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[ "Hi, @NoteDancing \r\n\r\nApologize for the delayed response and I was able to replicate the same issue on Google Colab with `Tensorflow==2.11` and it's giving me the same error what you're getting, for your reference I have added [gist-file ](https://colab.research.google.com/gist/gaikwadrahul8/4260c9fe4666543803dafb1549b0eab9/-59912-test.ipynb) and We have to dig more into this issue so we'll update you soon, Meanwhile could you please check this [stack-overflow answer](https://stackoverflow.com/questions/4674473/valueerror-setting-an-array-element-with-a-sequence) which may solve your issue. Thank you!", "@gaikwadrahul8 Thank you for your answer, I modified the code and have new question.[here](https://colab.research.google.com/gist/NoteDancing/12e89ace85e0a8f6d95cf82d569c0f8c/-59912-test.ipynb#scrollTo=FHED_9qoA6np&line=1&uniqifier=1)", "Hi, @NoteDancing \r\n\r\nApologize for the delayed response and I would suggest you to please refer our [Autograph reference guide ](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/index.md) and that official documentation has some sections which may help to solve your issue like [Better performance with tf.function](https://www.tensorflow.org/guide/function), [Error handling](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/error_handling.md), [Limitations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md), [Common AutoGraph errors](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/common_errors.md) till then I'll try to replicate same issue from my end and I'll update you soon.Thank you!", "@gaikwadrahul8 Thank you for your answer.", "Hi, @NoteDancing \r\n\r\nYou're welcome and Just to confirm, May I know your issue got resolved by following above documentation or still you need help with your issue ? Thank you!", "@gaikwadrahul8 No, I haven't resolved it yet.Thank you!" ]

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[ "@Kepnu4 Thanks for reporting the issue.\r\n\r\nThe supported for dynamic shapes by the TF Lite GPU Delegate is not available AFAIK. \r\n\r\nAlso, when tflite [analyzer](https://www.tensorflow.org/lite/api_docs/python/tf/lite/experimental/Analyzer) is used, I could observe the following:\r\n\r\n```\r\nSubgraph#0 main(T#0) -> [T#10]\r\n Op#0 SHAPE(T#0) -> [T#4]\r\nGPU COMPATIBILITY WARNING: Not supported op SHAPE\r\n Op#1 STRIDED_SLICE(T#4, T#3[1], T#2[3], T#3[1]) -> [T#5]\r\nGPU COMPATIBILITY WARNING: STRIDED_SLICE supports for 3 or 4 dimensional tensors only.\r\n Op#2 CAST(T#5) -> [T#6]\r\nGPU COMPATIBILITY WARNING: Not supported Cast case. Input type: INT64 and output type: FLOAT32\r\n Op#3 MUL(T#6, T#1) -> [T#7]\r\n Op#4 CAST(T#7) -> [T#8]\r\n Op#5 RESIZE_NEAREST_NEIGHBOR(T#0, T#8) -> [T#9]\r\nGPU COMPATIBILITY WARNING: Expected 1 runtime input tensor(s), but node has 2 runtime input(s).\r\n Op#6 RELU(T#9) -> [T#10]\r\n```\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/c1373679cf3769e62df9310b22df0997/59911.ipynb) and the list of supported ML operations on GPU [here](https://www.tensorflow.org/lite/performance/gpu#supported_ops).\r\n\r\nThanks.", "Thanks @pjpratik, \r\nI find that dynamic shapes actually work in Metal delegate, for example this network with downsampling convolution works fine. So basically encoder part works but i can't obtain working decode parts because of upsampling. \r\nFor unsupported operation i find it another problem, because resize got shape as input instead of upscaling factor. So it produces this shape operations when i just want to do 2x resize.\r\n\r\n<img width=\"212\" alt=\"image\" src=\"https://user-images.githubusercontent.com/2921717/224312426-73b45c71-9831-41e0-932a-50a77b0e98f8.png\">\r\n\r\n[test_net.tflite.zip](https://github.com/tensorflow/tensorflow/files/10941936/test_net.tflite.zip)\r\n", "@Kepnu4 Thanks for the information\r\n\r\n@sachinprasadhs could you please look into this. Thank you.", "I'm not entirely sure what you mean by dynamic shape. Your network has a fixed shape of [1, 1, 1, 3], no?\r\n\r\nAnyway, using the `SHAPE` op to extract the tensor dimensions and doing a runtime decision of what the tensor size should be is not supported. Things need to be statically assigned.", "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.", "@impjdi i don't know why it prints this shape in details, but if you run:\r\n```tf.lite.experimental.Analyzer.analyze(model_path=out_path, gpu_compatibility=True)```\r\non the model it will print:\r\n```\r\nYour TFLite model has '1' subgraph(s). In the subgraph description below,\r\nT# represents the Tensor numbers. For example, in Subgraph#0, the PAD op takes\r\ntensor #0 and tensor #3 as input and produces tensor #4 as output.\r\n\r\nSubgraph#0 main(T#0) -> [T#5]\r\n Op#0 PAD(T#0, T#3[0, 0, 1, 1, 1, ...]) -> [T#4]\r\n Op#1 CONV_2D(T#4, T#2, T#1) -> [T#5]\r\n\r\nTensors of Subgraph#0\r\n T#0(serving_default_image:0) shape_signature:[1, -1, -1, 3], type:FLOAT32\r\n T#1(convolution) shape:[3], type:FLOAT32 RO 12 bytes, buffer: 2, data:[0, 0, 0]\r\n T#2(convolution1) shape:[3, 3, 3, 3], type:FLOAT32 RO 324 bytes, buffer: 3, data:[0.0592289, -0.093541, 0.0682242, -0.0534223, -0.0468599, ...]\r\n T#3(Const) shape:[4, 2], type:INT32 RO 32 bytes, buffer: 4, data:[0, 0, 1, 1, 1, ...]\r\n T#4(Pad) shape_signature:[1, -1, -1, 3], type:FLOAT32\r\n T#5(PartitionedCall:0) shape_signature:[1, -1, -1, 3], type:FLOAT32\r\n\r\n\r\nYour model looks compatible with GPU delegate with TFLite runtime version 2.12.0.\r\nBut it doesn't guarantee that your model works well with GPU delegate.\r\nThere could be some runtime incompatibililty happen.\r\n```\r\n\r\nAs you can see the shape is `[1, -1, -1, 3]`. And it works when i resize tensors and pass input with different shapes.\r\n\r\nGot it about `SHAPE`. But is there any way to upscale without using `SHAPE`? Because `CONV2D` with stride 2 doesn't require `SHAPE` and it downsamples tensors. Is there any way to upscale it back?", "Hi @Kepnu4, we currently do not support dynamic shapes. If it works, then trying to build on top of it will lead to issues down the road because the system doesn't expect that state. Are you able to continue your work with static shapes?", "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/59911\">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/59911\">No</a>\n" ]

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[ "Hi @panhu \r\n\r\nCan you provide more details regarding the issue?\r\n\r\nYou can replace layers in the middle of the model by layer assignment. \r\n\r\nPlease refer to the transfer learning and fine tuning [tutorial](https://www.tensorflow.org/guide/keras/transfer_learning#fine-tuning). Also, please refer to the colab [gist](https://colab.research.google.com/gist/pjpratik/251fa430aa21f7e641e1e2baadfe749a/transfer_learning.ipynb) for the same.\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "hello @panhu \r\nsteps to remove the middle layer in the transfer learning model.You can remove it using running a for loop and using pop function to remove up to whichever layer you want and then add any other middle layer. then you can freeze the weights of all layers except the middle layer. after that, you can easily compile and fit your model \r\n\r\nyou can refer to the following GitHub gist given below\r\n[githubgist](https://colab.research.google.com/gist/Chinmayadas2003/6875e84ee2c7558fcc9d2e0c1cdaac7a/removing-middle-layers-in-a-pretrained-model.ipynb#scrollTo=kKWSJJ-_wbZQ)", "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/59908\">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/59908\">No</a>\n" ]

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[ "@DNJ-JINI,\r\nThis is not the issue from the Tensorflow side. In order to be able to offer computational resources at scale, Colab needs to maintain flexibility to adjust usage dynamically. Colab Pro subscribers have greater limits than the free version of Colab. You can read more at the FAQ: https://research.google.com/colaboratory/faq.html#resource-limits. \r\n\r\nPlease try to raise the issue on [Colab repo](https://github.com/googlecolab/colabtools) for better assistance. If you encounter this repeatedly, we'll need more information for the bug report. Please submit feedback within Colab at Help > Send feedback. 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/59906\">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/59906\">No</a>\n" ]

