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[ "Hi @optiluca ,\r\n\r\nThanks for reporting this. I have replicated the reported behaviour with TF2.13v and also tf-nightly versions whereas TF2.12v it seems working fine. Attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/85eef0845746d2c89e5b016bf7ea6676/61270.ipynb) for reference.\r\n\r\nThis seems regression issue for me. Needs to check the root cause. If you are willing to contribute please feel free to raise a PR.\r\n\r\nThanks!", "Hi @optiluca ,\r\n\r\nI have tried a simple code with custom object and it seems working fine and I can able to save and reload model. Please refer the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/08d7cc3ece073ec8e974d9d6c71b72dd/custom-objects_serialization_and_saving-2.ipynb) here. This seems not a regression issue. \r\n\r\nMay be we need to check the code for the Custom class (i.e.class MyCustomLSTM) of your model to check the root cause.There may be chance that it might not have implementation of `from_config()` and `get_config()` methods.\r\n\r\n\r\nPlease note that Custom class should implement `from_config()` which is a class method and `get_config()` methods to make it able to serialize and deserialize. Please refer the attached documentation [source](https://www.tensorflow.org/guide/keras/serialization_and_saving#custom_objects) for more details on how to implement custom classes.\r\n\r\nThanks!", "Hi @SuryanarayanaY , thanks for the prompt feedback. \r\n\r\nMy example code defines a custom layer that is *exactly* the same as the keras.layers.LSTM layer. For this reason, it does not need custom from_config and get_config methods. Clearly my real use case is more complex, and does define a custom get_config, but the minimal (not) working example is as per my original example code.\r\n\r\nThe example you share looks quite different to mine, apparently to a point that it sidesteps this bug, whatever the root cause.\r\n\r\nI modified your example to make it break again. It seems that the bug is triggered specifically if LSTM layers are involved, new gist [here ](https://colab.research.google.com/gist/optiluca/8b254fba9e7b0cdef59b9f4bbb0e0258/custom-objects_serialization_and_saving-2.ipynb)to demonstrate:\r\n\r\n", "Hi @optiluca ,\r\n\r\nIt seems custom layer sub classed from LSTM layer has problem when loading in `.keras` format but with .tf format it is working fine. The problem exists in tf-nightly also. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/c90a99527674a192b25cfe61a9192e0e/61270_custom-objects_serialization_and_saving-2.ipynb) for reference.\r\n\r\nThere seems some bug AFAIK which needs to look into. Will dig more to check or escalate it to concerned team. Thanks!" ]

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[ "Hi @misterBart, thanks for reporting the issue.\r\n\r\ngit & cmake are not available natively on windows command prompt so I have a couple of questions in order to reproduce your issue.\r\n\r\nAre you using powershell or command prompt? Are you using WSL? Are you using MinGW? Are you using git for windows? If you have trouble understanding these questions, a good first pass is to ask bard: https://bard.google.com/. Ex: \"How to tell if I'm using _______?\"\r\n\r\nUsually the more information you provide, the faster I am able to assist you. Thanks!", "I'm using Windows Command Prompt.\r\nInstalled Git with the Windows installer from https://git-scm.com/download/win\r\nInstalled CMake with the Windows installer from https://cmake.org/download/\r\nAfter installing, git and cmake are available in Windows Command Prompt\r\n\r\nBy the way, you can also solve the two mentioned compile errors if your replace `std::any_cast` with `absl::any_cast`. I believe this is the preferable solution, because I notice you use `absl::any_cast` more often in the two C++ files in question.", "Hi @misterBart, our internal tests/tools show it compiles correctly with clang, Is there a way to adjust your cmake installation to use clang? (I'm currently guessing it is using gcc but I'm not sure).", "I am using cmake with Visual Studio (also see title and opening post), so I'm using Microsoft Visual C++ (MSVC) compiler.\r\n\r\nAs for your follow-up email: \"Hi @misterBart, let us know if you have tried multiple models to help us look into the problem further.\"\r\nThe issue is building TfLite. I have to build TfLite before I can use TfLite models.\r\n\r\nTo make things clear, I never asked for help. I reported an issue and posted a solution in my opening post and a second solution in my previous comment. I posted these solutions so that one of them could be applied to the TfLite code, so that other people using Visual Studio will not experience this error. Hopefully things are clear now.", "Hi @misterBart, there are complications/restrictions which make applying those solutions not that simple, but we'll take a deeper look, in the mean time, can you use bazel to unblock yourself? Generally the bazel workflow is better supported for windows/macos and the cmake workflow is better supported for *nix systems.", "Hi @terryheo, can you please take a look? Thanks", "@terryheo Have you been able to look at the matter yet?" ]

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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/61268/checks?check_run_id=15005268842) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @TONG369 Can you please resolve conflicts? Thank you!", "Hi @TONG369 Can you please resolve conflicts? Thank you!", "Hi @TONG369 Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "You need to update the requirements.txt files used in all CI too, they have to test with a numpy that matches what is in setup.py", "Thanks. `tf-nightly` is broken in Colab right now (workaround: `pip install -U numpy`). This will fix it.\r\n\r\n@mihaimaruseac Here? https://github.com/tensorflow/tensorflow/blob/0ed8e904f4cb58826e2bd861ae508e38f49d3ea8/tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt#L13-L16", "I think so, unclear what are the current files being used by hermetic Python and all CIs. Tagging @angerson and @vam-google to confirm.", "\"I have found below requirements.txt files.\r\n\r\nI can see numpy version as >=1.22 in requirements.in file.\r\nhttps://github.com/tensorflow/tensorflow/blob/9fa5e774d4f86c2e41b04a6ac326578bdd516691/ci/official/requirements_updater/requirements.in#L1\r\n\r\nApart from this `requirements_lock_3_10.txt` and others (`requirements_lock_3_11.txt`, `requirements_lock_3_9.txt`, `requirements_lock_3_8.txt`) have numpy version set to 1.24.3 .\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/9fa5e774d4f86c2e41b04a6ac326578bdd516691/requirements_lock_3_10.txt#L265\r\n\r\nBut here in `ci_build/release/requirements_common.txt` requirements mentioned as below.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/9fa5e774d4f86c2e41b04a6ac326578bdd516691/tensorflow/tools/ci_build/release/requirements_common.txt#L12-L13\r\n\r\nUnder Docker SIG build:\r\nhttps://github.com/tensorflow/tensorflow/blob/aa7fbf11a7581009f08933273bde7a0bc2490aff/tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt#L15-L16\r\n\r\n", "Thanks @SuryanarayanaY , i think those are the 4 primary locations, and to my knowledge all should be updated to >= 1.23 given what you have said above. Normally the requirements lock files would need recomputed but given they are already locked above that value it should be fine to leave as is. If you can update the PR to include those today I can pull internal and make sure the requirement locks are fine, if not i will likely pull it in and adjust myself to fix the next build of tf-nightly. \r\n", "I am going to hold this till Monday, @georgiyekkert pointed out that this upgrade seems required due to when we build tf-nightly we are using numpy headers from CI, so our nightly is packed with 1.24.3 headers no matter what user has installed on their machine. \r\n\r\nWe will first try to modify and make 1.22 work again and discuss a 1.23 upgrade separately ", "Looks like there's a conflict wth this commit: https://github.com/tensorflow/tensorflow/commit/d9962234b8a33eb3b351ce69498f1671a832b98a", "Hi @SuryanarayanaY Can you please resolve conflicts? Thank you!", "The issue should have been fixed in https://github.com/tensorflow/tensorflow/commit/2c5f35e7e23c3298babf2227ea0ae128d1e6fb8e\r\nThe requirements files you are changing are outdated and not used in the build any more. Please let me know if you still have any issues otherwise please close.", "Perfect, then this can gen closed. Thanks @georgiyekkert " ]

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[ "@wuhongsheng,\r\nCould you please provide the complete code to reproduce the issue which helps us to analyse the issue in an effective way.\r\n\r\nAlso Segmentation faults may occur due to Out Of Bound errors,or Illegal Memory access errors. Tensorflow does provide some logs there unless its OS crashes due to failure of memory allocation. As you reported the behaviour occurs after some epochs which might happen due to Memory release/Memory Fragmentation issues.\r\n\r\n Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61266\">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/61266\">No</a>\n" ]

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[ "Hi @ngbusca ,\r\n\r\nI have tested the given code with tf-nightl(2.14.0-dev20230712) and its working fine for me. I can able to save and reload the model. Could you please check the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/ebb5ad981552360fa1f04541248e7914/61265.ipynb) and let me know whether the code is right one to reproduce the reported behaviour.\r\n\r\nThanks!", "Hi @SuryanarayanaY \r\nThanks for the response. Yes, the code is correct. I've downgraded to 2.13 and don't encounter the issue. I must have had an inconsistency on my system, I'll close the issue and reopen if I encounter it again.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61265\">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/61265\">No</a>\n" ]

