](https://github.com/tensorflow/tensorflow/files/12875033/Test61951_min22.zip) as per your instructions and updated following versions to 34. I can confirm that I am still getting the same crash on Android Emulators using Android 7 (API level 25) and below.\r\n```\r\ncompileSdk 34\r\ntargetSdk 34\r\n```", "> Conclusion: Android < 8 compatibility was suddenly lost with Tensorflow Lite >= 2.12.0.\r\n> Is that expected?\r\n\r\nThe minimum API level was increased to API 26 in order to provide guaranteed support for AHardwareBuffer,\r\nwhich is used by the GPU delegate for the asynchronous API.\r\n\r\nHowever, I don't think the consequences of this were fully anticipated, understood, and weighed off\r\nwhen that change was made. Breaking compatibility with ~6% of existing devices (based on the numbers on https://apilevels.com/) seems undesirable. I think TF Lite should strive to support ~99% of existing devices.", "@fergushenderson IMHO, it always feels really good when you know a piece of software works on a \"best endeavor\" basis, targeting the widest possible audience. (on a side note, I would point out the fact that Android OS versions may sometimes be upgraded for just a few years only for a given device, but it should be noted that many apps can target Android 5 - Android 5 has plenty to offer already).\r\n\r\nThe dealbreaker for me is that the Gpu delegate is extremely restrictive. Very few models work with it. None of my models do actually.", "Hi @pkgoogle Is there an update on this?\r\n\r\nPlease also note that the official [documentation](https://www.tensorflow.org/lite/android/development#minimum_android_sdk_versions_for_libraries) still indicates that the `minSdkVersion` is 19.", "We are going to fix this.\r\n\r\nI have been discussing this with the TF Lite team, and we have consensus that this is a problem that should be fixed.\r\nI have analyzed the cause and possible solutions, and produced a prototype solution that allows use of AHardwareBuffer on versions of Android that support it while still remaining compatible with earlier versions of Android back to Android API level 21. A TF Lite team member (@[turbotoribio](https://github.com/tensorflow/tensorflow/commits?author=turbotoribio)) has taken on the remaining tasks to get this fix landed.\r\n\r\nThe main part of the fix has landed:\r\nhttps://github.com/tensorflow/tensorflow/commit/c7c3135b2802f3b8886f2ecefc39de49799b1e9b\r\n\r\nThe remaining parts are to change ANDROID_NDK_API_LEVEL from 26 back to 21.\r\n\r\nThe fix will be included in TF Lite 2.16.", "Thanks for the reply @fergushenderson. That is a great news!\r\nAre you also considering patching the previous versions, namely 2.12.0, 2.13.0, 2.14.0?\r\n", "We are _considering_ patching 2.15, since that could be shipped a lot sooner than 2.16.\r\n\r\nThere doesn't seem to be much benefit from patching 2.12-2.14, since users who are affected by this issue could just use 2.15 instead. (But please correct me if I am wrong about that.)", "@fergushenderson Do we know when the version 2.15 can be released on Maven central?", "@fergushenderson Our project needs version 2.15 please help me to answer the release date", "@fergushenderson I have just tried tensorflow-lite version 2.15.0. Now I am getting the following compilation error:\r\n\r\n```\r\n> Failed to transform guice-5.1.0.jar (com.google.inject:guice:5.1.0) to match attributes {artifactType=android-dex, asm-transformed-variant=NONE, dexing-enable-desugaring=true, dexing-enable-jacoco-instrumentation=false, dexing-is-debuggable=true, dexing-min-sdk=21, org.gradle.category=library, org.gradle.libraryelements=jar, org.gradle.status=release, org.gradle.usage=java-runtime}.\r\n > Execution failed for DexingWithClasspathTransform: /Users/nijatahmadli/.gradle/caches/modules-2/files-2.1/com.google.inject/guice/5.1.0/da25056c694c54ba16e78e4fc35f17fc60f0d1b4/guice-5.1.0.jar.\r\n > Error while dexing.\r\n Increase the minSdkVersion to 26 or above.