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[ "@TheHellTower \r\nTensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2 for GPU support. Could you please refer to the [doc](https://tensorflow.org/install/pip#windows-wsl2) for reference.\r\n\r\nThank you !", "> @TheHellTower TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2 for GPU support. Could you please refer to the [doc](https://tensorflow.org/install/pip#windows-wsl2) for reference.\r\n> \r\n> Thank you !\r\n\r\nThe problem is I'm building the project on `Windows` with `Visual Studio` for `Windows` so it's not possible anymore ? Can I even build the project from `Windows` for `Linux` ?\r\nThis is not practical at all, why the support got dropped ?", "@TheHellTower \r\nSorry for the late reply, Unfortunately from TF 2.11 is not supported for GPU pip installation. If you want GPU support for windows try to build from source. Please refer to [Build from source on Windows](https://www.tensorflow.org/install/source_windows) for reference.\r\n\r\nThank you!", "> @TheHellTower Sorry for the late reply, Unfortunately from TF 2.11 is not supported for GPU pip installation. If you want GPU support for windows try to build from source. Please refer to [Build from source on Windows](https://www.tensorflow.org/install/source_windows) for reference.\r\n> \r\n> Thank you!\r\n\r\nHello,\r\n\r\nFirst of all I'm not using `Python` I want to use `C++`, then you said GPU support is not in 2.11 \"`TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows`\" so it would be useless for me to build from source ? I was enthusiastic and excited by the idea of using Tensorflow until I see that I can't even make my project with the 2.10 version it doesn't seem to work as intented (it was crashing).\r\n\r\nIf in C++ the GPU support is still there then I woud love to know how to get it on my side because I really need it I can't waste time with CPU it's way too long next to GPU...\r\n\r\nAnd like I can see [here](https://www.tensorflow.org/install/lang_c) \"`Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows.`\"\r\n\r\nRegards.", "Well hopefully you will not avoid the issue just like this and will give a answer at some point.", "Hi, @SuryanarayanaY \r\n\r\nCould you please take look into this issue? Thank you!", "Hi @TheHellTower ,\r\n\r\nWith Windows if you want to enable GPU support for Tf>2.10 versions, you can try DirectML Plugin option. Please refer the source [here](https://github.com/microsoft/tensorflow-directml-plugin#windows-10) and follow the instructions mentioned there.\r\n\r\nThe requirement should be:\r\n\r\n\r\n```\r\n- Windows 10 Version 1709, 64-bit (Build 16299 or higher)\r\n- Python x86-64 3.8, 3.9, 3.10 or 3.111\r\n- One of the following supported GPUs:\r\n AMD Radeon R5/R7/R9 2xx series or newer\r\n Intel HD Graphics 5xx or newer\r\n NVIDIA GeForce GTX 9xx series GPU or newer\r\n \r\n```\r\n\r\nIf you want to go with TF=2.10v only then you might need to install tensorflow first and then reinstall again with TF==2.10v.\r\n\r\nIncase of any problem with DirectML plugin please let us know.\r\n\r\nThanks!", "> Hi @TheHellTower ,\r\n> \r\n> With Windows if you want to enable GPU support for Tf>2.10 versions, you can try DirectML Plugin option. Please refer the source [here](https://github.com/microsoft/tensorflow-directml-plugin#windows-10) and follow the instructions mentioned there.\r\n> \r\n> The requirement should be:\r\n> \r\n> ```\r\n> - Windows 10 Version 1709, 64-bit (Build 16299 or higher)\r\n> - Python x86-64 3.8, 3.9, 3.10 or 3.111\r\n> - One of the following supported GPUs:\r\n> AMD Radeon R5/R7/R9 2xx series or newer\r\n> Intel HD Graphics 5xx or newer\r\n> NVIDIA GeForce GTX 9xx series GPU or newer\r\n> \r\n> ```\r\n> \r\n> If you want to go with TF=2.10v only then you might need to install tensorflow first and then reinstall again with TF==2.10v.\r\n> \r\n> Incase of any problem with DirectML plugin please let us know.\r\n> \r\n> Thanks!\r\n\r\nIsn't it for the Python bindings ? I need GPU support using C++ I'm writing my project in C++.", "Hi @TheHellTower ,\r\n\r\nIf you want GPU support with C++ then you need to go with tensorflow version 2.10 only using c-lang bindings [here](https://www.tensorflow.org/install/lang_c). For more details on this please refer this [source](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/extend/bindings.md).\r\n\r\nPlease note that there are some limitations in this bindings like mentioned below.\r\n\r\n<img width=\"1103\" alt=\"Screenshot 2023-03-18 at 7 49 06 PM\" src=\"https://user-images.githubusercontent.com/116063290/226111774-4df3d840-9d09-4405-a5fe-4858de52d2b3.png\">\r\n\r\n", "> Hi @TheHellTower ,\r\n> \r\n> If you want GPU support with C++ then you need to go with tensorflow version 2.10 only using c-lang bindings [here](https://www.tensorflow.org/install/lang_c). For more details on this please refer this [source](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/extend/bindings.md).\r\n> \r\n> Please note that there are some limitations in this bindings like mentioned below.\r\n> \r\n> <img alt=\"Screenshot 2023-03-18 at 7 49 06 PM\" width=\"1103\" src=\"https://user-images.githubusercontent.com/116063290/226111774-4df3d840-9d09-4405-a5fe-4858de52d2b3.png\">\r\n\r\nI think the support should be added back. See Issue #59918 that list 7 issues including this one about GPU support on Windows, It's very important for my project to be on the last version and I can't even use it and in 2.10 it doesn't work at all for some strange reasons.", "Bump.", "Hi @TheHellTower ,\r\n\r\nWith the inclusion of WSL,it will be comparatively easy to maintain the framework for both Linux and Windows.\r\nHere is the [link](https://blog.tensorflow.org/2022/09/announcing-tensorflow-official-build-collaborators.html) to the announcement blog which talks about official build collaborators.\r\n\r\nPlease refer to the Developer [comment](https://github.com/tensorflow/tensorflow/issues/59828#issuecomment-1454041070) related to drop of GPU support. \r\n\r\n\r\n\r\n\r\n", "> Hi @TheHellTower ,\r\n> \r\n> With the inclusion of WSL,it will be comparatively easy to maintain the framework for both Linux and Windows. Here is the [link](https://blog.tensorflow.org/2022/09/announcing-tensorflow-official-build-collaborators.html) to the announcement blog which talks about official build collaborators.\r\n> \r\n> Please refer to the Developer [comment](https://github.com/tensorflow/tensorflow/issues/59828#issuecomment-1454041070) related to drop of GPU support.\r\n\r\nhttps://github.com/tensorflow/tensorflow/issues/59918#issuecomment-1500529192", "> @TheHellTower Sorry for the late reply, Unfortunately from TF 2.11 is not supported for GPU pip installation. If you want GPU support for windows try to build from source. Please refer to [Build from source on Windows](https://www.tensorflow.org/install/source_windows) for reference.\r\n> \r\n> Thank you!\r\n\r\n@tiruk007 Hello, I tried building tf2.11 from source on Windows and it failed. I can't find the solution for the error in existing issues. May I ask that whether tensorflow developers already tried building from source on Windows since tf2.11 and succeeded? I'd like to build from source to use GPU for native-windows on tf2.11 and higher. However, if that means I need to modify the C++ code to make the build successful, the price is just too high.\r\n\r\nThe main part of error is listed below:\r\n```\r\n.\\tensorflow/core/kernels/segment_reduction_ops_gpu.cu.h(206): error: no suitable constructor exists to convert from \"int\" to \"tensorflow::AlignedVector<int, 4>\"\r\n detected during:\r\n instantiation of \"void tensorflow::SegmentOffsetsKernel(Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int, Tsegmentids=int]\"\r\n(233): here\r\n instantiation of \"tsl::Status tensorflow::LaunchSegmentOffsetsKernel(const tensorflow::GPUDevice &, Tindex, Tsegmentids, const Tsegmentids *, Tindex *) [with Tindex=int, Tsegmentids=int]\"\r\n(570): here\r\n instantiation of \"tsl::Status tensorflow::SegmentReduceGPUImpl<Treducevec,Tvec,Tindex,Tsegmentids,ReduceOp,Tinit>(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, ReduceOp, Tinit, Tinit, __nv_bool, __nv_bool, const Tvec *, const Tsegmentids *, const Tindex *, const Tinit *, Tvec *) [with Treducevec=tensorflow::AlignedVector<int, 4>, Tvec=tensorflow::AlignedVector<int, 4>, Tindex=int, Tsegmentids=int, ReduceOp=tensorflow::functor::Max, Tinit=int]\"\r\n(617): here\r\n instantiation of \"tsl::Status tensorflow::SegmentReduceGPUVectorized<Treduce>::Impl<vec_size>::operator()(tensorflow::OpKernelContext *, Tindex, Tindex, Tsegmentids, ReduceOp, T, T, __nv_bool, __nv_bool, const T *, const Tsegmentids *, const Tindex *, const T *, T *) [with Treduce=tsl::int32, vec_size=4, T=int, Tindex=int, Tsegmentids=int, ReduceOp=tensorflow::functor::Max]\"\r\n.\\tensorflow/core/util/gpu_kernel_helper.h(328): here\r\n instantiation of \"tsl::Status tensorflow::detail::DispatchToVectorizedHelper<VecSize, Functor>::operator()(int64_t, Args &&...) const [with VecSize=4LL, Functor=tensorflow::SegmentReduceGPUVectorized<tsl::int32>::Impl, Args=<tensorflow::OpKernelContext *&, int &, int &, int &, tensorflow::functor::Max &, int &, int &, __nv_bool &, __nv_bool &, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, const tsl::int32 *&, tsl::int32 *&>]\"\r\n\r\n```", "@mraunak Could you please take a look?", "Hey @learning-to-play this is the Intel TF Windows account. Is it possible to tag this account or does it need to be added? - Rajeev & Mayank", "@intel-tf-windows It seems I can now.\r\n@sachinprasadhs Thank you for assigning the issue!", "Hi @learning-to-play, I think TensorFlow GPU or Nvidia Team would be able to resolve this issue as the errors involve CUDA files", "Thank you @mraunak!", "News ?", "Some references that may be helpful:\r\n- See section \"Expanded GPU support on Windows\" in [this TensorFlow blog post](https://blog.tensorflow.org/2022/09/whats-new-in-tensorflow-210.html).\r\n- See [TensorFlow install page](https://www.tensorflow.org/install/pip) and [this page](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-tensorflow-plugin).\r\n", "> Some references that may be helpful:\r\n> \r\n> * See section \"Expanded GPU support on Windows\" in [this TensorFlow blog post](https://blog.tensorflow.org/2022/09/whats-new-in-tensorflow-210.html).\r\n> * See [TensorFlow install page](https://www.tensorflow.org/install/pip) and [this page](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-tensorflow-plugin).\r\n\r\nNo it's not okay for me because:\r\n- https://github.com/tensorflow/tensorflow/issues/59905#issuecomment-1474398835\r\n- https://github.com/tensorflow/tensorflow/issues/59905#issuecomment-1474864649", "Bump", "Bump", "Bump", "You can never wake up a bunch of people pretending to be asleep", "> You can never wake up a bunch of people pretending to be asleep\r\n\r\nDoesn't matter I have the time lol", "Bump", "@TheHellTower Unfortunately as mentioned in [this comment](https://github.com/tensorflow/tensorflow/issues/59905#issuecomment-1457386002) GPU on native-Windows isn't supported." ]

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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/59904/checks?check_run_id=11776460898) 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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[ "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/59903/checks?check_run_id=11776404720) 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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[ "There is a problem in converting of file (unsupported format) using tf 2x version.\r\nhow to convert pytorch model (.pt) files to tflite for YOLOv6 ? ", "Hi @abhishekbj97, Kindly let us know the steps you have followed to convert a pytorch model to tflite for better assistance.\r\n\r\nConverting a PyTorch model (.pt) file to TFLite for YOLOv6 involves several steps.\r\n\r\n1. Convert the PyTorch model to ONNX format using the `torch.onnx` module. \r\n2. Convert the ONNX model to TensorFlow format using the `onnx-tf` converter.\r\n3. Convert the TensorFlow model to TFLite format using the TensorFlow Lite Converter.\r\n4. Finally, test your TFLite model to make sure it is working correctly. You can use the TFLite interpreter to do this.\r\nThank you!", "Thank you for your response,\r\nit's working now " ]

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[ "@TheHellTower,\r\nThank you for reporting the doc issue. Could you please feel free to submit a PR for the requested change or share the code of the example where the requested change is to be made. Thank you!", "> @TheHellTower, Thank you for reporting the doc issue. Could you please feel free to submit a PR for the requested change or share the code of the example where the requested change is to be made. Thank you!\r\n\r\nHello, I don't have the updated code I opened a issue for a problem I got [here](https://github.com/tensorflow/tensorflow/issues/59905), it was just to know if it will be updated at some point or if I need to wait my problem to be solved to write myself a code and open a PR ?", "Well this one is not important I will focus on #59905.", "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/59900\">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/59900\">No</a>\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59900\">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/59900\">No</a>\n", "It didn't solve this issue @pjpratik, if you read betterI pointed out the [C example](https://github.com/tensorflow/tensorflow/blob/v2.10.0/tensorflow/examples/label_image/main.cc)", "Hi @TheHellTower, sorry for the inconvenience. \r\n\r\nWe are working on the C example as well. \r\n\r\nPlease feel free to raise a PR if you have the updated code implementation available. ", "> Hi @TheHellTower, sorry for the inconvenience.\r\n> \r\n> We are working on the C example as well.\r\n> \r\n> Please feel free to raise a PR if you have the updated code implementation available.\r\n\r\nI wanted to update it accordingly to TF 2.11 but I can't use it, see #59905 👌 ", "Hi @TheHellTower \r\n\r\nWe have tested the [example](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/label_image) on TF docker with 2.13 version and observed the example gives the expected output. Please find the logs below\r\n\r\n```\r\ntf-docker /tf/tensorflow > wget https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz \r\n--2023-03-29 06:36:03-- https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz\r\nResolving storage.googleapis.com (storage.googleapis.com)... 172.253.63.128, 142.250.31.128, 142.251.111.128, ...\r\nConnecting to storage.googleapis.com (storage.googleapis.com)|172.253.63.128|:443... connected.\r\nHTTP request sent, awaiting response... 200 OK\r\nLength: 88668554 (85M) [application/gzip]\r\nSaving to: 'inception_v3_2016_08_28_frozen.pb.tar.gz'\r\n \r\ninception_v3_2016_08_28_frozen.pb.tar.g 100%[=============================================================================>] 84.56M 99.5MB/s in 0.8s \r\n \r\n2023-03-29 06:36:04 (99.5 MB/s) - 'inception_v3_2016_08_28_frozen.pb.tar.gz' saved [88668554/88668554]\r\n \r\ntf-docker /tf/tensorflow > tar -xvpzf inception_v3_2016_08_28_frozen.pb.tar.gz -C tensorflow/examples/label_image/data --numeric-owner\r\ninception_v3_2016_08_28_frozen.pb\r\nimagenet_slim_labels.txt\r\ntf-docker /tf/tensorflow > ls tensorflow/examples/label_image/data \r\ngrace_hopper.jpg imagenet_slim_labels.txt inception_v3_2016_08_28_frozen.pb\r\ntf-docker /tf/tensorflow > bazel-bin/tensorflow/examples/label_image/label_image\r\n2023-03-29 06:48:48.658334: 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: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-29 06:48:51.529578: I tensorflow/examples/label_image/main.cc:252] military uniform (653): 0.834306\r\n2023-03-29 06:48:51.529646: I tensorflow/examples/label_image/main.cc:252] mortarboard (668): 0.0218695\r\n2023-03-29 06:48:51.529663: I tensorflow/examples/label_image/main.cc:252] academic gown (401): 0.0103581\r\n2023-03-29 06:48:51.529676: I tensorflow/examples/label_image/main.cc:252] pickelhaube (716): 0.00800816\r\n2023-03-29 06:48:51.529694: I tensorflow/examples/label_image/main.cc:252] bulletproof vest (466): 0.00535087\r\ntf-docker /tf > python -c \"import tensorflow as tf; print(tf.__version__)\"\r\n2023-03-29 05:27:29.371380: 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 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2.13.0\r\n```\r\n\r\nCan you please let us know if there is any help we can provide on running the example?\r\n\r\nThanks.\r\n\r\n\r\n", "> Hi @TheHellTower\r\n> \r\n> We have tested the [example](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/label_image) on TF docker with 2.13 version and observed the example gives the expected output. Please find the logs below\r\n> \r\n> ```\r\n> tf-docker /tf/tensorflow > wget https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz \r\n> --2023-03-29 06:36:03-- https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz\r\n> Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.63.128, 142.250.31.128, 142.251.111.128, ...\r\n> Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.63.128|:443... connected.\r\n> HTTP request sent, awaiting response... 200 OK\r\n> Length: 88668554 (85M) [application/gzip]\r\n> Saving to: 'inception_v3_2016_08_28_frozen.pb.tar.gz'\r\n> \r\n> inception_v3_2016_08_28_frozen.pb.tar.g 100%[=============================================================================>] 84.56M 99.5MB/s in 0.8s \r\n> \r\n> 2023-03-29 06:36:04 (99.5 MB/s) - 'inception_v3_2016_08_28_frozen.pb.tar.gz' saved [88668554/88668554]\r\n> \r\n> tf-docker /tf/tensorflow > tar -xvpzf inception_v3_2016_08_28_frozen.pb.tar.gz -C tensorflow/examples/label_image/data --numeric-owner\r\n> inception_v3_2016_08_28_frozen.pb\r\n> imagenet_slim_labels.txt\r\n> tf-docker /tf/tensorflow > ls tensorflow/examples/label_image/data \r\n> grace_hopper.jpg imagenet_slim_labels.txt inception_v3_2016_08_28_frozen.pb\r\n> tf-docker /tf/tensorflow > bazel-bin/tensorflow/examples/label_image/label_image\r\n> 2023-03-29 06:48:48.658334: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n> To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> 2023-03-29 06:48:51.529578: I tensorflow/examples/label_image/main.cc:252] military uniform (653): 0.834306\r\n> 2023-03-29 06:48:51.529646: I tensorflow/examples/label_image/main.cc:252] mortarboard (668): 0.0218695\r\n> 2023-03-29 06:48:51.529663: I tensorflow/examples/label_image/main.cc:252] academic gown (401): 0.0103581\r\n> 2023-03-29 06:48:51.529676: I tensorflow/examples/label_image/main.cc:252] pickelhaube (716): 0.00800816\r\n> 2023-03-29 06:48:51.529694: I tensorflow/examples/label_image/main.cc:252] bulletproof vest (466): 0.00535087\r\n> tf-docker /tf > python -c \"import tensorflow as tf; print(tf.__version__)\"\r\n> 2023-03-29 05:27:29.371380: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n> To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> 2.13.0\r\n> ```\r\n> \r\n> Can you please let us know if there is any help we can provide on running the example?\r\n> \r\n> Thanks.\r\n\r\nOh okay I just got it, these examples are aiming Linux right ?", "I think it is so I will close this issue it's not a important one.", "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/59900\">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/59900\">No</a>\n" ]

