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Isuru0x01

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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/60013/checks?check_run_id=12056369827) 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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[ "Hi @rahul-exp \r\n\r\nCan you please provide the steps you have followed. Did you setup `./configure` for android?\r\n\r\nThanks.", "@pjpratik Please find the ./configure options I selected for android:\r\nPlease specify the location of python: Correct path location provided(Python version 3.9)\r\nPlease input the desired Python library path to use: Correct path location provided(Python version 3.9)\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: N\r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: N\r\nNo CUDA support will be enabled for TensorFlow.\r\n\r\nDo you wish to download a fresh release of clang? (Experimental) [y/N]: N\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: Enabled just the default\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y\r\nPlease specify the home path of the Android NDK to use: Provided path for ndk/21.4.7075529\r\nPlease specify the (min) Android NDK API level to use: Default of 26\r\nPlease specify the home path of the Android SDK to use: Provided sdk path \r\nPlease specify the Android SDK API level to use: 33\r\nPlease specify an Android build tools version to use: 34.0.0-rc1\r\nDo you wish to build TensorFlow with iOS support? [y/N]: N\r\n", "Other builds for android with below commands are working fine:\r\n1) bazel build -c opt --config=android_arm64 --cpu=arm64-v8a --define tflite_with_xnnpack=true //tensorflow/lite:tensorflowlite\r\n2) bazel build -c opt --config=android_arm64 --cpu=arm64-v8a //tensorflow/lite/delegates/nnapi:nnapi_delegate_no_nnapi_implementation\r\n3) bazel build -c opt --config=android_arm64 --cpu=arm64-v8a //tensorflow/lite/nnapi:nnapi_implementation", "Hi @rahul-exp Sorry for the delayed response.\r\n\r\nCan you please provide which version of TF you are using?\r\n\r\nI couldn't reproduce it at my end and facing different error.\r\n\r\nThanks.", "TF version on my mac is 2.11.0. I had checked out the latest changes from the main branch of the repo for building the TFLite libraries. Should I switch to a release branch for building TFLite libraries?", "Hi @rahul-exp Thanks for the information.\r\n\r\nI have tried on MacOS (13.2) with Bazel 5.3.0 and using master branch. I was able to succesfully generate `libtensorflowlite_gpu_delegate.so` file. Please find the screenshot below\r\n\r\n<img width=\"806\" alt=\"Screenshot 2023-03-24 at 8 30 13 PM\" src=\"https://user-images.githubusercontent.com/118897289/227563831-edd158dc-ebf7-4e17-ac1a-0dac759e94d7.png\">\r\n\r\nI have tested on Ubuntu 20.04 with same config and found no issues.\r\n\r\nCan you try `bazel clean --expunge` and try the same again.\r\n\r\nThis is my config for your reference\r\n\r\n<img width=\"811\" alt=\"Screenshot 2023-03-24 at 8 35 04 PM\" src=\"https://user-images.githubusercontent.com/118897289/227564104-7aee04de-ab2b-4eaf-928e-8fb3faaeac58.png\">\r\n\r\nThanks.\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60012\">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/60012\">No</a>\n", "@pjpratik I get this error as well, can't build the GPU delegate.\r\n## Setup\r\n1. bazel 5.3.0\r\n1. Fresh clone of tensorflow\r\n1. git checkout v2.13.0\r\n1. run `configure` and use:\r\n```\r\nANDROID_NDK_HOME=\"/home/yuval/android/android-ndk-r21e\"\r\nANDROID_NDK_API_LEVEL=\"21\"\r\nANDROID_BUILD_TOOLS_VERSION=\"33.0.0\"\r\nANDROID_SDK_API_LEVEL=\"33\"\r\n```\r\n## Build\r\n```sh\r\n$ bazel build -c opt --config=android_arm64 //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so\r\n\r\nERROR: /home/yuval/MyProjects/tensorflow/tensorflow/lite/delegates/gpu/BUILD:225:11: Compiling tensorflow/lite/delegates/gpu/delegate.cc failed: (Exit 1): clang failed: error executing command external/androidndk/ndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -gcc-toolchain external/androidndk/ndk/toolchains/aarch64-linux-android-4.9/prebuilt/linux-x86_64 -target ... (remaining 192 arguments skipped)\r\ntensorflow/lite/delegates/gpu/delegate.cc:786:7: error: use of undeclared identifier 'AHardwareBuffer_acquire'; did you mean 'AHardwareBuffer_Plane'?\r\n AHardwareBuffer_acquire(ahwb);\r\n ^\r\nexternal/androidndk/ndk/sysroot/usr/include/android/hardware_buffer.h:320:3: note: 'AHardwareBuffer_Plane' declared here} AHardwareBuffer_Plane;\r\n ^\r\ntensorflow/lite/delegates/gpu/delegate.cc:794:9: error: use of undeclared identifier 'AHardwareBuffer_release'; did you mean 'AHardwareBuffer_Plane'?\r\n AHardwareBuffer_release(b);\r\n ^\r\nexternal/androidndk/ndk/sysroot/usr/include/android/hardware_buffer.h:320:3: note: 'AHardwareBuffer_Plane' declared here} AHardwareBuffer_Plane;\r\n ^\r\ntensorflow/lite/delegates/gpu/delegate.cc:802:7: error: use of undeclared identifier 'AHardwareBuffer_describe'\r\n AHardwareBuffer_describe(uptr_ahwb.get(), &desc_ahwb);\r\n ^\r\ntensorflow/lite/delegates/gpu/delegate.cc:1184:18: error: use of undeclared identifier 'AHardwareBuffer_lock'\r\n return AHardwareBuffer_lock(buffer, this->usage_, -1 /* fence */,\r\n ^\r\ntensorflow/lite/delegates/gpu/delegate.cc:1211:24: error: use of undeclared identifier 'AHardwareBuffer_unlock'\r\n return AHardwareBuffer_unlock(buffer, nullptr /* fence */);\r\n ^\r\n5 errors generated.\r\n```\r\n\r\nOS:\r\n```\r\n$ lsb_release -a\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription: Ubuntu 20.04 LTS\r\nRelease: 20.04\r\nCodename: focal\r\n```", "Hi @ValYouW, I encountered the same problem, did you solve it?", "@colorjam No :(", "@colorjam I'm back at it today and I was able to resolve the issue, you need to specify that the min NDK version to use is 26 (don't confuse it with ANDROID_SDK_API_LEVEL, which is what I did...).\r\nI just set these env vars on my bashrc\r\n```\r\nexport ANDROID_SDK_HOME=\"<path to where my android sdk is>\"\r\nexport ANDROID_NDK_HOME=\"<my android sdk home>/android-ndk-r21e\"\r\nexport ANDROID_NDK_API_LEVEL=\"26\"\r\nexport ANDROID_BUILD_TOOLS_VERSION=\"34.0.0\"\r\nexport ANDROID_SDK_API_LEVEL=\"34\"\r\n```" ]

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[ "Hi @JuanuMusic,\r\nAs mentioned in the error, as a workaround you can create your own custom implementation of an unsupported TensorFlow operator in TensorFlow Lite, known as a custom operator by following the instructions mentioned [here](https://www.tensorflow.org/lite/guide/ops_custom). Thank you!", "Hi! Yes, I know, but its also suggested to open an Issue to request to add. I think it's an important operator since there are some pre-procesdsing strategies that indeed require it." ]

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

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[ "In general, it's not advised to do so. `boringssl` is a safer version of `openssl`, with none of the design choices that resulted in the `openssl` vulnerabilities that are famous (Hearthbleed) or not", "I know boringssl may have better security and performance, but our service not for public, so security may not so important to us. for we must use brpc, so we must use openssl, from you answer, I guess I can modify tensorflow to use openssl", "thanks a lot", "for anyone has same demand, use repo_mapping = { \"@boringssl\": \"@openssl\",} is the fastest sustitution way", "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/60009\">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/60009\">No</a>\n", "> for anyone has same demand, use repo_mapping = { \"@boringssl\": \"@openssl\",} is the fastest sustitution way对于任何有相同需求的人，使用 repo_mapping = { “”： “”，} 是最快的 sustitution 方式\r\n\r\nthere?please\r\n\r\n" ]

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[ "No template filled in, new user, looks spam" ]

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[ "@wangdadaba \r\nI was able to execute a given code without error on colab using TF v2.11 & TF v2.8. `model(x, training = fasle)` and `model.predict()` getting the same results. Could you please confirm the same and find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/fa278bcb3c0c182fea74d9c0c983fa80/untitled155.ipynb) and [2.8](https://colab.research.google.com/gist/tiruk007/368f56317e9a51d878807532a189659a/untitled156.ipynb) for reference.\r\n\r\nThank you !", "我猜测可能是gpu得问题，我关闭了gpu进行测试结果是正确的，当我开启了gpu之后，结果仍然有问题。我使用的是docker容器，网址https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow/tags \r\nnvcr.io/nvidia/tensorflow:22.04-tf2-py3 .\r\n\r\n\r\n\r\n> @wangdadaba I was able to execute a given code without error on colab using TF v2.11 & TF v2.8. `model(x, training = fasle)` and `model.predict()` getting the same results. Could you please confirm the same and find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/fa278bcb3c0c182fea74d9c0c983fa80/untitled155.ipynb) and [2.8](https://colab.research.google.com/gist/tiruk007/368f56317e9a51d878807532a189659a/untitled156.ipynb) for reference.\r\n> \r\n> Thank you !\r\nI guessed that there might be a problem with the GPU, I turned off the GPU to test correctly, and when I turned on the GPU, the results are still problematic. I'm using a docker container with URLs ：https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow/tags \r\ntaf：nvcr.io/nvidia/tensorflow:22.04-tf2-py3 .\r\n\r\n\r\n", "@wangdadaba \r\nI was able to get similar results on the GPU as well on Colab using tf2.11. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/ee2704d70bbe51f4ecd45e429cbd7a83/untitled161.ipynb) \r\n\r\nCould you please try the latest docker container(nvcr.io/nvidia/tensorflow:23.02-tf2-py3) and let us know if it still persists.\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/60007\">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/60007\">No</a>\n" ]

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[ "Have you tested this? It looks like it relies on some data files that don't exist:\r\n\r\ntensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb", "Hi @MarkDaoust \r\n\r\nI have tested the changes by following the [instructions](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/label_image#to-buildinstallrun) given to run the label_image.py.\r\n\r\nThe model and sample data has also been provided as a part of it.\r\n\r\nPlease find the attached [gist](https://colab.research.google.com/gist/pjpratik/b596e519deb7da70e8639b3846b3e7e7/60006.ipynb) with tests using the proposed commit.\r\n\r\nThanks.", "SGTM thanks!" ]

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[ "@jpienaar gently remind of this patch for review : )" ]

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[ "Hi @samdabadei,\r\n \r\nThe error occurs because you are trying to set the trainable attribute of a non-trainable variable `self.a`.\r\n\r\nIn the __init__ method of your model class, you set `self.a` to a non-trainable variable with the statement `self.a = tf.Variable([1.0], trainable=False)`. Therefore, when you try to set the trainable attribute of `self.a` in the following line:\r\n\r\n```\r\nmodel.a.trainable = True\r\n```\r\nyou get the AttributeError: can't set attribute error. To fix this error, you can remove these statements `self.a.trainable = True` , `model.b.trainable = True` and set the trainable attribute of `self.b` to **True** in the `__init__` method by passing the `trainable=True` argument to the add_weight method and tf.Variable, as shown below:\r\n\r\nHere's the working code:\r\n```\r\nimport tensorflow as tf\r\nfrom keras import layers\r\nimport numpy as np\r\n\r\n\r\nclass model(tf.keras.Model):\r\n\r\n def __init__(self):\r\n super(model, self).__init__()\r\n self.b = self.add_weight(shape=(1, 5), trainable=True)\r\n self.a = tf.Variable([1.0], trainable=True)\r\n\r\n def call(self, inputs, training=None, mask=None):\r\n return self.a * self.b * inputs\r\n\r\n\r\ninput = np.random.randn(10, 5)\r\ntarget = np.random.randn(10, 1)\r\n\r\nmodel = model()\r\nmodel.compile(\"adam\", loss=\"mse\")\r\n\r\nmodel.fit(input, target, validation_data=(input, target), epochs=3)\r\n\r\n```\r\n\r\nThank you!", "Is there no way to change the trainable attribute of a Variable after the first creation? Between the model-init() and the model-compile(). Its possible for layers and models and its quite tedious to change this always in the init when doing transfer learning", "Hi @samdabadei,\r\nCould you please elaborate how the above model is used in transfer learning? ", "okay concrete example: I do transfer learning. first i transfer some parameters, layers etc and make them not trainable. after saving the model weights i want to load them again for finetuning but in order to do this i have to set the same variable to non trainable since the order of the weights would be different then when they where saved. but now there is no way for me to free them again. and again somehow it has to be possbile right? i mean layer are also just a collection of variables and there i can change the trainability like i want before compiling", "my current work-around for anybody interested:\r\n\r\n```\r\nclass VarablesLayer(tf.keras.layers.Layer):\r\n def __init__(self, shape, name, trainable=True, activation=None, **kwargs):\r\n super(VarablesLayer, self).__init__(name=name, trainable=trainable)\r\n self.var = self.add_weight(name=f\"{name}/var\", shape=shape, **kwargs)\r\n self.activation = activation\r\n\r\n\r\n def call(self, inputs, *args, **kwargs):\r\n if self.activation:\r\n return self.activation(self.var)\r\n return self.var\r\n```\r\n\r\nits a wrapper layer for a variable. you then can set the trainablitlity of the layer and get the values by calling the layer with None as argument", "Currently the variable trainable is set as [@property](https://docs.python.org/2/library/functions.html#property) object.\r\nhttps://github.com/tensorflow/tensorflow/blob/0db597d0d758aba578783b5bf46c889700a45085/tensorflow/python/ops/variables.py#L516-L518\r\n\r\nWhich means you will not be able to modify the variable unless it is defined under `@trainable.setter`\r\n\r\nConsider the below scenario.\r\n\r\n1. Trying to modify the trainable variable when it is defined as `@property`, it throws error as expected.\r\n\r\n```\r\nclass a:\r\n\r\n def __init__(self,trainable):\r\n self.trainable = trainable\r\n\r\n @property\r\n def trainable(self):\r\n return self._trainable\r\n\r\nc = a(False)\r\nc.trainable = True\r\n```\r\n\r\n2. Trying to modify the variable with the `@traiable.setter`\r\n```\r\nclass a:\r\n\r\n def __init__(self,trainable):\r\n self.trainable = trainable\r\n\r\n @property\r\n def trainable(self):\r\n return self._trainable\r\n\r\n @trainable.setter\r\n def trainable(self, value):\r\n self._trainable = value\r\n\r\n\r\nc = a(False)\r\nc.trainable = True\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/60004\">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/60004\">No</a>\n" ]