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[ "@sushreebarsa Could you please try to install the tensorflow_io and try again. \r\nTry with the following\r\n```\r\n!pip install tensorflow_io\r\n```\r\nI was able to run the code successfully after installing this module. Please have a look at this colab [gist](https://colab.research.google.com/gist/sushreebarsa/8adeb47ab958017b60d93c514ff6d932/61264.ipynb) for reference. \r\nPlease find the log below;\r\n```\r\nDownloading data from https://storage.googleapis.com/audioset/miaow_16k.wav\r\n215546/215546 [==============================] - 0s 0us/step\r\n./test_data/miaow_16k.wav\r\nWARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.\r\nSpeech\r\nChild speech, kid speaking\r\nConversation\r\nNarration, monologue\r\nBabbling\r\nSpeech synthesizer\r\nShout\r\nBellow\r\nWhoop\r\nYell\r\nChildren shouting\r\nScreaming\r\nWhispering\r\nLaughter\r\nBaby laughter\r\nGiggle\r\nSnicker\r\nBelly laugh\r\nChuckle, chortle\r\nCrying, sobbing\r\n...\r\nThe main sound is: Animal\r\nThe embeddings shape: (13, 1024)\r\nDownloading data from https://github.com/karoldvl/ESC-50/archive/master.zip\r\n 8192/Unknown - 0s 0us/stepWARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.\r\nModel: \"my_model\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param # \r\n=================================================================\r\n dense (Dense) (None, 512) 524800 \r\n \r\n dense_1 (Dense) (None, 2) 1026 \r\n \r\n=================================================================\r\nTotal params: 525,826\r\nTrainable params: 525,826\r\nNon-trainable params: 0\r\n_________________________________________________________________\r\nEpoch 1/20\r\n15/15 [==============================] - 11s 72ms/step - loss: 1.2337 - accuracy: 0.8229 - val_loss: 0.9781 - val_accuracy: 0.8687\r\nEpoch 2/20\r\n15/15 [==============================] - 0s 22ms/step - loss: 0.5836 - accuracy: 0.8771 - val_loss: 0.5496 - val_accuracy: 0.9187\r\nEpoch 3/20\r\n15/15 [==============================] - 0s 22ms/step - loss: 0.5533 - accuracy: 0.8958 - val_loss: 0.4514 - val_accuracy: 0.9187\r\nEpoch 4/20\r\n15/15 [==============================] - 0s 22ms/step - loss: 0.2929 - accuracy: 0.9083 - val_loss: 0.2570 - val_accuracy: 0.8750\r\nEpoch 5/20\r\n15/15 [==============================] - 0s 20ms/step - loss: 0.1942 - accuracy: 0.9146 - val_loss: 0.2387 - val_accuracy: 0.9125\r\nEpoch 6/20\r\n15/15 [==============================] - 0s 13ms/step - loss: 0.2205 - accuracy: 0.9229 - val_loss: 0.2080 - val_accuracy: 0.9187\r\nEpoch 7/20\r\n15/15 [==============================] - 0s 14ms/step - loss: 0.2351 - accuracy: 0.9167 - val_loss: 0.3459 - val_accuracy: 0.9187\r\nEpoch 8/20\r\n15/15 [==============================] - 0s 13ms/step - loss: 0.3472 - accuracy: 0.9125 - val_loss: 0.6668 - val_accuracy: 0.8687\r\n5/5 [==============================] - 0s 11ms/step - loss: 0.2761 - accuracy: 0.9000\r\nLoss: 0.27613919973373413\r\nAccuracy: 0.8999999761581421\r\nThe main sound is: cat\r\n```\r\n\r\nThank you!\r\n", "@sushreebarsa \r\nI tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io: \r\n\r\nERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0)\r\nERROR: No matching distribution found for tensorflow==2.11.1", "@sushreebarsa \r\nAny other recommendations?\r\nI found out that TensorFlow_io doesn't work on apple computers that have the m1 chip... Is there another way to resolve this or is there something other than that package that I could use?\r\n", "Please switch to using TF 2.13 instead of 2.11. That could possibly solve the issue.", "@mihaimaruseac \r\n\r\nI am currently using 2.13", "> @mihaimaruseac\r\n> \r\n> I am currently using 2.13\r\n\r\nSorry, was confused by the error message that is looking at `tensorflow==2.11.1`:\r\n\r\n> @sushreebarsa I tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io:\r\n> \r\n> ERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0) ERROR: No matching distribution found for tensorflow==2.11.1\r\n\r\n\r\nCan you post the output of `pip list`, please?", "> > @mihaimaruseac\r\n> > I am currently using 2.13\r\n> \r\n> Sorry, was confused by the error message that is looking at `tensorflow==2.11.1`:\r\n> \r\n> > @sushreebarsa I tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io:\r\n> > ERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0) ERROR: No matching distribution found for tensorflow==2.11.1\r\n> \r\n> Can you post the output of `pip list`, please?\r\n\r\nHere is output of pip list:\r\n\r\n```\r\nPackage Version\r\n----------------------- --------\r\nabsl-py 1.4.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\nastunparse 1.6.3\r\nasync-timeout 4.0.2\r\nattrs 22.2.0\r\ncachetools 5.3.1\r\ncertifi 2023.5.7\r\ncharset-normalizer 3.1.0\r\ncmake 3.25.2\r\ncontourpy 1.0.7\r\ncycler 0.11.0\r\nface-recognition-models 0.3.0\r\nflatbuffers 23.5.26\r\nfonttools 4.38.0\r\nfrozenlist 1.3.3\r\ngast 0.4.0\r\ngoogle-auth 2.21.0\r\ngoogle-auth-oauthlib 1.0.0\r\ngoogle-pasta 0.2.0\r\ngrpcio 1.56.0\r\nh5py 3.9.0\r\nidna 3.4\r\nkeras 2.13.1\r\nkiwisolver 1.4.4\r\nlibclang 16.0.0\r\nMarkdown 3.4.3\r\nMarkupSafe 2.1.3\r\nmatplotlib 3.6.3\r\nmultidict 6.0.4\r\nnumpy 1.24.2\r\noauthlib 3.2.2\r\nopt-einsum 3.3.0\r\npackaging 23.0\r\npandas 1.5.3\r\nPillow 9.4.0\r\npip 23.1.2\r\nprotobuf 4.23.3\r\npyasn1 0.5.0\r\npyasn1-modules 0.3.0\r\npyparsing 3.0.9\r\nPySimpleGUI 4.60.5\r\npython-dateutil 2.8.2\r\npytz 2022.7.1\r\nrequests 2.31.0\r\nrequests-oauthlib 1.3.1\r\nrsa 4.9\r\nsetuptools 65.5.0\r\nsix 1.16.0\r\ntensorboard 2.13.0\r\ntensorboard-data-server 0.7.1\r\ntensorflow 2.13.0\r\ntensorflow-estimator 2.13.0\r\ntensorflow-macos 2.13.0\r\ntermcolor 2.3.0\r\ntyping_extensions 4.5.0\r\nurllib3 1.26.16\r\nWerkzeug 2.3.6\r\nwheel 0.40.0\r\nwrapt 1.15.0\r\nyarl 1.8.2\r\n```", "Oh, I think the issue is that TF-IO did not have a release since Mar: https://pypi.org/project/tensorflow-io/0.32.0/#history, so the latest one is expecting TF 2.11.\r\n\r\nTagging @yongtang for a new release now that TF 2.13 has just been released.", "(sorry, closed by mistake, reopened)", "> Oh, I think the issue is that TF-IO did not have a release since Mar: https://pypi.org/project/tensorflow-io/0.32.0/#history, so the latest one is expecting TF 2.11.\r\n> \r\n> Tagging @yongtang for a new release now that TF 2.13 has just been released.\r\n\r\nOhhh I see. Thank you, I know a couple people have been having the same issue as me. Would you recommend I install TF 2.11 and try to work from that for right now? (I'm just curious as I need it for my job)", "Let's try with that one, though I also see you are on a Mac with M1 and I think full support for that is coming only in 2.13. But hopefully it works?\r\n\r\nIf not, I'd recommend trying the Google Colab Notebooks, if possible, as those run on Linux VMs and should work 99.999% of the time (plus, are very similar to Jupyter Notebooks, so experience transfers nicely). Of course, this is only if job requirements allow it.", "> Let's try with that one, though I also see you are on a Mac with M1 and I think full support for that is coming only in 2.13. But hopefully it works?\r\n> \r\n> If not, I'd recommend trying the Google Colab Notebooks, if possible, as those run on Linux VMs and should work 99.999% of the time (plus, are very similar to Jupyter Notebooks, so experience transfers nicely). Of course, this is only if job requirements allow it.\r\n\r\nyeah it won't even let me install the 2.11 version haha. I can try working on colab or something for now. When do you think the TF-io release for 2.13 will be completed?", "That depends on when @yongtang and other maintainers of TF IO can do the next release." ]

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

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[ "CC: @elfringham ", "The Py+CPP Test failure is due to an unrelated flaky test failure." ]

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[ "Hi @urplanet \r\n\r\nCould you please provide the minimal reproducible code for the colab environment?\r\n\r\nFacing errors while executing the provided code.\r\n\r\nThanks.", "Hi @pjpratik \r\nas requested, I have updated the code in the colab https://colab.research.google.com/drive/1Eea5Fi861_h3TzH1eGUaP-H9dSPBn3OJ?usp=sharing . I included the environment I used to run the training.\r\n", "Hi @urplanet \r\n\r\nThanks for the code.\r\n\r\nI have observed that `observation` has been converted with shape signature `[-1,5]` with type FLOAT32 using TFLite [Model Analyzer](https://www.tensorflow.org/lite/guide/model_analyzer) API.\r\n\r\nThe model has \r\n```\r\nObservation Spec:\r\nBoundedArraySpec(shape=(5,), dtype=dtype('float32'), name='observation', minimum=0.0, maximum=1.0)\r\n```\r\nand as per model analyzer\r\n```\r\n T#0(action_0_step_type:0) shape_signature:[-1], type:INT32\r\n T#1(action_0_discount:0) shape_signature:[-1], type:FLOAT32\r\n T#2(action_0_observation:0) shape_signature:[-1, 5], type:FLOAT32\r\n T#3(action_0_reward:0) shape_signature:[-1], type:FLOAT32\r\n```\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/52b533b493930883b98ea3ef91dc2174/61260.ipynb).\r\n\r\nThanks.\r\n", "So could you advice that this issue occurs because of incompatibility of the model or which root causes ? Because my conversion is very simple without any change in the conversion configuration.", "Hi @urplanet \r\n\r\nBased on the above gist, I don't see any issue in conversion process as the input shapes are not altered.\r\n\r\nYou have to access `input_details[2]['shape']` and `input_details[2]['index']`, if the `observation` data is of the interest.\r\n\r\n```\r\n# Create random input data with shape [1, 5]\r\ninput_shape = input_details[2]['shape']\r\ninput_data = np.random.random_sample(input_shape).astype(np.float32) \r\nprint(\"Input data: \\n\", input_data)\r\nprint(\"Input shape:\", input_shape)\r\n# Set input tensor to the random input data\r\ninterpreter.set_tensor(input_details[2]['index'], input_data)\r\ninterpreter.invoke()\r\n```\r\n\r\nThanks.", "that solved my problem. thank you so much !\r\n\r\nmeanwhile, I have a small question on the Saved Model selection. I followed this https://www.tensorflow.org/agents/tutorials/7_SAC_minitaur_tutorial and realized that there are different Saved_Model.pb in different folder in the tempdir.\r\n\r\nif I would like to convert the policy for further use in TFLite, which one I should pick to convert to represent my trained SAC Model ?", "Hi @urplanet \r\n\r\nThe Tensorflow Agents is being maintained as separate [repo](https://github.com/tensorflow/agents). Could you please raise a ticket there for faster resolution?\r\n\r\nPlease, let us know if we can help anything on TFLite.\r\n\r\nFeel free to close the issue if it is resolved.\r\n\r\nThanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61260\">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/61260\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/61259/checks?check_run_id=14978876953) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @adey4 It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727" ]

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[]

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[ "Opened pull request #61259 ", "@PhyllisJi ,\r\n\r\nThanks for reporting.I have tested a simple code with `alpha = -0.3` and `alpha = 0.3` just to check and both are generating output which are different as per attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/d999601062b0a57d5d8d1c014435c3d6/61257.ipynb). The source code is missing validation for values of `alpha < 0` .\r\n\r\n@adey4 , Could you please confirm whether you are interested to raise a PR for same on Keras repo ? The keras source code can be found [here](https://github.com/keras-team/keras/blob/master/keras/layers/activation/leaky_relu.py). If not I can raise a PR for this. \r\n\r\nThanks!\r\n\r\n\r\n\r\n\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61257\">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/61257\">No</a>\n", "Reopening as PR not approved in TF repo. Created a new PR in Keras repo", "Reference PR-[18336](https://github.com/keras-team/keras/pull/18336) in Keras repo", "I also observed the following alternative names of the API have the same behavior that accepts the negative value of `alpha` without throwing an Exception.\r\n\r\n- `(tf.keras.layers.LeakyReLU)`, `tf.compat.v1.keras.layers.LeakyReLU`\r\n\r\nThis behavior still exists in tensorflow nightly (2.15.0-dev20230906), and users should be cautious when using them on both CPU and GPU.\r\n\r\n<details>\r\n <summary>Code to reproduce the issue in <code>tf.compat.v1.keras.layers.LeakyReLU</code></summary>\r\n\r\n```python\r\nimport tensorflow as tf\r\nprint(tf.version.GIT_VERSION, tf.version.VERSION, flush=True)\r\nprint(tf.config.list_physical_devices(), flush=True)\r\n\r\n\r\ndef bilstm(num_units=25, input_shape=10):\r\n input_tensor = tf.keras.Input(shape=input_shape)\r\n x = tf.keras.layers.Embedding(input_dim=100, output_dim=10, input_length=8, embeddings_initializer=\"uniform\")(input_tensor)\r\n # x = tf.keras.layers.LeakyReLU(alpha=-0.2044550861511304)(x)\r\n x = tf.compat.v1.keras.layers.LeakyReLU(alpha=-0.2044550861511304)(x)\r\n output_tensor = x\r\n model = tf.keras.models.Model(inputs=input_tensor, outputs=output_tensor)\r\n return model\r\n\r\n\r\ntry:\r\n bilstm().summary()\r\nexcept Exception as e:\r\n print(\"Success! Error:\", str(e), flush=True)\r\nelse:\r\n print(\"Failed!\", flush=True)\r\n```\r\n\r\nHere is the output of the above code on my GPU machine:\r\n\r\n```text\r\nv2.14.0-rc0-34-gdd01672d9a9 2.14.0-rc1\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\nModel: \"model\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param # \r\n=================================================================\r\n input_1 (InputLayer) [(None, 10)] 0 \r\n \r\n embedding (Embedding) (None, 10, 10) 1000 \r\n \r\n leaky_re_lu (LeakyReLU) (None, 10, 10) 0 \r\n \r\n=================================================================\r\nTotal params: 1000 (3.91 KB)\r\nTrainable params: 1000 (3.91 KB)\r\nNon-trainable params: 0 (0.00 Byte)\r\n_________________________________________________________________\r\nFailed!\r\n```\r\n\r\nIt also fails on CPU:\r\n\r\n```text\r\nv2.14.0-rc0-34-gdd01672d9a9 2.14.0-rc1\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\nModel: \"model\"\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param # \r\n=================================================================\r\n input_1 (InputLayer) [(None, 10)] 0 \r\n \r\n embedding (Embedding) (None, 10, 10) 1000 \r\n \r\n leaky_re_lu (LeakyReLU) (None, 10, 10) 0 \r\n \r\n=================================================================\r\nTotal params: 1000 (3.91 KB)\r\nTrainable params: 1000 (3.91 KB)\r\nNon-trainable params: 0 (0.00 Byte)\r\n_________________________________________________________________\r\nFailed!\r\n```\r\n</details>\r\n", "Hi @PhyllisJi ,\r\n\r\nThis is fixed in #[18678](https://github.com/keras-team/keras/pull/18678).\r\n\r\nCould you please verify and close the issue. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61257\">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/61257\">No</a>\n" ]

