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[ "@youchangkim Sometimes nightly will be failing as the build is not stable. So it is recommended to use the stable version. Once the final release is done, we would expect it to work as expected. 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 is still failing on latest build. ", "@youchangkim Could you please share the exact TF version you are using ?\r\nThank you!", "@sushreebarsa \r\nI tried 2.15.0.dev20231011 and 2.16.0.dev20231023, and both are failing.", "@sushreebarsa \r\nI can reproduce this with 2.15.0rc0 wheel, and also from tensorflow/tensorflow:2.15.0rc0-gpu docker image.", "@sushreebarsa @sachinprasadhs Is there any plan to fix this before 2.15 release?", "Hi, 2.15 release is finalized and there won't be any additional code changes made, if the fix is made, it will be available in nightly version. " ]

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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/62080\">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/62080\">No</a>\n", "@majorisgit ,\r\n\r\nFor GPU package with all required CUDA libraries you need to use `pip install tensorflow[and-cuda]` command.\r\n\r\nWhether you resolved this ? Are you still having issues please feel free to discuss.\r\n", "I'm also having this problem, installed tensorflow 2.13 after using `pip install tensorflow[and-cuda] `command, but still can't use GPU calculations", "> I'm also having this problem, installed tensorflow 2.13 after using `pip install tensorflow[and-cuda] `command, but still can't use GPU calculations\r\n\r\nWindows native support for GPU is not supported any more in TensorFlow. \r\nI had to install Windows Subsystem for Linux (WSL 2). I ran Linux inside Windows. Then followed the instructions from this installation [script](https://gist.github.com/MihailCosmin/affa6b1b71b43787e9228c25fe15aeba)\r\nIt worked, the GPU is detected and I can use it for calculations. The problem is solved. \r\n" ]

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[ "Hi @jpienaar Can you please review this PR ? Thank you!", "Hi @jpienaar Can you please review this PR ? Thank you!", "Hi @jpienaar Can you please review this PR ? Thank you!", "Hi @jpienaar Can you please review this PR ? Thank you!", "Hi @jpienaar Can you please review this PR ? Thank you!", "Hi @sushreebarsa Can you please check the above [comment](https://github.com/tensorflow/tensorflow/pull/62079#pullrequestreview-1797233270) from @mihaimaruseac and keep posted. Thank you!", "> I'm actually thinking 4 is good here.\r\n\r\n@mihaimaruseac I did realize that and replied the same to the user in the issue thread. Thank you!", "Hi @sushreebarsa Any update on this PR? Please. Thank you!", "@gbaned The proposed changes as suggested by user is inappropriate it seems. The concerned issue has already been closed. So, closing this PR as well till new information comes. Thank you!" ]

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[ "@pmixer,\r\nThank you for reporting. Could you please elaborate about your Feature. Also, please specify the Use Case for this feature. Thank you!\r\n", "hi @tilakrayal , thank you for the followup.\r\n\r\nIn summary, the feature request is for pursuing a concrete example for what claimed in the doc https://tensorflow.google.cn/api_docs/python/tf/nn/safe_embedding_lookup_sparse?hl=en saying use of RaggedTensors can yield higher performance.\r\n\r\nRoughly speaking, we all know `embeddings = tf.nn.safe_embedding_lookup_sparse(embedding_matrix, lookup_ids)` works well when `lookup_ids` being `tf.SparseTensor`, but it's performance could still be improved, especially, for inference, as on-the-fly shape computation could lead to perf hit sometimes.\r\n\r\nThus, what I'm looking for is that hint in the doc, replacing `tf.SparseTensor` lookup ids to a batch of `tf.RaggedTensor`.\r\n\r\nI tried several approaches, all do not work(unless I call RaggedTensor's to_sparse method to cast it to SparseTensor). That's why I come to look for a piece of sample code to show the correct way to achieve the stuff what got claimed in the doc.\r\n\r\nBTW, high level keras API `tf.keras.layer.Embedding` makes the model accept tf.RaggedTensor as lookup ids, I just could not make it work for more fundamental `tf.nn.safe_embedding_lookup_sparse` API.", "More concretely, I had a piece of example code to show what I want if using keras:\r\n\r\n```python\r\nimport tensorflow as tf\r\nimport tf2onnx\r\n\r\n# Create a sample RaggedTensor of lookup IDs\r\ninput_data = tf.ragged.constant([[1, 2, 3], [4, 5], [6]])\r\n\r\n# Define the Embedding layer\r\nembedding_dim = 4\r\nembedding_layer = tf.keras.layers.Embedding(input_dim=7, output_dim=embedding_dim)\r\n\r\n# Build the model\r\ninputs = tf.keras.Input(shape=(None,), dtype=tf.int32, ragged=True)\r\nembedded_data = embedding_layer(inputs)\r\nmodel = tf.keras.Model(inputs=inputs, outputs=embedded_data)\r\n\r\n# Export the model as SavedModel\r\nsaved_model_path = \"./saved_model\"\r\ntf.saved_model.save(model, saved_model_path)\r\n\r\n# then tensorflow-onnx could be used for converting SavedModel to onnx\r\n```\r\n\r\nAnd what I'm looking for if how to feed RaggedTensor to lower level API, tf.nn.safe_embedding_lookup_sparse as input." ]

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[ "Hi @kevinkawchak,\r\n\r\nCould you please try with TF2.8 versions and higher.As TF2.7 version is quiet older and latest stable release is 2.14V and colab only fetching Tf 2.8 and higher versions only.\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/62077\">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/62077\">No</a>\n" ]

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[ "@rafaelubalmw We have raised a fix internally as per your suggestions. Once it is validated it can resolve this issue. Thank you!", "@rafaelubalmw Could you please have a look at this [comment](https://github.com/tensorflow/tensorflow/pull/62079) in the PR, in `TFL_NumElementsEqualsDim<3, 1, 4>` 4 would be good for the bias size.\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/62076\">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/62076\">No</a>\n" ]