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[ "@sdasgup3 is oncall for MHLO this week", "Hi @sdasgup3 Can you please review this PR ? Thank you!", "Closing because commit 11b7c33 addresses the problem." ]

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[ "This PR is dependent on https://github.com/tensorflow/tensorflow/pull/59675 being merged", "@cheshire There were some issues rebasing this branch. So I created a new PR with the same changes https://github.com/tensorflow/tensorflow/pull/60047. I am closing this one. Please approve the other PR. Sorry about the confusion." ]

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[ "cc @rainwoodman " ]

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[ "@mikel-brostrom Thanks for reporting the issue.\r\n\r\nThe benchmarking tool can be used with any tflite model. The example of Performance benchmark numbers for [starter model](https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1?lite-format=tflite) with SSD Mobilenet are generated with the tool [described here](https://www.tensorflow.org/lite/performance/benchmarks). Please refer to this [section](https://www.tensorflow.org/lite/examples/object_detection/overview#performance_benchmarks) for the same.\r\n\r\nThe [Model Customization](https://www.tensorflow.org/lite/examples/object_detection/overview#model_customization) section provides the information about choosing the model to customize and fine tuning the models on custom data.\r\n\r\nThanks\r\n\r\n\r\n", "Ok. Then I guess that the inference process fails because of the different output in each model... The output of the SSD Mobilenet you provided:\r\n\r\n\r\n\r\nMy model's output:\r\n\r\n\r\n", "Is there any tutorial on how to convert standard object detector output shape (bs, N, 5 + nr of classe) to what basically all the TFLite object detection tools in this repo expects?", "Hi @mikel-brostrom \r\n\r\nThe TFLite provides a way to use custom models and benchmark them. \r\n\r\nPlease check the [Model Customization](https://www.tensorflow.org/lite/examples/object_detection/overview#model_customization) and also a way of TF2 object detection models to convert into TF Lite [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md#running-tf2-detection-api-models-on-mobile) and let us know if it helps.\r\n\r\nFeel free to close the issue if it is resolved.\r\n\r\nThanks.", "> Please check the [Model Customization](https://www.tensorflow.org/lite/examples/object_detection/overview#model_customization) and also a way of TF2 object detection models to convert into TF Lite [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md#running-tf2-detection-api-models-on-mobile) and let us know if it helps.\r\n\r\nThanks. This was really helpful @pjpratik. I think it is unclear whether the benchmark App support different type of models or not. I would suggest that you add the customization information under https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark", "@mikel-brostrom Glad it helped. Feel free to create a PR if you are interested.\r\n\r\nCan you please close the issue if it is resolved?\r\n\r\nThanks.\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59997\">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/59997\">No</a>\n" ]

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[ "@pokecheater,\r\nIn order to expedite the trouble-shooting process, could you please provide a complete code and the TensorFlow version you are using. 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.", "Dear @tilakrayal,\r\n\r\nThank you for your response regarding my earlier request. I appreciate your suggestion to provide a simplified code example.\r\n\r\nHowever, I must reiterate that this error occurred in a highly complex application built on top of TensorFlow, and providing a full code example would take weeks due to the intricacies involved. Nonetheless, I wanted to bring this error to your attention because we were able to fix it by adapting the TensorFlow code itself. While this error has low priority for us because we were able to find a workaround, it could still be a significant issue for other users who do not have the same resources or expertise.\r\n\r\nAs I mentioned earlier, we believe that this error is related to packaging with PyInstaller and how the CurrentModuleFilter() method references modules. It would be extremely helpful if TensorFlow could provide more detailed documentation or even a dedicated feature to help users avoid this error in the future.\r\n\r\nThank you for your time and attention to this matter. \r\n\r\nBest regards", "@pokecheater,\r\nApologies for the delay. Without the reproducible code, it would be difficult for us to debug the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet and the TensorFlow version you are using. \r\n\r\nAlso could you please try to execute the below code before the main code, which might work in some cases.\r\n```\r\nimport tensorflow as tf\r\nimport pycoral.utils.edgetpu as edgetpu\r\ninterpreter = edgetpu.make_interpreter('model_edgetpu.tflite')\r\nedgetpu.supports_dmabuf(interpreter._native_handle())\r\n```\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/lite/Interpreter\r\n\r\nexperimental_op_resolver_type | The op resolver used by the interpreter. It must be an instance of OpResolverType. By default, we use the built-in op resolver which corresponds to tflite::ops::builtin::BuiltinOpResolver in C++.\r\n-- | --\r\n\r\n\r\n\r\nThank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Closing this as stale. Please reopen if this is still a valid request. Thank you!" ]

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[ "Note on testing: I've run ./tensorflow/tools/ci_build/ci_build.sh CPU.ARM64 bazel test on AArch64 on //tensorflow/core/... //tensorflow/tsl/... and //tensorflow/compiler/... and there was no regression.", "This looks good to me, but I'm not sure I'm the right person to review. I'll add @MichaelHudgins and @akuegel, hopefully one of them can review (or will know who to assign). Thanks for the PR!", "@vam-google would mind taking a look? ", "Hi @RoboTux Can you please check @akuegel's comments and keep us posted ? Thank you!", "> Hi @RoboTux Can you please check @akuegel's comments and keep us posted ? Thank you!\r\n\r\nTL;DR: saw that, I was trying to understand the current default behavior, will rework the patch accordingly and change it to default to enable only the host architecture.\r\n\r\nYes sorry I saw that but I realized in https://github.com/tensorflow/tensorflow/issues/59595#issuecomment-1473725625 that I erroneously thought all targets are enabled by default currently which is not the case, only X86 and the host CPU are enabled by default. I delayed a bit to find the time to analyze why despite llvm_targets having all targets in @llvm-project it did not cause all targets to be enabled. It's down to the config_settings operating on the --cpu value in //tensorflow/tsl which I thought did not work as intended because of how it's documented in bazel as having an empty default:\r\n\r\nhttps://bazel.build/reference/command-line-reference#flag--cpu\r\n\r\nBest regards,\r\nThomas", "Ah, it seems there are some merge conflicts that need to be resolved.", "What commit message will be used when this land after I solve the conflicts? Will it use the original message or what I last forced pushed?", "@RoboTux I believe it will use the original message on the PR (\"Default flag to current set of ...\")", "> \r\n\r\nOh, I'll fix the conflicts in a new PR then if you don't mind since this new version didn't add an option. I'll link it from here to help clarify things.", "I've just created https://github.com/tensorflow/tensorflow/pull/60161 which fixes the conflicts and contains an updated commit message." ]

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[ "Hi @Madhu5391111 Thanks for reporting this issue.\r\n\r\nThere might be different reasons for tensor allocation to be failed like incorrect tensor sizes.\r\n\r\nCan you provide the steps you have followed and the model settings provided inorder to further investigate the issue.\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." ]

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[ "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin I saw that the windows bazel build succeeded but showing failure here. could you please give me any hint?", "Hi @gbaned , could you help retriger the check? Thanks!", "Windows passed internally, and AMD ROCm test result:\r\n\r\n```\r\nTarget //tensorflow/tools/pip_package:build_pip_package up-to-date:\r\n\r\n bazel-bin/tensorflow/tools/pip_package/build_pip_package\r\n\r\nINFO: Elapsed time: 5299.297s, Critical Path: 820.89s\r\n\r\nINFO: 37654 processes: 4101 internal, 33553 local.\r\n\r\nINFO: Build completed successfully, 37654 total actions\r\n\r\nINFO: Build completed successfully, 37654 total actions\r\n\r\n++ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg\r\n\r\nWed Mar 29 23:47:58 UTC 2023 : === Preparing sources in dir: /tmp/tmp.I0vDbSk9pz\r\n\r\n/workspace /workspace\r\n\r\n/workspace\r\n\r\n/workspace/bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow /workspace\r\n\r\n/workspace\r\n\r\nbazel-bin/tensorflow/tools/pip_package/build_pip_package: line 201: patchelf: command not found\r\n```\r\n\r\nIt seems not the error coming from my PR..?", "> Hi @gbaned , could you help retriger the check? Thanks!\r\n\r\nHi @sirakiin I have retriggered the checks. Thank you!", "Hi @jaeyoo Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @jaeyoo Can you please resolve conflicts? Thank you!", "it's internally merged. closed." ]

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[ "@soosung80\r\n@joefernandez\r\n\r\nWhy can't I add you two as reviewers?", "Hi @soosung80 / @joefernandez Can you please review this PR ? Thank you!", "So, Copybara doesn't want to merge the PR after it created the commit (87197a464c7cb21135ce66bcf861a92d465f20fc) for it. This is because you have synced it to master after it was imported in the internal system (was in the Piper area in the diagram below):\r\n\r\n\r\n\r\nAs such, sadly, we'll need to manually close the PR." ]