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[ "@PhyllisJi Thank you for raising this issue!\r\nI was able to replicate the issue as provided [here](https://colab.research.google.com/gist/sushreebarsa/32c22320c0b7877f4fde23ce1270818d/61256.ipynb).\r\nCould you please share the link of the documentation where you are proposing the modifications?", "Both the dropout parameter and the recurrent_dropout parameter take values between 0 and 1 in the document.\r\n[GRU](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\r\n[LSTM](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM)\r\n[SimpleRNN](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNN)", "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/61256\">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/61256\">No</a>\n" ]

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[ "@PhyllisJi,\r\nFrom the docs here ([keras.io/layers/convolutional](http://keras.io/layers/convolutional)) it looks like you can input how many rows/cols to crop and whether it is from the top/bottom left/right using the cropping tuple you are passing, eg: \"If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop))\" If you only wanted to to crop the right of the image, for example, pass cropping=((0, 0), (0, 1))\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping2D\r\n\r\nThank you!", "> @PhyllisJi, From the docs here ([keras.io/layers/convolutional](http://keras.io/layers/convolutional)) it looks like you can input how many rows/cols to crop and whether it is from the top/bottom left/right using the cropping tuple you are passing, eg: \"If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop))\" If you only wanted to to crop the right of the image, for example, pass cropping=((0, 0), (0, 1)) https://www.tensorflow.org/api_docs/python/tf/keras/layers/Cropping2D\r\n> \r\n> Thank you!\r\n\r\nThank you for your reply, but I think according to the document, this parameter should not be filled in negative numbers, tf should check and verify the input.", "@PhyllisJi ,\r\nTensorFlow allows you to specify **-ve** values for cropping because there are some situations where you may want to crop pixels from the input tensor in the opposite direction. \r\n\r\nEg: If you are using a convolutional neural network (**CNN**) to classify images of objects, you may want to crop away the borders of the images before feeding them into the CNN. This can help to improve the performance of the CNN, as it can reduce the amount of unwanted information that the CNN has to process.\r\n\r\nIn most cases you want to specify **+ve** values for cropping. However, it is important to be aware that you can specify -ve values, and that TensorFlow will not check whether the values are `+ve or -ve`. If the cropping value is specified as an integer, TensorFlow is expected to handle both the +ve and -ve values. The +ve values would result in cropping from the corresponding edge, and -ve values would result in zero-padding from the corresponding edge. Thank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61255\">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/61255\">No</a>\n" ]

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[ "Hi @PhyllisJi ,\r\n\r\nThanks for reporting.I have replicated the reported behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/a140acaf103057f5627938cd1b92bba5/61254.ipynb) for reference.\r\n\r\nThe reason for this for l1 and l2 inputs validation part is missing.I may raise a PR to fix this after going in details.", "A PR #[18293](https://github.com/keras-team/keras/pull/18293) in Keras repo has been raised which may probably fix this once merged. Thanks!", "A new PR #[18688](https://github.com/keras-team/keras/pull/18688) has been proposed in Keras repo as previous PR ( mentioned above) got cancelled during migration process. ", "Hi @PhyllisJi ,\r\n\r\nThe proposed PR got merged now and changes can be reflected in Keras Master. Could you please verify and close the ticket now? Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61254\">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/61254\">No</a>\n" ]

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[ "Hi @rsuderman Can you please review this PR ? Thank you!", "Hi @rsuderman Can you please review this PR ? Thank you!", "Hi @jamwar01 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 @jamwar01 Can you please resolve conflicts? Thank you!\r\n\r\nConflicts have been resolved, thanks!", "Hi @jamwar01 Can you please resolve conflicts? Thank you!", "Hi @jamwar01 Can you please resolve conflicts? Thank you!", "Hi @jamwar01 I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you for your contribution!\r\n", "Hi @gbaned, I have resolved the conflicts and opened a new PR for this:\r\nhttps://github.com/tensorflow/tensorflow/pull/62829" ]

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[ "@sirvincent Could you please confirm if you are using the [link1](https://www.tensorflow.org/install/lang_c), and [link2](https://github.com/tensorflow/tensorflow/blob/master/README.md) for installation. Please make sure to share the steps you followed in order to replicate the issue. Thank you!", "@sushreebarsa if I am not mistaken those links show the build process for the C api not the C++ api?", "@sirvincent Sorry for the misread! From TF v2.13, the tensorflow build uses clang instead of GCC. As per this [documentation](https://www.tensorflow.org/install/source#install_clang_recommended_linux_only), please refer to building it using clang 16.0 and let us know if it helps. Could you let us know the steps to follow for reproducing the error reported here as well. Thank you!", "@sushreebarsa I have managed to solve my problem. \r\n\r\nThe problem was that libtensorflow._cc.so.2.13.0 links against libtensorflow_framework.so.2, which is not created by the above build target. But libtensorflow_framework.so.2.13.0 is created so I manually symbolic linked it:\r\n`ln -s libtensorflow_framework.so.2.13.0 libtensorflow_framework.so.2`\r\n\r\nFor completeness in the end I used the following command for compiling\r\n`bazel build --config=avx2_linux //tensorflow:tensorflow_framework //tensorflow:tensorflow_cc //tensorflow:install_headers`\r\nBuild with GCC version 9.4.0 & Bazel version 5.3.0 on Ubuntu 20.04 with linux kernel 5.4.0-59-generic.\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/61252\">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/61252\">No</a>\n", "@sirvincent Glad your issue is resolved now. Thank you!" ]

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[ "@p3achyjr,\r\nCould you please provide the complete code to reproduce the issue and it helps us to analyse the issue in an effective way. Thank you!", "```\r\nclass Model(tf.keras.Model):\r\n def call(self, x):\r\n return (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)\r\n \r\nmodel = Model()\r\nmodel(tf.convert_to_tensor(5))\r\nmodel.save(dir)\r\n```\r\n\r\n```\r\nsaved_model_cli show --dir dir --all\r\n```", "@p3achyjr,\r\nWhile executing the above mentioned code, I was facing a different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/92fd4e742af25fe8a1540e56df4514b6/untitled1242.ipynb) and provide the complete code and the dependencies to reproduce the issue. Thank you!", "sorry about that, try this?\r\n\r\nhttps://colab.research.google.com/gist/p3achyjr/b4930a63f215ff23615bdcd12a08fc4f/untitled1242.ipynb", "@p3achyjr,\r\nWhere do you call the loaded saved model?\r\n\r\nIf you call from python, you could directly do this:\r\n\r\n```\r\nrt = tf.ragged.constant([[[\"hey\"]]])\r\nt = tf.constant([1])\r\nloaded({\"ragged_tensor_input\": rt, \"nested_tensor_input\": {\"nested_tensor1\": t}})\r\n```\r\n\r\nAlso please take a look at this comment from the developer for the similar issue.\r\nhttps://github.com/tensorflow/tensorflow/issues/56819#issuecomment-1241215918\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/61251\">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/61251\">No</a>\n" ]

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[ "From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:\n * For TF-GPU - See point 1\n * For TF-CPU - See point 2\n-----------------------------------------------------------------------------------------------\n**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**\n\nMake sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table.\n| TF | CUDA |\n| :-------------: | :-------------: |\n| 2.5.0 | 11.2 |\n| 2.4.0 | 11.0 |\n| 2.1.0 - 2.3.0 | 10.1 |\n| 1.13.1 - 2.0 | 10.0 |\n| 1.5.0 - 1.12.0 | 9.0 |\n\n * If you have above configuration and using _**Windows**_ platform -\n * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.\n * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).\n * If you have above configuration and using _**Ubuntu/Linux**_ platform -\n * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.\n * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).\n * If error still persists then, apparently your CPU model does not support AVX instruction sets.\n * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).\n\n-----------------------------------------------------------------------------------------------\n**2. Installing **TensorFlow** (TF) CPU prebuilt binaries**\n\n*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*\n\nTherefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.\nApparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:\n\n * Try Google Colab to use TensorFlow.\n * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install``` to install any other preferred TF version.\n * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.\n * All you need is a good internet connection and you are all set.\n * Try to build TF from sources by changing CPU optimization flags.\n\n*Please let us know if this helps.*\n", "Hi @KaushalNaresh ,\r\n\r\nCould you please confirm the TF version you are using? \r\n\r\nCould you please refer to the similar issues attached here [#5344](https://github.com/tensorflow/tensorflow/issues/5343) and [#4944](https://github.com/BVLC/caffe/issues/4944).\r\n\r\nIf you are using TF1.0.* supported CUDA version is CUDA-8 and it seems you have installed CUDA 12.2 which is not compatible. Please refer to tested configurations [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\n\r\n\r\ntensorflow_gpu-1.0.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | 5.1 | 8\r\n-- | -- | -- | -- | -- | --\r\n\r\n\r\nAlso 1.x versions not supported now and hence request you to use latest versions of Tf2.x.\r\n\r\nThanks!\r\n\r\n\r\n", "Hi I was using tf-gpu 1.0.1 because I was trying to replicate one old repository.\r\n\r\nI will try to install compatible TF for my CUDA but the link you have provided doesn't have CUDA 12.2 so can you point me out the correct version of tf-gpu that goes well with my CUDA??\r\n\r\nThanks", "Hi @KaushalNaresh ,\r\n\r\nFor the tf-gpu 1.0.1 version required CUDA version has to be CUDA 8 and cuDNN 5.1 versions.\r\n\r\nBecause TF documentation instruction updates and keep them for latest stable release only.\r\n\r\nYou may refer to [Nvidia](https://developer.nvidia.com/cuda-80-ga2-download-archive) website for this or can search in internet for same.Since the version is too old I am not sure of official instructions at that time.\r\n\r\nThanks!\r\n\r\n", "Hi,\r\n\r\nI am not asking the CUDA version for tf-gpu 1.0.1. \r\n\r\nI have a CUDA version 12.2 but the link that you have provided doesnt list tf for CUDA version 12.2 Can you point me out the correct version of tf that goes well with my CUDA (12.2)?", "Hi @KaushalNaresh ,\r\n\r\nTensorflow users has to follow tested [configurations](https://www.tensorflow.org/install/source#gpu) mentioned in documentation. Each version of TF will be tested with some CUDA and cuDNN versions and users has to install those versions only as other advanced versions are yet to test with TF and may raise compatibility issues.\r\n\r\nFor latest version(TF2.13) CUDA 11.8 is the tested version hence CUDA>11.8 may raise compatibility issues.Hence you have to stick to the tested configurations only. In future versions TF might be tested with CUDA 12.2 also but for now you need to use tested configurations only.\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/61250\">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/61250\">No</a>\n" ]