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[ "Hi @Ke293-x2Ek-Qe-7-aE-B ,\r\n\r\nStarting from TF2.14 tensorflow provides CUDA package which can install all the cuDNN,cuFFT and cubLas libraries.\r\n\r\nYou can use `pip install tensorflow[and-cuda]` command for that.\r\n\r\nPlease try this command let us know if it helps. Thankyou!", "@SuryanarayanaY I did not know that it now came bundled with cuDNN. I installed tensorflow with the [and-cuda] part, though, but I also installed cuda toolkit and cuDNN separately. I will try just installing the cuda toolkit and then installing tensorflow[and-cuda].\r\nAlso, is there a way to install tensorflow for GPU without it coming with cuDNN? If I just `pip install tensorflow`, will that install with GPU support, just without cuDNN, so that I can manually install them? I don't really need to, but I am curious if it can be installed that way too.", "@SuryanarayanaY I tried several times, reinstalling Ubuntu, but it still doesn't work.", "I also have the same issue, and this seems not to be due to cuda environment as I rebulid cuda and cudnn to make them suit for tf-2.14.0.\r\n\r\nThis is log out I find:\r\n` python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\r\n\r\n`2023-10-11 18:21:57.387396: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-10-11 18:21:57.415774: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-10-11 18:21:57.415847: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-10-11 18:21:57.415877: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-10-11 18:21:57.421400: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-10-11 18:21:58.155058: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-10-11 18:21:59.113217: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-10-11 18:21:59.152044: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-10-11 18:21:59.152153: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]`", "@AthiemoneZero Because it still does output a GPU device at the bottom of the log, I am training on GPU, just without cuDNN. It will be slower, but it is better than nothing or training on CPU.", "> @AthiemoneZero Because it still does output a GPU device at the bottom of the log, I am training on GPU, just without cuDNN. It will be slower, but it is better than nothing or training on CPU.\r\n\r\nYeah. But I just found that when I downgrade to 2.13.0 version, errors in register won't appear again. It looks like this:\r\n\r\n\r\n`(TF) ephys3@ZhouLab-Ephy3:~$ python3 -c \"import tensorrt as trt;import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\r\n\r\n```\r\n2023-10-11 20:39:12.097457: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-10-11 20:39:12.130250: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-10-11 20:39:13.856721: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-10-11 20:39:13.870767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2023-10-11 20:39:13.870941: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:65:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nAlthough I haven't figured out how to solve NUMA node error, I found some clues from another [issue](https://github.com/microsoft/WSL/issues/5025) (as I operated all above in WSL Ubuntu). This bug seems not to be significant as explaination from [NVIDIA forums](https://forums.developer.nvidia.com/t/problem-running-tensorflow/249486) . So I guess errors in register might have something with the latest version and errors in NUMA might be caused by OS enviroment. Hope this information would help some guys. ", "@AthiemoneZero I tried downgrading as well, but it didn't work for me. The NUMA errors are (as stated in the error message) because the kernel provided by Microsoft for WSL2 is not built with NUMA support. I tried cloning the repo [(here)](https://github.com/microsoft/WSL2-Linux-Kernel) and building from source my own with NUMA support, but that didn't work, so I am just ignoring those errors for now.", "@Ke293-x2Ek-Qe-7-aE-B I rebuilt all in an independent conda environment as TF. My steps were to create a TF env with `python 3.9.8` and tried `python3 -m pip install tensorflow[and-cuda] --user` according to [instruction](https://www.tensorflow.org/install/pip). Following these I tried `python3 -m pip install tensorflow[and-cuda]=2.13.0 --user` and found it solved some bug.", "@AthiemoneZero Thanks for the instructions. I'll try and see if it works on my system. I have been using `python 3.10`, so maybe that's why it didn't work. Did you have to install the CUDA toolkit?", "@Ke293-x2Ek-Qe-7-aE-B I didnt execute `conda install cuda-toolkit ` here. I guess [and-cuda] argument help me install some dependencies. ", "But I did double check version of cuda and cudnn. For this I even downgrade them again and again.", "@AthiemoneZero Usually, I would install the CUDA toolkit according to these instructions ([here](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local)), then install cuDNN according to these instructions ([here](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html)). I installed CUDA toolkit version 11.8 and cuDNN version 8.7, because they are the latest supported by TensorFlow, according to their support table [here](https://www.tensorflow.org/install/source#tested_build_configurations). I guess using [and-cuda] installs all of that for you.", "@Ke293-x2Ek-Qe-7-aE-B Apologize for my misunderstanding. I did the same in installing cuda toolkit as what you described above before I went directly to debug tf_gpu. I made sure my gpu and cuda could perform well as I have tried another task smoothly using cuda but without tf. What I concerned is some dependencies of tf have to be pre-installed in a conda env and this might be treated by [and-cuda] (my naive guess", "@AthiemoneZero I always install CUDA toolkit and cuDNN globally for the whole system, and then install TensorFlow in a miniconda environment. This doesn't work anymore with the newest versions of TensorFlow, so I'll try your instructions. It does make sense to install everything in a conda env, I just hadn't thought of that since my other method had worked in the past. Thanks for sharing what you did to make it work.", "@Ke293-x2Ek-Qe-7-aE-B You're welcomed. BTW, I also followed the [instruction ](https://www.tensorflow.org/install/source) to configure development including suitable version of bazel and clang-16, just before all my operation digging into conda env.", "@AthiemoneZero Thanks, but it didn't work.", "\r\nHello,\r\n\r\nI'm experiencing the same issue, even though I meticulously followed all the instructions for setting up CUDA 11.8 and CuDNN 8.7. The error messages I'm encountering are as follows:\r\n\r\nUnable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered.\r\nUnable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered.\r\nUnable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered.\r\n\r\nI've tried this with different versions of Python. Surprisingly, when I used Python 3.11, TensorFlow 2.13 was installed without these errors. However, when I used Python 3.10 or 3.9, I ended up with TensorFlow 2.14 and the aforementioned errors.\r\n\r\nI've come across information suggesting that I may not need to manually install CUDA and CuDNN, as [and-cuda] should handle the installation of these components automatically.\r\n\r\nCould someone please guide me on the correct approach to resolve this issue? I've tried various methods, but unfortunately, none of them have yielded a working solution.\r\n\r\nP.S. I'm using conda in WSL 2 on Windows 11.\r\n", "I am having the same issue as FaisalAlj above, on Windows 10 with the same versions of CUDA and CuDNN. The package `tensorflow[and-cuda]` is not found by pip. I've tried different versions of python and tensorflow without success. In my case I'm using virtualenv rather than conda.\r\n\r\nEdit 1:\r\nI appear to be able to install `tensorflow[and-cuda]` as long as I use quotes around the package, like: `pip install \"tensorflow[and-cuda]\"`.\r\n\r\nEdit 2:\r\nI still appear to be getting these messages however, so I'm not sure I've installed things correctly.", "Hi @Ke293-x2Ek-Qe-7-aE-B ,\r\n\r\nI have checked the installation on colab(linx environment) and observed same logs as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/25f597c022f8720bb7620bf03f0f7b7d/62075.ipynb#scrollTo=FhXupzYJqxFq). \r\n\r\nThese logs seems generated from XLA compiler but GPU is able to detectable. Similar issue #62002 and already bought to Engineering team attention.\r\n\r\nCC: @learning-to-play \r\n\r\n\r\n\r\n", "@SuryanarayanaY After I did this: https://github.com/tensorflow/tensorflow/issues/62095#issuecomment-1763366758\r\nCuda, Gpu and Xla tests returned true and error was gone.", "@Syndicateeee \r\n\r\nThanks for the try, but did not really help. \r\n\r\nI'm still having problems with downloading the latest Tensorflow 2.14", "@SuryanarayanaY I still have the same issue. For now, I am just going to install Ubuntu 22.04 instead of using WSL2.", "@Ke293-x2Ek-Qe-7-aE-B go for Ubuntu 20.04 I tried 2 days to get it running on 22.04 didnt work.", "This [procedure step ](https://stackoverflow.com/questions/69812260/a100-tensorflow-gpu-error-failed-call-to-cuinit-cuda-error-not-initialized-i) made it work for me.\r\n\r\nI tried several recipes to get a virtual environmnet (for GPU) built, and this is what ended up working for me (Ubuntu 10.04, cuda-toolkit 11.8, GPU CUDA version 12.0, python 10.13)\r\n\r\n- initiate virtual environment\r\n- user Tensorflow [instructions ](https://www.tensorflow.org/install/pip)to install Tensorflow as follows\r\n pip install tensorflow[and-cuda]\r\n- create instance on GPU following the instructions in this [procedure ](https://stackoverflow.com/questions/69812260/a100-tensorflow-gpu-error-failed-call-to-cuinit-cuda-error-not-initialized-i) as follows\r\n sudo nvidia-smi mig -cgi 0 -C\r\n\r\nWith that done, Tensorflow was able to recognize my GPU.", "Hi, I'm running into the same issue as of today after installing `tensorflow[and-cuda]` (latest released version)\r\nFrom this thread, I'm not sure if people have found the root cause or a workaround. (for the record I dont have an MIG-enabled card)\r\n\r\nAny advice?", "Same issue here on both WSL2 and Ubuntu.\r\n\r\n```\r\n>>> import tensorflow as tf\r\n2023-10-28 13:19:49.412588: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-10-28 13:19:49.413083: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-10-28 13:19:49.437814: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n```", "same error on native ubuntu 20.04, fresh new python environment", "same error on native ubuntu 20.04 +1", "Running into a similar issue with a fresh conda environment.\r\n```\r\n2023-11-06 16:41:34.842775: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-11-06 16:41:34.863378: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-11-06 16:41:34.927139: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n```", "Same Error Me too on Ubuntu 22.04 with tensorflow version 2.14.0.\r\n\r\n023-11-07 16:36:12.218005: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-11-07 16:36:12.236705: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-11-07 16:36:12.236735: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-11-07 16:36:12.236749: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-11-07 16:36:12.240263: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-11-07 16:36:13.173565: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.176158: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.176267: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.177887: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.177940: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.177975: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.226379: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.226458: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.226509: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-11-07 16:36:13.226557: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9487 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4070, pci bus id: 0000:01:00.0, compute capability: 8.9\r\n2023-11-07 16:36:13.450493: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:231] Falling back to the CUDA driver for PTX compilation; ptxas does not support CC 8.9\r\n2023-11-07 16:36:13.450510: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:234] Used ptxas at ptxas\r\n2023-11-07 16:36:13.450538: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450549: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450561: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450575: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450586: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450595: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450611: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450658: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\n2023-11-07 16:36:13.450711: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\r\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/62074/checks?check_run_id=17532730903) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ ", "Also, please sign CLA and squash all commits in a single one, it does not make sense to have 3 different commits for editing a single file", "I think I have squashed all the commits successfully. please check. Sorry for my mistakes, I'm just learning and trying my best." ]

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[ "@ritesh4git,\r\nGPU 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.\r\n\r\nhttps://www.tensorflow.org/install/source_windows\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/62073\">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/62073\">No</a>\n" ]

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[ "Hi @drewshark ,\r\n\r\nThanks for reporting this. The issue persists with all unsigned dtypes i.e `'uint8','uint16','uint32','uint64'` which are causing overflow (i.e negative values being overflowed to positive end as unsigned integers can't hold -ve numbers) when the input (x) is of `non_decreasing` type.\r\n\r\nThe overflow is happening from this line of code.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/ops/check_ops.py#L1947\r\n\r\nWe will check and submit a fix for this. 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/62072\">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/62072\">No</a>\n" ]