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[ "Hi @edi-bice, \r\n\r\nSetting `os.environ['S3_USE_HTTPS'] = '0'` in TensorFlow can cause an error because TensorFlow by default uses HTTPS to communicate with S3. If you set S3_USE_HTTPS to '0', you are telling TensorFlow to use plain HTTP to communicate with S3, which can cause a security issue because the traffic is not encrypted and can be intercepted and modified by attackers.\r\n\r\nAdditionally, some S3 endpoints may not even allow non-HTTPS connections, so setting S3_USE_HTTPS to '0' could result in connection failures or other errors. Therefore, it is generally not recommended to set S3_USE_HTTPS to '0' in TensorFlow. Thank you!", "@synandi agree completely but should have made it clearer that this is TF running in TFX/KFP pipelines on Kubeflow accessing its cluster-local Minio storage via S3 protocol etc. Minio is bundled with Kubeflow and at least the gcp-blueprint distribution seems to have it configured without SSL.", "On a different Kubeflow 1.6 deployment on GCP, it seems to be making request against http endpoint though regardless of the value in os.environ['S3_USE_HTTPS'] (whether 0 or 1) and regardless of os.environ['S3_ENDPOINT'] being '10.12.2.51:9000' or 'http://10.12.2.51:9000' as is reported by Minio pod logs.\r\n\r\n2023-03-15 12:26:40.083090: I tensorflow/c/logging.cc:34] Making request to http://10.12.2.51:9000/edi_bice/models/clientXYZ/model_arch/poisson_model/model_layers.json\r\n\r\n`2023-03-15 12:26:30.063771: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.063836: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.063851: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.063857: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.063867: I tensorflow/c/logging.cc:34] Initializing config loader against fileName /home/jovyan/.aws/credentials and using profilePrefix = 0\r\n2023-03-15 12:26:30.063874: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.063879: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.063885: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.063890: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.063896: I tensorflow/c/logging.cc:34] Initializing config loader against fileName /home/jovyan/.aws/config and using profilePrefix = 1\r\n2023-03-15 12:26:30.063902: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.063907: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.063913: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.063918: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.063948: I tensorflow/c/logging.cc:34] Unable to open config file /home/jovyan/.aws/credentials for reading.\r\n2023-03-15 12:26:30.063955: I tensorflow/c/logging.cc:34] Failed to reload configuration.\r\n2023-03-15 12:26:30.063961: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.063970: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.063979: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.063988: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.064001: I tensorflow/c/logging.cc:34] Unable to open config file /home/jovyan/.aws/config for reading.\r\n2023-03-15 12:26:30.064010: I tensorflow/c/logging.cc:34] Failed to reload configuration.\r\n2023-03-15 12:26:30.064042: I tensorflow/c/logging.cc:34] Initializing Curl library with version: 7.85.0, ssl version: BoringSSL\r\n2023-03-15 12:26:30.064059: I tensorflow/c/logging.cc:34] ClientConfiguration will use SDK Auto Resolved profile: [default] if not specified by users.\r\n2023-03-15 12:26:30.064080: W tensorflow/c/logging.cc:37] Retry Strategy will use the default max attempts.\r\n2023-03-15 12:26:30.064096: I tensorflow/c/logging.cc:34] Creating AWSHttpResourceClient with max connections 2 and scheme http\r\n2023-03-15 12:26:30.064108: I tensorflow/c/logging.cc:34] Initializing CurlHandleContainer with size 2\r\n2023-03-15 12:26:30.064128: I tensorflow/c/logging.cc:34] ClientConfiguration will use SDK Auto Resolved profile: [default] if not specified by users.\r\n2023-03-15 12:26:30.064139: W tensorflow/c/logging.cc:37] Retry Strategy will use the default max attempts.\r\n2023-03-15 12:26:30.064164: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.064176: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.064187: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.064196: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.064207: I tensorflow/c/logging.cc:34] Initializing config loader against fileName /home/jovyan/.aws/credentials and using profilePrefix = 0\r\n2023-03-15 12:26:30.064218: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.064227: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.064237: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.064250: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.064261: I tensorflow/c/logging.cc:34] Checking HOME for the home directory.\r\n2023-03-15 12:26:30.064271: I tensorflow/c/logging.cc:34] Environment value for variable HOME is /home/jovyan\r\n2023-03-15 12:26:30.064280: I tensorflow/c/logging.cc:34] Home directory is missing the final / appending one to normalize\r\n2023-03-15 12:26:30.064288: I tensorflow/c/logging.cc:34] Final Home Directory is /home/jovyan/\r\n2023-03-15 12:26:30.064301: I tensorflow/c/logging.cc:34] Setting provider to read credentials from /home/jovyan/.aws/credentials for credentials file and /home/jovyan/.aws/config for the config file , for use with profile default\r\n2023-03-15 12:26:30.064314: I tensorflow/c/logging.cc:34] Setting process credentials provider to read config from default\r\n2023-03-15 12:26:30.064332: W tensorflow/c/logging.cc:37] Token file must be specified to use STS AssumeRole web identity creds provider.\r\n2023-03-15 12:26:30.064344: I tensorflow/c/logging.cc:34] Setting sso credentials provider to read config from default\r\n2023-03-15 12:26:30.064357: I tensorflow/c/logging.cc:34] The environment variable value AWS_CONTAINER_CREDENTIALS_RELATIVE_URI is \r\n2023-03-15 12:26:30.064366: I tensorflow/c/logging.cc:34] The environment variable value AWS_CONTAINER_CREDENTIALS_FULL_URI is \r\n2023-03-15 12:26:30.064375: I tensorflow/c/logging.cc:34] The environment variable value AWS_EC2_METADATA_DISABLED is \r\n2023-03-15 12:26:30.064386: I tensorflow/c/logging.cc:34] ClientConfiguration will use SDK Auto Resolved profile: [default] if not specified by users.\r\n2023-03-15 12:26:30.064397: W tensorflow/c/logging.cc:37] Retry Strategy will use the default max attempts.\r\n2023-03-15 12:26:30.064409: I tensorflow/c/logging.cc:34] Creating AWSHttpResourceClient with max connections 2 and scheme http\r\n2023-03-15 12:26:30.064419: I tensorflow/c/logging.cc:34] Initializing CurlHandleContainer with size 2\r\n2023-03-15 12:26:30.064430: I tensorflow/c/logging.cc:34] Creating Instance with default EC2MetadataClient and refresh rate 300000\r\n2023-03-15 12:26:30.064448: I tensorflow/c/logging.cc:34] Added EC2 metadata service credentials provider to the provider chain.\r\n2023-03-15 12:26:30.064477: I tensorflow/c/logging.cc:34] Found credential in environment with access key id minio\r\n2023-03-15 12:26:30.064491: I tensorflow/c/logging.cc:34] Found secret key\r\n2023-03-15 12:26:30.064547: I tensorflow/c/logging.cc:34] Initializing CurlHandleContainer with size 25\r\n2023-03-15 12:26:30.064729: I tensorflow/c/logging.cc:34] No content body, content-length headers\r\n2023-03-15 12:26:30.064795: I tensorflow/c/logging.cc:34] Found credential in environment with access key id minio\r\n2023-03-15 12:26:30.064811: I tensorflow/c/logging.cc:34] Found secret key\r\n2023-03-15 12:26:30.064824: I tensorflow/c/logging.cc:34] Using cached empty string sha256 e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 because payload is empty.\r\n2023-03-15 12:26:30.064870: I tensorflow/c/logging.cc:34] Canonical Header String: amz-sdk-invocation-id:CD996767-93DA-4B24-AC07-CF3DBD6AC7A3\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230315T122630Z\r\n\r\n2023-03-15 12:26:30.064883: I tensorflow/c/logging.cc:34] Signed Headers value:amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\n2023-03-15 12:26:30.064913: I tensorflow/c/logging.cc:34] Canonical Request String: HEAD\r\n/edi_bice/models/clientXYZ/model_arch/poisson_model/model_layers.json\r\n\r\namz-sdk-invocation-id:CD996767-93DA-4B24-AC07-CF3DBD6AC7A3\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230315T122630Z\r\n\r\namz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\ne3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-15 12:26:30.064938: I tensorflow/c/logging.cc:34] Final String to sign: AWS4-HMAC-SHA256\r\n20230315T122630Z\r\n20230315/us-east-1/s3/aws4_request\r\ne3cae3b1a464d54b492c8a1e21c4a99785ea29c76e307538f7af5db540f48360\r\n2023-03-15 12:26:30.064952: I tensorflow/c/logging.cc:34] Final computed signing hash: 89accf1cc9a4a9570c50324fa82195ca53aaae9764b2c70bdbafec1fe976609f\r\n2023-03-15 12:26:30.064965: I tensorflow/c/logging.cc:34] Signing request with: AWS4-HMAC-SHA256 Credential=minio/20230315/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=89accf1cc9a4a9570c50324fa82195ca53aaae9764b2c70bdbafec1fe976609f\r\n2023-03-15 12:26:30.064977: I tensorflow/c/logging.cc:34] Request Successfully signed\r\n2023-03-15 12:26:30.064997: I tensorflow/c/logging.cc:34] Making request to http://10.12.2.51:9000/edi_bice/models/clientXYZ/model_arch/poisson_model/model_layers.json\r\n2023-03-15 12:26:30.065011: I tensorflow/c/logging.cc:34] Including headers:\r\n2023-03-15 12:26:30.065021: I tensorflow/c/logging.cc:34] amz-sdk-invocation-id: CD996767-93DA-4B24-AC07-CF3DBD6AC7A3\r\n2023-03-15 12:26:30.065032: I tensorflow/c/logging.cc:34] amz-sdk-request: attempt=1\r\n2023-03-15 12:26:30.065042: I tensorflow/c/logging.cc:34] authorization: AWS4-HMAC-SHA256 Credential=minio/20230315/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=89accf1cc9a4a9570c50324fa82195ca53aaae9764b2c70bdbafec1fe976609f\r\n2023-03-15 12:26:30.065052: I tensorflow/c/logging.cc:34] content-type: application/xml\r\n2023-03-15 12:26:30.065063: I tensorflow/c/logging.cc:34] host: 10.12.2.51:9000\r\n2023-03-15 12:26:30.065073: I tensorflow/c/logging.cc:34] user-agent: aws-sdk-cpp/1.8.186 Linux/5.15.0-1024-gke x86_64 GCC/7.3.1\r\n2023-03-15 12:26:30.065082: I tensorflow/c/logging.cc:34] x-amz-api-version: 2006-03-01\r\n2023-03-15 12:26:30.065091: I tensorflow/c/logging.cc:34] x-amz-content-sha256: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-15 12:26:30.065100: I tensorflow/c/logging.cc:34] x-amz-date: 20230315T122630Z\r\n2023-03-15 12:26:30.065111: I tensorflow/c/logging.cc:34] Attempting to acquire curl connection.\r\n2023-03-15 12:26:30.065119: I tensorflow/c/logging.cc:34] No current connections available in pool. Attempting to create new connections.\r\n2023-03-15 12:26:30.065128: I tensorflow/c/logging.cc:34] attempting to grow pool size by 2\r\n2023-03-15 12:26:30.065153: I tensorflow/c/logging.cc:34] Pool grown by 2\r\n2023-03-15 12:26:30.065166: I tensorflow/c/logging.cc:34] Connection has been released. Continuing.\r\n2023-03-15 12:26:30.065177: I tensorflow/c/logging.cc:34] Returning connection handle 0x557f0495ca40\r\n2023-03-15 12:26:30.065186: I tensorflow/c/logging.cc:34] Obtained connection handle 0x557f0495ca40\r\n2023-03-15 12:26:40.081483: I tensorflow/c/logging.cc:34] HTTP/1.1 503 Service Unavailable\r\n\r\n2023-03-15 12:26:40.081573: I tensorflow/c/logging.cc:34] content-length: 91\r\n\r\n2023-03-15 12:26:40.081592: I tensorflow/c/logging.cc:34] content-type: text/plain\r\n\r\n2023-03-15 12:26:40.081603: I tensorflow/c/logging.cc:34] date: Wed, 15 Mar 2023 12:26:39 GMT\r\n\r\n2023-03-15 12:26:40.081612: I tensorflow/c/logging.cc:34] server: envoy\r\n\r\n2023-03-15 12:26:40.081620: I tensorflow/c/logging.cc:34] \r\n\r\n2023-03-15 12:26:40.081647: I tensorflow/c/logging.cc:34] Returned http response code 503\r\n2023-03-15 12:26:40.081655: I tensorflow/c/logging.cc:34] Returned content type text/plain\r\n2023-03-15 12:26:40.081661: I tensorflow/c/logging.cc:34] Releasing curl handle 0x557f0495ca40\r\n2023-03-15 12:26:40.081680: I tensorflow/c/logging.cc:34] Releasing curl handle 0x557f0495ca40\r\n2023-03-15 12:26:40.081689: I tensorflow/c/logging.cc:34] Notified waiting threads.\r\n2023-03-15 12:26:40.081713: I tensorflow/c/logging.cc:34] Request returned error. Attempting to generate appropriate error codes from response\r\n2023-03-15 12:26:40.081737: E tensorflow/c/logging.cc:40] HTTP response code: 503\r\nResolved remote host IP address: \r\nRequest ID: \r\nException name: \r\nError message: No response body.\r\n4 response headers:\r\ncontent-length : 91\r\ncontent-type : text/plain\r\ndate : Wed, 15 Mar 2023 12:26:39 GMT\r\nserver : envoy\r\n2023-03-15 12:26:40.081762: W tensorflow/c/logging.cc:37] If the signature check failed. This could be because of a time skew. Attempting to adjust the signer.\r\n2023-03-15 12:26:40.081771: I tensorflow/c/logging.cc:34] Server time is Wed, 15 Mar 2023 12:26:39 GMT, while client time is Wed, 15 Mar 2023 12:26:40 GMT\r\n2023-03-15 12:26:40.081778: W tensorflow/c/logging.cc:37] Request failed, now waiting 0 ms before attempting again.`", "Same code (and library versions) seems to work on Kubeflow 1.5 deployed on GCP via gcp-blueprints\r\n\r\n`2023-03-14 21:59:13.723573: I tensorflow/c/logging.cc:34] No content body, content-length headers\r\n2023-03-14 21:59:13.723658: I tensorflow/c/logging.cc:34] Found credential in environment with access key id minio\r\n2023-03-14 21:59:13.723680: I tensorflow/c/logging.cc:34] Found secret key\r\n2023-03-14 21:59:13.723696: I tensorflow/c/logging.cc:34] Using cached empty string sha256 e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 because payload is empty.\r\n2023-03-14 21:59:13.723766: I tensorflow/c/logging.cc:34] Canonical Header String: amz-sdk-invocation-id:5D5AC5F0-5091-4804-A1BE-EE45C33DAC84\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230314T215913Z\r\n\r\n2023-03-14 21:59:13.723792: I tensorflow/c/logging.cc:34] Signed Headers value:amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\n2023-03-14 21:59:13.723830: I tensorflow/c/logging.cc:34] Canonical Request String: HEAD\r\n/edi_bice/models/clientXYZ/model_arch/poisson_model/model_layers.json\r\n\r\namz-sdk-invocation-id:5D5AC5F0-5091-4804-A1BE-EE45C33DAC84\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230314T215913Z\r\n\r\namz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\ne3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-14 21:59:13.723874: I tensorflow/c/logging.cc:34] Final String to sign: AWS4-HMAC-SHA256\r\n20230314T215913Z\r\n20230314/us-east-1/s3/aws4_request\r\n1238bfb28a22deb82193ab1d9129bc2f72feaf1e511c934ff9912ff3f5ff759a\r\n2023-03-14 21:59:13.723911: I tensorflow/c/logging.cc:34] Final computed signing hash: 9aff3e1cfabe7f6bdabb78953a14b1d3da73a4ea72035a8da890706f7f115c4d\r\n2023-03-14 21:59:13.723931: I tensorflow/c/logging.cc:34] Signing request with: AWS4-HMAC-SHA256 Credential=minio/20230314/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=9aff3e1cfabe7f6bdabb78953a14b1d3da73a4ea72035a8da890706f7f115c4d\r\n2023-03-14 21:59:13.723956: I tensorflow/c/logging.cc:34] Request Successfully signed\r\n2023-03-14 21:59:13.723986: I tensorflow/c/logging.cc:34] Making request to http://10.12.2.51:9000/edi_bice/models/clientXYZ/model_arch/poisson_model/model_layers.json\r\n2023-03-14 21:59:13.724013: I tensorflow/c/logging.cc:34] Including headers:\r\n2023-03-14 21:59:13.724031: I tensorflow/c/logging.cc:34] amz-sdk-invocation-id: 5D5AC5F0-5091-4804-A1BE-EE45C33DAC84\r\n2023-03-14 21:59:13.724054: I tensorflow/c/logging.cc:34] amz-sdk-request: attempt=1\r\n2023-03-14 21:59:13.724069: I tensorflow/c/logging.cc:34] authorization: AWS4-HMAC-SHA256 Credential=minio/20230314/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=9aff3e1cfabe7f6bdabb78953a14b1d3da73a4ea72035a8da890706f7f115c4d\r\n2023-03-14 21:59:13.724085: I tensorflow/c/logging.cc:34] content-type: application/xml\r\n2023-03-14 21:59:13.724108: I tensorflow/c/logging.cc:34] host: 10.12.2.51:9000\r\n2023-03-14 21:59:13.724124: I tensorflow/c/logging.cc:34] user-agent: aws-sdk-cpp/1.8.186 Linux/5.4.217+ x86_64 GCC/7.3.1\r\n2023-03-14 21:59:13.724137: I tensorflow/c/logging.cc:34] x-amz-api-version: 2006-03-01\r\n2023-03-14 21:59:13.724161: I tensorflow/c/logging.cc:34] x-amz-content-sha256: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-14 21:59:13.724177: I tensorflow/c/logging.cc:34] x-amz-date: 20230314T215913Z\r\n2023-03-14 21:59:13.724200: I tensorflow/c/logging.cc:34] Attempting to acquire curl connection.\r\n2023-03-14 21:59:13.724216: I tensorflow/c/logging.cc:34] Connection has been released. Continuing.\r\n2023-03-14 21:59:13.724241: I tensorflow/c/logging.cc:34] Returning connection handle 0x55d7fe080310\r\n2023-03-14 21:59:13.724542: I tensorflow/c/logging.cc:34] Obtained connection handle 0x55d7fe080310\r\n2023-03-14 21:59:13.798382: I tensorflow/c/logging.cc:34] HTTP/1.1 200 OK\r\n\r\n2023-03-14 21:59:13.798456: I tensorflow/c/logging.cc:34] accept-ranges: bytes\r\n\r\n2023-03-14 21:59:13.798481: I tensorflow/c/logging.cc:34] content-length: 717\r\n\r\n2023-03-14 21:59:13.798505: I tensorflow/c/logging.cc:34] content-security-policy: block-all-mixed-content\r\n\r\n2023-03-14 21:59:13.798525: I tensorflow/c/logging.cc:34] content-type: application/json\r\n\r\n2023-03-14 21:59:13.798546: I tensorflow/c/logging.cc:34] etag: \"a88e35987284c3a006ee88c43f902a7a\"\r\n\r\n2023-03-14 21:59:13.798635: I tensorflow/c/logging.cc:34] last-modified: Thu, 04 Feb 2021 19:21:01 GMT\r\n\r\n2023-03-14 21:59:13.798665: I tensorflow/c/logging.cc:34] server: istio-envoy\r\n\r\n2023-03-14 21:59:13.798684: I tensorflow/c/logging.cc:34] vary: Origin\r\n\r\n2023-03-14 21:59:13.798703: I tensorflow/c/logging.cc:34] x-amz-request-id: 174C680A1BEFE503\r\n\r\n2023-03-14 21:59:13.798722: I tensorflow/c/logging.cc:34] x-xss-protection: 1; mode=block\r\n\r\n2023-03-14 21:59:13.798743: I tensorflow/c/logging.cc:34] date: Tue, 14 Mar 2023 21:59:13 GMT\r\n\r\n2023-03-14 21:59:13.798764: I tensorflow/c/logging.cc:34] x-envoy-upstream-service-time: 68\r\n\r\n2023-03-14 21:59:13.798789: I tensorflow/c/logging.cc:34] x-envoy-decorator-operation: minio-service.kubeflow.svc.cluster.local:9000/*\r\n\r\n2023-03-14 21:59:13.798849: I tensorflow/c/logging.cc:34] \r\n\r\n2023-03-14 21:59:13.798892: I tensorflow/c/logging.cc:34] Returned http response code 200\r\n2023-03-14 21:59:13.798919: I tensorflow/c/logging.cc:34] Returned content type application/json\r\n2023-03-14 21:59:13.798939: I tensorflow/c/logging.cc:34] Releasing curl handle 0x55d7fe080310\r\n2023-03-14 21:59:13.798964: I tensorflow/c/logging.cc:34] Releasing curl handle 0x55d7fe080310\r\n2023-03-14 21:59:13.799030: I tensorflow/c/logging.cc:34] Notified waiting threads.\r\n2023-03-14 21:59:13.799069: I tensorflow/c/logging.cc:34] Request returned successful response.\r\n2023-03-14 21:59:13.799099: I tensorflow/c/logging.cc:34] Request successful returning.\r\n2023-03-14 21:59:13.799232: I tensorflow/c/logging.cc:34] No content body, content-length headers\r\n2023-03-14 21:59:13.799291: I tensorflow/c/logging.cc:34] Found credential in environment with access key id minio\r\n2023-03-14 21:59:13.799315: I tensorflow/c/logging.cc:34] Found secret key\r\n2023-03-14 21:59:13.799331: I tensorflow/c/logging.cc:34] Using cached empty string sha256 e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 because payload is empty.\r\n2023-03-14 21:59:13.799428: I tensorflow/c/logging.cc:34] Canonical Header String: amz-sdk-invocation-id:6E5C4870-932F-495A-8CA3-BA80DEC0DB7A\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230314T215913Z\r\n\r\n2023-03-14 21:59:13.799458: I tensorflow/c/logging.cc:34] Signed Headers value:amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\n2023-03-14 21:59:13.799510: I tensorflow/c/logging.cc:34] Canonical Request String: GET\r\n/edi_bice\r\nlist-type=2&max-keys=1&prefix=models0.000000clientXYZ0.000000model_arch0.000000poisson_model0.000000model_layers.json0.000000\r\namz-sdk-invocation-id:6E5C4870-932F-495A-8CA3-BA80DEC0DB7A\r\namz-sdk-request:attempt=1\r\ncontent-type:application/xml\r\nhost:10.12.2.51:9000\r\nx-amz-api-version:2006-03-01\r\nx-amz-content-sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\nx-amz-date:20230314T215913Z\r\n\r\namz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date\r\ne3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-14 21:59:13.799561: I tensorflow/c/logging.cc:34] Final String to sign: AWS4-HMAC-SHA256\r\n20230314T215913Z\r\n20230314/us-east-1/s3/aws4_request\r\n47c1d82682b93d2b38c74ff49c8d39ea99c2a055e73ae8713f12cfe941631e4b\r\n2023-03-14 21:59:13.799581: I tensorflow/c/logging.cc:34] Final computed signing hash: 0e2b24c44f0bb058d251d2bddaab0f002dcaf531a9ca48bbafe2ea1d6a45ec1b\r\n2023-03-14 21:59:13.799607: I tensorflow/c/logging.cc:34] Signing request with: AWS4-HMAC-SHA256 Credential=minio/20230314/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=0e2b24c44f0bb058d251d2bddaab0f002dcaf531a9ca48bbafe2ea1d6a45ec1b\r\n2023-03-14 21:59:13.799634: I tensorflow/c/logging.cc:34] Request Successfully signed\r\n2023-03-14 21:59:13.799666: I tensorflow/c/logging.cc:34] Making request to http://10.12.2.51:9000/edi_bice?list-type=2&max-keys=1&prefix=models0.000000clientXYZ0.000000model_arch0.000000poisson_model0.000000model_layers.json0.000000\r\n2023-03-14 21:59:13.799743: I tensorflow/c/logging.cc:34] Including headers:\r\n2023-03-14 21:59:13.799805: I tensorflow/c/logging.cc:34] amz-sdk-invocation-id: 6E5C4870-932F-495A-8CA3-BA80DEC0DB7A\r\n2023-03-14 21:59:13.799846: I tensorflow/c/logging.cc:34] amz-sdk-request: attempt=1\r\n2023-03-14 21:59:13.799896: I tensorflow/c/logging.cc:34] authorization: AWS4-HMAC-SHA256 Credential=minio/20230314/us-east-1/s3/aws4_request, SignedHeaders=amz-sdk-invocation-id;amz-sdk-request;content-type;host;x-amz-api-version;x-amz-content-sha256;x-amz-date, Signature=0e2b24c44f0bb058d251d2bddaab0f002dcaf531a9ca48bbafe2ea1d6a45ec1b\r\n2023-03-14 21:59:13.800022: I tensorflow/c/logging.cc:34] content-type: application/xml\r\n2023-03-14 21:59:13.800108: I tensorflow/c/logging.cc:34] host: 10.12.2.51:9000\r\n2023-03-14 21:59:13.800226: I tensorflow/c/logging.cc:34] user-agent: aws-sdk-cpp/1.8.186 Linux/5.4.217+ x86_64 GCC/7.3.1\r\n2023-03-14 21:59:13.800283: I tensorflow/c/logging.cc:34] x-amz-api-version: 2006-03-01\r\n2023-03-14 21:59:13.800301: I tensorflow/c/logging.cc:34] x-amz-content-sha256: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\r\n2023-03-14 21:59:13.800315: I tensorflow/c/logging.cc:34] x-amz-date: 20230314T215913Z\r\n2023-03-14 21:59:13.800344: I tensorflow/c/logging.cc:34] Attempting to acquire curl connection.\r\n2023-03-14 21:59:13.800640: I tensorflow/c/logging.cc:34] Connection has been released. Continuing.\r\n2023-03-14 21:59:13.800677: I tensorflow/c/logging.cc:34] Returning connection handle 0x55d7fe080310\r\n2023-03-14 21:59:13.800793: I tensorflow/c/logging.cc:34] Obtained connection handle 0x55d7fe080310\r\n2023-03-14 21:59:13.878099: I tensorflow/c/logging.cc:34] HTTP/1.1 200 OK\r\n\r\n2023-03-14 21:59:13.878238: I tensorflow/c/logging.cc:34] accept-ranges: bytes\r\n\r\n2023-03-14 21:59:13.878309: I tensorflow/c/logging.cc:34] content-length: 319\r\n\r\n2023-03-14 21:59:13.878331: I tensorflow/c/logging.cc:34] content-security-policy: block-all-mixed-content\r\n\r\n2023-03-14 21:59:13.878356: I tensorflow/c/logging.cc:34] content-type: application/xml\r\n\r\n2023-03-14 21:59:13.878371: I tensorflow/c/logging.cc:34] server: istio-envoy\r\n\r\n2023-03-14 21:59:13.878396: I tensorflow/c/logging.cc:34] vary: Origin\r\n\r\n2023-03-14 21:59:13.878411: I tensorflow/c/logging.cc:34] x-amz-request-id: 174C680A2038EBCD\r\n\r\n2023-03-14 21:59:13.878427: I tensorflow/c/logging.cc:34] x-xss-protection: 1; mode=block\r\n\r\n2023-03-14 21:59:13.878451: I tensorflow/c/logging.cc:34] date: Tue, 14 Mar 2023 21:59:13 GMT\r\n\r\n2023-03-14 21:59:13.878470: I tensorflow/c/logging.cc:34] x-envoy-upstream-service-time: 75\r\n\r\n2023-03-14 21:59:13.878495: I tensorflow/c/logging.cc:34] x-envoy-decorator-operation: minio-service.kubeflow.svc.cluster.local:9000/*\r\n\r\n2023-03-14 21:59:13.878510: I tensorflow/c/logging.cc:34] \r\n\r\n2023-03-14 21:59:13.878530: I tensorflow/c/logging.cc:34] 319 bytes written to response.\r\n2023-03-14 21:59:13.878573: I tensorflow/c/logging.cc:34] Returned http response code 200\r\n2023-03-14 21:59:13.878594: I tensorflow/c/logging.cc:34] Returned content type application/xml\r\n2023-03-14 21:59:13.878608: I tensorflow/c/logging.cc:34] Response content-length header: 319\r\n2023-03-14 21:59:13.878621: I tensorflow/c/logging.cc:34] Response body length: 319\r\n2023-03-14 21:59:13.878644: I tensorflow/c/logging.cc:34] Releasing curl handle 0x55d7fe080310\r\n2023-03-14 21:59:13.878665: I tensorflow/c/logging.cc:34] Releasing curl handle 0x55d7fe080310\r\n2023-03-14 21:59:13.878687: I tensorflow/c/logging.cc:34] Notified waiting threads.\r\n2023-03-14 21:59:13.878711: I tensorflow/c/logging.cc:34] Request returned successful response.\r\n2023-03-14 21:59:13.878731: I tensorflow/c/logging.cc:34] Request successful returning.`", "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/59990\">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/59990\">No</a>\n" ]