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[ "@DeveshS215,\r\nI tried to execute the mentioned code and it was failing due to `SyntaxError: invalid syntax`. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/3aa1a8e614f036fa0e5dba1798617a1c/untitled1013.ipynb) and provide the complete code or the colab gist you are using. Thank you!", "@tilakrayal,\r\n\r\nThank you.\r\n\r\nI have provided the full code and remove that syntax error. The collab gist is [here](https://colab.research.google.com/gist/DeveshS215/cf9ede0d4bc127749a1b01a120eb961a/untitled1013.ipynb).", "@DeveshS215,\r\n\r\nApologies for the delay.Thank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n", "@tilakrayal \r\nI have posted on keras repo.", "@DeveshS215,\r\nCould you please feel free to move this issue to closed status, so that we can track the issue there. 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/59899\">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/59899\">No</a>\n" ]

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[ " @jyingl3 @penpornk can you help to take a look? thanks", "@jyingl3 I saw the PR is stuck with some internal checks Failed, if there is something need to be fixed in my PR, please let me know, thanks.", "Thanks Zhoulong! Could you update tensorflow/tensorflow/core/common_runtime/next_pluggable_device/BUILD to include tensorflow/core/tfrt/common:async_value_tensor? Sorry not noticing it earlier. Thank you!", "> Thanks Zhoulong! Could you update tensorflow/tensorflow/core/common_runtime/next_pluggable_device/BUILD to include tensorflow/core/tfrt/common:async_value_tensor? Sorry not noticing it earlier. Thank you!\r\n\r\n@jyingl3 done, please have a look. thanks", "Hi Zhoulong, we are actively testing some TPU implementation that uses NPD to meet deadline and worry this change may cause breakage. Hope you don't mind if we withhold merging this change temporarily. ", "> Hi Zhoulong, we are actively testing some TPU implementation that uses NPD to meet deadline and worry this change may cause breakage. Hope you don't mind if we withhold merging this change temporarily.\r\n\r\nsure, we will wait. Thanks", "hi, @jyingl3 , is there any update on this pr? thanks", "Thank you Zhoulong and sorry for the delay. Could you add a new flag so that the allocator can be switched? Left a comment about that. Thank you!!" ]

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[ "@gaikwadrahul8 \r\nI tried to replicate the issue on wsl2 using tf-nightly but facing a different error . Please refer to the following screenshot and output log for reference:\r\n\r\n\r\n**Output log**-[output_log](https://github.com/tensorflow/tensorflow/files/10952017/tf.root%40MoRaInE_.python.00_cifar.txt)\r\n\r\n\r\nCould you please look into this ?\r\n\r\nThank you !", "Hello! Sorry i forget to comment that I found the problem. The new 40 series GPU needs CUDA 11.8 or higher and cuDNN 8.6 or higher. And that versions aren't available on Conda, so you need to install on system-side or globally ", "Hi, @enmanuelmag \r\n\r\nApologize for the delayed response and if you're using `Nvidia GPU RTX 4080 ` GPU then you'll have use `cuda==11.8` and `cuDNN==8.6` as per [official Nvidia developer forum ](https://forums.developer.nvidia.com/t/tensorflow-and-cuda-12/237333/5?u=gaikwadrahul) and at the moment you're using `cuda==11.2.0` and `cuDNN==8.1.0 ` so I would request you to please give it try with `cuda==11.8` and `cuDNN==8.6` and check whether is it working as expected ? you can also refer our official [tested build configuration ](https://www.tensorflow.org/install/source#tested_build_configurations) section.\r\n\r\nIf you still persists issue please let us know or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!\r\n", "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/59897\">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/59897\">No</a>\n", "nice issues" ]

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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/59896/checks?check_run_id=11762859104) 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 @aneeshthakur67 Can you please resolve conflicts? Thank you!", "Francisco, I recall these being already integrated?", "Oh they are, the PR is a clone of an old change. Solving the conflicts shows that there's nothing new." ]

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

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[ "Hi @rajveerb, cuda 12.0 is not supported on Ubuntu 18.04. As you are using Ubuntu 18.04 and you need to use a newer version of CUDA. You will need to upgrade your operating system to a newer version that is supported. Alternatively, you can use an older version of CUDA that is compatible with Ubuntu 18.04. Thank you!", "@synandi \r\nThe support for CUDA 12.0 in Ubuntu 18.04 is available. Check [this](https://developer.nvidia.com/cuda-12-0-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu).", "@rajveerb, Apologies for the confusion. We are trying to dig deep into the issue. We will update here soon. Thank you! ", "Hi, For Tensorflow 2.12 release, we have tested the build configuration with CUDA 11.8 version and CuDNN 8.6 version, same details will be published in the table here https://www.tensorflow.org/install/source#gpu.\r\nCould you please test against the above configurations and let us know if you are still facing an error. 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/59890\">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/59890\">No</a>\n", "> @synandi The support for CUDA 12.0 in Ubuntu 18.04 is available. Check [this](https://developer.nvidia.com/cuda-12-0-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu).\r\n\r\nhi, any solution now?" ]

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[ "@dengpanyin Thanks for reporting the issue.\r\n\r\nIn order to expedite the troubleshooting process, please share a stand alone code/snippet to reproduce the issue.\r\n\r\nThanks.", "@pjpratik stand alone code is unavailable since this issue is not able to reproduce every time. We still want to understand the root cause since multi-models have the same issue.", "@dengpanyin Could you please provide more details to debug the issue. It will help us to investigate the issue further.\r\n\r\n Thanks.\r\n", "@dengpanyin can you explicitly specify the device type ,so that all the devices in the collective group have the same device type", "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/59888\">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/59888\">No</a>\n" ]

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[ "my initial intention is to build the tensorflow so with c++ standard which use std::basic_string_view instead of absl::string_view. \r\nI just follow the comments from these releated issues， but did not success.\r\nhttps://github.com/tensorflow/tensorflow/issues/57127，https://github.com/tensorflow/tensorflow/issues/23561\r\n\r\n\r\n", "it seems somewhere is not compiled with c++17？ ", "This seems like a recent regression", "Hi, @SuryanarayanaY \r\n\r\nCould you please take look on this issue ? Thank you!", "Hi @Weili17 ,\r\n\r\nPlease try the tested configurations below. As you are using TF 2.8v CUDA-11.2 and GCC 7.3.1 are the tested configurations.\r\n\r\n\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.11.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.3.0 | 8.1 | 11.2\r\ntensorflow-2.10.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.1.1 | 8.1 | 11.2\r\ntensorflow-2.9.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.0.0 | 8.1 | 11.2\r\ntensorflow-2.8.0 | 3.7-3.10 | GCC 7.3.1 | Bazel 4.2.1 | 8.1 | 11.2\r\n\r\n\r\nRequest you to use the suitable configurations for Tf2.8 version or Upgrade TF version to latest versions >= TF 2.9 and change CUDA to 11.2 version and let us know if still problem persists.\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/59887\">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/59887\">No</a>\n" ]

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[ "Hi ,\r\nI am a working professional and willing to participate in GSOC 2023 so want to solve this issue", "@rbmdotdev \r\nThis issue is more related to tfjs. So, Could you please post this issue on [tensorflow/tfjs](https://github.com/tensorflow/tfjs/issues/new/choose) for quick assistance.\r\n\r\nThank you !", "Ok done https://github.com/tensorflow/tfjs/issues/7454 thanks", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59886\">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/59886\">No</a>\n" ]

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[]

[]

[ "This pull request is identical to 890a846; please cherry-pick that instead (see issue 59878).\r\n" ]

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[ "Hi @trickiwoo, I was able to replicate the issue in Colab. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/60f540565de45d1b0cc988ed7819375d/59884.ipynb). It seems like we have to dig more into this issue, we will update soon here. Thank you! ", "@trickiwoo, Apologies for the delay. The reason why the code is not producing an identity matrix on GPU is because of the differences in how floating-point arithmetic is performed in GPUs. GPUs use a different set of instructions and floating-point precision, which can result in slightly different rounding errors and numerical precision when performing matrix operations. Thank you!", "\r\nThis 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.", "@synandi \r\nThanks for the explanation! I understand that GPUs might use a different floating-point precision. However, it seems that in GPU it produces a significantly different result for the matrix inversion, and according to the output value of `y` (expected to be an identity matrix), it seems more than numerical precision issues?", "@trickiwoo your matrix is not invertible. In particular, it has a nullspace vector `(0.5, -1, 0.5)`. The inverse operator is finding a pseudo-inverse (but we don't make any guarantees about which one), so multiplying it back out will not yield the identity. You're seeing different values on CPU vs GPU because they are returning different pseudo-inverses.", "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/59884\">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/59884\">No</a>\n" ]

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[ "Hi @hqt567 Thanks for reporting the issue.\r\n\r\nCan you provide the information regarding the steps you have followed and the tutorial you have referred?\r\n\r\nIs it from www.tensorflow.org?\r\n\r\nThanks.", "Thank You\r\nI am based on the following problems in the middle of the tutorial\r\nIs it from https://www.espressif.com/zh-hans/news/ESP32_meet_TensorFlow", "@hqt567 The TFLite for Microcontrollers codebase hasbeen moved to a [stand-alone github repository](https://github.com/tensorflow/tflite-micro).\r\n\r\nPlease refer to the hello world example [here](https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/hello_world) for TFlite Micro. \r\n\r\nAlso, please refer to this [example](https://github.com/espressif/tflite-micro-esp-examples/tree/master/examples/hello_world) for hello world on ESP32.\r\n\r\nThanks.", "Thank you, I will try again and give feedback。", "\r\n@hqt567 Could you please let us know if the issue still persists.\r\n\r\nFeel free to close the issue if the it is resolved. Thanks!\r\n\r\n", "I still can't use the TensorFlow example to directly generate the HelloWorld example that can be used by esp32. Later, I tried to use esp-idf to directly deploy the tflite-micro-esp-examples/examples/hello_world/ example but the following problems occurred when using idf.py build\r\n\r\nCMake Error at D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tools/cmake/build.cmake:245 (message):\r\n Failed to resolve component 'esp-nn'.\r\nCall Stack (most recent call first):\r\n D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tools/cmake/build.cmake:287 (__build_resolve_and_add_req)\r\n D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tools/cmake/build.cmake:288 (__build_expand_requirements)\r\n D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tools/cmake/build.cmake:574 (__build_expand_requirements)\r\n D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tools/cmake/project.cmake:440 (idf_build_process)\r\n CMakeLists.txt:4 (project)\r\n\r\n\r\n-- Configuring incomplete, errors occurred!\r\nSee also \"D:/exeespidf/Espressif/frameworks/esp-idf-v5.0.1/tflite-micro-esp-examples/examples/hello_world/build/CMakeFiles/CMakeOutput.log\".\r\ncmake failed with exit code 1, output of the command is in the d:\\exeespidf\\espressif\\frameworks\\esp-idf-v5.0.1\\tflite-micro-esp-examples\\examples\\hello_world\\build\\log\\idf_py_stderr_output_37188 and d:\\exeespidf\\espressif\\frameworks\\esp-idf-v5.0.1\\tflite-micro-esp-examples\\examples\\hello_world\\build\\log\\idf_py_stdout_output_37188", "Secondly, as a beginner, I don't know the detailed steps to deploy the TensorFlow lite model to the esp32\r\n\r\nI would appreciate it if you could guide me with some tutorials.Thank You.", "Hi @hqt567 Thanks for the information.\r\n\r\nCould you please post this issue in [espressif](https://github.com/espressif/tflite-micro-esp-examples/issues) repo for faster resolution.\r\n\r\nPlease refer to the examples for using tflite micro [here](https://www.tensorflow.org/lite/microcontrollers#explore_the_examples). Also tensorflow has [TensorFlow Lite for Microcontrollers Experiments](https://experiments.withgoogle.com/collection/tfliteformicrocontrollers) features work by developers combining Arduino and TensorFlow to exxamples and tools.\r\n\r\nFor esp32, you can refer to the espressif official examples [here](https://github.com/espressif/tflite-micro-esp-examples/tree/master/examples)\r\n\r\nThanks.\r\n\r\n", "Thank you. I have solved my problem with your help, deployed the esp official HelloWorld model to the board.really appreciate your help.\r\n\r\nI want to know if there are other models of tflite micro that can be deployed on esp32, such as OCR recognition. I want to try to run models other than the templates provided by espressif official on esp32, such as some OCR models, but I don’t know. Know if tflite micro is ok, and how to do it.\r\n\r\n\r\nThanks again for your help.\r\n", "Hi @hqt567, glad it helped.\r\n\r\nYou can check few lite example available in https://www.tensorflow.org/lite/examples and try them on esp32. Also, you can check tensorflow hub https://tfhub.dev/s?deployment-format=lite which provides pretrained lite models for different use cases.\r\n\r\nFeel free to close the issue if it is resolved.\r\n\r\nThanks.", "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/59883\">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/59883\">No</a>\n" ]