\r\n```\r\nLooks like `guice` was introduced in 2.15.0 and requires minSdkVersion 26 which I guess stems from the use of Java 8 language features. Unfortunately, upgrading to 26 is not an option for our project. ", "@fergushenderson I have the same issue with @nijat-ahmadli. Please support us", "> @fergushenderson I have just tried tensorflow-lite version 2.15.0. Now I am getting the following compilation error:\r\n> \r\n> ```\r\n> > Failed to transform guice-5.1.0.jar (com.google.inject:guice:5.1.0) to match attributes {artifactType=android-dex, asm-transformed-variant=NONE, dexing-enable-desugaring=true, dexing-enable-jacoco-instrumentation=false, dexing-is-debuggable=true, dexing-min-sdk=21, org.gradle.category=library, org.gradle.libraryelements=jar, org.gradle.status=release, org.gradle.usage=java-runtime}.\r\n> > Execution failed for DexingWithClasspathTransform: /Users/nijatahmadli/.gradle/caches/modules-2/files-2.1/com.google.inject/guice/5.1.0/da25056c694c54ba16e78e4fc35f17fc60f0d1b4/guice-5.1.0.jar.\r\n> > Error while dexing.\r\n> Increase the minSdkVersion to 26 or above.\r\n> ```\r\n> \r\n> Looks like `guice` was introduced in 2.15.0 and requires minSdkVersion 26 which I guess stems from the use of Java 8 language features. Unfortunately, upgrading to 26 is not an option for our project.\r\n\r\nSame problem here. ", "Thanks for raising that issue with the guice dependency. I have alerted the TensorFlow Lite team of this new issue.\r\n\r\nThe tensorflow 2.16.0 preview release is out. Is the issue still reproducible in 2.16.0?\r\n\r\nIn general it is possible to use (most) Java 8 language features while depending only on API level 21, rather than 26.\r\nSee <https://developer.android.com/studio/write/java8-support> for details.\r\nSo if guice is using Java 8 language features, that doesn't necessarily mean that it needs to be incompatible with API level 21.\r\n", "I have a problem too, I think it's the same problem\r\nERROR:C:\\Users\\hp\\.gradle\\caches\\transforms-3\\0e1dbadb4b8b682380e0f29f2a20be93\\transformed\\jetified-guice-5.1.0.jar: D8: com.android.tools.r8.internal.a2: MethodHandle.invoke and MethodHandle.invokeExact are only supported starting with Android O (--min-api 26)\r\n\r\nFAILURE: Build failed with an exception.\r\n\r\n* What went wrong:\r\nExecution failed for task ':app:mergeExtDexDebug'.\r\n> Could not resolve all files for configuration ':app:debugRuntimeClasspath'.\r\n > Failed to transform guice-5.1.0.jar (com.google.inject:guice:5.1.0) to match attributes {artifactType=android-dex, asm-transformed-variant=NONE, dexing-enable-desugaring=true, dexing-is-debuggable=true, dexing-min-sdk=21, org.gradle.category=library, org.gradle.libraryelements=jar, org.gradle.status=release, org.gradle.usage=java-runtime}.\r\n > Execution failed for DexingWithClasspathTransform: C:\\Users\\hp\\.gradle\\caches\\transforms-3\\0e1dbadb4b8b682380e0f29f2a20be93\\transformed\\jetified-guice-5.1.0.jar.\r\n > Error while dexing.", "According to the error messages, guice-5.1.0.jar is not compatible with Android 21. The Java support 8 of Android by desugaring seems not working with guice-5.1.0.jar, could you please consider removing it from the dependency list of tensorflow-lite?\r\n\r\n> @fergushenderson I have just tried tensorflow-lite version 2.15.0. Now I am getting the following compilation error:\r\n> \r\n> ```\r\n> > Failed to transform guice-5.1.0.jar (com.google.inject:guice:5.1.0) to match attributes {artifactType=android-dex, asm-transformed-variant=NONE, dexing-enable-desugaring=true, dexing-enable-jacoco-instrumentation=false, dexing-is-debuggable=true, dexing-min-sdk=21, org.gradle.category=library, org.gradle.libraryelements=jar, org.gradle.status=release, org.gradle.usage=java-runtime}.\r\n> > Execution failed for DexingWithClasspathTransform: /Users/nijatahmadli/.gradle/caches/modules-2/files-2.1/com.google.inject/guice/5.1.0/da25056c694c54ba16e78e4fc35f17fc60f0d1b4/guice-5.1.0.jar.\r\n> > Error while dexing.\r\n> Increase the minSdkVersion to 26 or above.\r\n> ```\r\n> \r\n> Looks like `guice` was introduced in 2.15.0 and requires minSdkVersion 26 which I guess stems from the use of Java 8 language features. Unfortunately, upgrading to 26 is not an option for our project.\r\n\r\n" ]