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[ "@setter43,\r\nFor Simple audio recognition and Audio classification, there are pre-built models available on the official tensorflow.org. \r\nThe model demonstrates how to preprocess audio files in the WAV format and build and train a basic [automatic speech recognition](https://en.wikipedia.org/wiki/Speech_recognition) (ASR) model for recognizing ten different words.\r\n\r\nAlso I tried to execute the demonstrated code for the Audio recognition which was executed successfully. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/11522da06ccca7071762085b7d3d007c/untitled1039.ipynb) and the reference.\r\n\r\nhttps://www.tensorflow.org/tutorials/audio/simple_audio\r\nhttps://www.tensorflow.org/lite/examples/audio_classification/overview#get_started\r\n\r\nThank you!\r\n", "I want to use my own Audio classification AI model that I build using Teachable Machine. I run into a number of issues when trying to adapt the model.py in the git project keyword-spotting.\r\n\r\nI can get the model working.\r\nThen I try to point the program to my own AI and it fails.\r\n\r\n`mendel@yellow-jet:~/newtest/project-keyword-spotter$ python3 run_model.py --model_file soundclassifier_with_metadata.tflite\r\nTraceback (most recent call last):\r\n File \"run_model.py\", line 60, in <module>\r\n main()\r\n File \"run_model.py\", line 50, in main\r\n interpreter = model.make_interpreter(args.model_file)\r\n File \"/home/mendel/newtest/project-keyword-spotter/model.py\", line 211, in make_interpreter\r\n {'device': device[0]} if device else {})\r\n File \"/usr/lib/python3/dist-packages/tflite_runtime/interpreter.py\", line 351, in __init__\r\n experimental_preserve_all_tensors))\r\nValueError: Could not open 'soundclassifier_with_metadata.tflite'.\r\nmendel@yellow-jet:~/newtest/project-keyword-spotter$\r\n`", "@setter43,\r\nCould you please confirm whether you are using this teachable machine official link to make an audio model.\r\nhttps://teachablemachine.withgoogle.com/train/audio\r\n\r\n\r\nThe pre trained lite models in TF hub provide metadata. However, you can add metadata given a tflite model using [TFLIte meta data writer API](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial).\r\n\r\nPlease find the overview of adding metada [here](https://www.tensorflow.org/lite/models/convert/metadata) along with the examples.\r\n\r\nAnd also were you able to get the tflite file, if yes could you provide the code which you are using to test which helps to analyse.\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.", "@setter43 \r\nTeachable Machine with Google gives you the option to download your model after it has been trained\r\nSee for TensorFlow Keras \r\nClick on it\r\nIt would generate a zip file with the name converted_keras\r\nThere would be 2 files in it:\r\n1) 1 would be your trained model in .h5 format\r\n2) 1 would be a simple text file with labels\r\n\r\nhttps://teachablemachine.withgoogle.com/train/audio\r\nOnce check for your dataset, it's source and how you are uploading it to be trained\r\nMy recommendation would be to create the dataset on your own", "I am using Teachable Machine for my audio model. I don't have the option to export in .h5 for audio. Just .js or .tflite. tflite would be compatible with the google coral I am working with. But the sample app Git link just leads to an error and it's hard to find an example where I can take my AI model and just plug and play.\r\n\r\n\r\n", "Hi @setter43 \r\n\r\nSorry for the delayed response.\r\n\r\nUnder the hood Teachable Machine uses [Tensorflow.js](https://www.tensorflow.org/js), a library for machine learning in Javascript, to train and run the models you make in your web browser as given [here](https://github.com/googlecreativelab/teachablemachine-community#what-is-this-repository-for). The option to download `.h5` file might not be available yet.\r\n\r\nCould you please to this issue in \r\n[teachablemachine-community](https://github.com/googlecreativelab/teachablemachine-community) repo for faster resolution?\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/59985\">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/59985\">No</a>\n" ]