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[ "@drewshark I have tried to replicate the issue reported here and faced different errors. Could you please have a look at the gist [here](https://colab.research.google.com/gist/sushreebarsa/2f80059b67d119a4ff880edf90ca9467/61249.ipynb) and let us know if I am missing something to replicate this one. Thank you!", "Hi, sorry that I forgot to import torch. Here is the corrected code snippet:\r\n```\r\nimport tensorflow as tf\r\nimport torch\r\nimport numpy as np\r\nt = np.array([[1 + 1j, 4 + 1j], [4 + 2j, 3 + 1j]])\r\ntf_res = tf.linalg.logdet(tf.constant(t, dtype=tf.complex128))\r\ntorch_res = torch.logdet(torch.tensor(t))\r\nprint(tf_res, torch_res)\r\n```", "@SuryanarayanaY I was able to replicate the issue in TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/410f0ed1fb8424d5641fe81dcae5285e/61249-tf-2-12.ipynb), TF [v2.13 ](https://colab.research.google.com/gist/sushreebarsa/1a2485c4778338e1fc8f814c0982c6c1/61249-tf-2-12.ipynb#scrollTo=cReA7k7qX5vF)and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/f683666aa23191354ea2e963192ebde3/61249-nightly.ipynb). Please have a look . Thank you!" ]

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[ "Hi @mohammedouahman Can you please resolve conflicts? Thank you!", "Hi @mohammedouahman 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 @mohammedouahman Can you please resolve conflicts? Thank you!", "Hi @mohammedouahman I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you for you contribution!" ]

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[ "@MH0386,\r\nAs you mentioned, the above code that was failing on tensorflow v2.10, but whereas on the latest tensorflow v2.12 it was executed without any issues/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0ad49bffc9c2679a4d32c5a196e92cef/untitled1239.ipynb). Thank you!", "\r\n\r\n1. Check TensorFlow Datasets version: Verify that you are using the latest version of TensorFlow Datasets. You can upgrade TFDS using the following command:\r\n```\r\npip install --upgrade tensorflow-datasets\r\n```\r\n\r\n2. Import correct method: Make sure you are importing the correct method from TensorFlow Datasets. Instead of using `load`, you might need to use other methods like `load_dataset` or `load_from_tfds`. For example:\r\n``\r\nimport tensorflow_datasets as tfds\r\n\r\ndataset = tfds.load_dataset('dataset_name')\r\n```\r\nBy following these steps, you should be able to resolve the issue and successfully load datasets using TensorFlow Datasets.", "> 1. Check TensorFlow Datasets version: Verify that you are using the latest version of TensorFlow Datasets. You can upgrade TFDS using the following command:\r\n> \r\n> ```\r\n> pip install --upgrade tensorflow-datasets\r\n> ```\r\n> \r\n> 2. Import correct method: Make sure you are importing the correct method from TensorFlow Datasets. Instead of using `load`, you might need to use other methods like `load_dataset` or `load_from_tfds`. For example:\r\n> ``\r\n> import tensorflow_datasets as tfds\r\n> \r\n> dataset = tfds.load_dataset('dataset_name')\r\n> \r\n> ```\r\n> By following these steps, you should be able to resolve the issue and successfully load datasets using TensorFlow Datasets.\r\n> ```\r\n\r\n`AttributeError: module 'tensorflow_datasets' has no attribute 'load_dataset'` with tensorflow 2.13 and tensorflow_datasets 4.9.2", "The correct method to load datasets in TensorFlow Datasets is 'load', not 'load_dataset'. Here's an example of how to load a dataset using TensorFlow Datasets:\r\n\r\nTry using load() instead of load_dataset\r\nimport tensorflow_datasets as tfds\r\n# Load the dataset\r\ndataset, info = tfds.load('dataset_name', split='train', as_supervised=True)\r\n\r\n", "Thanks for all; it works now.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61247\">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/61247\">No</a>\n", "for me its still not working even with the latest versions of tensorflow and tensorflow_datasets" ]

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[ "Hi @drewshark ,\r\n\r\nThanks for your time reporting this. I have replicated the issue with CPU runtime and attached [cpu-gist](https://colab.research.google.com/gist/SuryanarayanaY/6990939226d9d79dfe9225b13919c288/61246_cpu.ipynb) here. As the error log suggests this Op with float16 as input dtype not implemented on CPU which we need to dig more to rectify this.May be we need to register this Op for CPU device with tf.float16 also.\r\n\r\nWith GPU the code works as intended and can refer same in attached [gpu-gist](https://colab.research.google.com/gist/SuryanarayanaY/ed8801f338eaa04193e2b7fa35966652/61246_gpu.ipynb).\r\n\r\nThanks!\r\n\r\n\r\n\r\n" ]

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[ "@niemiaszek \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue reported here. I have tried to replicate [this](https://colab.research.google.com/gist/sushreebarsa/29378816f9ccca87549cb6617278aa8d/61245.ipynb#scrollTo=EZfM7r8QhGC7) issue and faced different errors. Thank you!", "Sorry for no response so far. I will prepare standalone code with some sample TFRecords prepared on public dataset. Then it should be easier to fiddle with this issue. For now I'm quite busy with other stuff and I was hoping for some expertise just based on code snippet, as my main question (how to efficiently filter `tf.data.Dataset` by classes) seem to be rather general than case-dependent.\r\n\r\n ", "@niemiaszek Thank you for your response!\r\nThough, in order to analyze the issue we need the complete code, you may perform filters on batches. Either unbatch and use tf.data.Dataset.filter or just use tf.data.Dataset.map which is more preferable. As an example you may refer to this following;\r\n```\r\nds = tfds.load('cats_vs_dogs', split='train', as_supervised=True)\r\nds = ds.filter(lambda img, label: label == 1)\r\n```\r\n\r\nWhen using the [Dataset.map](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), and [Dataset.filter](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#filter) transformations, which apply a function to each element, the element structure determines the arguments of the function. Please refer to this [document](https://www.tensorflow.org/api_docs/python/tf/data/Dataset). \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/61245\">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/61245\">No</a>\n" ]

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[ "\r\n@jyingl3 Can you help to have a look? thanks\r\n\r\n", "Thanks for making this change Zhoulong! It looks good to me." ]

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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/61243/checks?check_run_id=14944897492) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @rafaelubalmw Can you please sign CLA. Thank you!", "@gbaned Looks like my GitHub user was just successfully added to the MathWorks Google Group associated with an already signed corporate CLA. Happy to address any additional comments. Thanks!", "@jpienaar @eric-k256 Just pinging on this PR. Thanks!" ]

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[ "Here are the internal errors, @SuryanarayanaY can you please verify ? Thank you!\r\n\r\nTraceback (most recent call last):\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1486, in binary_op_wrapper\r\n out = r_op(x)\r\n File \"/tensorflow/python/util/traceback_utils.py\", line 141, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1509, in r_binary_op_wrapper\r\n y, x = maybe_promote_tensors(y, x, force_same_dtype=True)\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1441, in maybe_promote_tensors\r\n ops.convert_to_tensor(tensor, dtype, name=\"x\"))\r\n File \"/tensorflow/python/profiler/trace.py\", line 183, in wrapped\r\n return func(*args, **kwargs)\r\n File \"/tensorflow/python/framework/ops.py\", line 701, in convert_to_tensor\r\n return tensor_conversion_registry.convert(\r\n File \"/tensorflow/python/framework/tensor_conversion_registry.py\", line 209, in convert\r\n return overload(dtype, name) # pylint: disable=not-callable\r\n File \"/tensorflow/python/framework/tensor.py\", line 762, in __tf_tensor__\r\n raise ValueError(\r\nValueError: x: Tensor conversion requested dtype float32 for Tensor with dtype float64: <tf.Tensor 'embedding_lookup:0' shape=() dtype=float64>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/tensorflow/python/framework/test_util.py\", line 1634, in decorated\r\n return f(self, *args, **kwargs)\r\n File \"/tensorflow/python/kernel_tests/nn_ops/embedding_ops_test.py\", line 584, in testHigherRankMaxNorm\r\n simple = embedding_ops.embedding_lookup(params, ids, max_norm=1.0)\r\n File \"/tensorflow/python/util/traceback_utils.py\", line 141, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/tensorflow/python/util/dispatch.py\", line 1260, in op_dispatch_handler\r\n return dispatch_target(*args, **kwargs)\r\n File \"/tensorflow/python/ops/embedding_ops.py\", line 326, in embedding_lookup\r\n return _embedding_lookup_and_transform(\r\n File \"/tensorflow/python/ops/embedding_ops.py\", line 144, in _embedding_lookup_and_transform\r\n result = _clip(\r\n File \"/tensorflow/python/ops/embedding_ops.py\", line 73, in _clip\r\n return clip_ops.clip_by_norm(\r\n File \"/tensorflow/python/util/traceback_utils.py\", line 141, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/tensorflow/python/util/dispatch.py\", line 1260, in op_dispatch_handler\r\n return dispatch_target(*args, **kwargs)\r\n File \"/tensorflow/python/ops/clip_ops.py\", line 223, in clip_by_norm\r\n intermediate = values * clip_norm\r\n File \"/tensorflow/python/util/traceback_utils.py\", line 141, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1491, in binary_op_wrapper\r\n raise e\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1475, in binary_op_wrapper\r\n return func(x, y, name=name)\r\n File \"/tensorflow/python/ops/math_ops.py\", line 1856, in _mul_dispatch\r\n return multiply(x, y, name=name)\r\n File \"/tensorflow/python/util/traceback_utils.py\", line 141, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/tensorflow/python/util/dispatch.py\", line 1260, in op_dispatch_handler\r\n return dispatch_target(*args, **kwargs)\r\n File \"/tensorflow/python/ops/math_ops.py\", line 529, in multiply\r\n return gen_math_ops.mul(x, y, name)\r\n File \"/tensorflow/python/ops/gen_math_ops.py\", line 6590, in mul\r\n _, _, _op, _outputs = _op_def_library._apply_op_helper(\r\n File \"/tensorflow/python/framework/op_def_library.py\", line 778, in _apply_op_helper\r\n _ExtractInputsAndAttrs(op_type_name, op_def, allowed_list_attr_map,\r\n File \"/tensorflow/python/framework/op_def_library.py\", line 589, in _ExtractInputsAndAttrs\r\n raise TypeError(\r\nTypeError: Input 'y' of 'Mul' Op has type float32 that does not match type float64 of argument 'x'.", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @SuryanarayanaY Any update on this PR? Please. Thank you!", "I have gone through the error stack and root cause is as below.\r\n\r\n> File \"/tensorflow/python/kernel_tests/nn_ops/embedding_ops_test.py\", line 584, in testHigherRankMaxNorm\r\n> simple = embedding_ops.embedding_lookup(params, ids, max_norm=1.0)\r\n\r\nIn the above test the `params` is `float64` and max_norm=1.0 which are then passed to `clip_by_norm()` as `t` and `clip_norm` respectively. \r\n\r\n> \r\n> File \"/tensorflow/python/ops/clip_ops.py\", line 223, in clip_by_norm\r\n> intermediate = values * clip_norm\r\n\r\nThen in the above line of code `t` is converted to `values ` as `float64` tensor and `clip_norm=1.0`\r\n```\r\nvalues = ops.convert_to_tensor(\r\n t.values if isinstance(t, indexed_slices.IndexedSlices) else t,\r\n name=\"t\")\r\n```\r\n\r\nWith the proposed amendment `clip_norm = math_ops.maximum(clip_norm, 0)`, the `clip_norm` becoming float32 tensor and this is causing the error and test fail. Without modification it remains as 1.0 and hence `values * clip_norm` works fine here.\r\n\r\nOne observation is the args `t` and `clip_norm` of API `tf.clip_by_norm` both the args should be of same dtype else exception will be raised which is also not documented.\r\n\r\nMy proposed solution is:\r\n\r\n```\r\nclip_norm = tf.cast(math_ops.maximum(clip_norm, 0),dtype = values.dtype)\r\n```\r\n\r\nwhich can validate -ve inputs, resolves the issue mentioned above regarding dtype of `t` and `clip_norm` and it also eliminates testfail.\r\n\r\nI have added a [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/f01ac87fc5e19aebca7be360adef2ae2/clip_by_norm-pr-61242.ipynb) for above exercise for reference.\r\n\r\n@cantonios , Needs your review and comments for proceeding.\r\n\r\n\r\n\r\n", "Hi @cantonios, Can you please review this PR ? Thank you!", "Hi @cantonios, Can you please review this PR ? Thank you!", "Hi @SuryanarayanaY Can you please check @cantonios's [comments](https://github.com/tensorflow/tensorflow/pull/61242#discussion_r1344461449) and keep us posted ? Thank you!" ]