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[ "#61946", "Hi @captainst \r\n\r\nSorry for the delayed response.\r\n\r\nHave you observed the same in multiple runs, also can you check the same if it replicates with nightly pull as well?\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/61950\">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/61950\">No</a>\n" ]

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[ "@molokanov50,\r\nApologies for the delay. When i tried to save the model with the same configuration as you mentioned, it had only one .pb file saved_model.pb. Size of the model ~3.4MB.\r\n\r\nAlso try to use keras load model to load your model.\r\n\r\n```\r\nfrom tensorflow import keras\r\n\r\nnew_model = keras.models.load_model(\"save_model_keras\")\r\nnew_model.summary()\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.", "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/61949\">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/61949\">No</a>\n" ]

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[ "Hi @lindow2009 \r\n\r\nThis seems to be related to tensorflow js converter issue from the information provided.\r\n\r\nPlease post this issue in tensorflow.js repo for faster resolution,\r\nhttps://github.com/tensorflow/tfjs/issues\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@yap231995 \r\nGenerally, this error occurs when the version of the NVIDIA driver installed on your system is not compatible with the version of CUDA you are using. Specifically, the CUDA runtime version you are using requires a higher version of the NVIDIA driver than the one currently installed on your system.\r\nI tried to replicate the issue and faced a different [error](https://colab.research.google.com/gist/sushreebarsa/33c7d1045cde839dc7d1690e2785e933/61947.ipynb), could you please provide the complete standalone code to replicate the issue reported?\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/61947\">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/61947\">No</a>\n" ]

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[ "I am able to replicate:\r\n\r\n```sh\r\n(base) xxxxxx@xxxxxxxxx:~/issues/61946/tflite_build/tools/benchmark$ ./benchmark_model --graph=./mobilenet_v1_1.0_224_quant.tflite\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [./mobilenet_v1_1.0_224_quant.tflite]\r\nINFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite\r\nINFO: The input model file size (MB): 4.27635\r\nINFO: Initialized session in 0.53ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=5 first=124020 curr=116061 min=115177 max=124020 avg=117225 std=3412\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=50 first=121183 curr=115199 min=111222 max=121324 avg=116066 std=1906\r\n\r\nINFO: Inference timings in us: Init: 530, First inference: 124020, Warmup (avg): 117225, Inference (avg): 116066\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=3.75 overall=12.1914\r\n(base) xxxxxxx@xxxxxxxxx:~/issues/61946/tflite_build/tools/benchmark$ ./benchmark_model --graph=./mobilenet_v1_1.0_224_quant.tflite --use_xnnpack=true\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [./mobilenet_v1_1.0_224_quant.tflite]\r\nINFO: Use xnnpack: [1]\r\nINFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: XNNPACK delegate created.\r\nINFO: Though XNNPACK delegate is explicitly applied, the model graph will not be executed by the delegate.\r\nINFO: The input model file size (MB): 4.27635\r\nINFO: Initialized session in 1.624ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=5 first=118606 curr=115130 min=115130 max=118606 avg=115913 std=1351\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=50 first=118240 curr=115075 min=113462 max=118240 avg=115403 std=915\r\n\r\nINFO: Inference timings in us: Init: 1624, First inference: 118606, Warmup (avg): 115913, Inference (avg): 115403\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=4.125 overall=12.2188\r\n(base) xxxxxx@xxxxxxxxx:~/issues/61946/tflite_build/tools/benchmark$ ./benchmark_model --graph=./mobilenet_v1_1.0_224_quant.tflite --use_xnnpack=false\r\nINFO: STARTING!\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Graph: [./mobilenet_v1_1.0_224_quant.tflite]\r\nINFO: Use xnnpack: [0]\r\nINFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite\r\nINFO: The input model file size (MB): 4.27635\r\nINFO: Initialized session in 0.624ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=5 first=119736 curr=115120 min=115120 max=119736 avg=116948 std=1708\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=50 first=116137 curr=115145 min=114959 max=118105 avg=115743 std=829\r\n\r\nINFO: Inference timings in us: Init: 624, First inference: 119736, Warmup (avg): 116948, Inference (avg): 115743\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=3.75 overall=12.1914\r\n```\r\n\r\nSeems like maybe the cmake files might be out of date, @terryheo can you please take a look? Thanks.\r\n\r\n@captainst, can you please include the bazel build commands as well?\r\n", "@alankelly ", "This should do it: https://github.com/tensorflow/tensorflow/commit/db6395777b85e5eaa07651333bc3368b4c73a0ff" ]