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[ "@gaikwadrahul8 \r\nWhen I try to replicate the issue on the Virtual machine, I'm facing a different error. Please find the below screenshots for reference.\r\n\r\n\r\n\r\n\r\nThank you!\r\n", "Hi, @sdeoras \r\n\r\nApologize for the delayed response and I was able to replicate the same issue on `Apple Mac M1` system with command `bazel build //tensorflow/tools/pip_package:build_pip_package` and I've added screenshot below\r\n\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2023-03-23 at 10 48 47 AM\" src=\"https://user-images.githubusercontent.com/115997457/227163817-a274a342-11d1-404c-928a-eeb992c5d0ed.png\">\r\n\r\nI have also tried to replicate the same issue with your command `bazel test --config opt //tensorflow/tools/lib_package:libtensorflow_test` and I was getting below error and I have added error log for your reference and we'll 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(bazel-test-2.11) gaikwadrahul-macbookpro:tensorflow gaikwadrahul$ bazel test //tensorflow/tools/lib_package:libtensorflow_test\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=214\r\nINFO: Reading rc options for 'test' from /Users/gaikwadrahul/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'test' from /Users/gaikwadrahul/tensorflow/.bazelrc:\r\n Inherited '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 'test' from /Users/gaikwadrahul/tensorflow/.tf_configure.bazelrc:\r\n Inherited 'build' options: --action_env PYTHON_BIN_PATH=/Users/gaikwadrahul/miniconda/envs/bazel-test-2.11/bin/python3 --action_env PYTHON_LIB_PATH=/Users/gaikwadrahul/miniconda/envs/bazel-test-2.11/lib/python3.9/site-packages --python_path=/Users/gaikwadrahul/miniconda/envs/bazel-test-2.11/bin/python3\r\nINFO: Reading rc options for 'test' from /Users/gaikwadrahul/tensorflow/.bazelrc:\r\n Inherited '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: Reading rc options for 'test' from /Users/gaikwadrahul/tensorflow/.tf_configure.bazelrc:\r\n 'test' options: --flaky_test_attempts=3 --test_size_filters=small,medium\r\nINFO: Found applicable config definition build:short_logs in file /Users/gaikwadrahul/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/gaikwadrahul/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition test:v2 in file /Users/gaikwadrahul/tensorflow/.tf_configure.bazelrc: --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-nomac,-no_mac,-mac_excluded,-oss_serial,-v1only --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-nomac,-no_mac,-mac_excluded,-v1only\r\nINFO: Found applicable config definition build:macos in file /Users/gaikwadrahul/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/3e79dfac0ea7b7cf2c9687f798b6f63ba5b07c9d.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/0aaa6e679847a4eeb407136e7b0bcef93ec652e6.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/benchmark/archive/f7547e29ccaed7b64ef4f7495ecfff1c9f6f3d03.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://mirror.bazel.build/github.com/bazelbuild/rules_cc/archive/081771d4a0e9d7d3aa0eed2ef389fa4700dfb23e.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/openxla/stablehlo/archive/cc287f69b7115725164f26a5a164f09f9dae091e.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/boringssl/archive/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/glennrp/libpng/archive/v1.6.39.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Analyzed target //tensorflow/tools/lib_package:libtensorflow_test (12 packages loaded, 242 targets configured).\r\nINFO: Found 1 test target...\r\nERROR: /Users/gaikwadrahul/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/gaikwadrahul/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/tools/lib_package:libtensorflow_test failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 880.535s, Critical Path: 137.27s\r\nINFO: 4713 processes: 1630 internal, 3083 local.\r\nFAILED: Build did NOT complete successfully\r\n//tensorflow/tools/lib_package:libtensorflow_test FAILED TO BUILD\r\n\r\nFAILED: Build did NOT complete successfully\r\n(bazel-test-2.11) gaikwadrahul-macbookpro:tensorflow gaikwadrahul$ \r\n```\r\n`", "@sdeoras Your original issue looks like you have a bad version of tensorflow_io_gcs_filesystem installed. For building TensorFlow 2.11.x please use tensorflow_io_gcs_filesystem==0.31.0.", "@tiruk007 _ComputeOutputShape is not in the 2.11.x source code so I think maybe you were not building 2.11?", "@gaikwadrahul8 Your issue seems to be a Mac specific problem and not related to this issue as reported which was from a Raspberry Pi.", "@tiruk007 Sorry, I see that the r2.11 branch had a temporary build failure for a while which is what caused the error you found. This build error was not present in 2.11.0 nor is it present in 2.11.1 but was introduced and then fixed in between those two releases.", "@sdeoras Actually your build error looks more like there was a stale object in the bazel cache that was not rebuilt when it should have been. If this still needs resolving please try again but run 'bazel clean --expunge' first. If you can try 2.11.1 or even better 2.12.0 then that would be good.", "@elfringham thanks for your replies. I am now trying v2.12.0 per your suggestion and will reply back on how it goes.\r\n", "@elfringham , unfortunately the build failed for v2.12.0 as well.\r\n```\r\n[4,828 / 9,049] Compiling llvm/lib/Transforms/Utils/BasicBlockUtils.cpp; 29s local ... (4 actions running)\r\n[5,012 / 9,049] Compiling llvm/lib/CodeGen/CodeGenPassBuilder.cpp; 18s local ... (4 actions running)\r\n[5,260 / 9,049] Compiling llvm/lib/CodeGen/MachineCheckDebugify.cpp; 16s local ... (4 actions running)\r\n[5,626 / 9,049] Compiling llvm/lib/Target/X86/X86AvoidStoreForwardingBlocks.cpp; 34s local ... (4 actions running)\r\n[5,900 / 9,049] Compiling llvm/lib/Target/AArch64/MCTargetDesc/AArch64MCTargetDesc.cpp; 40s local ... (4 actions running)\r\n[6,754 / 9,049] Compiling tensorflow/compiler/xla/service/all_to_all_decomposer.cc; 34s local ... (4 actions running)\r\n[7,080 / 9,049] Compiling tensorflow/compiler/xla/service/cpu/cpu_compiler.cc; 211s local ... (4 actions running)\r\n[7,487 / 9,049] Compiling tensorflow/core/kernels/sync_ops.cc; 20s local ... (4 actions running)\r\n[7,763 / 9,049] Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc; 1513s local ... (4 actions running)\r\n[8,127 / 9,049] Compiling tensorflow/core/kernels/argmax_op.cc; 237s local ... (4 actions running)\r\n[8,311 / 9,049] Compiling tensorflow/core/kernels/cwise_op_less.cc; 162s local ... (4 actions running)\r\n[8,681 / 9,049] Compiling tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc; 67s local ... (4 actions running)\r\nERROR: /home/pi/tensorflow/tensorflow/cc/BUILD:673:22: Executing genrule //tensorflow/cc:random_ops_genrule failed: (Exit 127): bash failed: error executing command /bin/bash bazel-out/aarch64-opt/bin/tensorflow/cc/random_ops_genrule.genrule_script.sh\r\nbazel-out/aarch64-opt/bin/tensorflow/cc/ops/random_ops_gen_cc: symbol lookup error: bazel-out/aarch64-opt/bin/tensorflow/cc/ops/random_ops_gen_cc: undefined symbol: _ZN4absl12lts_2022062318container_internal11kEmptyGroupE\r\nERROR: /home/pi/tensorflow/tensorflow/cc/BUILD:673:22: Executing genrule //tensorflow/cc:manip_ops_genrule failed: (Exit 127): bash failed: error executing command /bin/bash bazel-out/aarch64-opt/bin/tensorflow/cc/manip_ops_genrule.genrule_script.sh\r\nbazel-out/aarch64-opt/bin/tensorflow/cc/ops/manip_ops_gen_cc: symbol lookup error: bazel-out/aarch64-opt/bin/tensorflow/cc/ops/manip_ops_gen_cc: undefined symbol: _ZN4absl12lts_2022062318container_internal11kEmptyGroupE\r\nERROR: /home/pi/tensorflow/tensorflow/cc/BUILD:673:22: Executing genrule //tensorflow/cc:map_ops_genrule failed: (Exit 127): bash failed: error executing command /bin/bash bazel-out/aarch64-opt/bin/tensorflow/cc/map_ops_genrule.genrule_script.sh\r\nbazel-out/aarch64-opt/bin/tensorflow/cc/ops/map_ops_gen_cc: symbol lookup error: bazel-out/aarch64-opt/bin/tensorflow/cc/ops/map_ops_gen_cc: undefined symbol: _ZN4absl12lts_2022062318container_internal11kEmptyGroupE\r\nTarget //tensorflow/tools/lib_package:libtensorflow_test failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 49702.061s, Critical Path: 2645.63s\r\nINFO: 8900 processes: 870 internal, 8030 local.\r\nFAILED: Build did NOT complete successfully\r\n//tensorflow/tools/lib_package:libtensorflow_test FAILED TO BUILD\r\n\r\nExecuted 0 out of 1 test: 1 fails to build.\r\nFAILED: Build did NOT complete successfully\r\n```", "@zyxkad Your problem is not related to the one that this issue refers to. I think you should open a new issue to cover your Mac specific build problem.", "> @zyxkad Your problem is not related to the one that this issue refers to. I think you should open a new issue to cover your Mac specific build problem.\r\n\r\nMoved to #60179", "```\r\n$ llvm-cxxfilt-12 _ZN4absl12lts_2022062318container_internal11kEmptyGroupE\r\nabsl::lts_20220623::container_internal::kEmptyGroup\r\n```\r\n\r\nmaybe the absl is compiled correctly?", "tf v2.14.0 built successfully on linux/arm64 using gcc #62357 ", "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/59984\">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/59984\">No</a>\n" ]

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[ "@Thank you for posting an issue!\r\nThe model accuracy might drop due to quantization applied to the model. To improve the accuracy you try to use post-training quantization. You can apply quantization on your trained model, and retrain it. Please refer to the following references and let us know if it helps?\r\nhttps://www.tensorflow.org/lite/performance/model_optimization\r\nhttps://www.tensorflow.org/model_optimization/guide/quantization/training\r\nThank you!", "Thank you @sushreebarsa for the answer! \r\nCould you please provide me some suggestions on how to do quantization aware training for this specific architecture? [Here](https://github.com/junyongyou/triq/blob/master/src/train/train_triq.py) is the code I use for training.\r\nThank you in advance! ", "@codingzebra333 I could see there is one duplicate [ticket](https://github.com/tensorflow/tensorflow/issues/59684) raised for the same issue. Could you please follow up with the other ticket and close this one?\r\nThank you!", "Thank you for pointing that out! I did not want to close that issue in case I get another suggestion that does not involve me retraining everything. Also, could you please provide suggestions for the best approach for quantization aware training for my use case? \r\nThank you! ", "Hi @codingzebra333 \r\n\r\nYou can wrap the `triq` model at https://github.com/junyongyou/triq/blob/4b206b593f904f3f330ca61308a8d220f9cd1de1/src/train/train_triq.py#L80\r\naround the `tfmot.quantization.keras.quantize_model` which requires the `tensorflow_model_optimization` toolkit. \r\n\r\nPlease refer to the [document](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide#quantize_whole_model) on how to quantize whole model and the tips for better model accuracy.\r\n\r\nAlso, please check the QAT training [guide](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide) and API [compatibility](https://www.tensorflow.org/model_optimization/guide/quantization/training#api_compatibility) for applying QAT.\r\n\r\nThanks.", "Hi, thank you for the suggestion. I tried it, but I got an ValueError: Quantizing a tf.keras Model inside another tf.keras Model is not supported.\r\n[Here](https://colab.research.google.com/drive/1BIj8iFssp0mnMTPBMk52k0r3Ibb-Pt8Q?usp=sharing) is the code. \r\n\r\nThank you in advance! ", "Hi @codingzebra333 \r\n\r\nThe QAT has few [compatability](https://www.tensorflow.org/model_optimization/guide/quantization/training#api_compatibility) limitations as mentioned earlier. \r\n\r\nThe quantization can be applied for [tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras) with only Sequential and Functional models.\r\n\r\n Can you please refer the Quantization Aware Training [guide](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide) and see if it fits for your use case?\r\n\r\nThanks.", "Hey! \r\nI have a functional model, so it should work for my use case. \r\nThank you for such a fast response! Appreciate it! ", "Hi @codingzebra333 \r\n\r\nAs the error suggests, using QAT tf.keras Model inside another tf.keras Model is also not supported.\r\n\r\nIn your case I can observe that the backbone and transformer model are compiled which may not be supported to quantize. In that case we may need to separate the backbone model from the main model and quantize them separately. \r\n\r\nCan you check the [experiments](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide#experiment_with_quantization) with the quantization and let us know if it helps.\r\n\r\nThanks.\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59983\">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/59983\">No</a>\n" ]

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[ " @p3achyjr Thanks for reporting this issue.\r\n\r\nLooks like inputs are not being provided to the model's session. Also, the minimal reproducible code for the issue is incomplete. \r\n\r\nCan you please provide the steps you have followed and also did you try setting up the [dtype policy](https://www.tensorflow.org/guide/mixed_precision#setting_the_dtype_policy) to mixed_float16.\r\n\r\nThanks.\r\n", "Thanks for getting back--the dtype policy is set to mixed_float16 in python. Not sure how to do that in c++.\r\n\r\nI also ended up solving this as it turns out my tensors were not zero-initialized. I wonder if there's a way to do that automatically?\r\n\r\nAlso let me know if there is a better way to do half precision in C++, as right now I need to construct and run a graph to cast my tensors from float -> half -> back to float every time I want to call into my model.", "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/59982\">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/59982\">No</a>\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi, Could you please check the `ZerosLike` function https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/zeros-like which returns tensor of zeros.\r\nFor half precision in C++, to the best of knowledge I can think of `Requantize` OP https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/requantize, check if that helps you. Thanks!", "```\r\nusing namespace tensorflow;\r\n\r\nconst static std::vector<std::string> kInputNames = {\r\n \"serving_default_args_0:0\",\r\n};\r\n\r\nconst static std::vector<std::string> kOutputNames = {\r\n \"StatefulPartitionedCall:0\",\r\n};\r\n\r\nint main(int argc, char** argv) {\r\n SavedModelBundleLite model_bundle_;\r\n LoadSavedModel(SessionOptions(), RunOptions(), \"/tmp/model\",\r\n {\"serve\"}, &model_bundle_);\r\n\r\n Tensor input_features(DataType::DT_HALF,\r\n {1, 8, 8});\r\n input_features.flat_half().setZero(); // Initialize to zero\r\n std::vector<std::pair<std::string, Tensor>> inputs{\r\n std::make_pair(kInputNames[0], input_features),\r\n };\r\n\r\n std::vector<Tensor> output_buf = {\r\n Tensor(DataType::DT_FLOAT, {1, 6, 6}),\r\n };\r\n\r\n // Run inference with mixed precision\r\n Status status = model_bundle_.GetSession()->Run(\r\n {ops::Cast(scope, input_features, DataType::DT_HALF)},\r\n {kOutputNames[0]}, {}, &output_buf);\r\n\r\n if (!status.ok()) {\r\n LOG(ERROR) << \"Inference failed: \" << status;\r\n return -1;\r\n }\r\n\r\n // Print the output tensor values for debugging\r\n auto output_flat = output_buf[0].flat<float>();\r\n for (int i = 0; i < output_flat.size(); i++) {\r\n LOG(INFO) << \"Output[\" << i << \"] = \" << output_flat(i);\r\n }\r\n\r\n return 0;\r\n}\r\n```\r\n\r\nNote that the code now initializes the **input_features** tensor values to zero using the **setZero()** method on the **flat_half()** accessor. The input tensor is also now created with **DataType::DT_HALF**, and the output tensor is created with **DataType::DT_FLOAT** to match the output of the Python model.\r\n\r\nDuring inference, the code now uses mixed precision by casting the input tensor to half-precision using **ops::Cast(scope, input_features, DataType::DT_HALF)**. The output tensor is also created as a **std::vector<Tensor>** so that the Run() method can fill in the output values.\r\nAfter running inference, the code prints out the values of the output tensor for debugging purposes.", "Thanks all! I think we can close this :)", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59982\">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/59982\">No</a>\n" ]