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[ "@jyingl3 Can you help to have a look? thanks" ]

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[ "@h2222,\r\nThe error occurred because you are trying to run the **32-bit Python on a 64-bit OS** which was incompatible. When I tried to execute the mentioned code with the compatible on the latest tensorflow `v2.13`, it was executed without any issues.\r\n\r\nKindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/cbdf08a73f6d4ca7b249d89dfc287e3a/untitled1237.ipynb) and please try with the right infra which helps to execute the code smoothly. Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61240\">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/61240\">No</a>\n" ]

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[ "Hi @yeoways ,\r\n\r\nTo use Distribution strategies first we need to create a strategy from applicable Distribution strategy class and then build the model inside that strategy scope. Then remaining steps are as usual.\r\n\r\n```\r\nstrategy = tf.distribute.MirroredStrategy()\r\n\r\nwith strategy.scope():\r\n # Model building/compiling need to be within `strategy.scope()`.\r\n multi_GPU_model = <call_your_model()>\r\n```\r\n\r\nYou can refer the attached tutorials [1](https://www.tensorflow.org/tutorials/distribute/keras) ,[2](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras) and [3](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl) for going in details on how to implement distribution strategies.\r\n\r\n\r\nThe attached code seems related to TF1.x versions. You can refer the resource 3 attached above for distribution training with custom training loop where we can override the `train_step` function to define our own training steps. \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/61239\">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/61239\">No</a>\n" ]

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[ "@adamw-u Thank you for raising this issue!\r\nCould you share the complete standalone code to reproduce this issue reported here. Could you please try to shuffle across the entire data in the training set and as a result the validation data would leak into the training set. Please let us know if that helps? Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61238\">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/61238\">No</a>\n" ]

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[ "@d0k Could you please review this? We would like to get some feedback and suggestions.", "@d0k Thanks for the comments. I have addressed those. Please check.", "@d0k Can we move FloatNormalization passes for CPU (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L655-L669) right before `CpuInstructionFusion`, for example, before https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L832", "> @d0k Can we move FloatNormalization passes for CPU (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L655-L669) right before `CpuInstructionFusion`, for example, before https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L832\r\n\r\nI'm not strictly opposed to that, but if your problem can be solved by configuring FloatNormalization (i.e. overriding FloatSupport) I'd prefer that solution.", "> > @d0k Can we move FloatNormalization passes for CPU (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L655-L669) right before `CpuInstructionFusion`, for example, before https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L832\r\n> \r\n> I'm not strictly opposed to that, but if your problem can be solved by configuring FloatNormalization (i.e. overriding FloatSupport) I'd prefer that solution.\r\n\r\n@d0k Thanks! I think we can solve by configuring FloatNormalization. I have one more question. Can we move `TreeReductionRewriter`, (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L740C22-L740C43),\r\nright before `CpuInstructionFusion`?", "> @d0k Thanks! I think we can solve by configuring FloatNormalization. I have one more question. Can we move `TreeReductionRewriter`, (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc#L740C22-L740C43), right before `CpuInstructionFusion`?\r\n\r\nI think right before layout assignment is the latest you can run it, feel free to move it there.", "@penpornk Now that all dependencies are merged in master branch, this PR is ready for review. Thanks!", "> There are 8 [Py+CPP Ubuntu CPU](https://source.cloud.google.com/results/invocations/50666fb9-baa8-4277-adf2-457e5bc701cd/log) test failures. Could you please help take a look? + Since you need to make changes anyway, could you please address my previous comments (that I said I would do from internally) as well? Thank you very much!\r\n> \r\n> ```\r\n> //tensorflow/compiler/tests:matrix_inverse_op_test_cpu FAILED in 3 out of 3 in 9.5s\r\n> //tensorflow/compiler/tests:matrix_inverse_op_test_cpu_mlir_bridge_test FAILED in 3 out of 3 in 8.5s\r\n> //tensorflow/compiler/tests:matrix_solve_op_test_cpu FAILED in 3 out of 3 in 5.1s\r\n> //tensorflow/compiler/xla/client/lib:qr_test_cpu FAILED in 3 out of 3 in 8.3s\r\n> //tensorflow/compiler/xla/service/cpu/tests:cpu_eigen_dot_operation_test FAILED in 3 out of 3 in 7.1s\r\n> //tensorflow/python/eager/polymorphic_function:polymorphic_function_xla_jit_test_cpu FAILED in 3 out of 3 in 13.9s\r\n> //tensorflow/python/eager/polymorphic_function:polymorphic_function_xla_jit_test_cpu_mlir_bridge_test FAILED in 3 out of 3 in 13.7s\r\n> //tensorflow/compiler/xla/tests:triangular_solve_test_cpu FAILED in 9 out of 9 in 28.0s\r\n> ```\r\n\r\nThanks @penpornk for the review. I will look into those failures and address your comments.", "@penpornk I think I have addressed your comments and fixed the UT failures. Please check. Thanks!", "@penpornk Now that 2.14 branch-cut is done, let us know if there is anything else needed for PR? ", "@penpornk We noticed that this PR got reverted. Could you please tell us more and a reproducer for jaxlib crash.", "@penpornk We noticed that this PR got reverted. Could you please tell us more and a reproducer for jaxlib crash.", "@mdfaijul Sorry for the late reply! The error message reported by the JAX team was \r\n```\r\n[ RUN ] LaxBackedNumpyTests.testCorrCoef0 (shape=(5,), dtype=<class 'ml_dtypes.bfloat16'>, rowvar=True)\r\ncould not create a primitive descriptor for a matmul primitive\r\nFatal Python error: Illegal instruction\r\n```\r\nI'm thinking this could be because oneDNN tried to create a bf16 matmul primitive on a machine that doesn't have AVX512_BF16. (I just realized that jaxlib builds with `--config=mkl_open_source_only`.) I'm trying to build jaxlib reproduce the issue. Will get back to you once I have the commands.", "@mdfaijul Here is the repro instruction. Please run this on an AVX2 machine. If you don't have one, constraining `ONEDNN_MAX_CPU_ISA=AVX2` might do the trick too.\r\n\r\n```bash\r\n# Use TF docker image to save time. JAX currently requires Python 3.9+ and NumPy 1.22+\r\nsudo docker run --name jax-dbg -w /root -it -d \\ \r\n -v \"${HOME}:/mnt\" \\\r\n tensorflow/build:latest-python3.9 \\\r\n bash\r\nsudo docker attach jax-dbg # Goes into the docker image\r\npip install numpy wheel build # Prereqs for building JAX\r\ngit clone https://github.com/google/jax.git\r\ncd jax\r\n\r\n# This JAX commit uses the XLA commit that still has the changes from your PR.\r\ngit checkout a1d8787e15914a24dc3e2f8cc46ce5542e602570 \r\n\r\n# The run failed test. Please run on an AVX2 machine.\r\nbazel test //tests:lax_numpy_test_cpu --test_filter=LaxBackedNumpyTests.testCorrCoef0\r\n```", "@penpornk Thanks for the reproducer. I have a fix and opened a new PR https://github.com/tensorflow/tensorflow/pull/61686", "@mdfaijul Thank you very much! How much was the PR changed? If it's not too much, maybe it's better that I reverted my rollback and add those addition fixes internally (I made some internal changes while merging your PR). If it's a big changes I can reapply my changes onto your new PR instead.", "@penpornk It's only small change. You can see the in this commit https://github.com/tensorflow/tensorflow/pull/61686/commits/2abff97921a566d143be67451aad85e735e9f1ab" ]

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[ "> Thank you very much for the PR, @agramesh1! The overall PR looks good to me. I have two minor comments.\r\n\r\n@penpornk I made the changes. Please take a look at it. " ]

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[ "@penpornk @nSircombe @milpuz01 @cfRod ", "Please also resolve merge conflicts / rebase. Thank you! :)", "> Please also resolve merge conflicts / rebase. Thank you! :)\r\n\r\nThe merge conflicts has been fixed.", "> cc: @TensorFlow-MKL: I don't think this will affect the x86 build.\r\n\r\nYes, that is right. This patch doesn't anymore uses single LRU cache when Eigen scheduler is used so the changes in TF are all around `ifdefs` for AArch64 to detect that case and the rest of changes are in Arm Compute Library (ACL) to have ACL scheduler per inter-thread and create it via stream in oneDNN (AArch64 code path) when it is required. \r\n", "Hi @davsva01 Can you please resolve conflicts? Thank you!", "> Hi @davsva01 Can you please resolve conflicts? Thank you!\r\n\r\nMerge conflicts fixed." ]

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[ "Some further digging on this I found out that there is probably an overflow in the paddings inside PadImpl:\r\n\r\n", "Hi @af-filby, I am not too familiar with emscripten, can you help me understand how you compile and execute your model?\r\n\r\nAre you in the tensorflow directory when you do your cmake command? You are also saying you are using emcmake but didn't include it in your command, was that a mistake or are you using the emcmake command like this?:\r\n```sh\r\nemcmake cmake ...\r\n```\r\n\r\nWhen compiling your project with emscripten, it looks like you are using a CMakeLists.txt?\r\n\r\nIs that where this is set?\r\n```\r\nSET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -s INITIAL_MEMORY=512MB\")\r\nSET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -s ALLOW_MEMORY_GROWTH=1\")\r\nSET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -s ALLOW_TABLE_GROWTH=1\")\r\n```\r\n\r\nCan you share your CMakeLists.txt if this is the case?\r\nCan you also share a minimally reproducible toy model as well?\r\n\r\nAre you using node to execute the model?\r\nI am not familiar with the emscripten workflow, so any details you share will be welcomed.\r\n\r\nPreferably, include as many cmd line commands you are doing as if you are writing a script/dockerfile.\r\nThanks for your help.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "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/61233\">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/61233\">No</a>\n" ]

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[]

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[ "@ujjwal-researcher,\r\nCould you please provide the complete code to reproduce the issue and it helps us to analyse the issue in an effective way. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/846ef0c0c96c968add15f82cd8ed2e0d/untitled1235.ipynb). Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61231\">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/61231\">No</a>\n" ]

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[ "The Py+CPP Test fail seems to be unrelated to this change." ]

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[ "Py+CPP Test failure is obviously completely unrelated.", "Yeah, will try to get this merged in today, thanks @elfringham " ]

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[ "Hi @sronen71 ,\r\n\r\nThanks for your time reporting this issue. The warning is actually related Tensorflow internal functions which is getting generated from the code below.This is not \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/8e2b6655c0c488290179ab90a0daed0f6d3006f7/tensorflow/python/ops/inplace_ops.py#L89-L93\r\n\r\nAs you see the warning is intended as its common that there will be some internal changes happens in source code.This will be taken care by the TF team and users need not do any changes here.\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/61228\">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/61228\">No</a>\n" ]