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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/61945\">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/61945\">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/61945\">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/61945\">No</a>\n" ]

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[ "Hi @andynewman10 \r\n\r\nI'm not sure if there is a build for 2.13.0 because the Flutter packages are not officially supported but rather contributed by the community.\r\n\r\nYou may try updating package dependencies https://docs.flutter.dev/packages-and-plugins/using-packages#updating-package-dependencies or use flutter tflite plugin https://github.com/tensorflow/flutter-tflite which has been recently migrated to tensorflow.\r\n\r\nPlease check this [blog](https://blog.tensorflow.org/2023/08/the-tensorflow-lite-plugin-for-flutter-officially-available.html?_gl=1*1gvogab*_ga*OTQ0MDM3NDM1LjE2Njg1Nzc1NzU.*_ga_W0YLR4190T*MTY5NTczMDE1MC45MDkuMS4xNjk1NzMyNzc1LjAuMC4w) for the same.\r\n\r\nThanks.", "I have solved this issue by using TensorFlow 2.12.0 (not 2.13.0) and by changing the way I create the model (ie. use TF Hub functions). Both were necessary.\r\n" ]

[]

[]

[ "Will newer versions of `ml-dtypes` be supported by tensorflow ?", "Main branch does not have the upper bound, so yes.", "Ok ! Thanks for the explanation on the PR :)" ]

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[ "@alerem18 Could you please provide the complete standalone code to replicate the [issue](https://colab.research.google.com/gist/sushreebarsa/1312d871663ec6136f188bc447d4b939/61942.ipynb) reported?\r\nThank you!", "https://keras.io/examples/nlp/neural_machine_translation_with_transformer/\r\n\r\nin decoder_sequence", "will there be any solution or i should wait for you to reply once a month?\r\nthis is why people prefer pytorch over tensorflow", "@alerem18 I tried to replicate the issue on colab. https://keras.io/examples/nlp/neural_machine_translation_with_transformer/ is working fine in [colab](https://colab.research.google.com/gist/sushreebarsa/e8ca05c13b4748b40911c8c00a86a7bc/neural_machine_translation_with_transformer.ipynb#scrollTo=7Wz8hlIULZul) . Please let me know if i have missed something to replicate the reported error. Have you tried to use the GPU for this issue and let me know if the memory leak would be there? Thank you!", "> @alerem18 I tried to replicate the issue on colab. https://keras.io/examples/nlp/neural_machine_translation_with_transformer/ is working fine in [colab](https://colab.research.google.com/gist/sushreebarsa/e8ca05c13b4748b40911c8c00a86a7bc/neural_machine_translation_with_transformer.ipynb#scrollTo=7Wz8hlIULZul) . Please let me know if i have missed something to replicate the reported error. Have you tried to use the GPU for this issue and let me know if the memory leak would be there? Thank you!\r\n\r\ni've not tested it on gpu, but i have memory issues when translating a lot of texts, in a loop\r\ninstead of eng_vectorization use input_vectorization, for spa_vectorization use output_vectorization\r\n\r\ncopy paste the code from the keras directly don't include my code", "@sachinprasadhs same issue on this but i didn't use @tf.function in translator, so i don't know if it's related to that\r\nhttps://www.tensorflow.org/text/tutorials/transformer", "is there any damn solution for this or not?" ]

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[ "It seems there is one test failure for this test:\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/batch_ops_test.py\r\n\r\n@pemeliya Can you please check whether you can reproduce this?", "@akuegel, I have tried to run this test on my branch, but for me it seems to work fine:\r\n\r\n```\r\nINFO: Build option --run_under has changed, discarding analysis cache.\r\nINFO: Analyzed 2 targets (2 packages loaded, 47879 targets configured).\r\nINFO: Found 2 test targets...\r\nINFO: Elapsed time: 64.678s, Critical Path: 50.77s\r\nINFO: 220 processes: 23 internal, 197 local.\r\nINFO: Build completed successfully, 220 total actions\r\n//tensorflow/python/ops:batch_ops_test_cpu PASSED in 7.5s\r\n//tensorflow/python/ops:batch_ops_test_gpu PASSED in 7.5s\r\n```\r\nHere is also my CUDA config:\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.2 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 Tesla V100-SXM2... On | 00000000:3D:00.0 Off | 0 |\r\n| N/A 35C P0 43W / 300W | 0MiB / 32768MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 1 Tesla V100-SXM2... On | 00000000:3E:00.0 Off | 0 |\r\n| N/A 35C P0 43W / 300W | 937MiB / 32768MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------+\r\n```", "Thanks for checking. It seems this was a flaky test, now it seems to pass." ]