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[ "@devharsh,\r\nI tried to execute the mentioned code and it was failing with the different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/da1944b0f1762f8e8b36ea97a76aebcb/untitled1035.ipynb) and provide the complete code which helps us to debug the issue in an effective way. Thank you!", "you have to put the code inside the init method of a class\r\n\r\n```\r\nclass your_class_name:\r\n\tdef __init__(self):\r\n\t\tmy_code_goes_here\r\n```", "@devharsh,\r\nUnfortunately I was facing a different error. It is better to provide the complete code or the colab gist which helps us to analyse the issue in an effective way. Thank you!", "Seems like the `TensorFlow` upgrade to version `2.12.0` from `2.12.0-rc1` fixed 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/59981\">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/59981\">No</a>\n" ]

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[ "@megleo \r\nAre you trying to reproduce the [example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android) locally for custom data and hitting problems?\r\n\r\nIf so, can you please mention what steps have you changed?\r\n\r\nThank you !", "@tiruk007 \r\nThanks for your reply.\r\n\r\nWe do nothing but download the sample code and compile into app and install app on our embedded device.\r\n\r\nBTW, we are embedded device manufacturer, and using the example app to test whether our embedded device tflite work well or not.\r\n\r\nBest\r\n\r\n", "@megleo \r\nThanks for the information, I have tried to reproduce the example on gpu and nnapi and didn't face any error.Please refer to the screenshot below:\r\n[NNAPI](https://user-images.githubusercontent.com/111861663/228489824-93c5509b-02aa-44cb-bc5e-0a29278ab983.jpg)\r\n[GPU](https://user-images.githubusercontent.com/111861663/228489851-d06ee7c9-1e29-4b9d-9e68-e919f5850408.jpg)\r\n\r\n\r\nCould you please provide more details and steps you followed to encounter the issue.\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.", "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/59980\">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/59980\">No</a>\n" ]

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[ "The issue with **tf.image.extract_patches** failing to compute gradients is that the function is not differentiable with respect to the input. The **extract_patches** operation is not implemented in a way that allows for gradients to be propagated through it.\r\n\r\nOne workaround for this issue is to implement the patch extraction manually using TensorFlow's built-in operations that support gradients. Here is an example implementation that achieves the same result as **tf.image.extract_patches**:\r\n```\r\nimport os\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nx = tf.Variable(tf.ones([1, 5, 5, 1]), tf.float32)\r\n\r\ndef func(x):\r\n patches = tf.image.extract_image_patches(\r\n x,\r\n ksizes=[1, 3, 3, 1],\r\n strides=[1, 1, 1, 1],\r\n rates=[1, 1, 1, 1],\r\n padding='VALID'\r\n )\r\n return tf.reshape(patches, [-1, 3, 3, 1])\r\n\r\nwith tf.GradientTape(persistent=True) as tape:\r\n tape.watch(x)\r\n output_rev = func(x)\r\n print(output_rev)\r\n\r\ngradient = tape.jacobian(output_rev, x)\r\n```\r\nIn this implementation, **tf.image.extract_image_patches** is used instead of **tf.image.extract_patches**, as **tf.image.extract_image_patches** supports gradients. The output of **tf.image.extract_image_patches** is reshaped to match the output of **tf.image.extract_patches**.", "@trickiwoo While replicating the issue reported here , I faced the following log;\r\n```\r\nTypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [int32, int64] that don't all match.\r\n Please find the [gist ]\r\n```\r\nPlease find the gist [here](https://colab.research.google.com/gist/sushreebarsa/878146fc2898f7db64e6a2231998c52f/59979.ipynb) here.\r\n\r\nCan we use tf.image.extract_image_patches instead for the implementation?\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/59979\">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/59979\">No</a>\n" ]

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[ "Hi @sirakiin Can you please review this PR ? Thank you!", "Please don't use \"update <file>\" commit messages. These make it harder to understand at a glance at commit history what the changes are.\r\n\r\nhttps://cbea.ms/git-commit/", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!" ]

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[ "Hi @tiruk007 Can you please resolve conflicts? Thank you!", "Hi @gbaned \r\nResolved the conflicts. Can you please check ?\r\nThanks !\r\n\r\n", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "This is superceded by https://github.com/tensorflow/tensorflow/commit/26612c8623c828c786dcebaa3151c5ea6d9ae0ec, closing." ]

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[ "Hi @ewenw , thanks for reporting this issue.\r\n\r\nSorry for the delayed response.\r\n\r\nCan you please provide the steps you have followed and also a toy model to reproduce the issue. That will help us to investigate the issue further.\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/59973\">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/59973\">No</a>\n" ]

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[ "cc @reedwm ", "CC @cantonios " ]

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[ "CC @reedwm.", "Can you resolve conflicts? a695fffc660994e892583391d69e4c1b9ce89320 has some minor conflicts." ]