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[ "@drewshark I wasn't able to replicate this issue as reported. Please check this [gist](https://colab.research.google.com/gist/sushreebarsa/db4856d8ad0174b72d422f564a7d621b/62071.ipynb) on the latest TF v2.14.\r\nThe result is as follows;\r\n```\r\nSuccess on dtype: bfloat16\r\nFail on dtype: bool\r\nFail on dtype: complex128\r\nFail on dtype: complex64\r\nSuccess on dtype: double\r\nSuccess on dtype: float16\r\nSuccess on dtype: float32\r\nSuccess on dtype: float64\r\nSuccess on dtype: half\r\nSuccess on dtype: int16\r\nSuccess on dtype: int32\r\nSuccess on dtype: int64\r\nSuccess on dtype: int8\r\nSuccess on dtype: uint16\r\nSuccess on dtype: uint32\r\nSuccess on dtype: uint64\r\nSuccess on dtype: uint8\r\n\r\n```\r\nThank you!\r\n\r\n", "Hi @sushreebarsa ,\r\nI apology for my mistake, my environment is tensorflow 2.10.0 instead of 2.14.0. I confirm your result on tensorflow 2.14.0. It looks like tf.truncatediv still does not support complex tensor.", "Hi @drewshark ,\r\n\r\nThe complex dtype still not supporting for `truncatediv` Op in tf-nightly also. We will look into this and do needful changes and will update you.\r\n\r\nThanks!\r\n\r\n", "Done PR #62409 ", "@drewshark,\r\nComplex types are not supported. This breaks a bunch of stuff, and as you can see from the functor, is only supported by real values. Floor/truncation doesn't make much sense for complex inputs. Thank you!" ]

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[ "Same issue applies for tf.math.igamma, which also is claimed to accept bfloat and half tensor but it does not in practical.", "Hi @drewshark ,\r\n\r\nI can confirm the Op `TruncateMod` registered for {int32, int64, bfloat16, half, float, double} dtypes. I checked with CPU the Op works fine except for `half` and `bfloat16`. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/bfdde4e69b4ef916bf583530793b07ca/62070_cpu.ipynb) for reference.\r\n\r\nBut some kernels has not been built due to unknown reasons. We will have a look into it and confirm whether it is omitted for some reason. If not maybe we can built kernels for same to fix it.\r\n\r\nThank you." ]

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[ "I think I have sought the answer, a comment on _How to handle non-determinism when training on a GPU?_(https://stackoverflow.com/questions/50744565/how-to-handle-non-determinism-when-training-on-a-gpu),\r\nThe problem comes from Tensorflow does not currently guarantee determinism for all of its operations. So, you just need to change to Linux or `set TF_DETERMINISTIC_OPS=0`", "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/62067\">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/62067\">No</a>\n" ]

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[ "Java Inference, the same question。", "Hi @chuanshanjia ,\r\n\r\nTF1.x versions are not supported anymore. Will you upgrade it to latest TF version and let us know if any issue there. Thanks!", "Hi @chuanshanjia ,\r\n\r\nI have checked with TF1.15V and there kernels found registered for only GPU devices as per below code.\r\nhttps://github.com/tensorflow/tensorflow/blob/590d6eef7e91a6a7392c8ffffb7b58f2e0c8bc6b/tensorflow/python/lib/core/py_func.cc#L398\r\n\r\nNow in TF2.14v I can see that kernels are registered for all as DEVICE_DEFAULT.Here it should work for all devices.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/lib/core/py_func.cc#L422\r\n\r\nYou can see the list of `dtypes` supported in TF2.14 for the Op **PyFunc** here.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/4dacf3f368eb7965e9b5c3bbdd5193986081c3b2/tensorflow/python/lib/core/py_func.cc#L413-L420\r\n\r\n\r\nYou can test the code with TF2.14v and let us know if you still have any problem.Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62066\">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/62066\">No</a>\n" ]

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[ "I updated the issue description and the gist code to pinpoint that the second `gather` call in the function `gather_classification_data_distributed` fails. This is the gather call that should gather all predicted labels from the different devices.", "Hi @visionscaper ,\r\n\r\nI tried the code on colab with GPU runtime with TF2.14v & 2.13v but getting a different error for even `cross_device_ops=None` also. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/bbb645cda29db582dc17ceb167777bcf/62065.ipynb). Could you please check whether tho code block is same as you tested ? \r\n\r\n", "Hi @SuryanarayanaY,\r\n\r\nThe likely reason you get this error is because you only have one GPU, see this print output in your gist:\r\n```\r\nGPUs: ['GPU:0']\r\n```\r\n\r\nI think the mirrored strategy doesn't return a `PerReplica` instance when you only have one device available, as it has no purpose with only one device. A `PerReplica` object instance has the `values` property, but because this is not returned by `strategy.run` the code can't find a `values` property and thus fails.\r\n\r\nSo, obviously, you need to test this on a machine with multiple GPUs (at least two).\r\n\r\nKind regards,\r\n\r\n -- Freddy ", "Hi @SuryanarayanaY,\r\n\r\nHave you made any progress?\r\nWere you able to reproduce my issue using a multi-GPU system?", "@sushreebarsa @SuryanarayanaY Is there any progress in reproducing this issue? Due to this issue my project is still stuck. I put in effort to create a gist to reproduce the issue, could please respond? Thank you.", "Same Issue here...", "Thanks for the detailed error report and the gist to reproduce. I was unable to reproduce this on a 2-gpu machine with 100 test runs, suggesting a possible platform issue. Could you share more details about your machine setup?\r\n\r\nNot being able to use GPU on tf-nightly sounds like an issue, though the nightly builds are not 100% reliable and it may work on a retry with the latest nightly release. If it persists feel free to file a separate issue", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62065\">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/62065\">No</a>\n", "I'm facing issues when I tried to run model using mirror distributed strategy.\r\nI want to run multiples model one by one in a loop using distributed strategy. First model runs well but is shows error before start the next model.\r\n\r\n```\r\nCollective ops is aborted by: Shape mismatch in the collective instance 100. Op at device /job:localhost/replica:0/task:0/device:GPU:2 expected shape [28678440] but another member in the group expected shape [39166184]. This is likely due to different input shapes at different members of the collective op.\r\nThe error could be from a previous operation. Restart your program to reset.\r\n\t [[{{node CollectiveReduceV2_2}}]] [Op:__inference_train_function_51026]\r\n```\r\n\r\nHere is my code-\r\n\r\n```\r\n\r\nstrategy = tf.distribute.MirroredStrategy()\r\n# Set up model within scope\r\nwith strategy.scope():\r\n\r\n model = Create_model()\r\n\r\n optimizer = SGD(learning_rate=0.01, weight_decay=1e-4,momentum=0.9)\r\n model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['sparse_categorical_accuracy','sparse_top_k_categorical_accuracy'])\r\n\r\ncheckpoint = ModelCheckpoint('Best_model.h5', \r\n monitor='val_loss', \r\n save_best_only=True, \r\n mode='min', \r\n verbose=0)\r\nlr_reduce = ReduceLROnPlateau(\r\n monitor='val_loss',\r\n factor=0.1,\r\n patience=5,\r\n verbose=0,\r\n mode='min',\r\n )\r\n\r\n\r\nmodel.fit(train_generator,validation_data=valid_generator,steps_per_epoch=len(train_generator),\\\r\n validation_steps=len(valid_generator), epochs = 50,\\\r\n callbacks=[checkpoint,lr_reduce], verbose=1\r\n )\r\n\r\n```" ]

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[ "@myalos Glad, you are able to run the code successfully. \r\nThe warning might be occurring due to the versions you installed for TensorFlow and CUDA probably aren't compatible. Try using one of the versions tested here: [Tensorflow GPU Source Install](https://www.tensorflow.org/install/source#gpu). \r\nPlease use the latest TF version with all compatible versions.\r\n\r\n\r\nThank you!", "Thanks for reply! I changed the version of tensorflow 2.11 to 2.12 and the original problem was solved, but unpleasant message showed up which is `[/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor`\r\nAnd i searched the reason and solution for this problem, and found related issue on https://github.com/tensorflow/tensorflow/issues/59779. It seems that the problem about this unpleasant message is still unsolved.\r\n", "@myalos Thank you for your response here!\r\nThough there are some workarounds to avoid such warnings but the original issue is under tracking. Once that will be resolved such warnings will not occur. \r\nAs you confirmed that the original issue has been resolved so could you please move this issue to closed status ?\r\nThank you! ", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62064\">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/62064\">No</a>\n" ]

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[ "@Maxwell12345,\r\nCould you please confirm whether you are trying to execute the below code or the different one?\r\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/model_main_tf2.py\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/62063\">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/62063\">No</a>\n" ]