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[ "Any progress on this issue? I believe the autocluster should process `-0.0` as negative, otherwise, the sign of the whole computation is wrong", "Hi @YangChenyuan ,\r\n\r\nI have replicated the reported behaviour and its observed with `multiplication` operation for value 0.0 (i.e float) only and for 0( i.e int) also there is no difference . For any other values the results are same and as intended.\r\n\r\nI am bit curious personally as this is observed in only multiplication operation with `0.0` and the result here will be zero anyways and how it may affect the computation. This might be fixable but to prioritize it we may need a strong demo that demonstrate how this behaviour will affect the computations.\r\n\r\nThank you!", "Hi @SuryanarayanaY, thanks for your further exploration!\r\n\r\nHere is one possible case that would be affected:\r\n```py\r\nclass Model(tf.keras.Model):\r\n def __init__(self):\r\n super(Model, self).__init__()\r\n self.v3_weight = tf.Variable(123.45)\r\n\r\n def call(self, x1):\r\n x4 = (self.v3_weight * (- 0.0))\r\n x4 = 1 / x4\r\n return x4\r\nm = Model()\r\nx = tf.constant(702.89)\r\nprint(m(x)) # tf.Tensor(-inf, shape=(), dtype=float32)\r\n```\r\n\r\n```py\r\nimport os\r\nos.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit'\r\nclass Model(tf.keras.Model):\r\n def __init__(self):\r\n super(Model, self).__init__()\r\n self.v3_weight = tf.Variable(123.45)\r\n\r\n @tf.function\r\n def call(self, x1):\r\n x4 = (self.v3_weight * (- 0.0))\r\n x4 = 1 / x4\r\n return x4\r\nm = Model()\r\nx = tf.constant(702.89)\r\nprint(m(x)) # tf.Tensor(inf, shape=(), dtype=float32)\r\n```\r\nIf we divide the results 0, one of them will return `inf` and the other is `-inf`. Then if we do some check for their signs, the results will be much different.\r\n\r\nBesides, if we use `@tf.function(jit_compile=True)`, the result is consistent with naive execution", "Hi @YangChenyuan ,\r\n\r\nThe above case can demonstrate the severity. Thanks for it. We will dig more to fix this.", "Thanks for your exploration and confirmation!", "@YangChenyuan ,\r\n\r\nThis also seems issue with tf.function only. I have replicated the reported behaviour with `jit_compile=True` and `jit_compile=False` both. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/9849f49df56c21960083dad41b5db025/61939_r1.ipynb) for reference . Seems its a bug in tf.function. We will work on it and update you.", "@YangChenyuan This is happening because we have a graph optimization that replaces `x * 0` with zero, regardless of what `x` is [here](https://github.com/tensorflow/tensorflow/blob/eb44a46d62a5312af79d69eaf56e26b1d4439236/tensorflow/core/grappler/optimizers/constant_folding.cc#L3073).\r\n\r\nI'm not sure I'm convinced it's important to keep the -0. What is your use-case?", "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/61939\">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/61939\">No</a>\n" ]

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[ "@YangChenyuan,\r\nApologies for the delay. I tried to execute the mentioned code on tf-nightly(2.16.0-dev20231101) and the code was executed without any issue/error and the respective outputs are also the same in both the cases. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/74a405b63752afb269015e829ad5a1b0/untitled1475.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/61938\">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/61938\">No</a>\n" ]