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[ "Hi @Bajirak \r\n\r\nIf your model has dynamic input shapes and you didn't specify a dimension size when building and converting your model, the converter will use 1 as the default dimension size. \r\n\r\nFrom TFLite [Model Analyzer](https://www.tensorflow.org/lite/guide/model_analyzer) I can see that \r\n\r\n`T#140(predict/MobilenetV3/expanded_conv/add) shape_signature:[-1, -1, -1, 16], type:FLOAT32`\r\n\r\nThe dimensions labeled with -1, these dimensions are the dimensions that are dynamic and can be resized.\r\nThat means that the model you are converting has shape [1, None, None, 3] and you can resize the 'None' dimensions at runtime.\r\n\r\nThe interpreter uses the static shape which starts with [1, 224,224,3] and get the shape of [1,112,112,3] in the process at that node.\r\n\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/604ab3d2ae70e8317fc57fd164063907/61227.ipynb).\r\n\r\nThanks.\r\n\r\n", "@pjpratik \r\nI appreciate you responding. so quickly to my \bquestion. :)\r\n\r\nI'm not familiar with tensorflow, but could you tell me where the guide document you explained is located?\r\nI mean, \r\n1) Is there any documentation on how to fix the input size of a tflite model?\r\n2) Is there any documentation related to dynamic input dimensions for tflite models?", "Hi @Bajirak \r\n\r\nPlease refer to this [documentation](https://www.tensorflow.org/lite/guide/inference.md#run_inference_with_dynamic_shape_model) related to dynamic input tensors. Regarding fixing the input size, we need to provide the batch and input size while model building.\r\n\r\nFor ex:\r\n```\r\nimg = tf.keras.Input(shape=(64, 64, 3), name=\"img\")\r\nconst = tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])\r\nval = img + const\r\nout = tf.identity(val, name=\"out\")\r\n\r\n# Convert to TF Lite format\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.Model(inputs=[img], outputs=[out]))\r\ntflite_model = converter.convert()\r\n```\r\nPlease find the [gist](https://colab.research.google.com/gist/pjpratik/56ea53502a5396f8dcec8c9011fc769e/61227.ipynb) for the same.\r\n\r\nFeel free to close the issue if it is resolved.\r\n\r\nThanks.\r\n", "@pjpratik \r\nThanks to you, all my doubts related to this have been resolved. 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/61227\">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/61227\">No</a>\n" ]

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[ "@ajithrp123,\r\n Apologies for the delay. Could you please run the above command again on the latest commit and let us know the issue you are facing? Also most of the bugs are resolved on the latest tensorflow commit. Thank you!", "@tilakrayal \r\nThanks for the reply\r\n\r\nI need to build TensorFlow source for my C++ application in both CPU and GPU machines. \r\nAs per TensorFlow.org website, GPU support is available only till TensorFlow 2.10.0 version.\r\nHence I have chosen the version https://github.com/tensorflow/tensorflow/tree/v2.10.0\r\n\r\nIs it possible to continue the build with this version itself? \r\nOr, is there any other option to build for the GPU also?", "@mraunak , Could you please take a look into this?", "Hi, @ajithrp123, for using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin. Please follow the link https://www.tensorflow.org/install/source_windows", "@mraunak Thanks for the reply\r\nI need to build TensorFlow source for my C++ application in both CPU and GPU machines.\r\nI using tensorflow 2.10.0. As per tensorflow.org(https://www.tensorflow.org/install/source_windows) WSL or direct ML plugin needed for tensorflow versions from 2.11.\r\n\r\nIs WSL needed to build tensorflow 2.10.0?\r\nIs there any additional steps needed to build tensorflow 2.10.0 in native windows for cpp?", "Hi @ajithrp123, got it, thank you, could you please share the steps you are following and the errors you are getting while running TF 2.10.0? No WSL is not needed for TF 2.10.0", "hi @mraunak thanks for the response\r\n\r\n### **Please find the below steps which I have followed**\r\n1. Installed Bazel version 5.1.1 and set the environment variables.\r\n2. Installed msys2 and set the environment variables.\r\n3. Installed visual studio professional 2019 for C++ build .\r\n4. Installed python 3.9.13\r\n5. Installed python dependencies like numpy, wheel, keras\r\n6. Downloaded tensorflow 2.10.0 source from tensorflow github repository\r\n7. Set BAZEL_SH, VC,VS,FULL_VERSION environment variables\r\n8. Configured TensorFlow with default configuration using ‘./configure’\r\n9. Build TensorFlow using the command\r\n‘bazel --host_jvm_args=-Xmx4g --output_base=D:\\0 build --jvmopt=\"-server -Xms2g\" --config=opt tensorflow:tensorflow_cc’\r\n\r\n### **Build Error :**\r\nD:\\TF_Building\\cc_tensorflow-2.10.0>bazel --host_jvm_args=-Djavax.net.ssl.trustStore=\"C:\\Program Files\\Java\\jdk1.8.0_144\\jre\\lib\\security\\cacerts\" --host_jvm_args=-Djavax.net.ssl.trustStorePassword=changeit --host_jvm_args=-Xmx4g build --jvmopt=\"-server -Xms2g\" --config=opt //tensorflow:tensorflow_cc\r\nWARNING: Running Bazel server needs to be killed, because the startup options are different.\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=157\r\nINFO: Reading rc options for 'build' from d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Options provided by the client:\r\n 'build' options: --python_path=C:/Users/1024182/AppData/Local/Programs/Python/Python39/python.exe\r\nINFO: Reading rc options for 'build' from d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc:\r\n 'build' options: --define framework_shared_object=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\r\nINFO: Reading rc options for 'build' from d:\\tf_building\\cc_tensorflow-2.10.0\\.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=C:/Users/1024182/AppData/Local/Programs/Python/Python39/python.exe --action_env PYTHON_LIB_PATH=C:/Users/1024182/AppData/Local/Programs/Python/Python39/lib/site-packages --python_path=C:/Users/1024182/AppData/Local/Programs/Python/Python39/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true\r\nINFO: Reading rc options for 'build' from d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1INFO: Found applicable config definition build:opt in file d:\\tf_building\\cc_tensorflow-2.10.0\\.tf_configure.bazelrc: --copt=/arch:AVX --host_copt=/arch:AVX\r\nINFO: Found applicable config definition build:windows in file d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/experimental:preprocessor --host_copt=/experimental:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --distinct_host_configuration=false\r\nINFO: Found applicable config definition build:monolithic in file d:\\tf_building\\cc_tensorflow-2.10.0\\.bazelrc: --define framework_shared_object=false --experimental_link_static_libraries_once=false\r\nINFO: Analyzed target //tensorflow:tensorflow_cc (269 packages loaded, 20666 targets configured).\r\nINFO: Found 1 target...\r\nERROR: D:/tf_building/cc_tensorflow-2.10.0/tensorflow/BUILD:1156:21: Linking tensorflow/tensorflow_cc.dll failed: (Exit 1120): link.exe failed: error executing command\r\n cd /d C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow\r\n SET LIB=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\VC\\Tools\\MSVC\\14.29.30133\\ATLMFC\\lib\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\VC\\Tools\\MSVC\\14.29.30133\\lib\\x64;C:\\Program Files (x86)\\Windows Kits\\NETFXSDK\\4.8\\lib\\um\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.19041.0\\ucrt\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.19041.0\\um\\x64\r\n SET PATH=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\\\Extensions\\Microsoft\\IntelliCode\\CLI;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX64\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\VC\\VCPackages;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\TestWindow;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\TeamFoundation\\Team Explorer;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\MSBuild\\Current\\bin\\Roslyn;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Team Tools\\Performance Tools\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Team Tools\\Performance Tools;C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Common\\VSPerfCollectionTools\\vs2019\\\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Common\\VSPerfCollectionTools\\vs2019\\;C:\\Program Files (x86)\\Microsoft SDKs\\Windows\\v10.0A\\bin\\NETFX 4.8 Tools\\x64\\;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\FSharp\\Tools;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\Tools\\devinit;C:\\Program Files (x86)\\Windows Kits\\10\\bin\\10.0.19041.0\\x64;C:\\Program Files (x86)\\Windows Kits\\10\\bin\\x64;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\\\MSBuild\\Current\\Bin;C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\Tools\\;;C:\\WINDOWS\\system32;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\CMake\\bin;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\CommonExtensions\\Microsoft\\CMake\\Ninja;C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\Common7\\IDE\\VC\\Linux\\bin\\ConnectionManagerExe\r\n SET PWD=/proc/self/cwd\r\n SET PYTHON_BIN_PATH=C:/Users/1024182/AppData/Local/Programs/Python/Python39/python.exe\r\n SET PYTHON_LIB_PATH=C:/Users/1024182/AppData/Local/Programs/Python/Python39/lib/site-packages\r\n SET RUNFILES_MANIFEST_ONLY=1\r\n SET TEMP=C:\\Users\\1024182\\AppData\\Local\\Temp\r\n SET TF2_BEHAVIOR=1\r\n SET TMP=C:\\Users\\1024182\\AppData\\Local\\Temp\r\n C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Professional\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX64\\x64\\link.exe @bazel-out/x64_windows-opt/bin/tensorflow/tensorflow_cc.dll-2.params\r\n**# Configuration: 49364d498d2e60c64c08a94404aa46742b697c047bdd88e3c7913989a8abad35\r\n**# Execution platform: @local_execution_config_platform//:platform****\r\nstdout (C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130) exceeds maximum size of --experimental_ui_max_stdouterr_bytes=1048576 bytes; skipping\r\nTarget //tensorflow:tensorflow_cc failed to build\r\nINFO: Elapsed time: 8002.569s, Critical Path: 968.80s\r\nINFO: 7482 processes: 878 internal, 6604 local.\r\nFAILED: Build did NOT complete successfully", "@ajithrp123 Could you please share the file content C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130 to us? It should contain the error detail information. \r\n\r\nThank you.", "@shangerxin thanks for the response\r\nI have added the C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130 file in [https://github.com/ajithrp123/tensorflow_2.10.0_build_error/blob/main/stdout-12130.txt]\r\nplease find the file", "> @shangerxin thanks for the response I have added the C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130 file in [https://github.com/ajithrp123/tensorflow_2.10.0_build_error/blob/main/stdout-12130.txt] please find the file\r\n\r\nOK. I have got the file. I will check and test it locally. Thanks.", "> > @shangerxin thanks for the response I have added the C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130 file in [https://github.com/ajithrp123/tensorflow_2.10.0_build_error/blob/main/stdout-12130.txt] please find the file\r\n> \r\n> OK. I have got the file. I will check and test it locally. Thanks.\r\n\r\n@shangerxin thanks for the response and your time\r\ntensorflow 2.9.0 cc build is working properly. But issue only in 2.10.0 cc build.\r\nIs there any additional steps or any build configurations to be modified to build cc in tensorflow 2.10.0?", "> > > @shangerxin thanks for the response I have added the C:/users/1024182/_bazel_1024182/gigze6o2/execroot/org_tensorflow/bazel-out/_tmp/actions/stdout-12130 file in [https://github.com/ajithrp123/tensorflow_2.10.0_build_error/blob/main/stdout-12130.txt] please find the file\r\n> > \r\n> > \r\n> > OK. I have got the file. I will check and test it locally. Thanks.\r\n> \r\n> @shangerxin thanks for the response and your time tensorflow 2.9.0 cc build is working properly. But issue only in 2.10.0 cc build. Is there any additional steps or any build configurations to be modified to build cc in tensorflow 2.10.0?\r\n\r\n@ajithrp123 I have tried to add new link option into the compilation command. \r\n```\r\n --linkopt=/FORCE:MULTIPLE\r\n```\r\nThe duplicated symbol error is mitigated but there are new link errors. I will work on it and let you know the result. Thanks. ", "@shangerxin thanks for the update , looking forward for your solution\r\nOne doubt, Is it possible to build tensorflow_cc.dll in windows for tf2.10.0 or is there any bug in the build setup? ", "> @shangerxin thanks for the update , looking forward for your solution One doubt, Is it possible to build tensorflow_cc.dll in windows for tf2.10.0 or is there any bug in the build setup?\r\n\r\n@ajithrp123 Yes it is supported. I can see the target tensorflow_cc from the file\r\n```\r\ntensorflow\\BUILD\r\n\r\ntf_cc_shared_library(\r\n name = \"tensorflow_cc\",\r\n additional_linker_inputs = [\r\n \"//tensorflow:tf_exported_symbols.lds\",\r\n \"//tensorflow:tf_private_symbols.lds\",\r\n \"//tensorflow:tf_version_script.lds\",\r\n ],\r\n...\r\n```\r\nI'm still investigating the why there are so many link errors. Wil update the result later. Thanks. ", "@ajithrp123 \r\n\r\n> > @shangerxin thanks for the update , looking forward for your solution One doubt, Is it possible to build tensorflow_cc.dll in windows for tf2.10.0 or is there any bug in the build setup?\r\n> \r\n> @ajithrp123 Yes it is supported. I can see the target tensorflow_cc from the file\r\n> \r\n> ```\r\n> tensorflow\\BUILD\r\n> \r\n> tf_cc_shared_library(\r\n> name = \"tensorflow_cc\",\r\n> additional_linker_inputs = [\r\n> \"//tensorflow:tf_exported_symbols.lds\",\r\n> \"//tensorflow:tf_private_symbols.lds\",\r\n> \"//tensorflow:tf_version_script.lds\",\r\n> ],\r\n> ...\r\n> ```\r\n> \r\n> I'm still investigating the why there are so many link errors. Wil update the result later. Thanks.\r\n\r\nI have double confirmed with the Team. The target tensorflow_cc is not support Windows platform. You can check these files:\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/3d79c0e4a663a4dd846c38cb5cdc0beeb3292d83/tensorflow/python/BUILD#L630-L640\r\nhttps://github.com/tensorflow/tensorflow/blob/1dbc8d1b0126dcac5edea9a65a1fddbfe5e337ce/tensorflow/compiler/mlir/lite/experimental/tac/py_wrapper/BUILD#L37-L41\r\n\r\nThe Windows platform has no dependency files. \r\n", "Thank you @shangerxin", "@shangerxin thanks for the confirmation.\r\n@tilakrayal @mraunak @sachinprasadhs thanks for the support.\r\nClosing this issue with the following confirmation ,\r\n**tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**", "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/61226\">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/61226\">No</a>\n", "> @shangerxin thanks for the confirmation. @tilakrayal @mraunak @sachinprasadhs thanks for the support. Closing this issue with the following confirmation , **tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**\r\n\r\nCan older version of tensorflow support C++ tensorflow_cc in windows?", "> @ajithrp123\r\n> \r\n> > > @shangerxin thanks for the update , looking forward for your solution One doubt, Is it possible to build tensorflow_cc.dll in windows for tf2.10.0 or is there any bug in the build setup?\r\n> > \r\n> > \r\n> > @ajithrp123 Yes it is supported. I can see the target tensorflow_cc from the file\r\n> > ```\r\n> > tensorflow\\BUILD\r\n> > \r\n> > tf_cc_shared_library(\r\n> > name = \"tensorflow_cc\",\r\n> > additional_linker_inputs = [\r\n> > \"//tensorflow:tf_exported_symbols.lds\",\r\n> > \"//tensorflow:tf_private_symbols.lds\",\r\n> > \"//tensorflow:tf_version_script.lds\",\r\n> > ],\r\n> > ...\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > I'm still investigating the why there are so many link errors. Wil update the result later. Thanks.\r\n> \r\n> I have double confirmed with the Team. The target tensorflow_cc is not support Windows platform. You can check these files:\r\n> \r\n> https://github.com/tensorflow/tensorflow/blob/3d79c0e4a663a4dd846c38cb5cdc0beeb3292d83/tensorflow/python/BUILD#L630-L640\r\n> \r\n> \r\n> https://github.com/tensorflow/tensorflow/blob/1dbc8d1b0126dcac5edea9a65a1fddbfe5e337ce/tensorflow/compiler/mlir/lite/experimental/tac/py_wrapper/BUILD#L37-L41\r\n> \r\n> The Windows platform has no dependency files.\r\nit is meaning of this. Can not C++ project with tensorflow on windows os?", "> > @shangerxin thanks for the confirmation. @tilakrayal @mraunak @sachinprasadhs thanks for the support. Closing this issue with the following confirmation , **tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**\r\n> \r\n> Can older version of tensorflow support C++ tensorflow_cc in windows?\r\n\r\n@hidon Yes , I have tried tensorflow 2.9.0 and I am able to generate tensorflow_cc.dll in windows using the steps mentioned in previous replies.", "> > > @shangerxin thanks for the confirmation. @tilakrayal @mraunak @sachinprasadhs thanks for the support. Closing this issue with the following confirmation , **tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**\r\n> > \r\n> > \r\n> > Can older version of tensorflow support C++ tensorflow_cc in windows?\r\n> \r\n> @hidon Yes , I have tried tensorflow 2.9.0 and I am able to generate tensorflow_cc.dll in windows using the steps mentioned in previous replies.\r\n\r\n@ajithrp123 thank you very much. I will try 2.9.0.", "> > > @shangerxin thanks for the confirmation. @tilakrayal @mraunak @sachinprasadhs thanks for the support. Closing this issue with the following confirmation , **tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**\r\n> > \r\n> > \r\n> > Can older version of tensorflow support C++ tensorflow_cc in windows?\r\n> \r\n> @hidon Yes , I have tried tensorflow 2.9.0 and I am able to generate tensorflow_cc.dll in windows using the steps mentioned in previous replies.\r\n\r\nWhich version of Python use with tensorflow 2.9.0? I got error python 3.9.13 version.", "> > > > @shangerxin thanks for the confirmation. @tilakrayal @mraunak @sachinprasadhs thanks for the support. Closing this issue with the following confirmation , **tensorflow 2.10.0 c++ build(tensorflow_cc) in windows is not supported**\r\n> > > \r\n> > > \r\n> > > Can older version of tensorflow support C++ tensorflow_cc in windows?\r\n> > \r\n> > \r\n> > @hidon Yes , I have tried tensorflow 2.9.0 and I am able to generate tensorflow_cc.dll in windows using the steps mentioned in previous replies.\r\n> \r\n> Which version of Python use with tensorflow 2.9.0? I got error python 3.9.13 version.\r\n\r\n@hidon which error you are getting ? It will work in python 3.9.13 if not can you try with python 3.8.0. For me i have tried in these two python versions and both worked for me.", "@ajithrp123 thanks for reply. build is succesfully completede with 3.8.0. but get unresolved external error with ms visual stdio C++ desktop when build it windwos 10.\r\n\r\nadded additional dependencies (bazel build --config=opt //tensorflow:tensorflow.lib)\r\ntensorflow.lib\r\ntensorflow_cc.lib\r\ninclude directory (bazel build --config=opt //tensorflow:install_headers)\r\n../include\r\n../include/src\r\n\r\nms visul stdio configuration is release and platform is x64.\r\n\r\nActullay my target is compiling saved_model_bundle_test.cc (tensorflow/cc/saved_model ) but I can not do it on windows 10.\r\n then I will adapt my project.\r\n\r\n\r\nCan you give me suggestion? thanks.", "> > @ajithrp123\r\n> > > > @shangerxin thanks for the update , looking forward for your solution One doubt, Is it possible to build tensorflow_cc.dll in windows for tf2.10.0 or is there any bug in the build setup?\r\n> > > \r\n> > > \r\n> > > @ajithrp123 Yes it is supported. I can see the target tensorflow_cc from the file\r\n> > > ```\r\n> > > tensorflow\\BUILD\r\n> > > \r\n> > > tf_cc_shared_library(\r\n> > > name = \"tensorflow_cc\",\r\n> > > additional_linker_inputs = [\r\n> > > \"//tensorflow:tf_exported_symbols.lds\",\r\n> > > \"//tensorflow:tf_private_symbols.lds\",\r\n> > > \"//tensorflow:tf_version_script.lds\",\r\n> > > ],\r\n> > > ...\r\n> > > ```\r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > I'm still investigating the why there are so many link errors. Wil update the result later. Thanks.\r\n> > \r\n> > \r\n> > I have double confirmed with the Team. The target tensorflow_cc is not support Windows platform. You can check these files:\r\n> > https://github.com/tensorflow/tensorflow/blob/3d79c0e4a663a4dd846c38cb5cdc0beeb3292d83/tensorflow/python/BUILD#L630-L640\r\n> > \r\n> > https://github.com/tensorflow/tensorflow/blob/1dbc8d1b0126dcac5edea9a65a1fddbfe5e337ce/tensorflow/compiler/mlir/lite/experimental/tac/py_wrapper/BUILD#L37-L41\r\n> > \r\n> > The Windows platform has no dependency files.\r\n> > it is meaning of this. Can not C++ project with tensorflow on windows os?\r\n\r\n@hidon It means the target of TF 2.10.0 is not support on Windows. ", "@shangerxin Actually Correct question, which version of tensorflow is support on windows?\r\n\r\nI need for my desktop C++ project. this project will use saved tensorflow model. I did not find good guide documents like this https://www.tensorflow.org/install/source_windows.\r\nthanks.\r\n", "> @ajithrp123 thanks for reply. build is succesfully completede with 3.8.0. but get unresolved external error with ms visual stdio C++ desktop when build it windwos 10.\r\n> \r\n> added additional dependencies (bazel build --config=opt //tensorflow:tensorflow.lib) tensorflow.lib tensorflow_cc.lib include directory (bazel build --config=opt //tensorflow:install_headers) ../include ../include/src\r\n> \r\n> ms visul stdio configuration is release and platform is x64.\r\n> \r\n> Actullay my target is compiling saved_model_bundle_test.cc (tensorflow/cc/saved_model ) but I can not do it on windows 10. then I will adapt my project.\r\n> \r\n> Can you give me suggestion? thanks.\r\n\r\n@hidon Is this link error. Link error is expected one because not all the symbols exported to tensorflow_cc.dll. We need to manually export (using TF_EXPORT) needed symbols to dll.\r\nPlease look into the following tutorial for reference\r\n[https://medium.com/vitrox-publication/deep-learning-frameworks-tensorflow-build-from-source-on-windows-python-c-cpu-gpu-d3aa4d0772d8](url)", "@ajithrp123, @shangerxin thanks to you. Tensorflow model exported C++ on Windows is completed successfully. shared my route this link --> https://github.com/hidon/Tensorflow-to-C-", "@hidon nice to hear from you. It would be great if you can share what are the functions/classes exported manually using TF_EXPORT. So that if anyone doing the same then they can easily do it. It would save more time for others." ]