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[ "Hi @Craigacp ,\r\n\r\nIf my understanding is correct you want to build tensorflow-java using pre-built binaries of respective OS and for Linux and Mac it is success but for windows OS there is problem due to .pyd file format. Is that right ?\r\n\r\n`tensorflow-intel` is maintained by `intel` team and officially meant for Windows OS. Pinging them for a response.\r\n\r\nCC: @TensorFlow-MKL ", "Yes, that's correct. Our build works just fine for Linux & Mac on multiple platforms, but due to the pyd files it doesn't work on Windows.", "Hi @Craigacp and @SuryanarayanaY, I am looking into it. ", "@mraunak , Any update on this??", "Hi @Craigacp,\r\n\r\nWe don't have build for SIG-JVM/TensorFlow-Java. The file type pyd is dynamic link library of Python. What's your expectation for this case? I would suggest to build TensorFlow from source for your specific requirements. Thanks. ", "We do currently build TF from source. I'd like the libtensorflow and tensorflow framework libraries to be packaged as DLLs, because they are dependently loaded by the actual python binding and so I assume they don't need to be pyd files (because on other platforms they are independent of the API in use). If you package the python `tensorflow-intel` package like that then we can use the binaries you build which will make it easier for us to keep TF-Java up to date. We don't have dedicated build resources, TF-Java uses GitHub actions and the Windows build is something which times out and we also don't have much experience with.", "\r\nHi @craigacp, this is regarding https://discuss.tensorflow.org/t/tensorflow-version-update/22277. My reply on the discuss forum is awaiting some approval. Hence, pasted my comments here\r\n\r\nTo resolve long path issues while building with Bazel, set --output_user_root=c:\\\r\ne.g. bazel --output_user_root=c:\\ build --config=opt --repo_env=TF_PYTHON_VERSION=3.10 --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package\r\n\r\nApart from this, also consider looking into the system settings to ensure the long path is enabled. https://www.backupery.com/how-to-enable-ntfs-long-paths-in-windows/\r\n\r\nFor https://github.com/tensorflow/tensorflow/issues/62579, PR from our end is in progress\r\n" ]

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[ "Closing because this can be fixed by relying on non-binary wheels.", "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/62061\">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/62061\">No</a>\n" ]

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[ "@sachinprasadhs I was able to replicate this issue on colab using [TFv2.13](https://colab.research.google.com/gist/sushreebarsa/290f255e733d113c7026e0827a77629b/62060.ipynb), [2.14](https://colab.research.google.com/gist/sushreebarsa/0f4eaed473ff891444fc54c5875b4f3c/62060.ipynb#scrollTo=V5-zf5sEd_DG) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/03ea89c61270eec12cdda2c4b24778de/62060.ipynb). Please find the attached gits. Thank you!", "We will transfer this issue to keras org.", "Thanks @sachinprasadhs. I will close this issue now.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62060\">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/62060\">No</a>\n" ]

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[ "Check out this pull request on&nbsp; <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/62059\"><img align=\"absmiddle\" alt=\"ReviewNB\" height=\"28\" class=\"BotMessageButtonImage\" src=\"https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png\"/></a> \n\n See visual diffs & provide feedback on Jupyter Notebooks. \n\n---\n\n <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>", "Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/ ", "Reopening as I was wrong, sorry about that" ]

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[ "I was able to reproduce the issue. Please find this [gist](https://colab.research.google.com/gist/pjpratik/456c0d5c4035cf68fbd88c380a9861e0/62058.ipynb).\r\n\r\n@pkgoogle Could you please look into this issue. Thanks.", "I was able to replicate, [gist](https://colab.sandbox.google.com/gist/pkgoogle/15855d323971676c2e64ec00355e2e23/62058.ipynb), I added some comments for readability.\r\n\r\n@alankelly, can you please take a look? Thanks." ]

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[ "i am curious as to why this was dropped. I would like to see wrapt unpinned." ]

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[ "Hi @ckstanton This PR is in draft, any update on this? Please. Thank you!", "> Hi @ckstanton This PR is in draft, any update on this? Please. Thank you!\r\n\r\nThis was just a PR to demonstrate an issue we're seeing, I wasn't planning on submitting this... sorry! I'll close this PR now." ]

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[ "@sachinprasadhs,\r\nI was able to reproduce the issue/error on tensorflow v2.13 and tf-nightly. Kindly find the screenshot below for the reference.\r\n\r\n<img width=\"567\" alt=\"image (22)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/9e4c9db2-d7f3-4b83-b719-4165cd035e20\">\r\n\r\n<img width=\"567\" alt=\"image (23)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/be607840-eeae-4e4e-923e-ad7af2990250\">\r\n", "@GwiHwan-Go,\r\nI tried to execute the mentioned code on tf-nightly and the code was executed with the error and also observed that the crash did not happen. And the same has been in the respective files. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/b1597c68ee31839c1f0d8e006770fbb0/untitled1687.ipynb) for the [reference](https://colab.research.google.com/gist/tilakrayal/47842479824cd16fa3773a49640bf860/untitled1689.ipynb).\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L22\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/resource_variable_ops.cc#L1120\r\n\r\n```\r\n// Check data type of update and resource to scatter.\r\nconst DataType update_dtype = c->input(2).dtype();\r\nOP_REQUIRES(c, v->tensor()->dtype() == update_dtype,\r\nerrors::InvalidArgument(\r\n\"DType of scatter resource and updates does not match.\"));\r\n\r\n```\r\n\r\nThank you!", "close the issue as it is fixed on latest version of 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/62055\">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/62055\">No</a>\n" ]

[]

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[ "`I hoping anyone know what the issue here`\r\n\r\nI get it, the issue is with Python 3.12. Use a different version and you'll be good to go.", "Thanks\r\nI installed python 3.10 and now its working", "Canonical issue #62003" ]

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[ "From the Relevant Log Output above, if I remove `tf.group` from this line `File \"/opt/conda/envs/tf214/lib/python3.10/site-packages/keras/src/optimizers/legacy/optimizer_v2.py\", line 844, in _distributed_apply`, the issue seems to be resolved. I am not sure how to properly resolve this issue though, any help would be appreciated.", "@ayulockin Thank you for raising this issue!\r\n I was able to replicate the issue on colab using TF [v2.14](https://colab.research.google.com/gist/sushreebarsa/407893cdeb17f20c5bb4bda8bdecec2f/untitled.ipynb) and [nightly](https://colab.research.google.com/gist/sushreebarsa/4c4f13e3c4233a27d3545041d78d10ca/untitled.ipynb). This issue is not appearing in [2.13](https://colab.research.google.com/gist/sushreebarsa/b27971ea8f0027c6addf9eae2fc5b3c2/62053-2-13.ipynb). \r\n@sachinprasadhs Could you please have a look. Thank you!", "Thanks for quickly replicating it @sushreebarsa. Looking for a response from @sachinprasadhs :)", "I solved the issue like this:\r\n\r\nclass CustomSGD(tf.keras.optimizers.legacy.Optimizer): \r\n pass\r\n... \r\n\r\n# Creating an instance of the custom SGD optimizer\r\ncustom_sgd_optimizer = CustomSGD(learning_rate=0.01)\r\n\r\nWhen compiling the model, I added the run_eagerly=True attribute:\r\n\r\nmodel3.compile(optimizer=custom_sgd_optimizer, loss='mse', metrics=['mse', 'mae'], run_eagerly=True)\r\n\r\nThen, I trained the model with:\r\n\r\nhistory3 = model3.fit(x, y, epochs=1200)\r\n\r\nAnd it proceeded correctly without any issues.\r\n\r\nHope this helps! 🙂\r\n혹시 이해가 안되는 분있으면 메일주세요~" ]

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[]

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[ "@ezhulenev - the AMD ROCm failure is not related to changes in this PR. Can you please help review this PR?", "Hi @ezhulenev, Can you please review this PR ? Thank you!", "@cantonios Can you please review this PR also? Looks like the tests passed. Let me know if I need to change anything. Thanks!", "It was originally reverted because it broke a set of internal tests across Google back in August. But those logs are now purged, so I can't see why exactly.\r\n\r\nWe can try again to see if it triggers the same errors." ]

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[ "Hi @SagaraBattousai This PR is duplicate of [PR#62037](https://github.com/tensorflow/tensorflow/pull/62037). Hence we are closing this PR. Thank you for your contribution! " ]

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[ "Duplicate of #62003", "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/62049\">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/62049\">No</a>\n" ]

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[ "@SachinBM-CE,\r\nCould you please note that using `tfp-nightly` in the meantime should work for the above issue. Also stable (ie non-nightly) TFP releases are generally tied to a particular stable TF release and won't generally work with a subsequent TF release. TFP nightlies are tested against tf-nightly and more likely to work with a recent TF stable release.\r\n\r\nAnd also, it looks more related to tensorflow-propability, where in that repo there is an issue that was raised and a conversation is happening. https://github.com/tensorflow/probability/issues/1752. I request that please feel free to close this issue and follow up on that issue for a quick resolution. Thank you!", "@tilakrayal \r\nThank you very much for your suggestion & directing me to the right conversation. ", "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/62048\">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/62048\">No</a>\n" ]