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[ "@hayyaw \r\nIn order to expedite the trouble-shooting process here, could you please fill the issue [template](https://github.com/tensorflow/tensorflow/issues/new/choose),\r\nThank you!", "System information\r\n\r\nOS Platform and Distribution ：Linux Ubuntu 18.04\r\nTensorFlow installed from (github):\r\nTensorFlow version (dev分支):\r\nProvide the text output from tflite_convert\r\nnone\r\n\r\nStandalone code to reproduce the issue\r\n\r\ntensorflow/lite/delegates/hexagon/builders/resize_nearest_neighbor_builder.cc\r\n\r\nAny other info / logs\r\nSTARTING!\r\nDuplicate flags: num_threads\r\nLog parameter values verbosely: [0]\r\nMin num runs: [1]\r\nGraph: [/home/wangzhiqun/yolox_resize_nearest_neighbor.tflite]\r\nUse Hexagon: [1]\r\nLoaded model /home/wangzhiqun/yolox_resize_nearest_neighbor.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nHexagon delegate created.\r\nINFO: TfLiteHexagonDelegate delegate: 1 nodes delegated out of 1 nodes with 1 partitions.\r\n\r\nINFO: Replacing 1 node(s) with delegate (TfLiteHexagonDelegate) node, yielding 1 partitions.\r\n[hexagon/nn] Add Node: tid(-1) nid(1)\r\n[hexagon_nn] hexagon_nn_append_const_node(gid=1 tid=2 nid=2 type=0x3)\r\n[hexagon/nn] Add Node from Op(331) mid(0) nid(3)\r\n[hexagon_nn] hexagon_nn_append_const_node(gid=1 nid=4 type=0x3)\r\n[hexagon_nn] hexagon_nn_append_const_node(gid=1 nid=5 type=0x3)\r\n[hexagon/nn] Add Node: tid(-1) nid(6)\r\n[hexagon_nn] hexagon_nn_append_node(gid=1 nid=1 type=0x0)\r\n[hexagon_nn] hexagon_nn_append_node(nis=0 nos=1)\r\n[hexagon_nn] hexagon_nn_append_node(outputs[0].elementsize=1)\r\n[hexagon_nn] hexagon_const_node(gid=1 nid=2 type=0x3)\r\n[hexagon_nn] hexagon_nn_append_node(gid=1 nid=3 type=0x14b)\r\n[hexagon_nn] hexagon_nn_append_node(nis=6 nos=3)\r\nError adding node: id:3, op_type:331\r\n[hexagon_nn] hexagon_const_node(gid=1 nid=4 type=0x3)\r\n[hexagon_nn] hexagon_const_node(gid=1 nid=5 type=0x3)\r\n[hexagon_nn] hexagon_nn_append_node(gid=1 nid=6 type=0x1)\r\n[hexagon_nn] hexagon_nn_append_node(nis=1 nos=0)\r\nTimestamp: Thu Sep 21 08:06:06 2023\r\n\r\nLog\r\nhexagon/src/newnode.c:413:node 3 (ResizeNearestNeighbor_8): bad input count 6\r\nhexagon/src/newnode.c:763:node id=0x3 ctor fail\r\nhexagon/src/prepare.c:4830:input 0x6:0 refers to nonexistent node 0x3\r\nhexagon/src/prepare.c:4644:can't find id 0x3\r\n\r\nERROR: Failed: Failed to prepare graph.\r\n.\r\nERROR: Node number 1 (TfLiteHexagonDelegate) failed to prepare.\r\nERROR: Restored original execution plan after delegate application failure.\r\nFailed to apply Hexagon delegate.\r\nBenchmarking failed.", "Hi @hayyaw \r\n\r\nCould you please provide a reproducible code and steps you have followed? It is hard to understand the issue from error log. Please please fill this issue [template](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=TFLiteConverter&projects=&template=tflite-converter-issue.md).\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.", "I am having the exact same problem with multiple TFLITE models I have made full quantized versions to run on the hexagon." ]