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[ "Hi @rsuderman Can you please review this PR ? Thank you!", "Hi @Tai78641 Can you please resolve conflicts? Thank you!", "rebased" ]

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[ "@david507huang,\r\n In the mentioned comment I did not see the inclusion of Select tf ops.\r\nCould you please try to add the below snippet in your code and let us know the outcome. \r\n\r\n```\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS_INT8, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n]\r\ntflite_model = converter.convert()\r\n```\r\nAlso kindly note that all ops are not available right now in INT8. Thank you!", "Still the same errors. I tried converting the model to frozen graph and then to tflite. But the final tflite model outputs incorrect output and runs very slowly. ", "Hi @david507huang \r\n\r\nSorry for the delayed response.\r\n\r\nThe conversion error is not provided. If it is successfully converted, can you provide more information regarding the results obtained and on which platform inference is done.\r\n\r\nAlso, if possible can you share a reproducible gist as the code provided is not reproducible since it has other dependencies.\r\n\r\nThanks.", "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/59879\">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/59879\">No</a>\n" ]

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[ "\"Me too\"\r\n", "Backporting the relevant support from the master branch seems to do the trick:\r\n\r\n[naurwlock.patch](https://github.com/tensorflow/tensorflow/files/10877485/naurwlock.patch)\r\n\r\nI have also created a [pull request](https://github.com/tensorflow/tensorflow/pull/59885).", "@plopresti Thank you for creating the pull request. We can merge your fix to master first, run all the tests (including nightly tests). If everything is green we can send the cherry pick to r2.12 for review. WDYT?\r\n@reedwm Could you please take a look and share your guidance?", "Looks like this was broken by https://github.com/tensorflow/tensorflow/pull/59842, which cherry-picked 740005d28413923af3f38f7d6401f7f99d26f8f3 into TF 2.12. The PR uses a BUILD target introduced by 890a846bdc26e0e1e66120adb1e2fb2aa33c7323, which was not cherry-picked, which is why the error is occurring. Either https://github.com/tensorflow/tensorflow/pull/59842 should be rolled back or 890a846bdc26e0e1e66120adb1e2fb2aa33c7323 (and potentially other commits 740005d28413923af3f38f7d6401f7f99d26f8f3 relies on) should also be cherry-picked.\r\n\r\n@venkat2469 @rishikasinha-tf @anlunx, how should this be handled?", "@learning-to-play @reedwm Yeah, my pull request is identical to 890a846. I can confirm that this change alone fixes this PR, and there are no further dependencies. I suggest cherry-picking 890a846 into r2.12.", "This is very helpful, thank you both for the additional information!", "Created https://github.com/tensorflow/tensorflow/pull/59889.", "Hi, @vyepishov1 \r\n\r\nIt seems like this PR [#59889](https://github.com/tensorflow/tensorflow/pull/59889) got merged so could you please try to build tensorflow from source now and check whether are you getting any error or not ? Thank you!", "> Hi, @vyepishov1\r\n> \r\n> It seems like this PR [#59889](https://github.com/tensorflow/tensorflow/pull/59889) got merged so could you please try to build tensorflow from source now and check whether are you getting any error or not ? Thank you!\r\n\r\nHi @gaikwadrahul8,\r\n\r\nI have managed to rebuild recently, so it seems to work now. Thank you for quick response and support!", "Hi, @vyepishov1 \r\n\r\nGood to hear that and thank you for the confirmation, Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? or if you need any further assistance please let us know? Thank you!\r\n", "> \r\n\r\nHi @gaikwadrahul8,\r\n\r\nMy apologies for the last unclear answer.\r\nYes, the issue has been resolved.\r\nI was able to both build the new version of tensorflow as well as test it.\r\nThank you for your help!", "Hi, @vyepishov1 \r\n\r\nNo problem and Good to hear that your issue got resolved and thank you for the confirmation so now I'm closing this issue from my end. If you need any further assistance please feel free to create new issue. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59878\">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/59878\">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/59877/checks?check_run_id=11725913953) 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 @rdzhabarov Can you please review this PR ? Thank you!", "Hi @rdzhabarov 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/59876/checks?check_run_id=11725816657) 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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[ "This is my model. You can do conversion with my code and reproduce my result.\r\nhttps://drive.google.com/file/d/1nTVfurH8gEjSU8WOx7XHsCqMJwT5g2IT/view?usp=sharing", "Hi @ShashmurinSergey, I was able to replicate the issue in Colab using TF v2.10, TF v2.11 and tf-nightly(2.13.0.dev20230305). Please find the gists [here(2.10)](https://colab.sandbox.google.com/gist/synandi/888a32265c1e6136f29fadb263c1e3b6/59870_2-10.ipynb), [here(2.11) ](https://colab.sandbox.google.com/gist/synandi/3c247df43db3d42d47644e4735b2af86/59870.ipynb) and [here(tf-nightly)](https://colab.sandbox.google.com/gist/synandi/32e0fddd8e463066a2776277e9e17495/59870_nightly.ipynb). Thank you!", "@synandi Yes, replicate is correct, Thank you!\r\nWhat can you suggest to solve the problem?", "Hi @ShashmurinSergey \r\n\r\nSorry for the delayed response.\r\n\r\nThe `BATCH_TO_SPACE_ND` and `SPACE_TO_BATCH_ND` are not compatible on GPU. Please find the list of of supported ops on GPU [here](https://www.tensorflow.org/lite/performance/gpu#supported_ops). \r\n\r\nAlso, as given in the [documentation](https://www.tensorflow.org/lite/performance/gpu#optimizing_for_gpus),\r\n\r\n> Reshape operations - Some operations that are quick on a CPU may have a high cost for the GPU on mobile devices. Reshape operations are particularly expensive to run, including BATCH_TO_SPACE, SPACE_TO_BATCH, SPACE_TO_DEPTH, and so forth. You should closely examine use of reshape operations, and consider that may have been applied only for exploring data or for early iterations of your model. Removing them can significantly improve performance.\r\n\r\nThe list of ops in the model compatible with GPU can be found using [Analyzer](https://www.tensorflow.org/lite/guide/model_analyzer#check_gpu_delegate_compatibility). Please find the [gist](https://colab.sandbox.google.com/gist/pjpratik/0eb0c5e3c148065331966412c4040d51/59870.ipynb#scrollTo=jqZ1mD1ZJhlV) on the usage of the tool for your use case.\r\n\r\nThanks. ", "Hi @pjpratik!\r\nThank you for the detailed answer!\r\nWhy do these layers appear? After all, everything works correctly when we use dilation=1.\r\n\r\nIs there any way for me to convert this model to TFLite with GPU support? Can I somehow remove these layers?", "Hi @ShashmurinSergey \r\n\r\nThey are commonly used in conv2d transpose operation AFAIK.\r\n\r\nHave you tried in latest TF 2.11 and TF nightly with dilation!=1 and see if issue still exists?\r\n\r\nThanks.", "@pjpratik But I am not using Conv2d transpose, the error occurs on Conv2d layers with dilation=2, 4, 8.\r\nI am using a Docker container with the latest version of TensorFlow, I also tried using a container with tf-nightly image, but I still get the same error.\r\nIs there any other way to convert Conv2d layer with dilation != 1 without adding BatchToSpaceND and SpaceToBatchND layers?", "Hi @ShashmurinSergey \r\n\r\nThanks for the information. I have created a toy model with dilation = 1 and dilation = 4. Please find the gist [here](https://colab.sandbox.google.com/gist/pjpratik/8ffe5de6e2caca1e33284deab31949e1/59870.ipynb).\r\n\r\nThe Conv2D adds the BatchToSpaceND and SpaceToBatchND ops when the dilation rate !=1 for appropriate padding to avoid holes in the output.\r\n\r\nModel with dilation = 4\r\n\r\n\r\n\r\nModel with dilation = 1\r\n\r\n\r\n\r\nThis can also be observed at \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/d5b57ca93e506df258271ea00fc29cf98383a374/tensorflow/python/ops/nn_ops.py#L1465\r\n\r\nas a result those ops are added which are [not compatible](https://www.tensorflow.org/lite/performance/gpu#supported_ops_) for tflite GPU support.\r\n\r\nThanks.", "Hi @pjpratik!\r\nThank for your answer!\r\nAm I correct in understanding that there is no workaround for converting Conv2d with dilation != 1 with GPU support? Could this be possible in the future?", "@ShashmurinSergey That could be on roadmap. I am not sure about it.\r\n\r\n@sachinprasadhs Could you please look into this. Thanks." ]

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[ "@pmatsibekker Thanks for reporting the issue.\r\n\r\nWe see that you are using 1.x version which is not supported anymore.\r\n\r\nI was able to run the code without any error on TF 2.11. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/912de7b163af0087ae00eea2e15061a1/59869.ipynb) and let us know if it helps.\r\n\r\nThank you.", "@pmatsibekker I guess this might work. Does it help to you, or still it persist.\r\n\r\nIn Python, objects have a special attribute called __dict__, which is a dictionary that stores the object's attributes. However, there are some special objects called _DictWrapper that don't support the __dict__ attribute. If you try to access __dict__ on a _DictWrapper object, you'll get a TypeError.\r\nTo fix this error, you can try to access the __wrapped__ attribute of the _DictWrapper object, which will give you access to the underlying object's dictionary. Or, you can try to avoid using __dict__ on _DictWrapper objects and instead use the methods provided by the wrapper to access and manipulate its attributes.\r\n\r\nCode:\r\nwrapped_obj = my_dict_wrapper.__wrapped__\r\nwrapped_obj_dict = wrapped_obj.__dict__\r\n", "Solved after removing saving model callback. It seems that error consists in model preservation procedure, which handled via transparent dictionary wrapper incorrectly.\r\nAs temporary solution (for those who faced this problem) saving model data can be done through `model.save_weights`", "This issue also happens in version 2.12.0-rc0 on ubuntu\r\nThis code produces error.\r\ntf.keras.callbacks.BackupAndRestore may be broken.\r\n\r\n``` python\r\nimport tensorflow as tf\r\n\r\nmnist = tf.keras.datasets.mnist\r\n\r\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n\r\nx_train, x_test = x_train / 255.0, x_test / 255.0\r\nmodel = tf.keras.models.Sequential([\r\n tf.keras.layers.Flatten(input_shape=(28, 28)),\r\n tf.keras.layers.Dense(128, activation='relu'),\r\n tf.keras.layers.Dropout(0.2),\r\n tf.keras.layers.Dense(10, activation='softmax')\r\n])\r\n\r\nmodel.compile(optimizer='adam',\r\n loss='sparse_categorical_crossentropy',\r\n metrics=['accuracy'])\r\n\r\ncallbacks = [\r\n tf.keras.callbacks.BackupAndRestore('backup'),\r\n]\r\n\r\nmodel.fit(x_train, y_train, epochs=5, callbacks=callbacks)\r\n```\r\n\r\n```\r\nEpoch 1/5\r\n1854/1875 [============================>.] - ETA: 0s - loss: 0.2973 - accuracy: 0.9132Traceback (most recent call last):\r\n File \"/home/ubuntu/mnist.py\", line 23, in <module>\r\n model.fit(x_train, y_train, epochs=5, callbacks=callbacks)\r\n File \"/home/ubuntu/venv/tensorflow2.12.0rc0/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/home/ubuntu/venv/tensorflow2.12.0rc0/lib/python3.10/site-packages/tensorflow/python/trackable/data_structures.py\", line 823, in __getattribute__\r\n return super().__getattribute__(name)\r\nTypeError: this __dict__ descriptor does not support '_DictWrapper' objects\r\n```", "tf_nightly-2.13.0.dev20230302 works fine same code.", "https://github.com/tensorflow/tensorflow/commit/b016eb2a4a9d9d95cfa9809ca4af937c8caeee38\r\n\r\nwrapt >= 1.11.0, <1.15 works, so tf_nightly works fine", "upgrade worked. python had a hard time letting go of the version 1.x tensorflow library but a wipe and rebuild worked. 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/59869\">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/59869\">No</a>\n", "In the solving [commit](https://github.com/tensorflow/tensorflow/commit/b016eb2a4a9d9d95cfa9809ca4af937c8caeee38) it is mentioned that the error has to be understood and solved more in depth. Maybe the [issue and discussion](https://github.com/GrahamDumpleton/wrapt/issues/231) on the wrapt repo can be helpful.", "At this point I believe a more appropriate fix is to change the `__getattribute__()` override to:\r\n\r\n```\r\n def __getattribute__(self, name):\r\n import types\r\n\r\n #if (hasattr(type(self), name)\r\n # and (isinstance(getattr(type(self), name), property)):\r\n\r\n if hasattr(type(self), name):\r\n obj = getattr(type(self), name)\r\n\r\n if isinstance(obj, property):\r\n return object.__getattribute__(self, name)\r\n elif type(obj) == types.MappingProxyType and name == \"__dict__\":\r\n raise AttributeError(\"'mappingproxy' object has no attribute '__dict__'\")\r\n else:\r\n return super().__getattribute__(name)\r\n else:\r\n return super().__getattribute__(name)\r\n```\r\n\r\nThe change in wrapt 1.15.0 was to fix a bug whereby exceptions were suppressed by a wrapper and not raised when doing attribute access. That these exceptions are now raised has subtly changed how `__gettattribute__()` in Python may see things, having implications for tensorflow as a result. Best guess I can make, not really understanding what tensorflow wrapper does, is to have it explicitly check for `mappingproxy` type and raise an explicit `AttributeError` so that higher levels of Python internal `__getattribute__()` flow through and do what else it has to do properly rather than raising an exception. Can't see any other options at this point as overriding `__getattribute__()` can get quite gnarly.\r\n\r\nReally need people in the tensorflow community to check this change with all your test suites and verify things still work as expected. I can't really do much else as have no idea what those wrappers in tensorflow do.\r\n\r\nFor more details see https://github.com/GrahamDumpleton/wrapt/issues/231.", "BTW, forgot to highlight this, but the trigger for this is that the exception that tensorflow raises:\r\n\r\n```\r\nTypeError: this __dict__ descriptor does not support '_DictWrapper' objects\r\n```\r\n\r\nuses `TypeError` when it arguably should be raising `AttributeError`.\r\n\r\nIf it changed to `AttributeError`, this likely would all work without needing to change `__getattribute__()` to add a special check for `__dict__` access on a `mappingproxy`.", "\r\n\r\n\r\n> [b016eb2](https://github.com/tensorflow/tensorflow/commit/b016eb2a4a9d9d95cfa9809ca4af937c8caeee38)\r\n> \r\n> wrapt >= 1.11.0, <1.15 works, so tf_nightly works fine\r\n\r\nThanks!\r\n\r\nIt works for me when I download to 1.14.1 in conda using:\r\n`conda install -c conda-forge wrapt=1.14.1`" ]