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[ "Hi @OH-AU \r\n\r\nTensorflow supports `--copt=-mavx` flag as per [source](https://github.com/tensorflow/tensorflow/blob/master/.bazelrc#L421C1-L422C1) ,but not sure of the `avx512fp16` flag.\r\n\r\nNeed to hear from Engg team.Thanks you!\r\n", "Hi,\r\n\r\nYou seem to be using very old bazel version, could you please use below listed configurations and let us know the outcome.\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.14.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.7 | 11.8\r\n\r\n", "My apologies - not sure where that came from in the original report - the version of bazel used in the original report was 6.1.0 (I also had 6.3.2 available however that was too new for tensorflow to work with).", "Can you try with above published CUDA and cuDNN version.", "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.", "Unfortunately I don't have access to older versions of python, cuda or cudnn on this system. I attempted to compile with llvm/clang 17.0.1 but not being familiar with how to build in that environment I just got an error of:\r\nclang-17: error: unknown argument: '-fno-canonical-system-headers' - which points me to:\r\nhttps://github.com/llvm/llvm-project/issues/61699\r\nand\r\nhttps://github.com/bazelbuild/bazel/issues/16107\r\nI can probably grab a clang 16 binary, and the older cuda/cudnn but that won't optimize well for the h100 hardware - and presumably I'd run into the same build issue with -fno-canonical...? python/3.12.0 is also the only python available on this test system at this time.\r\nI did try with CC=clang CXX=clang++ and then using:\r\nPlease specify which gcc should be used by nvcc as the host compiler. [Default is /gcc/12.3/bin/gcc]\r\nto see if I could somehow combine clang+gcc but that resulted in the same error as the original ticket. I may try gcc/13.2.", "Canonical issue #62003", "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/62047\">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/62047\">No</a>\n", "I have the same static_cast build error on 2.16.1 building with CUDA 12.4, cudnn 8.9.7, TritonRT 8.6, clang 17, and python 3.10 (stock) on Ubuntu 22.04 if I use either copt \"-march=native\" or \"-mavx\". I can successfully build without either march=native or mavx if I also have copt \"-Wno-error=unused-command-line-argument\" (see issue 62459)." ]

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[ "@nikraj01 Thank you for raising the issue!\r\nCould you please go to SDK location find NDK folder and check the folders if one of them is empty or corrupted? If so please delete it and let it use the latest version you have,\r\nThank you!", "Hi @sushreebarsa ,\r\n\r\nDoesn't look like both NDK folders are empty/corrupted to me\r\n\r\nAndroidSDK/ndk/21.4.7075529$ ls\r\nbuild meta ndk-gdb ndk-which NOTICE.toolchain platforms python-packages shader-tools source.properties sysroot wrap.sh\r\nCHANGELOG.md ndk-build ndk-stack NOTICE package.xml prebuilt README.md simpleperf sources toolchains\r\n\r\n\r\nAndroidSDK/ndk/25.0.8775105$ ls\r\nbuild meta ndk-gdb ndk-stack NOTICE package.xml python-packages shader-tools source.properties toolchains\r\nCHANGELOG.md ndk-build ndk-lldb ndk-which NOTICE.toolchain prebuilt README.md simpleperf sources wrap.sh\r\n\r\nThanks", "@nikraj01 Sorry for the late response!\r\nCould you please follow the steps from the document [here](https://www.tensorflow.org/lite/android/lite_build) for the android build. Please let us know which NDK version you are specifying? The current recommended version is 21e, which may be found [here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-21e-downloads).\r\nThank you!", "Hello @sushreebarsa ,\r\n\r\nThanks for the reply. I am not building Tflite for Android, I am specifically building Tf Delegate Performance Benchmark tool. \r\nIts working fine if I use NDK version 21. But I suppose later versions of NDK should also support it?\r\n\r\nThanks", "@nikraj01 Thanks for your reply!\r\nI could see there has been a [PR](https://github.com/tensorflow/tensorflow/pull/59422) already raised before on this. Could you please refer to this [PR](https://github.com/tensorflow/tensorflow/pull/59422#issuecomment-1733975406) and let us know if it helps?\r\nIt seems like nkd v25 will be supported in tf-nightly and the next release. \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/62046\">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/62046\">No</a>\n" ]

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[ "Hi @JasmineKaur-eaton \r\n\r\nThe TensorFlow provides TensorFlow Lite for Microcontrollers which is designed to run machine learning models on microcontrollers and other devices with only a few kilobytes of memory. \r\n\r\nPlease check the list of [supported platforms](https://www.tensorflow.org/lite/microcontrollers#supported_platforms) which includes [STM32F746 Discovery kit](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html). \r\n\r\nThe documentation provides a hello world template and other examples which can be used as templates to create own projects.\r\n\r\nThis [documentation](https://www.tensorflow.org/lite/microcontrollers/library) outlines the basic structure of the C++ library and provides information about creating your own project.\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi, thank you for your response.\r\nI have tried the Hello World Example with Arduino Nano BLE and the TensorFlow library gives no errors. When trying the same with Arduino Mega I am getting errors in the TensorFlow Lib.\r\nBelow is the error-\r\n\\Arduino\\libraries\\Arduino_TensorFlowLite\\src/tensorflow/lite/core/api/error_reporter.h:18:10: fatal error: cstdarg: No such file or directory\r\n\r\n#include <cstdarg>\r\n ^~~~~~~~~\r\ncompilation terminated.\r\n\r\nWhy am I getting error when I am trying to build using TF with some other board and example. \r\n", "Hi @JasmineKaur-eaton \r\n\r\nAs per the [documentation](https://www.tensorflow.org/lite/microcontrollers#supported_platforms), the [Arduino Nano 33 BLE Sense](https://store-usa.arduino.cc/products/arduino-nano-33-ble-sense-with-headers) is supported and Arduino Mega is not listed in the supported platforms. Hence the incompatiblity might be causing the issue.\r\n\r\nThanks.", "Is there any other library similar to TensorFlow which supports microcontrollers/platforms other than listed as per documentation?", "Hi @JasmineKaur-eaton ,\r\n\r\nI am not sure about other libraries but as far as TFLite Micro is concerned please refer to the list of [supported platforms](https://www.tensorflow.org/lite/microcontrollers#supported_platforms) as mentioned above. Feel free to post in [TFLite Micro](https://github.com/tensorflow/tflite-micro/issues) repo for reporting the possible feature request.\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/62045\">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/62045\">No</a>\n" ]

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[ "@Kuree,\r\nCould you please elaborate about your Feature. Also, please specify the Use Cases for this feature to understand in the effective way. Thank you!", "We have a custom compiler that takes tflite models and convert them into some custom MLIR dialect. To do so, we use `flatbuffer_translate` and `tf-opt` to obtain MLIR in standard dialects (TODA + Linalg). So the compilation flow looks something like this:\r\n1. Call `flatbuffer_translate` to get MLIR in TFL dialect\r\n2. Call `tf-opt` to lower the dialect to TOSA and Linalg\r\n3. Call the custom compiler to continue the compilation\r\n\r\nOur compiler users have to compile tensorflow from source to obtain those two binaries. It would be great if those two can be shipped with standard tensorflow distribution. It doesn't have to be in the main python wheel, but can be included as extra dependency, e.g. `pip install tensorflow[mlir]`.\r\n\r\nPlease let me know if you have additional questions.", "Hi @Kuree,\r\n\r\nThese are C++ binaries which generally I don't think we want to install with TF, though as a custom option this might make sense. @terryheo Can you please take a look? Thanks.\r\n\r\n", "This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you.", "> We have a custom compiler that takes tflite models and convert them into some custom MLIR dialect. To do so, we use `flatbuffer_translate` and `tf-opt` to obtain MLIR in standard dialects (TODA + Linalg). So the compilation flow looks something like this:\r\n> \r\n> 1. Call `flatbuffer_translate` to get MLIR in TFL dialect\r\n> 2. Call `tf-opt` to lower the dialect to TOSA and Linalg\r\n> 3. Call the custom compiler to continue the compilation\r\n> \r\n> Our compiler users have to compile tensorflow from source to obtain those two binaries. It would be great if those two can be shipped with standard tensorflow distribution. It doesn't have to be in the main python wheel, but can be included as extra dependency, e.g. `pip install tensorflow[mlir]`.\r\n> \r\n> Please let me know if you have additional questions.\r\n\r\nIt would be really great if all the tools from `/tensorflow/compiler/mlir` are shipped with tensorflow." ]

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[ "Hi @ndeepesh ,\r\n\r\nI am not sure of the patch that resolved this bug. Maybe it's possible that its internal fix also and hence difficult to find it out for me. You may try git diff command to know the differences in the file for master branch and v2.11.0.\r\n\r\nAFAIK most of the vulnerability fixes happens at C++ backend. You may try the command below to check the changes in code.\r\n\r\n`!git diff master v2.11.0 -- tensorflow/core/kernels/sparse_to_dense_op.cc`\r\n\r\nI hope this may be of some help. 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/62043\">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/62043\">No</a>\n" ]

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[ "The AMD ROCm error appears unrelated to change and is compile error on C++ code." ]