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[ "Hi,\r\n\r\nIs there any way to track `stat:awaiting tensorflower` ?\r\n\r\nThanks", "Hi, Any update or information will be posted in this issue thread.", "Hi @RobertFlatt \r\n\r\nI have tried to build for `armeabi-v7a` using the android cross compilation build [instructions](https://www.tensorflow.org/lite/guide/build_cmake#specifics_of_android_cross-compilation) on r2.12 and was able build successfully without any error. Please find the screenshot below.\r\n\r\n<img width=\"655\" alt=\"Screenshot 2023-04-22 at 9 11 52 AM\" src=\"https://user-images.githubusercontent.com/118897289/233760390-c4493dd8-713d-4fff-92ad-b16c19b6c51a.png\">\r\n\r\nWith XNNPACK using the command \r\n`cmake -DCMAKE_TOOLCHAIN_FILE=android-ndk-r25/build/cmake/android.toolchain.cmake -DANDROID_ABI=armeabi-v7a tensorflow/lite -DTFLITE_ENABLE_XNNPACK=ON`\r\n\r\nCan you please let us know if you are still facing the issue?\r\n\r\nThanks.", "> Can you please let us know if you are still facing the issue?\r\n\r\nyes ☹️\r\n\r\n- I can replicate your response (I used `git clone -b v2.12.0 --single-branch https://github.com/tensorflow/tensorflow.git tensorflow_src` and `ndk 25b`), **but** a following `cmake --build . -j` fails with the same issue that was reported in the original post:\r\n\r\n```\r\nIn file included from /home/bobf/ex/tflite_build/xnnpack/src/qc8-gemm/gen/qc8-gemm-1x1c4-minmax-fp32-armsimd32.c:15:\r\n/home/bobf/ex/tflite_build/xnnpack/src/xnnpack/math.h:316:13: error: invalid output constraint '=t' in asm\r\n : [i] \"=t\" (i)\r\n```\r\n\r\n- FYI my use case is slightly different as I am building a pip package for Python (yes, on Android), but the use case in your post illustrates presumably the same issue.\r\n\r\n**Edit**: same result with NDK \"current LTS release\" `25c` https://github.com/android/ndk/wiki#current-lts-release", "I was able to replicate on r2.13 branch:\r\n\r\n```\r\ncmake -DCMAKE_TOOLCHAIN_FILE=~/Android/Sdk/ndk/25.2.9519653/build/cmake/android.toolchain.cmake -DANDROID_ABI=armeabi-v7a ../tensorflow/lite -DTFLITE_ENABLE_XNNPACK=ON\r\ncmake --build . -j\r\n```\r\n\r\n```\r\n...\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16694: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-igemm/gen/qs8-igemm-1x2c4-minmax-fp32-armsimd32.c.o] Error 1\r\nIn file included from In file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qu8-gemm/gen/qu8-gemm-1x1c4-minmax-fp32-armsimd32.c/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qs8-vlrelu/gen/qs8-vlrelu-armsimd32-x4.c::1515:\r\n:\r\n/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h:332:13/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h: :332:error: 13: invalid output constraint '=t' in asm\r\nerror: invalid output constraint '=t' in asm\r\n : [i] \"=t\" (i)\r\n ^\r\n : [i] \"=t\" (i)\r\n ^\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16680: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-igemm/gen/qs8-igemm-1x1c4-minmax-fp32-armsimd32.c.o] Error 1\r\n1 error generated.\r\n1 error generated.\r\n1 error generated.\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16554: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qc8-gemm/gen/qc8-gemm-2x2c4-minmax-fp32-armsimd32.c.o] Error 1\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16666: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-gemm/gen/qs8-gemm-2x2c4-minmax-fp32-armsimd32.c.o] Error 1\r\nIn file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qs8-vlrelu/gen/qs8-vlrelu-armsimd32-x8.c:15:\r\n/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h:332:13: error: invalid output constraint '=t' in asm\r\n : [i] \"=t\" (i)\r\n ^\r\n1 error generated.\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16708: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-igemm/gen/qs8-igemm-2x1c4-minmax-fp32-armsimd32.c.o] Error 1\r\nIn file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qu8-igemm/gen/qu8-igemm-1x1c4-minmax-fp32-armsimd32.c:15:\r\n/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h:332:13: gmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16736: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-vcvt/gen/qs8-vcvt-armsimd32-x4.c.o] Error 1\r\nerror: invalid output constraint '=t' in asm\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16750: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qs8-vcvt/gen/qs8-vcvt-armsimd32-x8.c.o] Error 1\r\n : [i] \"=t\" (i)\r\n ^\r\nIn file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qu8-gemm/gen/qu8-gemm-2x2c4-minmax-fp32-armsimd32.c:15:\r\n/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h:332:13: error: invalid output constraint '=t' in asm\r\n : [i] \"=t\" (i)\r\n ^\r\n1 error generated.\r\n1 error generated.\r\nIn file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qu8-vcvt/gen/qu8-vcvt-armsimd32-x4.c:15:\r\n/usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/xnnpack/math.h:332:13: error: invalid output constraint '=t' in asm\r\ngmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:16596: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/qc8-igemm/gen/qc8-igemm-2x1c4-minmax-fp32-armsimd32.c.o] Error 1\r\n : [i] \"=t\" (i)\r\n ^\r\nIn file included from /usr/local/google/home/pisethk/tensorflow/tflite_build/xnnpack/src/qu8-vlrelu/gen/qu8-vlrelu-armsimd32-x4.c:15:\r\n```", "There seems to have been some analysis \r\n\r\nhttps://github.com/google/XNNPACK/issues/4775#issuecomment-1542579254\r\nand\r\nhttps://github.com/google/XNNPACK/issues/4775#issuecomment-1542725430\r\n\r\nBut I can't see (I'm outside Google) if this has resulted in an updated implementation.....\r\n\r\nAnd I can infer from some of those posts 'just wait for some future NDK where some future Clang will be fixed', I would consider that an insufficient response.\r\n\r\n", "@RobertFlatt \r\n\r\nDoes the recommended work around in both those threads suffice for you so far?", "Hi @RobertFlatt\r\n\r\nAndroid NDK version=25 is actually not currently supported, the official documentation https://www.tensorflow.org/lite/android/lite_build currently recommends version 21e.\r\n\r\nCan you try with that version instead?", "@pkgoogle \r\n\r\nThanks for your interest.\r\n\r\nFor the recommended workaround https://github.com/google/XNNPACK/issues/4775#issuecomment-1571617394 `-DXNNPACK_ENABLE_ARM_BF16=OFF` :\r\n\r\n\r\nEDIT:\r\n\r\nDue to typo in NDK specification, my previous comments (below) were using gcc not Clang, and thus incorrect.\r\n\r\n`cmake -DCMAKE_TOOLCHAIN_FILE=../android-ndk-r25c/build/cmake/android.toolchain.cmake -DANDROID_ABI=armeabi-v7a -DTFLITE_ENABLE_XNNPACK=ON -DXNNPACK_ENABLE_ARM_BF16=OFF ../tensorflow_src/tensorflow/lite`\r\n\r\nFails with\r\n\r\n`\r\n/home/bobf/ex/tflite_build/xnnpack/src/qc8-igemm/gen/qc8-igemm-1x2c4-minmax-fp32-armsimd32.c:15:\r\n/home/bobf/ex/tflite_build/xnnpack/src/xnnpack/math.h:316:13: error: invalid output constraint '=t' in asm\r\n : [i] \"=t\" (i)\r\n ^\r\n`\r\n\r\nI think the issue still exists. Can you confirm?\r\n\r\n==================================\r\nTHESE PREVIOUS COMMENTS ARE INCORRECT\r\nThe compile flag does enable an error free compile with the test case above, which is a good first step.\r\n\r\nBut I'm still exploring unexpected issues.\r\n\r\nFor example the value of the flag (ON/OFF) is ignored, the flag's *presence* disables the bogus assembly code error - the argument is always interpreted as OFF. \r\n\r\nSo maybe this will be usable, I need to do more testing. \r\n\r\nBut I still think the project build script should implement the fix. Because the long term viability and side effects of a workaround are not visible to an end user. And the core issue which is build interaction with Clang is clearly in the domain of the tool developers.\r\n\r\n===================================\r\n\r\n> Android NDK version=25 is actually not currently supported, the official documentation https://www.tensorflow.org/lite/android/lite_build currently recommends version 21e.\r\n\r\nI think the referenced page has bit rot.\r\n\r\nThe default NDK is 25c as shown here https://developer.android.com/ndk/downloads\r\nThis is confirmed by the tflite CMake build which defaults to 25c if an NDK is not set.\r\n\r\nI use 25c.\r\n \r\n", "Hi @RobertFlatt,\r\n\r\nWhile there is always some bit rot in documentation, I have confirmed that that part is accurate. We more or less only support NDK 19, 20, 21. We do not currently support NDK version=25 for now, as such we can't help with this issue unless we are seeing this issue with the recommended version.", "> We do not currently support NDK version=25 for now, \r\n\r\nThis statement is clearly wrong.\r\n\r\n", "Hi @RobertFlatt ,\r\n\r\nPlease check the below configuration file which mentions about the supported NDK versions.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/6088a22ddb8fc299d18a34596b91ccf077b86b17/configure.py#L38-L41", "@sachinprasadhs \r\n\r\nOK, but that is NDK versions for Bazel https://github.com/tensorflow/tensorflow/blob/master/configure.py#L737-L738 \r\n*The issue at hand is CMake.*\r\n\r\nThe following simple tests use Tensorflow v2.13.0-rc1\r\n\r\n**Moving away from the issue at hand we use arm64-v8a to test CMake builds with 3 NDKs**\r\n\r\nBoth NDK LTS r25c and r23b build without error, try:\r\n```\r\ncmake -DCMAKE_TOOLCHAIN_FILE=../android-ndk-r25c/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a ../tensorflow_src/tensorflow/lite \r\ncmake --build . -j\r\n```\r\n```\r\ncmake -DCMAKE_TOOLCHAIN_FILE=../android-ndk-r23b/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a ../tensorflow_src/tensorflow/lite \r\ncmake --build . -j\r\n```\r\n\r\nHowever r19c fails with\r\n> clang: error: the clang compiler does not support '-march=armv8.2-a+bf16'\r\n```\r\ncmake -DCMAKE_TOOLCHAIN_FILE=../android-ndk-r19c/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a ../tensorflow_src/tensorflow/lite \r\ncmake --build . -j\r\n``` \r\n\r\nYou can easily validate this for yourself using the commands above.\r\n\r\n**Returning to the issue at hand we use armeabi-v7a and CMake to test the proposed workaround**\r\n\r\nThe proposed workaround `-DXNNPACK_ENABLE_ARM_BF16=OFF `\r\n```\r\ncmake -DCMAKE_TOOLCHAIN_FILE=../android-ndk-r25c/build/cmake/android.toolchain.cmake -DANDROID_ABI=armeabi-v7a -DTFLITE_ENABLE_XNNPACK=ON -DXNNPACK_ENABLE_ARM_BF16=OFF ../tensorflow_src/tensorflow/lite \r\ncmake --build . -j\r\n```\r\nBut this fails in the usual `asm : [i] \"=t\" (i)` way.\r\n\r\nPresumably I don't understand how to use the workaround.\r\n\r\nPlease explain how to use `-DXNNPACK_ENABLE_ARM_BF16=OFF ` , and if you could test the explaination as well that might save us some back and forth. Thank you.\r\n\r\n\r\n", "Further investigation reveals `-DXNNPACK_ENABLE_ARM_BF16=OFF` does not workaround the issue.\r\n\r\nAnd we should not expect it to because this is defined in the default armeabi-v7a build behavior https://github.com/google/XNNPACK/blob/master/scripts/build-android-armv7.sh#L59 and we know the default fails to build.\r\n\r\nNot clear why it was suggested as a workaround, as adding it clearly has no impact.\r\n\r\nThis *not-a-workaround* was apparently suggested here https://github.com/google/XNNPACK/issues/4775#issuecomment-1571617394 by the same account that provided *non-resolutions* to this issue here https://github.com/google/XNNPACK/issues/4348#issuecomment-1465093478 , and here https://github.com/google/XNNPACK/issues/4348#issuecomment-1465239898 , and (in retrospect) here https://github.com/google/XNNPACK/issues/4348#issuecomment-1445437613 . Two of these non-resolutions are referenced in the first post in this issue.\r\n\r\nThis code used to work, but somebody broke it between 2.9 and 2.12 .\r\n\r\nEnough already! Please, a resolution.\r\n", "@terryheo can you please take a look? Also can we verify NDK supported versions for CMake Android workflow?", "@pkgoogle \r\n5 weeks have passed, any clarity on this issue? \r\n", "Trying to compile v2.15.0 for armeabi-v7a, with NDK 25c (officially supported now I believe) and running into the same problem.\r\nApplying the fixes from https://github.com/microsoft/onnxruntime/commit/8d298f6f78f3280ece8d42ddb50caa6e81a6826f to XNNPACK's CMakeLists.txt gets me past the first issue, but the build then later fails on XNNPACK microkernel compilation with below (rather cryptic) error:\r\n\r\n<details>\r\n<summary>Long cryptic error (click me)</summary>\r\n\r\n```sh\r\n[ 23%] Building C object _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c.o\r\nfatal error: error in backend: Cannot select: 0x83465a8: v4bf16 = ARMISD::VEXT 0x836ab38, 0x836ab38, Constant:i32<2>, xnnpack/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c:119:22\r\n 0x836ab38: v4bf16,ch = CopyFromReg 0x84e41b8, Register:v4bf16 %54, xnnpack/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c:117:9\r\n 0x851e1f0: v4bf16 = Register %54\r\n 0x836ab38: v4bf16,ch = CopyFromReg 0x84e41b8, Register:v4bf16 %54, xnnpack/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c:117:9\r\n 0x851e1f0: v4bf16 = Register %54\r\n 0x836afb0: i32 = Constant<2>\r\nIn function: xnn_bf16_gemm_minmax_ukernel_1x4c8__neonbf16_bfdot\r\nPLEASE submit a bug report to https://github.com/android-ndk/ndk/issues and include the crash backtrace, preprocessed source, and associated run script.\r\nStack dump:\r\n0.\tProgram arguments: /home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang --target=armv7-none-linux-androideabi26 --sysroot=/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/sysroot -DEIGEN_MPL2_ONLY -DFXDIV_USE_INLINE_ASSEMBLY=0 -DNOMINMAX=1 -DPTHREADPOOL_NO_DEPRECATED_API=1 -DXNN_ENABLE_ARM_BF16=1 -DXNN_ENABLE_ARM_DOTPROD=1 -DXNN_ENABLE_ARM_FP16_SCALAR=1 -DXNN_ENABLE_ARM_FP16_VECTOR=1 -DXNN_ENABLE_ARM_I8MM=1 -DXNN_ENABLE_ASSEMBLY=1 -DXNN_ENABLE_CPUINFO=1 -DXNN_ENABLE_DWCONV_MULTIPASS=0 -DXNN_ENABLE_GEMM_M_SPECIALIZATION=1 -DXNN_ENABLE_JIT=0 -DXNN_ENABLE_MEMOPT=1 -DXNN_ENABLE_RISCV_VECTOR=1 -DXNN_ENABLE_SPARSE=1 -I/home/peter/dev/sandbox/tensorflow/tensorflow-2.15.0/third_party/xla/third_party/tsl -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/opencl_headers -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/vulkan_headers/include -I/home/peter/dev/sandbox/tensorflow/tensorflow-2.15.0/tensorflow/lite/delegates/gpu/common -I/home/peter/dev/sandbox/tensorflow/tensorflow-2.15.0/tensorflow/lite/delegates/gpu/common/task -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/xnnpack/src -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/pthreadpool-source/include -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/FXdiv-source/include -I/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/FP16-source/include -g -DANDROID -fdata-sections -ffunction-sections -funwind-tables -fstack-protector-strong -no-canonical-prefixes -D_FORTIFY_SOURCE=2 -march=armv7-a -mthumb -Wformat -Werror=format-security -O3 -DNDEBUG -std=c99 -fPIC -O2 -pthread -fno-math-errno -marm -march=armv8.2-a+bf16 -mfpu=neon-fp-armv8 -MD -MT _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c.o -MF CMakeFiles/microkernels-all.dir/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c.o.d -o CMakeFiles/microkernels-all.dir/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c.o -c /home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/xnnpack/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c\r\n1.\t<eof> parser at end of file\r\n2.\tCode generation\r\n3.\tRunning pass 'Function Pass Manager' on module '/home/peter/dev/sandbox/tensorflow/build_2.15.0_ndk_25c_armeabi-v7a/xnnpack/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c'.\r\n4.\tRunning pass 'ARM Instruction Selection' on function '@xnn_bf16_gemm_minmax_ukernel_1x4c8__neonbf16_bfdot'\r\n #0 0x00000000047d91d8 llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x47d91d8)\r\n #1 0x00000000047d8340 llvm::sys::RunSignalHandlers() (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x47d8340)\r\n #2 0x00000000047a3dc3 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x47a3dc3)\r\n #3 0x00000000047a3d7b (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x47a3d7b)\r\n #4 0x00000000047d7a87 llvm::sys::Process::Exit(int, bool) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x47d7a87)\r\n #5 0x00000000040dc70a (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x40dc70a)\r\n #6 0x0000000003083072 llvm::report_fatal_error(llvm::Twine const&, bool) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x3083072)\r\n #7 0x000000000282b5f5 llvm::SelectionDAGISel::CannotYetSelect(llvm::SDNode*) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x282b5f5)\r\n #8 0x0000000006cf4e77 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x6cf4e77)\r\n #9 0x000000000641f425 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x641f425)\r\n#10 0x0000000005e86b63 llvm::SelectionDAGISel::DoInstructionSelection() (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5e86b63)\r\n#11 0x0000000005e8710a llvm::SelectionDAGISel::CodeGenAndEmitDAG() (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5e8710a)\r\n#12 0x0000000006417d3c llvm::SelectionDAGISel::SelectAllBasicBlocks(llvm::Function const&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x6417d3c)\r\n#13 0x0000000006457ad3 llvm::SelectionDAGISel::runOnMachineFunction(llvm::MachineFunction&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x6457ad3)\r\n#14 0x00000000064572df (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x64572df)\r\n#15 0x0000000005d9faea llvm::MachineFunctionPass::runOnFunction(llvm::Function&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5d9faea)\r\n#16 0x0000000005da0113 llvm::FPPassManager::runOnFunction(llvm::Function&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5da0113)\r\n#17 0x0000000005d9fc6f llvm::FPPassManager::runOnModule(llvm::Module&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5d9fc6f)\r\n#18 0x00000000063aa794 llvm::legacy::PassManagerImpl::run(llvm::Module&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x63aa794)\r\n#19 0x00000000065d6968 clang::EmitBackendOutput(clang::DiagnosticsEngine&, clang::HeaderSearchOptions const&, clang::CodeGenOptions const&, clang::TargetOptions const&, clang::LangOptions const&, llvm::StringRef, llvm::Module*, clang::BackendAction, std::__1::unique_ptr<llvm::raw_pwrite_stream, std::__1::default_delete<llvm::raw_pwrite_stream> >) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x65d6968)\r\n#20 0x00000000060524d5 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x60524d5)\r\n#21 0x0000000005ea25a9 clang::ParseAST(clang::Sema&, bool, bool) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x5ea25a9)\r\n#22 0x00000000063c128d clang::FrontendAction::Execute() (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x63c128d)\r\n#23 0x00000000063c112d clang::CompilerInstance::ExecuteAction(clang::FrontendAction&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x63c112d)\r\n#24 0x00000000063c1541 clang::ExecuteCompilerInvocation(clang::CompilerInstance*) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x63c1541)\r\n#25 0x00000000066a9f54 cc1_main(llvm::ArrayRef<char const*>, char const*, void*) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a9f54)\r\n#26 0x00000000066a6de3 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a6de3)\r\n#27 0x00000000066a6c92 (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a6c92)\r\n#28 0x00000000066a6c61 llvm::CrashRecoveryContext::RunSafely(llvm::function_ref<void ()>) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a6c61)\r\n#29 0x00000000066a69f4 clang::driver::CC1Command::Execute(llvm::ArrayRef<llvm::Optional<llvm::StringRef> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >*, bool*) const (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a69f4)\r\n#30 0x00000000066a685f clang::driver::Compilation::ExecuteCommand(clang::driver::Command const&, clang::driver::Command const*&) const (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a685f)\r\n#31 0x00000000066a66f2 clang::driver::Driver::ExecuteCompilation(clang::driver::Compilation&, llvm::SmallVectorImpl<std::__1::pair<int, clang::driver::Command const*> >&) (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66a66f2)\r\n#32 0x00000000066752ee main (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x66752ee)\r\n#33 0x00007f6833640cd0 (/usr/lib/libc.so.6+0x27cd0)\r\n#34 0x00007f6833640d8a __libc_start_main (/usr/lib/libc.so.6+0x27d8a)\r\n#35 0x00000000064cce69 _start (/home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang+0x64cce69)\r\nclang: error: clang frontend command failed with exit code 70 (use -v to see invocation)\r\nAndroid (9352603, based on r450784d1) clang version 14.0.7 (https://android.googlesource.com/toolchain/llvm-project 4c603efb0cca074e9238af8b4106c30add4418f6)\r\nTarget: armv7-none-linux-android26\r\nThread model: posix\r\nInstalledDir: /home/peter/dev/sandbox/tensorflow/android-ndk-r25c/toolchains/llvm/prebuilt/linux-x86_64/bin\r\nclang: note: diagnostic msg: \r\n********************\r\n\r\nPLEASE ATTACH THE FOLLOWING FILES TO THE BUG REPORT:\r\nPreprocessed source(s) and associated run script(s) are located at:\r\nclang: note: diagnostic msg: /tmp/bf16-gemm-1x4c8-minmax-neonbf16-bfdot-463081.c\r\nclang: note: diagnostic msg: /tmp/bf16-gemm-1x4c8-minmax-neonbf16-bfdot-463081.sh\r\nclang: note: diagnostic msg: \r\n\r\n********************\r\nmake[2]: *** [_deps/xnnpack-build/CMakeFiles/microkernels-all.dir/build.make:49874: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/src/bf16-gemm/gen/bf16-gemm-1x4c8-minmax-neonbf16-bfdot.c.o] Error 70\r\nmake[1]: *** [CMakeFiles/Makefile2:6653: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/all] Error 2\r\nmake: *** [Makefile:136: all] Error 2\r\n```\r\n</details>\r\n\r\nAny update regarding this? \r\n\r\n----- Update\r\nBuilding with NDK 21e also fails with below error\r\n```shell\r\n[ 1%] Building C object _deps/xnnpack-build/CMakeFiles/microkernels-prod.dir/src/amalgam/gen/neoni8mm.c.o\r\nclang: error: the clang compiler does not support '-march=armv8.2-a+i8mm'\r\n```", "I was trying to build according to this docs: https://www.tensorflow.org/lite/guide/build_cmake_arm - but it failed. I was trying to use ndk in the same way as you - the same error `invalid output constraint '=t' in asm`. Bazel built without error.", "the same issue, test at r2.16 with ndk:26.1.10909125.", "Hi @pfk-beta, @RobertFlatt, I got a workaround. You can directly modifiy the xnnpack source code which is inside the tflite build folder.\r\n\r\n\r\nThe main idea is to bypass the error code and use the default branch.", "related onnx issue: https://github.com/google/XNNPACK/issues/6164, pr: https://github.com/google/XNNPACK/pull/6179", "Still no solution for this problem?", "It's been a year since any meaningful or misleading comments from Google, and 3 months since the original assignee was unassigned. A separate and earlier thread https://github.com/google/XNNPACK/issues/4348 showed the developer responding with what appeared to be misdirection.\r\n\r\nI started both threads. I interpret these events as nobody at Google is interested. " ]