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[ "Hi @KnSun99 ,\r\n\r\nThanks for reaching us. Trying to access Windows files from WSL might be considerably slower than using the native Linux file system.\r\n\r\nWindows drives are mounted in the Linux at `/mnt/` directory. You can prefix /mnt/ to your local directory to access then in WSL. For example, your personal Users folder at `C:\\Users\\<yourname>` is available at: `/mnt/c/Users/<yourname>`.\r\n\r\nFor more details you may refer the attached external sources from here [source1](https://www.sitepoint.com/wsl2/) and [source2](https://ling123labs.com/posts/WSL-files-in-Windows-and-vice-versa/#:~:text=Accessing%20your%20Windows%20files%20in%20the%20WSL%20terminal&text=Just%20use%20the%20cd%20command,to%20access%20my%20Documents%20folder.).\r\n\r\nPlease let us know if it helps. Thanks!\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "Thank you for the response! Will there be future versions that support Windows native GPU?", "@KnSun99 ,\r\n\r\nDue to performance issues Windows native GPU support has been dropped. I doubt whether support for windows native GPU will bring back in near future.", "Thanks for your reply!", "@KnSun99 ,\r\n\r\nPlease let us know if the issue is resolved for you. Please feel free to close if resolved.Let us know if you get any further issue. Thanks!", "OK", "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/61225\">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/61225\">No</a>\n" ]

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[ "CC @fcoUnda " ]

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[ "@drewshark,\r\nI tried to execute the mentioned code on tensorflow v2.13, where it was executed with the error stating that `AttributeError: image dtype must be either floating point or integer` which was clearly mentioning to the user that the dtype should be either floating or interger. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/2caebf81876962bd026cc0dbbaf32cb4/untitled1233.ipynb). Thank you!\r\n\r\n", "Thanks for your response, I tried it on tf1.12.0. It seems that this issue has been fixed.", "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/61220\">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/61220\">No</a>\n" ]