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[ "Hi @rsuderman, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @Tessil Can you please resolve conflicts? 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 @Tessil Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@plooney The latest release of TensorFlow CPU binary image is Default. Could you please mention the exact commands to replicate the error reported. Thank you!", "If I create a docker file with the commands\r\n\r\n`FROM tensorflow/tensorflow:2.14.0-gpu\r\n\r\nRUN dpkg --configure -a\r\n`\r\n\r\nand try to build it I get the errors \r\n\r\ndpkg: error: error executing hook 'if { test \"$DPKG_HOOK_ACTION\" = add-architecture || test \"$DPKG_HOOK_ACTION\" = remove-architecture; } && test -x /usr/share/pkg-config-dpkghook; then /usr/share/pkg-config-dpkghook update; fi', exit code 32512\r\n\r\nIf I create a docker file with \r\n\r\n`FROM tensorflow/tensorflow:2.13.0-gpu\r\n\r\nRUN dpkg --configure -a\r\n`\r\n\r\nI can build it successfully", "@plooney Thank you for the response! Could you please have a look at this [official](https://www.tensorflow.org/install/docker) documentation and let us know if you have followed the steps ?\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/62040\">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/62040\">No</a>\n" ]

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[ "Looks like urllib3 is up-to-date now, so this is no longer needed." ]

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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/62037/checks?check_run_id=17334909298) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @zichuan-wei, Can you please review this PR ? Thank you!", "Hi @SagaraBattousai Can you please check @zichuan-wei's comments and keep us posted? Thank you!", "Hi @SagaraBattousai Any update on this PR? Please. Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Can reproduce this on ubuntu and colab.\r\n\r\nThis issue looks related https://github.com/NVIDIA/TensorRT/issues/2933\r\n\r\nPotentially a simple fix, I am not sure we really need to be [pinning](https://github.com/tensorflow/tensorflow/blob/67f519e727605f502c711cd340df46af89c4085e/tensorflow/tools/pip_package/setup.py#L178-L180) `tensorrt-bindings` and `tensorrt-libs` directly. `pip install tensorrt==8.6.1.post1` seems to pull in the dependent libraries just fine.\r\n\r\n", "I found failure of same package i.e `tensorflow[and-cuda]>=2.14.0` with Python 3.11 environment mainly due to Tensorrt issue with Python 3.11v. Related issue #61986 .\r\n", "It seems this one does not exist: `'tensorrt-libs == 8.6.1',` unless you install it like this:\r\n`pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com tensorrt-libs==8.6.1`", "Hi, I am unable to install tensorflow nightly. Any suggestions ?\r\n\r\nI am running tensor flow 2.13.1 ?\r\n", "@NikhielRahulSingh ,\r\n\r\nCould you please confirm the install command and logs ? Please note that current nightly version is 2.16.0-dev20231018.", "I ran into the same issue with Version 2.15.0 rc0.\r\nPlease fix this for the release.\r\nThe fix from @picobyte saved the day\r\n\r\n\r\n```\r\nERROR: Ignored the following versions that require a different python version: 0.28.0 Requires-Python >=3.7, <3.11; 1.21.2 Requires-Python >=3.7,<3.11; 1.21.3 Requires-Python >=3.7,<3.11; 1.21.4 Requires-Python >=3.7,<3.11; 1.21.5 Requires-Python >=3.7,<3.11; 1.21.6 Requires-Python >=3.7,<3.11; 1.6.2 Requires-Python >=3.7,<3.10; 1.6.3 Requires-Python >=3.7,<3.10; 1.7.0 Requires-Python >=3.7,<3.10; 1.7.1 Requires-Python >=3.7,<3.10; 1.7.2 Requires-Python >=3.7,<3.11; 1.7.3 Requires-Python >=3.7,<3.11; 1.8.0 Requires-Python >=3.8,<3.11; 1.8.0rc1 Requires-Python >=3.8,<3.11; 1.8.0rc2 Requires-Python >=3.8,<3.11; 1.8.0rc3 Requires-Python >=3.8,<3.11; 1.8.0rc4 Requires-Python >=3.8,<3.11; 1.8.1 Requires-Python >=3.8,<3.11; 1.9.5 Requires-Python >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <3.7; 6.0.0 Requires-Python >=3.6, <3.10; 6.0.0a1.dev1606911628 Requires-Python >=3.6, <3.10; 6.0.1 Requires-Python >=3.6, <3.10; 6.0.2 Requires-Python >=3.6, <3.10; 6.0.3 Requires-Python >=3.6, <3.10; 6.0.4 Requires-Python >=3.6, <3.10; 6.1.0 Requires-Python >=3.6, <3.10; 6.1.1 Requires-Python >=3.6, <3.10; 6.1.2 Requires-Python >=3.6, <3.10; 6.1.3 Requires-Python >=3.6, <3.10; 6.2.0 Requires-Python >=3.6, <3.11; 6.2.1 Requires-Python >=3.6, <3.11; 6.2.2 Requires-Python >=3.6, <3.11; 6.2.2.1 Requires-Python >=3.6, <3.11; 6.2.3 Requires-Python >=3.6, <3.11; 6.2.4 Requires-Python >=3.6, <3.11; 6.3.0 Requires-Python <3.11,>=3.6; 6.3.1 Requires-Python <3.11,>=3.6; 6.3.2 Requires-Python <3.11,>=3.6; 6.4.0 Requires-Python <3.11,>=3.6\r\nERROR: Could not find a version that satisfies the requirement tensorrt-libs==8.6.1; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 9.0.0.post11.dev1, 9.0.0.post12.dev1, 9.0.1.post11.dev4, 9.0.1.post12.dev4, 9.1.0.post11.dev4, 9.1.0.post12.dev4)\r\nERROR: No matching distribution found for tensorrt-libs==8.6.1; extra == \"and-cuda\"\r\n```\r\n", "This issue still exists in the stable 2.15.0 version." ]

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[ "Hi @rsuderman, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @rdzhabarov, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!" ]

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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/62033/checks?check_run_id=17322440679) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @franz101 It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727", "Thanks for the quick reply @gbaned, unfortunately i looked in the repos of keras-team and the type `DistributedDatasetInterface` is nowhere mentioned. I have opened a new issue in keras. Or do is there a specific build pipeline how `tensorflow/python/keras/engine/` is created" ]