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[ "Have you considered a Re install i or re-writing the\r\ninterpreter to make sure that it matched what is required.\r\nSincerely, Hamish Leahy", "@Bajirak Could you please try to reinstall the interpreter and check the compatibility. Please try using the command Python: Clear cache. Also do mention the expected output for this operation. 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.", "Sorry for being late. @Hamish-Leahy , @sushreebarsa \r\n\r\nI created a new virtual environment and installed the nightly version of tensorflow.\r\n\r\n`python3.8 -m venv issue_tracking`\r\n`source issue_tracking/bin/activate`\r\n`pip install --upgrade pip`\r\n`pip install tf-nightly-cpu`\r\n`pip freeze | grep tf-nightly`\r\n\r\nIt says\r\n`tf-nightly-cpu==2.14.0.dev20230706`\r\n\r\nAfter installation, I tried the tests described above again, but the output is still incorrect. \r\n(*The weight data of DwConv is randomly generated. Therefore, the same result as the issue cannot be derived.)\r\n\r\n0) input[0, 61:64, 65:68, 24]\r\n[[0.978893 0.03620292 0.9301994 ]\r\n [0.34403667 0.4492776 0.8247143 ]\r\n [0.8075199 0.77361435 0.1642472 ]]\r\n\r\n1) weight[..., :24]\r\n[[[ 0.04345933 -0.04809386 0.07144216]\r\n [-0.08781118 0.01062268 -0.05774678]\r\n [-0.03802773 0.07661463 0.0889118 ]]]\r\n\r\n2) bias[24]\r\n-0.029987096786499023\r\n\r\n3) Deptwise Convoultion output\r\ntflite output[0, 31, 33, 24] : 0.01883140206336975\r\ntest output[0, 31, 33, 24]: 0.04737246036529541", "Hi @Bajirak \r\n\r\nI have tested both the keras and TFLite model and they produce same output given the same input. Please find this [gist](https://colab.research.google.com/gist/pjpratik/ece6465f351b841bc40e8810a0732989/61934.ipynb).\r\n\r\nAs their result goes I don't think there is an issue. Can you elaborate more about why the partial data is being tested? \r\n\r\nThanks.\r\n\r\n", "Hi @pjpratik \r\n\r\nYou are right. This isn't an issue. I thought the interpreter's operation result was invalid.\r\n\r\nSo, I tried to prove that the interpreter's results were wrong by using partial input and partial weight.\r\n\r\nHowever, I was considering the 'SAME' Pad value incorrectly. After considering this, I checked my results and the interpreter's results.\r\n\r\nThank you for checking so carefully. Sorry for taking up your time.", "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/61934\">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/61934\">No</a>\n" ]

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[ "@pentney,\r\nIn the given code snippet you have defined the class and its methods but are not calling them anywhere. Could you please provide the complete code to debug the issue. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/d4baeba4cdc407efec93501bbb2ce525/untitled1374.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/61932\">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/61932\">No</a>\n" ]

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[ "Hi @cnovel ,\r\n\r\nYou can find the required pacakges for specific version packages in [setup.py](https://github.com/tensorflow/tensorflow/blob/r2.9/tensorflow/tools/pip_package/setup.py#L75) file.\r\n\r\nFor TF 2.9v the required numpy version should be >=1.20\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/a5ed5f39b675a1c6f315e0caf3ad4b38478fa571/tensorflow/tools/pip_package/setup.py#L87\r\n\r\nApart from that you need to install CUDA and cuDNN also as per tested [configurations](https://www.tensorflow.org/install/source#gpu) mentioned here wrt TF version. For Tf2.9v the tested configurations are mentioned below.\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\n\r\n\r\n\r\n\r\n\r\ntensorflow-2.9.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.0.0 | 8.1 | 11.2\r\n-- | -- | -- | -- | -- | --\r\n\r\n\r\nPlease try the build again and let us know if still have problem.\r\n\r\nThanks!\r\n\r\n\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61926\">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/61926\">No</a>\n" ]

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[ "@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/5551526739f9cda1b55893287fc16a3d/61925.ipynb). Thank you!", "Hi @Prashant-THRSL ,\r\n\r\nApologies for the delayed response.The issue got fixed with keras3 and tf-keras 2.15v. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/ceac0d7b22dba33b40b3a46be5eecf07/61925.ipynb).\r\n\r\nSince Keras3 is now multi backend supporting and for TF specific you need to install tf-keras package.\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/61925\">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/61925\">No</a>\n" ]

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[ "Hi @Gaurang-Belekar ,\r\n\r\nThis issue observed in Tf2.12v also. Its due to issue in compilation problem in boringssl and it was fixed in nightly at that time. Please refer to similar issue #60191 which resolved subsequently in nightly version. \r\n\r\nCould you please check the build with Tf2.13v as the changes might not be cherry picked in Tf2.12 and earlier.\r\n\r\nThank 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/61924\">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/61924\">No</a>\n" ]

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[ "@TkinterinShanghai , Thanks for reporting the issue.\r\n\r\nSince this feature request is specific to Keras, could you please close this and open a new issue in Keras repo https://github.com/keras-team/keras/issues\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." ]