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[ "@sampathrajapaksha,\r\nCould you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you!", "@tilakrayal \r\nThis is related to TF pruning. Even though TF provides the model pruning capability, it lacks retraining pruning models. \r\nI have pruned a model up to 80% . If I want to retrain the algorithm only using non-pruned weights (Non-zero weights), there's no way to achieve this in TF. \r\nMight be 0,1 masks can be used to convert gradients to zero for pruned weights, But, this introduces additional calculation, and therefore it's not computationally efficient. \r\nIs there any way to calculate gradients only for non-zero weights and retrain unpruned weights efficiently? ", "Hi @sampathrajapaksha ,\r\n\r\nCould you please refer to this model_optimization documentation related to model pruning [here](https://www.tensorflow.org/model_optimization/guide/pruning) and an [example](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras) here. \r\nThis will help us in reducing model size at cost of Accuracy.Please let us know if this is helpful. \r\n\r\nIf your requirement is different please provide gist of an end to end example which helps us to evaluate this feature.\r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Test successful" ]

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[ "* i have NOT reproduced with tf nightly", "Hi, @boocheck \r\n\r\nApologize for the delay and if you're using single machine with multiple GPU's (in your case 2 GPU's) then you'll have to go with [tf.distribute.MirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy) and you can create `MirroredStrategy` as below which will create a `MirroredStrategy` instance, which will use all the GPUs that are visible to TensorFlow :\r\n\r\n```\r\nmirrored_strategy = tf.distribute.MirroredStrategy()\r\n\r\n## If you wish to use only some of the GPUs on your machine, you can do so like this:\r\n\r\nmirrored_strategy = tf.distribute.MirroredStrategy(devices=[\"/gpu:0\", \"/gpu:1\"])\r\n```\r\n\r\nYou'll have to compile and build your model within `MirroredStrategy` scope so do something like below :\r\n\r\n```\r\nwith mirrored_strategy.scope():\r\n model = tf.keras.Model(inputs=inputs, outputs=x)\r\n model.compile(...)\r\n model.fit(...)\r\n```\r\n\r\nPlease refer our official documentation of [Distributed training with TensorFlow](https://www.tensorflow.org/guide/distributed_training) and please refer these references [Ref-1](https://towardsdatascience.com/multi-gpus-and-custom-training-loops-in-tensorflow-2-15b4b86b53bd), [Ref-2](https://www.youtube.com/watch?v=S1tN9a4Proc), I hope it will help you to resolve your issue\r\n\r\nIf issue still persists please let us know ? Thank you!", "I am sorry, but this is not solving my problem. I am aware of existance of MirroredStrategy, but too my knowledge, it tries to place whole model on specified GPUs. I cannot use it, because after CNN part (here: VGG16) I need to use heavy memory operation, which cannot fit any GPU memory. \r\n\r\nMy problem is solvable by architecture like this:\r\n\r\n\r\nSo what would preferably do is:\r\n\r\n\r\nWhat is puzzling, and makes me believe this may be not intended behavior, when put some plain matmul in place of VGG16 (look: model2 in my original post) it is all working like intended\r\n", "Hi, @SuryanarayanaY \r\n\r\nCould you please look into this issue? Thank you!", "Hi @boocheck ,\r\n\r\nRight now Tensorflow supports Data Parallelism i.e Data will be splitted across the accelerators but the same model will be copied across all these devices(each device have same copy of model but different slices of data).\r\n\r\nYour ask is for Model parallelism which is not yet supported by Tensorflow. i.e you can't train different models/layers on different machines.This is still under development stage only.\r\n\r\nPlease refer this official tensorflow video [tutorial](https://www.youtube.com/watch?v=cdNLtMszdLs) for more detail on this concept.\r\n\r\nRight now you can use data parallelism using Mirrored strategy.\r\n\r\nThankyou!", "Hello @SuryanarayanaY \r\n\r\nthis seems not to be true. Have a look at this https://www.tensorflow.org/guide/gpu#manual_device_placement", "Hi @boocheck ,\r\n\r\nYou can choose the device manually using `tf.device` for Ops.AFAIK It does execution of code line by line but not parallel. \r\n\r\nBut in your case on same model you want to use different devices for different blocks of model which is nothing but Model parallelism which is not yet supported in Tensorflow.For model training on GPUs right now Tensorflow supports data parallelism concept using Distribution training.Since you are trying to use same model but on different devices which is not supported yet on tensorflow.Distribution training is not straight forward, it needs copying the data/model across replicas and it uses all reduce algorithms for cross device communication which is implemented using distribution strategies and only for Data Parallelism as of now.\r\n\r\nPlease refer the attached [source](https://www.tensorflow.org/api_docs/python/tf/distribute#:~:text=Glossary,async%20between%20groups.) that states limitations and status regarding model parallelism.\r\n\r\nAlso Please refer to distribution training [documentation](https://www.tensorflow.org/guide/distributed_training) for more details.", "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/59865\">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/59865\">No</a>\n" ]

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[ "Hi @Caolongjie, Apologies for the delay. \r\n\r\nAnything under` tf.python.*` is private, intended for development only, rather than for public use.\r\nImporting from tensorflow.python or any other modules (including import tensorflow_core...) is not supported, and can break unannounced.So, it is suggested not to use anything with tf.python.*.\r\n\r\nYou can use `tf.keras.layers.InputSpec` instead of `tensorflow.python.keras.engine.input_spec`. Please find the gist of working code [here](https://colab.sandbox.google.com/gist/synandi/b7fa1ca299d9901f650e8a8bdf55c992/59863.ipynb). Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "The issue should be opened for bert project not tf. You can close it.", "Anyway, I used my tempory workaound to use bert on tf 2.6.0 gpu version based on python3.9, and will update when bert is updated .", "> The issue should be opened for bert project not tf. You can close it.\r\n\r\nThank you for confirming. Closing the issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59863\">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/59863\">No</a>\n" ]

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[ "I would like to work on this issue \r\n ", "@shelper Would you like to create a PR?\r\n\r\nThanks.", "i am not a bazel guy, played with it a little bit, but i didnt figure out how to access this api_level variable in this bazel file. \r\nI do have api_level defined in my root WORKSPACE file as below, but just dont know how can i get it \r\n```\r\nandroid_ndk_repository(\r\n name=\"androidndk\",\r\n path=\"/path/to/android-ndk-r21\",\r\n api_level=21,\r\n)\r\n\r\n```\r\n", "This implementation is due to the implementation of async API for GPU delegate, implementation depends on `AHardwareBuffer` which is available from API 26.\r\nThe intended behavior is to always return the `lnativewindow` for API 26 or higher.\r\n@terryheo , Could you please look into the issue to include a condition for pre API 26.", "I also couldn't find a way to get API level in Bazel BUILD rules.\r\n\r\nThat's why TF's Android builds requires API level 26 or higher.\r\nhttps://github.com/tensorflow/tensorflow/blob/master/configure.py#L760\r\n\r\nIf you want to use lower level, you need to modify the file manually.", "This issue has been marked as stale because it has no recent 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/59861\">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/59861\">No</a>\n" ]

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[ "Hi @yijie-yang Can you please review this PR ? Thank you!" ]

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[ "cc @eric-k256 @dominicsymes", "Hello @jpienaar , seems something failed in `feedback/copybara` and I don't have access to that. Could you help me double check? Tks!\r\n", "I'm seeing\r\n\r\n```\r\nmlir/tosa/tests/tfl-to-tosa-pipeline.mlir:358:45: error: undefined variable: VAL_0\r\n// CHECK: %[[VAL_3:.*]] = \"tosa.rescale\"(%[[VAL_0]]) {double_round = false, input_zp = 128 : i32, multiplier = array<i32: 1073741824>, output_zp = 0 : i32, per_channel = false, scale32 = true, shift = array<i32: 30>}\r\n ^\r\n```\r\n\r\non internal result.", "> \r\n\r\ngot it. will fix it soon.", "Still see failure tosa/tests/tfl-to-tosa-pipeline.mlir:390:45 and :358", "There's one environment difference between our setup and github: we were not setting the `--enable-var-scope` in FileCheck which was causing the failing test cases can still pass on our side. With the new patch set, everything should be ok now. " ]

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[ "cc @matthiaskramm", "@gaikwadrahul8,\r\nI was able to reproduce the issue on tensorflow v2.11 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a6272839ae621a7b289186743d654b08/stablehlo_read_variable.ipynb).", "Interesting. This uses a different entry point (`pywrap_mlir.import_function` instead of `pywrap_mlir.experimental_convert_saved_model_to_mlir`). As such, the frozen function has less information:\r\n\r\n```\r\n@__inference_infer_func_97(\r\n %arg0: tensor<32x32x32x3xf32> {tf._user_specified_name = \"data\"}\r\n %arg1: tensor<*x!tf_type.resource>,\r\n %arg2: tensor<*x!tf_type.resource>, ...) {\r\n %0 = tf_executor.graph {\r\n %outputs, %control = tf_executor.island wraps \"tf.ReadVariableOp\"(%arg2)\r\n ...\r\n} \r\n```\r\n\r\n(I got the MLIR before the `tf-lower-to-mlprogram-and-hlo` pass by setting the pipeline to \"\")\r\n\r\nNote how there's no `GlobalTensor` op corresponding to the \"resource\" arguments. And also, since this is not a saved_model, we don't have the `tf_saved_model.bound_input` we usually use to map arguments to resources.\r\n\r\nWhat should the ReadVariable op be converted to? Should it become an argument? Or should we create a `ml_program.GlobalOp` with a machine-generated name that represents (otherwise unassigned) resource arguments to public functions?\r\n(Either is fine with me, I'm mostly curious what kind of stablehlo/ml_program output will be the easiest to consume.)", "Thank you for taking a look, @matthiaskramm! @christopherbate could you opine here please?", "@matthiaskramm could we maybe replace the ReadVariableOp by the MHLO equivalent: https://www.tensorflow.org/mlir/hlo_ops#mhloinfeed_mlirmhloinfeedop and the StableHLO: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed", "We convert all other variable operations to `ml_program`. It would be weird to convert only some of them into `stablehlo` instead.", "@matthiaskramm could we work at making this not SavedModel specific maybe ?\r\nhttps://github.com/tensorflow/tensorflow/blob/b737ee48f1de346151b1e32e4f2d8351071e64d7/tensorflow/compiler/mlir/tensorflow/transforms/tf_savedmodel_passes.td#L93-L113", "Right, that's the idea. I think based on what we've discussed so far, the best option is to we create a ml_program.GlobalOp with a machine-generated name that represents (otherwise unassigned) resource arguments to public functions, in the case where we convert a Graph instead of a savedmodel. That's what I've seen other converters do, as well." ]