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[ "@schrummy14,\r\nCould you please confirm if this is the document which you are looking for https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html\r\n\r\nand also provide if you are facing any issue/error while the running code mentioned above or the executed smoothly. Thank you!", "@tilakrayal,\r\nNo, not the Nvidia doc. This was part of the tensorflow docs. (`https://www.tensorflow.org/install/pip`)\r\nThe code does not complete and errors out.\r\n\r\nBelow is the full error.\r\nError occurs for both k80s and v100s.\r\n\r\n```txt\r\n2023-10-02 16:01:32.416512: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x31b68fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-10-02 16:01:32.416555: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla K80, Compute Capability 3.7\r\n2023-10-02 16:01:32.416570: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): Tesla K80, Compute Capability 3.7\r\n2023-10-02 16:01:32.416580: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): Tesla K80, Compute Capability 3.7\r\n2023-10-02 16:01:32.416590: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): Tesla K80, Compute Capability 3.7\r\n2023-10-02 16:01:32.423430: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.423550: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.423628: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.423777: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n2023-10-02 16:01:32.426575: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.426663: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.426717: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.426873: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n2023-10-02 16:01:32.430015: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.430104: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.430150: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.430300: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n2023-10-02 16:01:32.433232: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.433321: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.433368: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.433499: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n2023-10-02 16:01:32.437404: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.437495: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.437554: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.437678: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n2023-10-02 16:01:32.440590: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:447] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\r\n2023-10-02 16:01:32.440676: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:451] Memory usage: 466550784 bytes free, 11996954624 bytes total.\r\n2023-10-02 16:01:32.440731: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:461] Possibly insufficient driver version: 450.203.8\r\n2023-10-02 16:01:32.440884: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.\r\n---------------------------------------------------------------------------\r\nFailedPreconditionError Traceback (most recent call last)\r\nCell In[6], line 2\r\n 1 # Run GPU Benchmark\r\n----> 2 gpuBench()\r\n\r\nCell In[5], line 8, in gpuBench()\r\n 6 with tf.device('/GPU:0'):\r\n 7 model_gpu = get_model()\r\n----> 8 model_gpu.fit(X_train_scaled, y_train_encoded, epochs = 10)\r\n\r\nFile /usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)\r\n 67 filtered_tb = _process_traceback_frames(e.__traceback__)\r\n 68 # To get the full stack trace, call:\r\n 69 # `tf.debugging.disable_traceback_filtering()`\r\n---> 70 raise e.with_traceback(filtered_tb) from None\r\n 71 finally:\r\n 72 del filtered_tb\r\n\r\nFile /usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py:60, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)\r\n 53 # Convert any objects of type core_types.Tensor to Tensor.\r\n 54 inputs = [\r\n 55 tensor_conversion_registry.convert(t)\r\n 56 if isinstance(t, core_types.Tensor)\r\n 57 else t\r\n 58 for t in inputs\r\n 59 ]\r\n---> 60 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\n 61 inputs, attrs, num_outputs)\r\n 62 except core._NotOkStatusException as e:\r\n 63 if name is not None:\r\n\r\nFailedPreconditionError: Graph execution error:\r\n\r\nDetected at node SGD/StatefulPartitionedCall_4 defined at (most recent call last):\r\n File \"<frozen runpy>\", line 198, in _run_module_as_main\r\n\r\n File \"<frozen runpy>\", line 88, in _run_code\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel_launcher.py\", line 17, in <module>\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 1046, in launch_instance\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 736, in start\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 195, in start\r\n\r\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 604, in run_forever\r\n\r\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1909, in _run_once\r\n\r\n File \"/usr/lib/python3.11/asyncio/events.py\", line 80, in _run\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 505, in process_one\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 740, in execute_request\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 422, in do_execute\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 546, in run_cell\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\r\n\r\n File \"/tmp/ipykernel_89/3941288529.py\", line 2, in <module>\r\n\r\n File \"/tmp/ipykernel_89/898971160.py\", line 8, in gpuBench\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1783, in fit\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1377, in train_function\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1360, in step_function\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1349, in run_step\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1130, in train_step\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 544, in minimize\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 1223, in apply_gradients\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 652, in apply_gradients\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 1253, in _internal_apply_gradients\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 1345, in _distributed_apply_gradients_fn\r\n\r\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/optimizers/optimizer.py\", line 1340, in apply_grad_to_update_var\r\n\r\nDNN library initialization failed. Look at the errors above for more details.\r\n\t [[{{node SGD/StatefulPartitionedCall_4}}]] [Op:__inference_train_function_727]\r\n```\r\n\r\nnvidia-smi output\r\n```bash\r\nnimbix@jarvice-job-85995-jt7l8:/data$ nvidia-smi \r\nWed Oct 4 21:21:57 2023 \r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 450.203.08 Driver Version: 450.203.08 CUDA Version: 11.0 |\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 K80 Off | 00000000:00:04.0 Off | 0 |\r\n| N/A 33C P8 27W / 149W | 0MiB / 11441MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 1 Tesla K80 Off | 00000000:00:05.0 Off | 0 |\r\n| N/A 46C P8 28W / 149W | 0MiB / 11441MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 2 Tesla K80 Off | 00000000:00:06.0 Off | 0 |\r\n| N/A 73C P8 33W / 149W | 0MiB / 11441MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 3 Tesla K80 Off | 00000000:00:07.0 Off | 0 |\r\n| N/A 32C P8 27W / 149W | 0MiB / 11441MiB | 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```", "Hi, \r\n\r\nFor 2.14 version, could you please try with the below configuration.\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.14.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.7 | 11.8\r\ntensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0 | 8.6 | 11.8\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/62032\">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/62032\">No</a>\n", "will cudnn latest versions work (say 8.9)?\r\n" ]

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[ "Needed to add a `release_linux_base` so arm could inherit from it. Everything i believe is the same as x86 cpu except the crosstool which bazel did not like being defined twice. ", "@angerson do you think we should put this one in official for now or over in devinfra? ", "I'd say to put it in Official with a WIP tag somewhere." ]

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[ "It seems there are 2 failing tests:\r\n\r\n```\r\n[ FAILED ] [Test/FusedMatMulWithBiasOpTest/1].MatMul256x128x64WithActivation, where TypeParam = Eigen::half\r\n[ FAILED ] [Test/FusedMatMulWithBiasOpTest/1].MatMul1x256x256WithActivation, where TypeParam = Eigen::half\r\n```\r\n\r\nAnd the errors look like:\r\n\r\n```NOT_FOUND: No algorithm worked!```", "> It seems there are 2 failing tests:\r\n> \r\n> ```\r\n> [ FAILED ] [Test/FusedMatMulWithBiasOpTest/1].MatMul256x128x64WithActivation, where TypeParam = Eigen::half\r\n> [ FAILED ] [Test/FusedMatMulWithBiasOpTest/1].MatMul1x256x256WithActivation, where TypeParam = Eigen::half\r\n> ```\r\n> \r\n> And the errors look like:\r\n> \r\n> `NOT_FOUND: No algorithm worked!`\r\n\r\nOK, I will have a look at it tomorrow, possibly it depends on CUDNN version and GPU architecture (I have tested on SM 7.0 GPU). It says that CUDNN cannot not find a suitable mamul algorithm for the problem. \r\n", "I would wait until the related PR is merged to upstream: https://github.com/tensorflow/tensorflow/pull/61941\r\nbecause it applies some modifications to fused_matmul_op, then I can rebase and try running it once again.", "> FYI: I am using a different fix internally, we can just avoid create Eigen::half test cases on CPU.\r\n\r\nYes, if you have a better approach how to skip these tests, can you share it with me ?\r\nBasically I wanted to skip those tests for which no OpKernel is available but it seems that GTEST_SKIP() does not break the test execution immediately, so there are some \"patches\" to make it working.. \r\n", "@akuegel: do you know why this PR has been rejected ? I last updated it over the weekend and was hoping that the tests will run fine now..", "Can you explain what you mean with rejected? This (i.e. the version I had approved, + the fixes I added) got merged 46 minutes ago. It took a while because I needed internal approvals from US-based people, so they happened after my working hours. And this morning I was OOO as well.", "ah sorry, I was confused by this message: \r\n```\r\n[PR Queue] automation moved this from Approved by Reviewer to Closed/Rejected [1 hour ago]\r\n```\r\nmany thanks for the help !\r\n", "It seems my idea of a fix doesn't work, because we seem to run the _cpu version of the test also with --config=cuda. Some followup might be needed, maybe also a temporary revert.", "OK, then I guess the only way is to check for the errors at runtime.. \r\n\r\nFor example, in Python there are exceptions, and some tests do it in this way:\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/kernel_tests/nn_ops/depthwise_conv_op_base.py#L377-L387\r\n\r\nIn C++, I also tried to selectively skip subtests depending on tsl::Status() message but without exceptions it's more cumbersome..", "I think I found a solution, adding the tag no_cuda_on_cpu_tap should avoid that the test is automatically run on CPU with --config=cuda. That should be good enough." ]

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[ "Hi @Allen-Webb, can you make a PR for us to review? Alternatively, internal systems will probably be faster.", "> Hi @Allen-Webb, can you make a PR for us to review? Alternatively, internal systems will probably be faster.\r\n\r\nThanks. I will have to come back to us after I resolve the other blocking issues with the upgrade.", "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.", "Honestly I don't know I will get to this before the bots close it unless I ping it, so here is one ping but it is likely to get closed before I get to it.", "Hi @Allen-Webb, I think it's OK to leave it closed until you are ready to get back to it.", "> AFAICT nativewindow is an Android library and it looks like it isn't really supposed to be added to all operating systems building tensorflow lite with the GPU delegate.\r\n> \r\n> Can you all confirm? \r\n\r\nConfirmed. \"-lnativewindow\" should only be added for Android.", "Hi @Allen-Webb, seems like the fix is in -- can you test to see if your issue is resolved? Thanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62029\">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/62029\">No</a>\n" ]

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[ "@YaroslavPavlovich I was able to import tensorflow on colab , please find the [gist](https://colab.research.google.com/gist/sushreebarsa/c1e21d1a8fed7c92c12b271f2e92cefa/62028.ipynb) here. Could you please have a look at the system [requirements](https://www.tensorflow.org/install/pip#software_requirements) and follow the instructions provided in the documentation. The Python version needs to be between 3.9–3.11. Thank you!", "I also run with ` docker run -it --rm tensorflow/tensorflow bash` with latest version of tensorflow.\r\nBut I have the same problem:\r\n```\r\nroot@b533c1553888:/# python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\nIllegal instruction (core dumped)\r\n```", "@YaroslavPavlovich Thank you for your response!\r\nCould you please make sure that [these](https://www.tensorflow.org/install/docker) instructions were followed properly. Thank you!", "Here is an instruction on how to run a Docker container using TensorFlow. There are also links provided for installing Docker with GPU support and without it.\r\n\r\nEverything is ok with GPU in docker. Docker is installed correctly.\r\nIf I installed tensorflow for another computer with Intel COre I7 10 Gen, it is working ok.\r\n", "@YaroslavPavlovich Thank you for your response again!\r\nDid you try upgrading the Python version and check if your system satisfies all the requirements?\r\nThank you!", "it is python3.11 in docker\r\nCUDA version 11.8 (nvidia-smi returns 12.0)\r\nDriver version is 525.85.12\r\npip version in docker is normal too\r\nOS is suitable\r\nanything else?\r\n", "I tried with python 3.11 in docker example\r\n\r\n\r\n", "Hi @YaroslavPavlovich, \r\n\r\nFor GPU package you need to use `pip install tensorflow[and-cuda]` command.With this only we can able to get GPU list. Could you please try and confirm?\r\n \r\nThanks!\r\n", "https://github.com/tensorflow/tensorflow/issues/61986?ysclid=lnn9iziiq693897091\r\nI have the sane problem but with python3.9", "I tried this\r\n`pip install tensorflow==2.14.0 nvidia-cuda-runtime-cu11==11.8.89 nvidia-cublas-cu11==11.11.3.6 nvidia-cufft-cu11==10.9.0.58 nvidia-cudnn-cu11==8.7.0.84 nvidia-curand-cu11==10.3.0.86 nvidia-cusolver-cu11==11.4.1.48 nvidia-cusparse-cu11==11.7.5.86 nvidia-nccl-cu11==2.16.5 nvidia-cuda-cupti-cu11==11.8.87 nvidia-cuda-nvcc-cu11==11.8.89\r\n`\r\nthis is the same result with Illegal instruction (core dumped)\r\n\r\nI think this is a problem with my PC, but I don't know why. I tried install on another PC with Intel Core I7 10 Gen and Nvidia GeForce 1650 Ti it is ok.\r\nThis problem with Intel Core I9 12 Gen and Nvidia GeForce 3090 TI", "Hi @YaroslavPavlovich ,\r\n\r\nThe issue might be related to your environment.You can find the suitable Nvidia driver for your GPU from [here](https://www.nvidia.com/Download/index.aspx). For Geoforce 3090 TI , it is suggesting 535.129.03 driver. Maybe you can try with it. ", "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/62028\">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/62028\">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/62027/checks?check_run_id=17297420385) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @rmackUF, Please submit multiple typo fixes in a single PR as the CPU/GPU hours are wasted on CI.\r\nHence, we do not encourage one liner grammatical changes as it is an expensive process. Thank you for your contribution!" ]