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[ "Hi @fndppx \r\n\r\nCan you please check if the preprocessing is done properly as I can see that the python api as well as model metadata normalize the values between [0,1].\r\n\r\nThanks.", "> Hi @fndppx \r\n> \r\n> Can you please check if the preprocessing is done properly as I can see that the python api as well as model metadata normalize the values between [0,1].\r\n> \r\n> Thanks.\r\n\r\n\r\n\r\nbut i use TensorFlowLiteTaskVision this framework ，not found model metadata normalize the values between [0,1]. only could set option 😂😂", "Hi @fndppx \r\n\r\nThe ImageClassifier API expects a TFLite model with mandatory [TFLite Model Metadata](https://www.tensorflow.org/lite/models/convert/metadata). [TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/overview) can handle normalization for you if you set up NormalizationOptions in metadata.\r\n\r\nThe downloaded [tflite model](https://tfhub.dev/google/lite-model/imagenet/mobilenet_v3_large_100_224/classification/5/metadata/1) contains the metadata and a file containing all the class labels.\r\n\r\nCan you try [this](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial#image_classifiers) steps with \r\n\r\n```\r\n_INPUT_NORM_MEAN = 0\r\n_INPUT_NORM_STD = 255\r\n```\r\nThanks.\r\n", "> Hi @fndppx\r\n> \r\n> The ImageClassifier API expects a TFLite model with mandatory [TFLite Model Metadata](https://www.tensorflow.org/lite/models/convert/metadata). [TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/overview) can handle normalization for you if you set up NormalizationOptions in metadata.\r\n> \r\n> The downloaded [tflite model](https://tfhub.dev/google/lite-model/imagenet/mobilenet_v3_large_100_224/classification/5/metadata/1) contains the metadata and a file containing all the class labels.\r\n> \r\n> Can you try [this](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial#image_classifiers) steps with\r\n> \r\n> ```\r\n> _INPUT_NORM_MEAN = 0\r\n> _INPUT_NORM_STD = 255\r\n> ```\r\n> \r\n> Thanks.\r\n\r\nthank you very mach！！this way solve this issue！", "Hi @fndppx \r\n\r\nGlad the issue is solved. Please feel free to close the issue.\r\n\r\nThanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61219\">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/61219\">No</a>\n" ]

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[ "@syedshahbaaz Could you please make sure to use latest stable version of TF 2.13 and check the tested build configurations [here](https://www.tensorflow.org/install/source#tested_build_configurations). Thank you!", "Here is the command to reproduce the failure:\r\n`bazel test --copt=-march=skylake --copt=-mavx512 //tensorflow/python/tools:aot_compiled_test`\r\n\r\nFirst bad commit from Eigen: [554fe02a](https://gitlab.com/libeigen/eigen/-/commit/554fe02ae3f3fbc2fd320c26a522f1e59b2d6342)\r\n", "CC @cantonios ", "> CC @cantonios\r\n\r\nThanks, I have a pending fix waiting for review. Should be out today or tomorrow.", "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/61216\">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/61216\">No</a>\n" ]

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[ "this is the same error I had: AttributeError: module 'tensorflow.python.distribute.input_lib' has no attribute 'DistributedDatasetInterface'. This error comes up even when I try model.fit and any other operation with the model beyond its compile line. ", "> this is the same error I had: AttributeError: module 'tensorflow.python.distribute.input_lib' has no attribute 'DistributedDatasetInterface'. This error comes up even when I try model.fit and any other operation with the model beyond its compile line.\r\n\r\nI've solved it by replacing DistributedDatasetInterface by DistributedDatasetSpec right on: \r\n```\r\n...\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\", line 1699, in _is_distributed_dataset\r\n```\r\n\r\nThis is what I did:\r\n```\r\ndef _is_distributed_dataset(ds):\r\n# return isinstance(ds, input_lib.DistributedDatasetInterface)\r\n return isinstance(ds, input_lib.DistributedDatasetSpec)\r\n```\r\n\r\nDefinetely not a good practice, I know, but... ", "> return isinstance(ds, input_lib.DistributedDatasetSpec)\r\n\r\ndo you have any update if input_lib.DistributedDatasetSpec carries same functionality as input_lib.DistributedDatasetInterface ?", "@samuelsennev,\r\n**tensorflow/python/keras** code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. \r\n\r\nPlease remove any import of **tensorflow.python.keras** and use the public API with **from tensorflow import keras** or **import tensorflow as tf; tf.keras.**\r\n\r\nI tried to execute the mentioned code on tensorflow **v2.13** by using below imports and it was executed without any issues.\r\n\r\n```\r\nfrom tensorflow.keras.layers import Dense, Input\r\nfrom tensorflow.keras import Sequential\r\nfrom tensorflow.keras.activations import sigmoid\r\n```\r\n\r\nKindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/6975de68760fead5d9b255261812163d/untitled.ipynb). \r\nhttps://github.com/tensorflow/tensorflow/releases\r\n\r\nThank you!", "@tilakrayal\r\nGot it! \r\n\r\nWorked with the public API and **from tensorflow import keras**\r\n\r\nThanks ;)", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61215\">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/61215\">No</a>\n", "> @samuelsennev, **tensorflow/python/keras** code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release.\r\n> \r\n> Please remove any import of **tensorflow.python.keras** and use the public API with **from tensorflow import keras** or **import tensorflow as tf; tf.keras.**\r\n> \r\n> I tried to execute the mentioned code on tensorflow **v2.13** by using below imports and it was executed without any issues.\r\n> \r\n> ```\r\n> from tensorflow.keras.layers import Dense, Input\r\n> from tensorflow.keras import Sequential\r\n> from tensorflow.keras.activations import sigmoid\r\n> ```\r\n> \r\n> Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/6975de68760fead5d9b255261812163d/untitled.ipynb). https://github.com/tensorflow/tensorflow/releases\r\n> \r\n> Thank you!\r\n\r\nit does works!\r\nbut after i change the imports code, the IDE(vs code) cannot recognize any type under keras， is there any way to resolve it?", "https://stackoverflow.com/a/77733620/13086128" ]

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[ "Hey @SuryanarayanaY . you working on this ?\r\n", "Hi @zhumakhan ,\r\n\r\nTensorflow provides C_API that can be used to build [bindings for other languages](https://github.com/tensorflow/docs/tree/master/site/en/r1/guide/extend/bindings.md) which is defined in [c_api.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h).\r\n\r\nPlease refer the above sources and let us know if it helps. Thanks!\r\n", "Thanks for your suggestion, will check that and let you know!", "@SuryanarayanaY as I understood, c_api.h is to create extension for other languages. But my problem was that current python api of tensorflow does not allow to get raw data pointer of tf.Tensor (e.g. torch.Tensor.data_ptr(), jax.Array.unsafe_data_pointer()). Hence I decided to make a C++ extension which returns tensorflow::Tensor::data() and bind it to python with pybind11. So, currently I can pass tf.Tensor (python api's tensor) into that C++ extension and take it as PyObject* argument inside the C++ extension. Further I should convert PyObject* into tensorflow::Tensor, call data() method and return its result. I stuck at the last step at the moment. Would really appreciate your suggestions on that.\r\nThanks!", "Hi @zhumakhan ,\r\n\r\nCould you please try converting a tf.tensor into serialized TensorProto prototype by using [tf.io.serialize_tensor](https://www.tensorflow.org/api_docs/python/tf/io/serialize_tensor) API, and then using tensorflow::ops::[ParseTensor](https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/parse-tensor) parse the serialized TensorProto back to tf:Tensor.\r\n\r\nPlease try this and let us know if this doesn't work. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61214\">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/61214\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/61213/checks?check_run_id=14862648204) 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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[ "@AkshayRoyal I tried to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/e19e257d31ab246426050348c2e56b5f/tensorboard_profiling_keras.ipynb#scrollTo=K4KJ3S-odd-a) and didn't see the error. Could you please check the attached gist and let me know if I am missing something here. Thank you!", "The first image is taken from the guide itself which shows the profile tab\r\n\r\nThe second image is being shown while running the same colab notebook showing in the guide but it does not give us the profile\r\n\r\nNow try selecting the Profile option from the list drop down on the right\r\n\r\nOnce you select it, either you will see blank output or an error as mentioned in the issue\r\n\r\n@sushreebarsa Can you see the \"Profile\" tab in the tensorboard? It doesn't show the \"Profile\" of the training run. There is no error in the run itself. But when you click the \"Profile\" tab in the tensorboard extension, you will be able to see the error or it'll be empty. ", "@AkshayRoyal Thank you for the quick response!\r\n@sachinprasadhs I was able to replicate the issue reported here in both TF [v2.12](https://colab.research.google.com/gist/sushreebarsa/e19e257d31ab246426050348c2e56b5f/tensorboard_profiling_keras.ipynb#scrollTo=lugpLFAflkeI) and [v2.13](https://colab.research.google.com/gist/sushreebarsa/2973acfbebd86f365b95ad5fac6b1e3c/tensorboard_profiling_keras.ipynb#scrollTo=sYCj8WzWhhMf). Please find the attached gists.\r\nThank you!", "@sachinprasadhs @ Any updates?", "I believe the reason for this is that the profile data is written in a different place in the hierarchy starting with 2.12. Why this is the case/how this happened, I do not know. I'm able to reproduce/fix this by doing the following (go into directory with events.out... first):\r\n\r\n```\r\ncp -Rpv ../plugins .\r\n```\r\n\r\n", "> I believe the reason for this is that the profile data is written in a different place in the hierarchy starting with 2.12. Why this is the case/how this happened, I do not know. I'm able to reproduce/fix this by doing the following (go into directory with events.out... first):\r\n> \r\n> ```\r\n> cp -Rpv ../plugins .\r\n> ```\r\n\r\nThis is the right answer, it works for me on tf 2.15.0. Saved my life. Thanks!", "> > I believe the reason for this is that the profile data is written in a different place in the hierarchy starting with 2.12. Why this is the case/how this happened, I do not know. I'm able to reproduce/fix this by doing the following (go into directory with events.out... first):\r\n> > ```\r\n> > cp -Rpv ../plugins .\r\n> > ```\r\n> \r\n> This is the right answer, it works for me on tf 2.15.0. Saved my life. Thanks!\r\n\r\nCould you please add you code? I couldn't get mine working ", "The solution is here. The page is not in English but just use the browser's translation\r\n\r\nhttps://beiznotes.org/how-to-solve-a-tensorflow-profiler-bug-of-no-profile-data-was-found/" ]

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[ "@epetrovski,\r\nThank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\n\r\n\r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n", "Moved to https://github.com/keras-team/tf-keras/issues/44", "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/61211\">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/61211\">No</a>\n", "Hi, I had the same issue initially, and I think it had to do with the tensorflow version. I had version 2.15.0 installed in another laptop and 2.16.1 was installed in my pc. The 2.15.0 version didn't give me this error and ran fine, but the 2.16.1 version did. I also had Python 3.10 installed in my laptop and 3.11 installed in my pc, so it seems like Python version doesn't matter. To fix this, this is what I did:\r\n\r\n1. Checked my tensorflow version (In my terminal, I ran: py -c 'import tensorflow as tf; print(tf.__version__)'\r\n2. Un-installed tensorflow, since I saw it was version 2.16.1 (pip uninstall tensorflow)\r\n3. Re-installed tensorflow version 2.15.0 (pip install tensorflow==2.15.0 )" ]

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[ "I am facing the same problem. Were you able to sort this out?", "I have the same problem on TF 2.14. Same config, Ubuntu 22.04 WSL2 on Windows 10.", "I am seeing the same problem. Have you found a solution?", "Unfortunately no", "Same here. Any suggestions would be appreciated." ]

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[ "Hi, \r\n\r\nIt is difficult to point to the exact commit and mention what caused this change without any simple reproducible code.\r\n\r\nAlso, did you test with the latest Nightly version and observed the same behavior in that as well?\r\n", "Thanks. I can confirm that the latest nightly version has the same behavior.\r\n\r\nI will try my best to provide a reproducible code.", "I find the reason: in https://github.com/tensorflow/tensorflow/commit/9d1262082e761cd85d6726bcbdfdef331d6d72c6, the stream becomes non-blocking (in `CU_STREAM_NON_BLOCKING` mode). Our codes use the default CUDA stream, so this change breaks our program. The workaround is to call `cudaDeviceSynchronize` before executing our CUDA codes.", "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/61209\">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/61209\">No</a>\n" ]