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[ "@MartinKlefas \r\nSorry for the late reply. Could you please try to install using `pip install tflite-model-maker` with python 3.8. I was able to install the tflite-model-maker with python 3.8. Please refer to the below screenshot:\r\n\r\n\r\nAnd also please refer to the similar [issue](https://github.com/tensorflow/tensorflow/issues/59323) and let us know if it helps.\r\n\r\nThank you!\r\n", "Thanks for the suggestion, this time it got a lot further than usual, and didn't download all the nightly builds, but I did still end up with the below error:\r\n`\r\nINFO: pip is looking at multiple versions of dm-tree to determine which version is compatible with other requirements. This could take a while.\r\nCollecting dm-tree\r\n Downloading dm_tree-0.1.7-cp38-cp38-win_amd64.whl (91 kB)\r\n ---------------------------------------- 91.0/91.0 kB ? eta 0:00:00\r\n Downloading dm_tree-0.1.6-cp38-cp38-win_amd64.whl (75 kB)\r\n ---------------------------------------- 75.1/75.1 kB 4.1 MB/s eta 0:00:00\r\n Downloading dm_tree-0.1.5-cp38-cp38-win_amd64.whl (83 kB)\r\n ---------------------------------------- 84.0/84.0 kB ? eta 0:00:00\r\nINFO: pip is looking at multiple versions of etils[enp,epath] to determine which version is compatible with other requirements. This could take a while.\r\n Downloading dm-tree-0.1.4.tar.gz (33 kB)\r\n Preparing metadata (setup.py) ... done\r\n Downloading dm-tree-0.1.2.tar.gz (31 kB)\r\n Preparing metadata (setup.py) ... done\r\n Downloading dm-tree-0.1.1.tar.gz (30 kB)\r\n Preparing metadata (setup.py) ... done\r\nINFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\r\nINFO: pip is looking at multiple versions of decorator to determine which version is compatible with other requirements. This could take a while.\r\nCollecting decorator>=3.0.0\r\n Downloading decorator-5.1.0-py3-none-any.whl (9.1 kB)\r\nINFO: pip is looking at multiple versions of dm-tree to determine which version is compatible with other requirements. This could take a while.\r\nINFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\r\n Downloading decorator-5.0.9-py3-none-any.whl (8.9 kB)\r\n Downloading decorator-5.0.8-py3-none-any.whl (8.9 kB)\r\n Downloading decorator-5.0.7-py3-none-any.whl (8.8 kB)\r\n Downloading decorator-5.0.6-py3-none-any.whl (8.8 kB)\r\n Downloading decorator-5.0.5-py3-none-any.whl (8.8 kB)\r\n Downloading decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\r\nINFO: pip is looking at multiple versions of decorator to determine which version is compatible with other requirements. This could take a while.\r\n Downloading decorator-4.4.1-py2.py3-none-any.whl (9.2 kB)\r\n Downloading decorator-4.4.0-py2.py3-none-any.whl (8.3 kB)\r\n Downloading decorator-4.3.2-py2.py3-none-any.whl (9.1 kB)\r\n Downloading decorator-4.3.1-py2.py3-none-any.whl (8.8 kB)\r\n Downloading decorator-4.3.0-py2.py3-none-any.whl (9.2 kB)\r\nINFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\r\n Downloading decorator-4.2.1-py2.py3-none-any.whl (9.3 kB)\r\n Downloading decorator-4.1.2-py2.py3-none-any.whl (9.1 kB)\r\n Downloading decorator-4.1.1-py2.py3-none-any.whl (9.0 kB)\r\n Downloading decorator-4.1.0-py2.py3-none-any.whl (9.0 kB)\r\n Downloading decorator-4.0.11-py2.py3-none-any.whl (8.9 kB)\r\n Downloading decorator-4.0.10-py2.py3-none-any.whl (9.2 kB)\r\n Downloading decorator-4.0.9-py2.py3-none-any.whl (22 kB)\r\n Downloading decorator-4.0.6-py2.py3-none-any.whl (21 kB)\r\n Downloading decorator-4.0.4-py2.py3-none-any.whl (8.7 kB)\r\n Downloading decorator-4.0.3-py2.py3-none-any.whl (8.5 kB)\r\n Downloading decorator-4.0.2-py2.py3-none-any.whl (8.5 kB)\r\n Downloading decorator-4.0.1-py2.py3-none-any.whl (8.5 kB)\r\n Downloading decorator-4.0.0-py2.py3-none-any.whl (8.6 kB)\r\n Downloading decorator-3.4.2.tar.gz (6.1 kB)\r\n Preparing metadata (setup.py) ... error\r\n error: subprocess-exited-with-error\r\n\r\n × python setup.py egg_info did not run successfully.\r\n │ exit code: 1\r\n ╰─> [1 lines of output]\r\n error in decorator setup command: use_2to3 is invalid.\r\n [end of output]\r\n\r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\nerror: metadata-generation-failed\r\n\r\n× Encountered error while generating package metadata.\r\n╰─> See above for output.\r\n\r\nnote: This is an issue with the package mentioned above, not pip.\r\nhint: See above for details.\r\n\r\n`\r\n\r\nFull install log is here: https://1drv.ms/t/s!AjAaK-BVbphXliztpWTdIGmr3Kf3?e=O5nb8I", "If I follow the suggestion in the other issue and install with `--no-dependencies` then pip complains about missing dependencies next time I need a package, and the example code won't run.\r\n\r\nI'm using the same code on a docker though that came with pre-installed tflite model maker - so I know it's this conda environment, and not the code that's the problem.", "Hi, I was able to install `tflite_model-maker` and `tflite-model-maker-nightly` using pip without any errors. \r\nPlease find the gist attached [here](https://colab.sandbox.google.com/gist/sachinprasadhs/5ef2dab8f4ed8636eae3368120f35061/59855.ipynb) for reference. Thanks!", "Thanks for the update. In almost all your pip lines it states: Requirement already satisfied\r\nMy install still failed from a completely clean conda environment though. Pip still could not work out which of the various dependent packages to use. Maybe it's because I'm on windows or something??\r\n\r\nPlease see attached the full log below - far too much to paste into this box, but suffice to say it's a fail.\r\n[tflite model maker install.txt](https://github.com/tensorflow/tensorflow/files/11157832/tflite.model.maker.install.txt)\r\n", "I'm getting requirement already satisfied because I ran the `tflite_model-maker` in same session before trying nightly.\r\n\r\nTo understand more about the issue, could you please create a new conda environment using `conda create -n venv_name`\r\nand install pip using `conda install pip` and try again installing `pip install tflite_model-maker`", "OK, doing that will result in the even worse errors of my first post, because it'll be python 3.11.2 that gets used. It'll take an hour or so to fail, and will download hundreds of tf_models_nightly packages with dates at the end of their names. I'll post back the results shortly.", "I was wrong, it took a little under an hour, and output 1382 lines before hitting an error. As mentioned though, when not specifying a lower version of Python it tries installing every daily build.\r\n\r\n[Python 3.11.2 install log](https://github.com/tensorflow/tensorflow/files/11221760/install_log.txt)\r\n\r\nToday when I made another completely fresh python 3.8 environment it seems to have installed successfully, which is a first. It didn't use any nightly packages, but still needed to test hundreds of different packages for compatibility and took over 20 minutes to do the install - and my computer is reasonably well specced. \r\n[Python 3.8 install](https://github.com/tensorflow/tensorflow/files/11221811/old_install.txt)\r\n\r\nAnyway despite it looking like it worked, some of the python import lines still seem to fail:\r\n\r\n`2023-04-13 12:54:25.349815: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\r\n2023-04-13 12:54:25.349995: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\nC:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\read_weights.py:28: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\r\n np.uint8, np.uint16, np.object, np.bool]\r\nTraceback (most recent call last):\r\n File \"main.py\", line 8, in <module>\r\n from tflite_model_maker import model_spec\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tflite_model_maker\\__init__.py\", line 44, in <module>\r\n from tflite_model_maker import audio_classifier\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tflite_model_maker\\audio_classifier\\__init__.py\", line 24, in <module>\r\n from tensorflow_examples.lite.model_maker.core.data_util.audio_dataloader import DataLoader\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflow_examples\\lite\\model_maker\\core\\data_util\\audio_dataloader.py\", line 27, in <module>\r\n from tensorflow_examples.lite.model_maker.core.task.model_spec import audio_spec\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflow_examples\\lite\\model_maker\\core\\task\\model_spec\\__init__.py\", line 20, in <module>\r\n from tensorflow_examples.lite.model_maker.core.task.model_spec import audio_spec\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflow_examples\\lite\\model_maker\\core\\task\\model_spec\\audio_spec.py\", line 29, in <module>\r\n from tensorflow_examples.lite.model_maker.core.task import model_util\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflow_examples\\lite\\model_maker\\core\\task\\model_util.py\", line 28, in <module>\r\n from tensorflowjs.converters import converter as tfjs_converter\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\__init__.py\", line 21, in <module>\r\n from tensorflowjs import converters\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\converters\\__init__.py\", line 21, in <module>\r\n from tensorflowjs.converters.converter import convert\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\converters\\converter.py\", line 35, in <module>\r\n from tensorflowjs.converters import keras_h5_conversion as conversion\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\converters\\keras_h5_conversion.py\", line 33, in <module>\r\n from tensorflowjs import write_weights # pylint: disable=import-error\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\write_weights.py\", line 25, in <module>\r\n from tensorflowjs import read_weights\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\tensorflowjs\\read_weights.py\", line 28, in <module>\r\n np.uint8, np.uint16, np.object, np.bool]\r\n File \"C:\\Users\\martin\\miniconda3\\envs\\13042023OldPython\\lib\\site-packages\\numpy\\__init__.py\", line 305, in __getattr__\r\n raise AttributeError(__former_attrs__[attr])\r\nAttributeError: module 'numpy' has no attribute 'object'.\r\n`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.\r\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\r\n https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations`\r\n", "Same problem for me, in a google colab notebook ", "Another nice way to do this within colab notebooks is to clone the tensorflow repo and then modify the requirements.txt file so you can control the versions of packages installed. Had a few errors, but was able to modify versions and everything worked fine. Make sure you remove the -q flag so you can get the verbose output to find where the dependency errors are.\r\n\r\nClone the repo:\r\n!git clone https://github.com/tensorflow/examples.git\r\n\r\nNavigate to the directory with the setup.py script\r\ncd examples/tensorflow_examples/lite/model_maker/pip_package\r\n\r\nInstall tflite-model-maker and the required dependencies\r\n!pip3 install -e .\r\n\r\nIf there are errors, modify the requirements.txt file in the model maker dir - when running verbose it will tell you where the error is\r\nand you can modify accordingly. \r\nexamples/tensorflow_examples/lite/model_maker\r\n\r\nThen keep running (!pip3 install -e . )until there are no errors and you'll be good to go.\r\n", "I had this same issue using python 3.10 so I tried to use 3.8. Then I hit a new issue with the `orbax` package which was recently renamed.\r\n\r\nCreating a new conda environment with python 3.9 successfully installed without issue.\r\n```\r\nconda create -n object-detection python=3.9\r\nconda activate object-detection\r\npip install tflite-model-maker\r\n```", "The installation of the tflite-model-maker with the pip command keeps crashing on Google Colab. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.", "> I had this same issue using python 3.10 so I tried to use 3.8. Then I hit a new issue with the `orbax` package which was recently renamed.\r\n> \r\n> Creating a new conda environment with python 3.9 successfully installed without issue.\r\n> \r\n> ```\r\n> conda create -n object-detection python=3.9\r\n> conda activate object-detection\r\n> pip install tflite-model-maker\r\n> ```\r\n\r\nWorked for me! Thanks, man!", "> The installation of the tflite-model-maker with the pip command keeps crashing on Google Colab. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.\r\n\r\nI'm having the same issues, tried updating pip but it didn't help, I also tried reverting to python 3.9 on my own machine but the program still has problems with the _numba_ version, the required one would be the 0.53 but that particular version is not available", "> > The installation of the tflite-model-maker with the pip command keeps crashing on Google Colab. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.\r\n> \r\n> I'm having the same issues, tried updating pip but it didn't help, I also tried reverting to python 3.9 on my own machine but the program still has problems with the _numba_ version, the required one would be the 0.53 but that particular version is not available\r\n\r\nTry installing numba 0.53.0 with: pip install numba==0.53.0 \r\nThis version works with python 3.9", "> > > The installation of the tflite-model-maker with the pip command keeps crashing on Google Colab. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.\r\n> > \r\n> > \r\n> > I'm having the same issues, tried updating pip but it didn't help, I also tried reverting to python 3.9 on my own machine but the program still has problems with the _numba_ version, the required one would be the 0.53 but that particular version is not available\r\n> \r\n> Try installing numba 0.53.0 with: pip install numba==0.53.0 This version works with python 3.9\r\n\r\nI also got the issue where it tried downloading a million nightly builds. I tried installing this version of numba but got this\r\n\r\n`Failed to build numba llvmlite\r\nERROR: Could not build wheels for numba, llvmlite, which is required to install pyproject.toml-based projects`", "Yo, @StainlessStlRat ,\r\nYou can always clone the tflite-model-maker repo and troubleshoot by modifying the 'requirements.txt' file (this is in the model_maker directory). That way as missing dependencies come up you can modify as needed or install versions of packages with conflicts. The big one to change is the scann version to scann==1.2.8. There are a couple of others that will cause issue, but I'll leave that to you.\r\n\r\nLink to pypi: https://pypi.org/project/tflite-model-maker/0.4.3/\r\n\r\ngit clone https://github.com/tensorflow/examples\r\ncd examples/tensorflow_examples/lite/model_maker/pip_package\r\npip install -e .\r\n\r\nI only imported the tflite_model_maker packages and confirmed they import properly, but did not run any of the examples. That said, this worked as of an hour ago.", "@ldiangelis \r\nwhats your python version? are u running in docker? whats the runtime arch? Linux x86_64?\r\nI figured there is no way to run model-maker on m1 chips unless you run in docker", "I finally busted out my old Mac and followed the guide and got it to work, but I can't believe I didn't think to run it in Docker. Is there a pre-built image for that? I already trained my models but of course will need to do it again in the future.", "- I was not able to install model maker lib on differnt docker images (mostly older ubuntu)\r\n- Based on other threads and commentary I got a feeling that tensorflow is not being actively maintained (dead?)\r\n- Trying pytorch approach", "I did manage to use tensorflow lite inside colab by modifying the requirements.txt contained in the model training tutorial. \r\nI am not sure if this can help since the last time I worked on that piece of software was six months ago.\r\n\r\nHere is my [requirements.txt](https://github.com/FilippoGuarda/Colabs/blob/main/requirements.txt) file\r\n\r\n> * Based on other threads and commentary I got a feeling that tensorflow is not being actively maintained (dead?)\r\n\r\n yeah, it looks like tensorflow lite has been left on the sideline for a while. " ]