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[ "@sagi21805 Generally error will occur if the model you are trying to load was saved using a different version of TensorFlow than the one you are currently using. Also try to use the following for loading the model and for more information on saving or loading the model please have a look at [this](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model) doc.\r\n```\r\n model = tf.keras.saving.load_model(\"path\")\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/62026\">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/62026\">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/62025/checks?check_run_id=17289172663) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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[ "@HNanditha with the provided information it is difficult to figure out what is going on, would it be possible for you to share the code or a way by which we can reproduce the issue?", "My **train.py** program is like this :\r\n\r\nimport functools\r\nimport json\r\nimport os\r\nimport tensorflow as tf\r\n\r\nfrom object_detection.builders import dataset_builder\r\nfrom object_detection.builders import graph_rewriter_builder\r\nfrom object_detection.builders import model_builder\r\nfrom object_detection.legacy import trainer\r\nfrom object_detection.utils import config_util\r\n\r\ntf.logging.set_verbosity(tf.logging.INFO)\r\n\r\nflags = tf.app.flags\r\nflags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')\r\nflags.DEFINE_integer('task', 0, 'task id')\r\nflags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')\r\nflags.DEFINE_boolean('clone_on_cpu', False,\r\n 'Force clones to be deployed on CPU. Note that even if '\r\n 'set to False (allowing ops to run on gpu), some ops may '\r\n 'still be run on the CPU if they have no GPU kernel.')\r\nflags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '\r\n 'replicas.')\r\nflags.DEFINE_integer('ps_tasks', 0,\r\n 'Number of parameter server tasks. If None, does not use '\r\n 'a parameter server.')\r\nflags.DEFINE_string('train_dir', '',\r\n 'Directory to save the checkpoints and training summaries.')\r\n\r\nflags.DEFINE_string('pipeline_config_path', '',\r\n 'Path to a pipeline_pb2.TrainEvalPipelineConfig config '\r\n 'file. If provided, other configs are ignored')\r\n\r\nflags.DEFINE_string('train_config_path', '',\r\n 'Path to a train_pb2.TrainConfig config file.')\r\nflags.DEFINE_string('input_config_path', '',\r\n 'Path to an input_reader_pb2.InputReader config file.')\r\nflags.DEFINE_string('model_config_path', '',\r\n 'Path to a model_pb2.DetectionModel config file.')\r\n\r\nFLAGS = flags.FLAGS\r\n\r\n\r\[email protected](None, 'Use object_detection/model_main.py.')\r\ndef main(_):\r\n assert FLAGS.train_dir, '`train_dir` is missing.'\r\n if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)\r\n if FLAGS.pipeline_config_path:\r\n configs = config_util.get_configs_from_pipeline_file(\r\n FLAGS.pipeline_config_path)\r\n if FLAGS.task == 0:\r\n tf.gfile.Copy(FLAGS.pipeline_config_path,\r\n os.path.join(FLAGS.train_dir, 'pipeline.config'),\r\n overwrite=True)\r\n else:\r\n configs = config_util.get_configs_from_multiple_files(\r\n model_config_path=FLAGS.model_config_path,\r\n train_config_path=FLAGS.train_config_path,\r\n train_input_config_path=FLAGS.input_config_path)\r\n if FLAGS.task == 0:\r\n for name, config in [('model.config', FLAGS.model_config_path),\r\n ('train.config', FLAGS.train_config_path),\r\n ('input.config', FLAGS.input_config_path)]:\r\n tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),\r\n overwrite=True)\r\n\r\n model_config = configs['model']\r\n train_config = configs['train_config']\r\n input_config = configs['train_input_config']\r\n\r\n model_fn = functools.partial(\r\n model_builder.build,\r\n model_config=model_config,\r\n is_training=True)\r\n\r\n def get_next(config):\r\n return dataset_builder.make_initializable_iterator(\r\n dataset_builder.build(config)).get_next()\r\n\r\n create_input_dict_fn = functools.partial(get_next, input_config)\r\n\r\n env = json.loads(os.environ.get('TF_CONFIG', '{}'))\r\n cluster_data = env.get('cluster', None)\r\n cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None\r\n task_data = env.get('task', None) or {'type': 'master', 'index': 0}\r\n task_info = type('TaskSpec', (object,), task_data)\r\n\r\n ps_tasks = 0\r\n worker_replicas = 1\r\n worker_job_name = 'lonely_worker'\r\n task = 0\r\n is_chief = True\r\n master = ''\r\n\r\n if cluster_data and 'worker' in cluster_data:\r\n # Number of total worker replicas include \"worker\"s and the \"master\".\r\n worker_replicas = len(cluster_data['worker']) + 1\r\n if cluster_data and 'ps' in cluster_data:\r\n ps_tasks = len(cluster_data['ps'])\r\n\r\n if worker_replicas > 1 and ps_tasks < 1:\r\n raise ValueError('At least 1 ps task is needed for distributed training.')\r\n\r\n if worker_replicas >= 1 and ps_tasks > 0:\r\n # Set up distributed training.\r\n server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',\r\n job_name=task_info.type,\r\n task_index=task_info.index)\r\n if task_info.type == 'ps':\r\n server.join()\r\n return\r\n\r\n worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)\r\n task = task_info.index\r\n is_chief = (task_info.type == 'master')\r\n master = server.target\r\n\r\n graph_rewriter_fn = None\r\n if 'graph_rewriter_config' in configs:\r\n graph_rewriter_fn = graph_rewriter_builder.build(\r\n configs['graph_rewriter_config'], is_training=True)\r\n\r\n trainer.train(\r\n create_input_dict_fn,\r\n model_fn,\r\n train_config,\r\n master,\r\n task,\r\n FLAGS.num_clones,\r\n worker_replicas,\r\n FLAGS.clone_on_cpu,\r\n ps_tasks,\r\n worker_job_name,\r\n is_chief,\r\n FLAGS.train_dir,\r\n graph_hook_fn=graph_rewriter_fn)\r\n\r\n\r\nif __name__ == '__main__':\r\n tf.app.run()\r\n\r\nAnd am using tensorflow 2.13 , protobuf 3.20 , python 3.8", "@HNanditha,\r\nI tried to execute the mentioned code and it was failing with the error \"module 'tensorflow' has no attribute 'contrib'\" which means the mentioned code was written for the tensorflow 1.x version which is not supported. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/50b4b452162079c4fd72d9cb24d83a40/untitled1380.ipynb) and also this issue looks more related to models. Please raise the request in the [models](https://github.com/tensorflow/models/issues/) repo for quick response. 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/62024\">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/62024\">No</a>\n" ]

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[ "Hi @HNanditha ,\r\n\r\nThis issue seems duplicate of #62024 which also raised by yourself with the required details. Could you please close it here and track there ?\r\n\r\nThank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62023\">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/62023\">No</a>\n", "yes", "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/62023\">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/62023\">No</a>\n", "yes" ]

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[ "Hi @maybeLee,\r\n\r\nYou can use legacy optimizers for using the mentioned hyper parameters.Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/192742c52d367a81d4d9213881981240/62021.ipynb#scrollTo=0Xsa7A_GJ2Ns). It seems this option has been dropped in latest version of optimizers.", "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/62021\">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/62021\">No</a>\n" ]