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[ "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @pjpratik Can you please resolve conflicts? Thank you!", "Hi @gbaned \r\n\r\nI have updated the conflicts.\r\n\r\nThanks.", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @pjpratik Can you please resolve conflicts? Thank you!", "Hi @gbaned \r\n\r\nI have resolved the conflicts.\r\n\r\nThank you.", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "Hi @sirakiin Can you please review this PR ? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "https://github.com/tensorflow/tensorflow/pull/59969/files#r1312915151 is still not resolved. If there won't be an update to that I'd suggest closing the PR instead", "Hi @mihaimaruseac @gbaned \r\n\r\nThe Hello World example has been refactored with the commit https://github.com/tensorflow/tensorflow/commit/f263e19e227da0d325f15477f57a359291fc387c and broken links are fixed.\r\n\r\nI think this PR can be closed.\r\n\r\nThanks." ]

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[ "@Cranky-cat,\r\nCould you please provide the complete code and the dependencies to reproduce the issue in the effective way and helps us to debug the issue and also provide the tensorflow version you are using. Thank you!", "[train_mobilenet.zip](https://github.com/tensorflow/tensorflow/files/10966111/train_mobilenet.zip)\r\nI used this py to train the model, and converting the trained H5 model file to a tflite model file caused the loss of metadata", "@tilakrayal [test.zip](https://github.com/tensorflow/tensorflow/files/10966144/test.zip)\r\nThis is the py of converting the file format I am using the tensorflow 2-11 version\r\n", "I convert file format without label .txt file, so my conversion will fail.\r\nSo I would like to ask how to get the label .txt file of the model.", "@tilakrayal I can train the model file, but I need the model file to be used on Android, so I need to convert to tflite format.\r\nThank you for your reply", "Hi @Cranky-cat \r\n\r\nI have observed that in https://github.com/tensorflow/tensorflow/files/10966111/train_mobilenet.zip, the model conversion into tflite is not done.\r\n\r\nAlso, please refer to the model compatability [requirements](https://www.tensorflow.org/lite/inference_with_metadata/task_library/object_detector#model_compatibility_requirements), which has example lable.txt for pretrained mobile net model. You can create a similar one for your case with class names according to your training dataset.\r\n\r\nI have tried metadata tutorial for [object detector](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial#object_detectors) and I was able to run without any error. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/841aaa2471d1e75deafd253266c0be20/59968.ipynb). \r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59968\">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/59968\">No</a>\n" ]

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[ "@PhilippWillms we have uploaded the Python 3.9 wheel for TF 2.10.1\r\n https://pypi.org/project/tensorflow-intel/2.10.1/#files\r\n", "Thank you very much. As of now it is working fine!", "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/59967\">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/59967\">No</a>\n" ]

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[ "@tsender Could you please have a look at [this](https://www.tensorflow.org/install/source#gpu) tested build configuration for different compatible versions and let us know if it helps?\r\nThank you!", "Thank you for pointing me to that table. I am currently using cuDNN 8.1 and CUDA 11.2 without any issues. In this past, however, I may have been using a version of cuDNN between 8.1 and 8.7 without problem, hence why I thought that TF would work with any cuDNN 8.x version and submitted this ticket (though, I do not know exactly which version I was using previously as that docker image has been rewritten).", "@tsender Thank you for the response!\r\nCould you please confirm if the issue resolves in the latest TF version?\r\nThank you!", "I'm rather busy at the moment and likely won't be able to get back to this until the end of next week. But if I have time before then I will try.", "@tsender Could you please let us know if the issue still persists?\r\nThank you!", "Hi @sushreebarsa. \r\n\r\nI tried using the tensorflow/tensorflow:2.12.0-gpu docker image, however, it seems to require that I upgrade my cuda version, which I don't have the time to deal with. Seeing that this entire issue was created because I had accidentally created a setup in which TF was not tested/guaranteed to work in, I do not really see a need to keep this ticket open. Once you told me about the driver compatibility page, I had since downgraded back to cuDNN 8.1 and have not had any issues. I also do not have the time to do any further compatibility testing, nor do I even use the latest version of TF in my work. Hence, I think we can close this issue.", "@tsender Glad it is working for you!\r\nThank you for your response. Closing this ticket as per your above comment, please feel free to reopen if you still face any 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/59966\">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/59966\">No</a>\n" ]

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[ "Hi @chauxb Thanks for reporting this issue.\r\n\r\nCan you please mention the steps you have followed for using the hub model and tflite conversion.\r\n\r\nI have tried converting the https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1 model and was able to successfully convert into tflite model.\r\n\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/9a5b7af9d09b2d6e988c333df8b02d47/59965.ipynb) and let us know if it helps.\r\n\r\nThanks.", "Hello @pjpratik \r\n\r\nFirst thank you for your answer and the help you gave me.\r\nYour gist indeed works very well and I was able to convert the model. I think what I was not doing is using the tensorflow hub with the kera layers and using the flag as you did.\r\nHere is the exact code I used :\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\n# Convert the model\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory\r\ntflite_model = converter.convert()\r\n\r\n# Save the model.\r\nwith open('model.tflite', 'wb') as f:\r\n f.write(tflite_model)\r\n```\r\n\r\nEdit : it seems tensorflow lite metadata needs metadata to work in the app : is it not provided by pretrained models ?\r\n\r\n```\r\njava.lang.AssertionError: Error occurred when initializing ObjectDetector: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.\r\n```", "Hi @chauxb \r\n\r\nThe pre trained lite models in TF hub provide metadata. However, you can add metadata given a tflite model using [TFLIte meta data writer API](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial).\r\n\r\nPlease find the overview of adding metada [here](https://www.tensorflow.org/lite/models/convert/metadata) along with examples.\r\n\r\nPlease feel free to close the issue if it is resolved.\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/59965\">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/59965\">No</a>\n" ]

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[ "Hi @trixirt Can you please check @sirakiin's [comments](https://github.com/tensorflow/tensorflow/pull/59964#discussion_r1139400198) and keep us posted ? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "@RinnetenseiQ,\r\nMay i know how many CPU cores you have? The following are the reasons why the CPU performance is higher than the GPU in some of the model training cases.\r\n- For small models there will be no performance improvements when using GPU and you are going to benefit from GPU only when you have `large weight matrices`.\r\n\r\n- For small matrices it will be faster on CPU because it has higher **frequency** and this overhead of invoking kernels and copying is only worsens the situation. Try multiplying matrices of these shapes **(10000, 10000)x(10000x10000)**. You will see that the **GPU is much faster**.\r\n\r\n- Try increasing number of parameters in your model. There is an overhead when invoking GPU kernels and copying data to/from GPU. \r\n\r\nAlso the overhead of invoking GPU kernels, and copying data to and from GPU, is very high. For operations on models with very little parameters it is not worth using GPU since frequency of CPU cores is much higher. If you compare matrix multiplication, you will see that for large matrices GPU outperforms CPU significantly. [Reference](https://github.com/tensorflow/tensorflow/issues/31654). Thank you!\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59963\">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/59963\">No</a>\n" ]

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[ "close this, since it's fixed int de91449fde1f5c4694780a72b4637a821cec3ab4" ]

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[ "@anshkumar \r\nThis issue seems to be a keras related issue. Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999.\r\n\r\nThank you!", "Posted issue to [keras](https://github.com/keras-team/tf-keras/issues/238)", "@anshkumar \r\nThis issue will be triaged on [Keras repo](https://github.com/keras-team/tf-keras/issues/238). So, Could you please close this issue ?\r\n\r\nThank you !", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59961\">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/59961\">No</a>\n" ]

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[ "assign this to me\r\ni have a working solution in mind", "Based on the error message, it appears that the issue is with the _QuantizeAndDequantizeV4GradGrad() function in TensorFlow's backend. One possible workaround is to use a custom gradient for the quantize_and_dequantize_v2 function. \r\n\r\nbelow is code with the custom gradient implementation:\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\ntensor = tf.random.normal(shape=[1, 1], mean=127, stddev=50, dtype=tf.float32)\r\n\r\[email protected]_gradient\r\ndef func(tensor):\r\n quanti = tf.quantization.quantize_and_dequantize_v2(tensor, \r\n input_min = 0.0,\r\n input_max = 255.0,\r\n num_bits = 8,\r\n range_given=False)\r\n \r\n def grad(dy):\r\n return dy\r\n return quanti, grad\r\n\r\noutput = func(tensor) # pass\r\nwith tf.autodiff.ForwardAccumulator(tensor, tf.constant([[0.], [1.]])) as acc:\r\n output = func(tensor)\r\n\r\nHere, we've wrapped the quantize_and_dequantize_v2 call inside a @tf.custom_gradient decorator. This allows us to define a custom gradient function that overrides the default gradient implementation.\r\n\r\nIn this case, we've defined the gradient function as def grad(dy): return dy, which simply returns the incoming gradient dy unchanged. This is the identity gradient, which is appropriate for this operation because it doesn't modify the input tensor in any way.\r\n\r\nWith this change, the code should now run without errors.", "@CrazyShenanigan Thank you for your response here. \r\n@trickiwoo Thank you for raising this issue! Could you please refer to the comment above and let us know if that helped?\r\n\r\n ", "@sushreebarsa @CrazyShenanigan Sorry for the delay. Thanks for providing the workaround for the issue! I tried implementing the workaround you suggested, but it doesn't seem to resolve the problem. It fails in gradient computation with the same error message:\r\n```\r\nTypeError: in user code:\r\n TypeError: _QuantizeAndDequantizeV4GradGrad() takes 2 positional arguments but 4 were given\r\n```", "Hi @trickiwoo ,\r\n\r\nIt seems you are not using the API correct way. I am not sure why you are calling `tf.quantization.quantize_and_dequantize_v2` under the accumulator `tf.autodiff.ForwardAccumulator`. \r\n\r\nThe API `tf.autodiff.ForwardAccumulator` expects `loss` function to be defined under this context like we do under GradientTape class and then the method `.jvp(loss)` shall be called to compute JVPs.\r\n\r\nI have rewritten the code that the API `tf.autodiff.ForwardAccumulator` expects and it throws no error and JVPs are None as there is no connection between the `primals` and the Tensor returned by `quantize_and_dequantize_v2`.\r\n\r\nPlease refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/ad96bef34d45bc2a8e95c0fa84069ee2/59960.ipynb).", "Hi @SuryanarayanaY , thanks for taking a look into it!\r\n\r\nI followed the example in the [documentation](https://www.tensorflow.org/api_docs/python/tf/autodiff/ForwardAccumulator), where it also calls the function within the scope of `tf.autodiff.ForwardAccumulator`." ]

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[ "@acridland Thanks for reporting this.\r\n\r\nPR #59969 has been created. Issue will be closed once PR is merged.\r\n\r\nThanks.", "Hi @acridland \r\n\r\nThe Hello World example has been refactored with the commit https://github.com/tensorflow/tensorflow/commit/f263e19e227da0d325f15477f57a359291fc387c and broken links are fixed.\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/59959\">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/59959\">No</a>\n" ]

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[ "CC @fcoUnda @mihaimaruseac @learning-to-play ", "This issue might be related: https://github.com/google/benchmark/issues/1543" ]

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[ "Please don't spam" ]

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[ "To call a specific signature, you need to use the **GetSignatures()** method to get a map of all available signatures and their corresponding input and output tensors. You can then use this map to create input and output tensor maps that you can pass to **Session::Run()**.\r\n```\r\nauto signatures = model_bundle_->GetSignatures();\r\nauto& signature = signatures.at(\"sig_a\");\r\n\r\n// create input tensor map\r\ntensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 1}));\r\nauto input_tensor_map = std::map<std::string, tensorflow::Tensor>{{\"sig_a_x:0\", input_tensor}};\r\n\r\n// create output tensor map\r\nstd::vector<tensorflow::Tensor> output_tensors;\r\nfor (const auto& output : signature.outputs()) {\r\n output_tensors.emplace_back();\r\n output_tensors.back().set_shape(output.second.shape());\r\n}\r\nauto output_tensor_map = std::map<std::string, tensorflow::Tensor>{{\"StatefulPartitionedCall:0\", output_tensors[0]}};\r\n\r\n// run the session\r\nmodel_bundle_->GetSession()->Run(input_tensor_map, output_tensor_map, {}, &output_tensors);\r\n```" ]