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[ "Hi @heguanyu Thanks for reporting the issue.\r\n\r\nCan you please provide the steps followed along with the toy model to reproduce the issue.\r\n\r\nThanks.\r\n", "Hi @pjpratik , please find the toy model attached. \r\n\r\nReproduce is simple\r\n\r\n1. Fetch tensorflow repo\r\n2. Run ./configure and enable iOS while disable all others. \r\n3. Copy the model into repo's directory\r\n4. bash tensorflow/lite/ios/build_frameworks.sh --input_models=toy_tflite_model_20230308.tflite --target_archs=x86_64,armv7,arm64\r\n\r\nAnd will see the error like \r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=280\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Applications/Xcode.app/Contents/Developer/usr/bin/python3 --action_env PYTHON_LIB_PATH=/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages --python_path=/Applications/Xcode.app/Contents/Developer/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: --verbose_explanations has no effect when --explain=<file> is not enabled\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework (138 packages loaded, 7435 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/xx/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD:4:24: Compiling tensorflow/lite/ios/tmp/custom_c_api_registration_registration.cc failed: undeclared inclusion(s) in rule '//tensorflow/lite/ios/tmp:custom_c_api_create_op_resolver':\r\nthis rule is missing dependency declarations for the following files included by 'tensorflow/lite/ios/tmp/custom_c_api_registration_registration.cc':\r\n 'tensorflow/lite/core/shims/builtin_ops_list.inc'\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework failed to build\r\nINFO: Elapsed time: 12.037s, Critical Path: 5.02s\r\nINFO: 15 processes: 11 internal, 4 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\n[toy_tflite_model_20230308.tflite.zip](https://github.com/tensorflow/tensorflow/files/10925472/toy_tflite_model_20230308.tflite.zip)\r\n", "@heguanyu Thanks for the information\r\n\r\nI was able to reproduce this issue on Mac M1 with Bazel 5.3.0. Please find the screenshot below.\r\n\r\n<img width=\"784\" alt=\"Screenshot 2023-03-09 at 4 50 06 PM\" src=\"https://user-images.githubusercontent.com/118897289/224008908-90afea11-2ada-4a54-a707-f5be9288733a.png\">\r\n\r\n@sachinprasadhs Could you please look into this issue? Thanks.", "@yishuangP is this a known issue?", "> @yishuangP is this a known issue?\r\n\r\nThis is not a known issue. Will take a look.", "We need to update tensorflow/lite/ios/build_frameworks.sh, it is outdated. In the meantime, can you try building each framework manually? For example, the c framework https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/ios/BUILD.apple#L102, and the selected ops framework:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/ios/BUILD.apple#L153", "Hi, update on this, submitting a change to fix this. In the meantime if you wanna build selective build, can you try adding the following snippet here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/ios/build_frameworks.sh#L88:\r\n```\r\n' deps = ['\r\n' \"//tensorflow/lite/core/shims:builtin_ops_list\",'\r\n' ],'\r\n```", "> Hi, update on this, submitting a change to fix this. In the meantime if you wanna build selective build, can you try adding the following snippet here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/ios/build_frameworks.sh#L88:\r\n> \r\n> ```\r\n> ' deps = ['\r\n> ' \"//tensorflow/lite/core/shims:builtin_ops_list\",'\r\n> ' ],'\r\n> ```\r\n\r\nHi @yishuangP I tried adding the deps in build_frameworks.sh line 88, and the same build script shows a different error as below:\r\n\r\nghe-mn2:tensorflow ghe:master]$ bash tensorflow/lite/ios/build_frameworks.sh --input_models=toy_tflite_model_20230308.tflite --target_archs=x86_64,armv7,arm64\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=317\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Applications/Xcode.app/Contents/Developer/usr/bin/python3 --action_env PYTHON_LIB_PATH=/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages --python_path=/Applications/Xcode.app/Contents/Developer/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/a2802dd24f62c14ae00d1691bb19a20d130ec68d.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/4df50417fbbaec08d3f61c5d837bdb3b3f1915ae.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\", line 4, column 24, in <toplevel>\r\n\t\ttflite_custom_c_library(\r\n\tFile \"/Users/ghe/projects/tensorflow/tensorflow/lite/build_def.bzl\", line 630, column 26, in tflite_custom_c_library\r\n\t\tnative.cc_library(\r\nError in cc_library: cc_library(...) got multiple values for parameter 'deps'\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': no such target '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': target 'TensorFlowLiteC_framework' not declared in package 'tensorflow/lite/ios/tmp' defined by /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such target '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': target 'TensorFlowLiteC_framework' not declared in package 'tensorflow/lite/ios/tmp' defined by /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\r\nINFO: Elapsed time: 0.056s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (1 packages loaded)", "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/59853\">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/59853\">No</a>\n", "Hi Guanyu, my change is merged. Can you try again? I've verified it on my side.", "Hi @yishuangP I tried updated the repo and run the command again, but seeing the error of missing TensorFlowLiteC_framework\r\n\r\n**target 'TensorFlowLiteC_framework' not declared in package 'tensorflow/lite/ios/tmp' defined by /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD**\r\n\r\n\r\n```\r\nghe-mn2:tensorflow ghe:master]$ bash tensorflow/lite/ios/build_frameworks.sh --input_models=toy_tflite_model_20230308.tflite --target_archs=x86_64,armv7,arm64\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=314\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Developer/CommandLineTools/usr/bin/python3 --action_env PYTHON_LIB_PATH= /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages --python_path=/Library/Developer/CommandLineTools/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nERROR: Traceback (most recent call last):\r\n\tFile \"/Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\", line 4, column 24, in <toplevel>\r\n\t\ttflite_custom_c_library(\r\n\tFile \"/Users/ghe/projects/tensorflow/tensorflow/lite/build_def.bzl\", line 630, column 26, in tflite_custom_c_library\r\n\t\tnative.cc_library(\r\nError in cc_library: cc_library(...) got multiple values for parameter 'deps'\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': no such target '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': target 'TensorFlowLiteC_framework' not declared in package 'tensorflow/lite/ios/tmp' defined by /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such target '//tensorflow/lite/ios/tmp:TensorFlowLiteC_framework': target 'TensorFlowLiteC_framework' not declared in package 'tensorflow/lite/ios/tmp' defined by /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp/BUILD\r\nINFO: Elapsed time: 0.082s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (1 packages loaded)\r\n```", "That is very weird. The `TensorFlowLiteC_framework` is supposed to be generated by this function https://github.com/tensorflow/tensorflow/blob/7fc287d1aa879420885098e03197f1c5fe09990f/tensorflow/lite/ios/build_frameworks.sh#L80 and writes it to the build file here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/ios/build_frameworks.sh#L110. In this case, can you try creating the BUILD file in the tmp directly manually? You just need to copy the content from build_frameworks.sh", "Hi @yishuangP ,\r\n\r\nI checked the BUILD file in tmp directory, and its content looks identical to the one in shell script, can you check if its contents are correct?\r\n\r\n```\r\nload(\"@build_bazel_rules_apple//apple:ios.bzl\", \"ios_static_framework\")\r\nload(\"//tensorflow/lite:build_def.bzl\", \"tflite_custom_c_library\")\r\nload(\"//tensorflow/lite/ios:ios.bzl\", \"TFL_MINIMUM_OS_VERSION\")\r\ntflite_custom_c_library(\r\n name = \"custom_c_api\",\r\n deps = [\r\n \"//tensorflow/lite/core/shims:builtin_ops_list\",\r\n ],\r\n models=[\"toy_tflite_model_20230308.tflite\",],\r\n)\r\nios_static_framework(\r\n name = \"TensorFlowLiteC_framework\",\r\n hdrs = [\r\n \"//tensorflow/lite/c:c_api_types.h\",\r\n \"//tensorflow/lite/ios:common.h\",\r\n \"//tensorflow/lite/ios:c_api.h\",\r\n \"//tensorflow/lite/ios:xnnpack_delegate.h\",\r\n ],\r\n bundle_name = \"TensorFlowLiteC\",\r\n minimum_os_version = TFL_MINIMUM_OS_VERSION,\r\n deps = [\r\n \":custom_c_api\",\r\n \"//tensorflow/lite/delegates/xnnpack:xnnpack_delegate\",\r\n ],\r\n)\r\n```", "Yes, can you try building the TensorFlowLiteC_framework and see if it works?", "I think your model also has flex ops right? So you also need TensorFlowLiteSelectTfOps_frameworkhttps://github.com/tensorflow/tensorflow/blob/db7a2b3b3c01ba2039b8bb0e78b4a4e85b83e672/tensorflow/lite/ios/build_frameworks.sh#L120. \r\n\r\nWhat confuses me is that why this script doesn't automatically generate the BUILD files for you. It should and I can verify it on my side. Can you try replacing BUILD with BUILD.apple?", "Hi @heguanyu sorry I just realized you'll need to use `tflite_custom_cc_library` instead of `tflite_custom_c_library`. I'm sending out another PR for review.", "Hi @yishuangP Thanks for the update! After changing to `tflite_custom_cc_library` in build_frameworks.sh, the above error is gone, but now seeing these errors:\r\n\r\n```\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for host] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 1132.618s, Critical Path: 66.45s\r\nINFO: 12518 processes: 3574 internal, 8944 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "Just pulled the latest version from git, still seeing the `'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule` error\r\n\r\n```\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for host] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 591.530s, Critical Path: 121.72s\r\nINFO: 2120 processes: 20 internal, 2100 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "`This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)`\r\nHmm what's the python version you have? We have an internal job that runs this script and the job is working fine. It uses python 3.9.9. I think this error is related to some configuration issue.", "@yishuangP I'm using 3.9.6 Let me update to 3.9.9 and try again. Is there anything I need to configure when running ./configure file? I only select \"yes\" for iOS question, all others are \"No\". ", "> @yishuangP I'm using 3.9.6 Let me update to 3.9.9 and try again. Is there anything I need to configure when running ./configure file? I only select \"yes\" for iOS question, all others are \"No\".\r\n\r\nI think that is enough. Let me know if you run into any issues.", "@yishuangP Unfortunately updating the python to 3.9.16 doesn't solve the problem. (I was not able to update to 3.9.9 specifically via homebrew) ", "Hi guanyu, this seems a tooling issue on tensorflow side. Could you help file a separate issue for this?", "Sure will do. thanks for looking into ios build part!", "@yishuangP I filed a separate ticket https://github.com/tensorflow/tensorflow/issues/60088 can you help assign it to the correct team to look into it? Thank you!", "Thanks for filing the issue!", "Hey guys, stuck with the same problem, but trying to build an android ndk c tflite lib\r\ncompletely followed the reduce the binary size page instructions, tmp/BUILD file is the same\r\nNDK: 19.2.5345600, Python 3.9, every bazel var was set\r\n\r\n```\r\nERROR: /home/sait/tf_short/tensorflow/tmp/BUILD:7:24: Compiling tmp/selectively_built_c_lib_registration_registration.cc failed: undeclared inclusion(s) in rule '//tmp:selectively_built_c_lib_create_op_resolver':\r\nthis rule is missing dependency declarations for the following files included by 'tmp/selectively_built_c_lib_registration_registration.cc':\r\n 'tensorflow/lite/core/shims/builtin_ops_list.inc'\r\nTarget //tmp:tensorflowlite_c failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 20.528s, Critical Path: 8.48s\r\nINFO: 92 processes: 8 internal, 84 local.\r\nFAILED: Build did NOT complete successfully\r\n```\r\n\r\nwhen added \"//tensorflow/lite/core/shims:builtin_ops_list\", to deps\r\n```\r\nERROR: /home/sait/tf_short/tensorflow/tmp/BUILD:21:24: in cc_binary rule //tmp:libtensorflowlite_c.so: target '//tensorflow/lite/core/shims:builtin_ops_list' is not visible from target '//tmp:libtensorflowlite_c.so'. Check the visibility declaration of the former target if you think the dependency is legitimate\r\nERROR: /home/sait/tf_short/tensorflow/tmp/BUILD:21:24: Analysis of target '//tmp:libtensorflowlite_c.so' failed\r\nERROR: Analysis of target '//tmp:tensorflowlite_c' failed; build aborted: \r\nINFO: Elapsed time: 0.088s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (1 packages loaded, 5 targets configured)\r\n```\r\n\r\nbazel line is:\r\n`bazel build -c opt --cxxopt=--std=c++17 --config=android_arm64 //tmp:tensorflowlite_c`\r\n\r\nmicro update: \r\ntried adding aforementioned dep line into almost every cc_library thing in different BUILD files (mostly with word \"resolver\" in it, still doesnt work :(\r\n\r\n\r\n\r\n\r\n\r\n", "Hi @heguanyu I was successfully able to build the TFLite framework after merges from @yishuangP. Please refer the screenshot here. FYI I have python 3.10.8 installed and it works fine.\r\n\r\n<img width=\"744\" alt=\"Screenshot 2023-03-24 at 9 48 04 PM\" src=\"https://user-images.githubusercontent.com/118897289/227582964-f88c8ef7-34d5-4a92-b6e4-4da122e699dd.png\">\r\n", "update: downgraded to 2.11.0, everything worked!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you." ]