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[ "Hi @ihh,\r\n\r\nThis seems to be environment issue. We have observed that with protobuf 4.24.3 version there seems some problem as in other ticket #61551 user confirmed segmentation fault with this protobuf version. That issue is under review now.\r\n\r\nCould you please try with any other protobuf version mentioned below.May be 4.23.4 or something and let us know the outcome.\r\n\r\n 'protobuf>=3.20.3,<5.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5'\r\n\r\nThank you!", "Thanks @SuryanarayanaY. Unfortunately this does not resolve the problem:\r\n\r\n~~~\r\n$ pip uninstall tensorflow protobuf\r\n$ python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 protobuf==4.23.4 tensorflow==2.13.*\r\n...\r\nUsing cached protobuf-4.23.4-cp37-abi3-manylinux2014_x86_64.whl (304 kB)\r\nUsing cached tensorflow-2.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (524.2 MB)\r\nInstalling collected packages: protobuf, tensorflow\r\nSuccessfully installed protobuf-4.23.4 tensorflow-2.13.0\r\n$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/yam/miniconda3/envs/jax-cuda11/lib/python3.11/site-packages/tensorflow/__init__.py\", line 38, in <module>\r\n from tensorflow.python.tools import module_util as _module_util\r\n File \"/home/yam/miniconda3/envs/jax-cuda11/lib/python3.11/site-packages/tensorflow/python/__init__.py\", line 36, in <module>\r\n from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow\r\n File \"/home/yam/miniconda3/envs/jax-cuda11/lib/python3.11/site-packages/tensorflow/python/pywrap_tensorflow.py\", line 26, in <module>\r\n self_check.preload_check()\r\n File \"/home/yam/miniconda3/envs/jax-cuda11/lib/python3.11/site-packages/tensorflow/python/platform/self_check.py\", line 63, in preload_check\r\n from tensorflow.python.platform import _pywrap_cpu_feature_guard\r\nImportError: /home/yam/miniconda3/envs/jax-cuda11/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2: undefined symbol: _ZN6google8protobuf7Message19CopyWithSourceCheckERS1_RKS1_\r\n~~~", "Hi @ihh ,\r\n\r\nWe are able to install tensorflow==2.13 successfully on Ubuntu22. Just to cross check,could you please uninstall tensorflow and try with fresh environment and also with latest version i.e. 2.14 as well and let us know the outcome.\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/61922\">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/61922\">No</a>\n", "> Hi @ihh ,\r\n> \r\n> We are able to install tensorflow==2.13 successfully on Ubuntu22. Just to cross check,could you please uninstall tensorflow and try with fresh environment and also with latest version i.e. 2.14 as well and let us know the outcome.\r\n\r\nApologies for the slow reply. A fresh install worked, thank you. I think there was some cross-contamination between different package versions." ]

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[ "Hi @lorenzodellagiustina, my current best guess is you are suffering from too much virtualization and the memory isn't being allocated across the virtual layers as well as you think it is. Is there any way to remove one of the layers, if only temporarily to figure out where the root of this problem lies? Otherwise can you check how much memory is actually allocated to WSL 2 and then how much memory is actually accessible to the docker?", "> Hi @lorenzodellagiustina, my current best guess is you are suffering from too much virtualization and the memory isn't being allocated across the virtual layers as well as you think it is. Is there any way to remove one of the layers, if only temporarily to figure out where the root of this problem lies? Otherwise can you check how much memory is actually allocated to WSL 2 and then how much memory is actually accessible to the docker?\r\n\r\n@Luca-Stefanescu\r\n\r\nThanks for your quick response. Turned out to be a problem with the `-j` flag of the command `cmake --build`. This flags is used to specify the number of parallel build jobs to run simultaneously. It should be used followed by a number (the number of cpu cores of my machine worked fine for me). I guess that if a number is not specified the build will be parallelized as much as possible trying to allocate way too memory (even github actions failed to build the \"minimal\" example).\r\nI think that this should be written in the guide for building TFLite with CMake. Just adding a note about the usage of `-j` flag would have saved me a lot of hours. Maybe I'll just open a pull request and I'll link it to this issue in the next days.\r\n\r\nThanks for your help.", "@lorenzodellagiustina np, Thanks for letting us know, can you please close this issue as completed if you have no more open items? 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/61921\">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/61921\">No</a>\n" ]