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[ "@frgossen any idea what caused the rollback here?", "There was a missing dependency to TSL, which we fixed. The rollback was automatic and likely did not see that fix. Will try rolling it forward. ", "https://github.com/tensorflow/tensorflow/commit/d094920c58070e179fd8e7737073923dc7cd6fa7\r\n" ]

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[ "Hi @ezhulenev Can you please review this PR ? Thank you!", "@ezhulenev, friendly ping :) Would you have some time to review this PR? Thank you!", "Hi @ezhulenev Can you please review this PR ? Thank you!", "TFTR @ezhulenev!", "@ezhulenev @gbaned this PR has been approved for a while now. Is there something I need to do for it to be merged?", "> @ezhulenev @gbaned this PR has been approved for a while now. Is there something I need to do for it to be merged?\r\n\r\nHi @jonathan-albrecht-ibm Sorry for the delay, this PR processing internally, nothing pending from you end. Thank you!", "Here are the internal errors, @jonathan-albrecht-ibm can you please verify ? Thank you!\r\n\r\n/tensorflow/core/util/tensor_bundle/byte_swap_tensor.cc:150:56: error: no member named 'data' in 'absl::Cord'\r\n 150 | char* buff = const_cast<char*>((tp->tensor_content().data()));\r\n | ~~~~~~~~~~~~~~~~~~~~ ^\r\n1 error generated.\r\n\r\ntensorflow/core/util/tensor_bundle/byte_swap_tensor.cc:150:56: error: no member named 'data' in 'absl::Cord'\r\n 150 | char* buff = const_cast<char*>((tp->tensor_content().data()));\r\n | ~~~~~~~~~~~~~~~~~~~~ ^\r\n1 error generated.", "@gbaned thanks for providing the details. Is there somewhere I can see the logs with the internal errors above?\r\n\r\nNot completely sure what's going on but it looks like `absl::Cord` is being used instead of `std::string` for the protobuf `tensor_content` field in this build? `tensor_content` has the protobuf type `bytes` which should be `std::string` in the c++ generated classes so not sure how this is happening. \r\n\r\n@gbaned @ezhulenev Any pointers on how to configure my local build to reproduce this error?", "Hi @ezhulenev Can you please take a look on the above comment from @jonathan-albrecht-ibm. Thank you!", "Hi @ezhulenev Any update on this PR? Please. Thank you!", "@ezhulenev, @gbaned Not sure if something else changed but the MacOS build passed this time. The `Py+CPP Test Suite - Ubuntu CPU, Python 3.9` build failed though. I looked at the logs that I can access and it looks like the errors were due to timeouts IIUC.\r\n\r\nCould someone with more access confirm?", "@gbaned just wanted to check on the status of this issue since the ready to pull label was removed. I don't have access to the internal CI. Are there any problems?" ]

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[ "@LJKS,\r\nCould you please elaborate about the mentioned Feature. Also, please specify the Use Cases for this feature which helps us to analyse. Thank you!", "tf.scan applies a function (arg: fn) in a stateful manner. It splits the input tensor (arg: elem) along axis 0 into n elements, where n=tf.shape(elem)[0]. It then applies the function iteratively while also updating/making use of the state to these n elements. The resulting elements are put together (stacked) into a tensor with leading dimension of equal size again. \r\n\r\nThe feature I'm requesting simple allows for splitting (and iterating over) the tensor along a different axis than the leading axis. Let's run through a potentially typical example, with a tensor of shape=[batch_size, time_steps, features]. Now instead of scanning (tf.scan) over the leading (batch) dimension, one might want to tf.scan over the time_steps axis. Let's assume 'my_bs_ts_feat_tensor' is such a tensor: \r\n\r\nI can currently scan along the timesteps axis by applying: \r\n\r\n```\r\nmy_bs_ts_feat_tensor = tf.transpose(my_bs_ts_feat_tensor, perm=[1,0,2])\r\nmy_bs_ts_feat_tensor = tf.scan(fn=some_scan_fn, elem=my_bs_ts_feat_tensor)\r\nmy_bs_ts_feat_tensor = tf.transpose(my_bs_ts_feat_tensor, perm=[1,0,2])\r\n```\r\n\r\nThe code above swaps the timestep-axis to the front via transpose, then scans the function along the now leading timestep axis, then transposes the timestep axis back and gets the batch-dim again as the leading dimension via transpose again. \r\n\r\nThe above should be equal to the result of calling tf.scan(my_bs_ts_feat_tensor, axis=1) in what I propose. Or generally instead the proposed behaviour could be achieved (and cheaply implemented) as follows: \r\n\r\nlet n_axes be the number of axes in the elem tensor (e.g. for my_bs_ts_feat_tensor of shape [batch_size, time_steps, features]: n_axes=3)\r\nlet axis the the axis argument I would like to propose in the scan function\r\n\r\nthen the proposed better scan with axis would be:\r\n\r\n```\r\ndef tf.better_scan(fn, elems, axis):\r\n #transpose target axis to front\r\n perm_1 = [axis] + [i for i in range(n_axes) if not i==axis]\r\n elems = tf.transpose(elems, perm=perm_1)\r\n #apply the actual scan\r\n elems = tf.scan(fn=fn, elems=elems)\r\n #permute the dimensions back to where they belong, by inserting the target axis (with axis-index zero in elem) into permutations where it belongs\r\n perm_2 = list(range(1,n_axes)).insert(axis,0) \r\n elems = tf.transpose(elems, perm=perm_2)\r\n return elem\r\n```\r\n\r\nUse-cases: \r\n - Lots of stuff working with time-dimensions\r\n - RL applications: E.g. computing the discounted rewards-to-go, TD-errors, Advantages, GAEs, etc.\r\n - Literally anything else where scan would be applied, and where not by chance the correct split is the batch dimension\r\n - Makes some really annoying preprocessing much easier\r\n - Makes some comparatively hard-to-read code much easier to read", "Notes: \r\n - I can of course also provide a more TF appropriate version (permutations via tf.range and inferring n_axes via tf.shape(tf.shape(elems))) if there is any upside with that\r\n - The same change of adding a axis argument as proposed for scan here would of course also be great for tf.map_fn", "Hi Thanks for the feature request.\r\nAs already suggested by you as the workaround to transpose and apply scan and transpose back again.\r\n```\r\nmy_bs_ts_feat_tensor = tf.transpose(my_bs_ts_feat_tensor, perm=[1,0,2])\r\nmy_bs_ts_feat_tensor = tf.scan(fn=some_scan_fn, elem=my_bs_ts_feat_tensor)\r\nmy_bs_ts_feat_tensor = tf.transpose(my_bs_ts_feat_tensor, perm=[1,0,2])\r\n``` \r\nWouldn't it makes more sense to use the same methods in all your code implementation, rather than adding a new feature. Thanks! ", "Hi, \r\nI'm not sure I understand your answer correctly, but let me try: \r\n - The workaround obviously works perfectly\r\n - However it makes the code much less readable and creates high risk of mistakes for users with less experience\r\n - E.g. When scanning along the 4th axis (i.e. axis with index 3) of a tensor with 6 axes: \r\n - Perm1: [3,0,1,2,4,5]\r\n - Perm2: [1,2,3,0,4,5]\r\n - IMO the situation above is a very likely source for creating bugs, especially when code is handed over to other developers, as this is very much not 'intuitive'\r\n - From a usage perspective for operations which happen along a certain axis, in TF users are used to the axis argument\r\n \r\nAs such, with the fix being only ~4 lines of easy&quick code, and the change being non-breaking for existing code, this seems like an attractive feature to me. ", "Hi @LJKS!\r\n\r\nThanks for the request! Would you like to contribute a PR to add this functionality alongside some tests?" ]

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[ "@josemalope \r\n\r\nCould you please try to install TensorFlow 2.11 as per the [tested build configurations](https://www.tensorflow.org/install/source#gpu) and also try to update the GPU drivers. \r\n\r\nPlease refer to the similar [issue_1](https://github.com/tensorflow/tensorflow/issues/53341) & [issue_2](https://github.com/tensorflow/tensorflow/issues/41937) for reference and let us know if it helps.\r\n\r\nThank you !", "Hi @tiruk007,\r\n\r\nThe message is expected since we are using CPU only (we already switched to tensorflow-cpu).\r\n\r\nHowever, I created the ticket to point out the inconsistency between the warning message: \r\nW tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\r\nand the level at which it is logged: ERROR\r\n\r\nRegards,\r\nJose", "@josemalope \r\nSorry for the late reply, tensorflow and tensorflow-gpu have been in the same package since TensorFlow 2.1([REF](https://pypi.org/project/tensorflow-gpu/)). Could you please use `pip install tensorflow-cpu` for cpu inferences to avoid unwanted warning like `W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)`. \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/59849\">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/59849\">No</a>\n" ]

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[ "@maifeeulasad,\r\nApologies for the delay!\r\nCould you please provide the complete code to reproduce the issue which helps us to analyse the issue in an effective way. Thank you", "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/59848\">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/59848\">No</a>\n" ]

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[ "Hi @HJL8055. Thanks for reporting the issue.\r\n\r\nSorry for the delayed response.\r\n\r\nAs per the [documentation](https://www.tensorflow.org/lite/performance/quantization_spec#symmetric_vs_asymmetric), the zero point of the weights in fully-connected layers is enforced to be zero to avoid the runtime cost of multiplying the zero point of the weight with the activation value. \r\n\r\nCan you please provide the model and steps you have followed in order to reproduce the issue and help troubleshooting process.\r\n\r\nThanks.", "Closing as stale. Please reopen if you'd like to work on this further. Thanks!" ]

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[ "@mengran1234,\r\nThanks for opening the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet you are trying to execute. Thank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59845\">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/59845\">No</a>\n" ]

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[ "@gaikwadrahul8 \r\nI was able to reproduce the issue on Colab using TF 2.11 and tf-nightly. Please find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/cc8ffbbf3cc69f51227cc4ae93b7c078/untitled149.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/739b3634108ad660e60f8769fe1d2284/untitled149-nightly.ipynb) for reference. \r\nThank you !", "Hi, @shijy16 \r\n\r\nApologize for the delay and I was able to replicate the same issue on Google Colab with `tf-nightly `and `tensorflow==2.12.0rc0` and code is not executing completely successfully, Here is [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/88dc21f81d4fbfe850ea8dfda20f56cb/-59844-test.ipynb) and session is getting crashed. It's giving warning `2023-03-03 15:03:16.216167: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -2147483648`, for your reference I have added screenshot below:\r\n\r\n\r\n\r\nI have executed the same code multiple times on `Ubuntu 20.04` with `tf-nightly` and `tensorflow==2.12.0rc0` I have added detailed log below, It seems like we have to dig more into this issue and we'll update you soon, Thank you for noticing this issue. Thank you!\r\n\r\n```\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:10.809221: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:11.628435: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:12.447280: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:12.447673: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:12.592711: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n<tf.RaggedTensor [[], [], [], [], [], [], [], [], [], [], []]>\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:17.896998: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:18.697086: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:19.442879: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:19.443243: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:19.453230: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n2023-03-03 15:20:19.476306: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -2147483648\r\nAborted (core dumped)\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:26.245838: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:27.079848: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:27.877856: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:27.878182: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:27.888476: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n2023-03-03 15:20:27.911686: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -2147483648\r\nAborted (core dumped)\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:32.161218: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:32.984919: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:33.728023: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:33.728350: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:33.738221: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n2023-03-03 15:20:33.761200: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -2147483648\r\nAborted (core dumped)\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:39.988125: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:40.836736: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:41.593577: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:41.593877: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:41.603714: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n<tf.RaggedTensor [[], [], [], [], [], [], [], [], [], [], []]>\r\n(tf-cpu-tf-nightly) gaikwadrahul@rahul-ubuntu-20-gpu:~$ python _59844_test.py \r\n2023-03-03 15:20:50.296064: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-03 15:20:51.125175: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-03-03 15:20:51.897238: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:51.897710: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] 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\n2023-03-03 15:20:51.907990: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\n2023-03-03 15:20:51.931164: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -2147483648\r\nAborted (core dumped)\r\n\r\n```", "Hi, @SuryanarayanaY \r\n\r\nCould you please look into this issue ? Thank you!", "Hi @shijy16 ,\r\n\r\nThe issue got resolved now with tf-nightly(2.14.0-dev20230514) and now TF is successfully able to raise the intended Error.\r\nPlease refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/85f3d42c0ec2d25e26093ea3f2ae32c9/59844_tf-nightly-2-14.ipynb). \r\n\r\nPlease check and confirm and let us know if we can close the issue as it resolved now. \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/59844\">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/59844\">No</a>\n" ]

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[ "Hi @buptzyb This PR is in draft, any update on this? Please. Thank you!", "Hi @buptzyb This PR is in draft, any update on this? Please. Thank you!", "> Hi @buptzyb This PR is in draft, any update on this? Please. Thank you!\r\n\r\nHi, after discussing with reviewers, the changes will be split into several PRs for merge, such as [#60322](https://github.com/tensorflow/tensorflow/pull/60322) and [#60323](https://github.com/tensorflow/tensorflow/pull/60323) so far. So this PR will work as a reference and will not be merged directly.", "Hi @buptzyb This PR is in draft, any update on this? Please. Thank you!" ]

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