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Isuru0x01

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[ "CC @penpornk This PR fixes breakage in building TF with --config=mkl.", "@penpornk Can we please get this merged? All CI jobs will fail until it is. The failures in the Py+CPP Test Suite do not seem related and I cannot reproduce them." ]

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[ "@TensorFlow-MKL ", "@elfringham thanks for letting us know and providing a fix.", "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/60724\">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/60724\">No</a>\n" ]

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[ "Hi @penpornk - some performance improvements to the AArch64 build with oneDNN, would you be able to take a look please?" ]

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[ "@k-w-w @liangyaning33 I need a solution to upgrade the code.", "Hi, unfortunately the _SingleDeviceSaver is not part of the public API and can change at any time. \r\n\r\nIf you're trying to add custom save/restore steps we do have a `register_checkpoint_saver` mechanism for doing this (also private but more likely to be surfaced as a public API symbol): \r\nhttps://github.com/tensorflow/tensorflow/blob/f104fe5d323dddd20cfa41e12ac2efbc08baf598/tensorflow/python/saved_model/registration/registration.py#L198-L321\r\n\r\nThere is an example here:\r\nhttps://github.com/tensorflow/tensorflow/blob/f104fe5d323dddd20cfa41e12ac2efbc08baf598/tensorflow/python/saved_model/registration/registration_saving_test.py#L45-L116\r\n\r\nFor your case, since (at a brief glance) the Variable is being saved to another file, your `save_fn` and `restore_fn` would look something like:\r\n\r\n```\r\ndef save_fn(trackables, file_prefix):\r\n \"\"\"Save stack and part objects to a checkpoint shard.\"\"\"\r\n for v in trackables:\r\n # Write v to file\r\n return []\r\n\r\ndef restore_stacks_and_parts(trackables, merged_prefix):\r\n for v in trackables:\r\n # Read and assign v from file\r\n```\r\n\r\n\r\n", "> Hi, unfortunately the _SingleDeviceSaver is not part of the public API and can change at any time.\r\n> \r\n> If you're trying to add custom save/restore steps we do have a `register_checkpoint_saver` mechanism for doing this (also private but more likely to be surfaced as a public API symbol):\r\n> \r\n> https://github.com/tensorflow/tensorflow/blob/f104fe5d323dddd20cfa41e12ac2efbc08baf598/tensorflow/python/saved_model/registration/registration.py#L198-L321\r\n> \r\n> There is an example here:\r\n> \r\n> https://github.com/tensorflow/tensorflow/blob/f104fe5d323dddd20cfa41e12ac2efbc08baf598/tensorflow/python/saved_model/registration/registration_saving_test.py#L45-L116\r\n> \r\n> For your case, since (at a brief glance) the Variable is being saved to another file, your `save_fn` and `restore_fn` would look something like:\r\n> \r\n> ```\r\n> def save_fn(trackables, file_prefix):\r\n> \"\"\"Save stack and part objects to a checkpoint shard.\"\"\"\r\n> for v in trackables:\r\n> # Write v to file\r\n> return []\r\n> \r\n> def restore_stacks_and_parts(trackables, merged_prefix):\r\n> for v in trackables:\r\n> # Read and assign v from file\r\n> ```\r\n\r\nThank you very much for your reply. And I have another question, is this mechanism compatible with apis such as tf.keras.models.save_model?", "This is compatible with SavedModel but must be used with `register_serializable`. When loading, the same objects and checkpoint saver must be registered otherwise you may see errors.", "> This is compatible with SavedModel but must be used with `register_serializable`. When loading, the same objects and checkpoint saver must be registered otherwise you may see errors.\r\n\r\nNot only in python API restoring, but also the save_fn would be serialized to op graph saved in the saved_model.pb? I mean when I restore my model using C++ API (model serving), does it would run the custom restoring op graph just like old patching way." ]

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[ "@alejopaullier96,\r\nCould you please provide the simple standalone code to reproduce the issue which helps us to analyse the issue in an effective way. \r\n\r\nThe error is clearly describing that the error is due to shape or Rank. \r\n\r\nmatrix | A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Shape is [..., M, M].\r\nrhs | A Tensor. Must have the same type as matrix. Shape is [..., M, K].\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/linalg/solve#args\r\n\r\n\r\nThank you!", "Hi, is this open to pick up, I would like to contribute to this. I am new to open source contribution, but I am a quick learner.\r\n", "@tilakrayal I have solved the issue by squeezing one dimension from the first input tensor:\r\n```\r\nM = tf.squeeze(M, axis=1)\r\n```\r\nThis way my first tensor `M` which had shape `(batch_size, 1, 4, 4)` after squeezing has shape `(batch_size, 4, 4)`. My second tensor `E` had shape `(batch_size, 4, 1)`, so essentially they ended up with the same number of dimensions (3). \r\n\r\nJust for clarification, the current documentation does not specify that the two input tensors/matrices must have the same number of dimensions, but rather that it specifies how the two last dimensions must be.", "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/60721\">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/60721\">No</a>\n" ]

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[ "Hi @haider-khan333 \r\n\r\nOn android 12, please add the following to the Android manifest in order to detect GPU delegate.\r\n\r\n```\r\n<uses-library android:name=\"libOpenCL.so\"\r\n android:required=\"false\"/>\r\n\r\n<uses-library android:name=\"libOpenCL-pixel.so\"\r\n android:required=\"false\"/>\r\n```\r\n\r\nThanks.", "> Hi @haider-khan333\r\n> \r\n> On android 12, please add the following to the Android manifest in order to detect GPU delegate.\r\n> \r\n> ```\r\n> <uses-library android:name=\"libOpenCL.so\"\r\n> android:required=\"false\"/>\r\n> \r\n> <uses-library android:name=\"libOpenCL-pixel.so\"\r\n> android:required=\"false\"/>\r\n> `\r\n\r\n\r\nThanks a lot. It solved my issue.\r\nI'll be closing the issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60720\">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/60720\">No</a>\n" ]

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[ "TensorFlow version: 2.11.0 \r\nPython version: 3.10.10 \r\nGPU/CPU: GPU T4 x 2 on Kaggle\r\n\r\nAdditional steps taken:\r\nExecuted tf.config.run_functions_eagerly(True) , but the issue persists.\r\nConfirmed all image sizes are (128, 128, 3) and model.input_shape is (None, 128, 128, 3) . \r\nValidated that the datasets are not empty by running the following code:\r\n\r\n```\r\nfor images, masks in val_dataset.take(1): \r\n sample_image, sample_mask = images[31], masks[31] \r\n display([sample_image, sample_mask])```\r\n\r\nThe displayed images and masks are as expected.", "@woahthere2,\r\nI request you to take a look at this [issue](https://github.com/keras-team/keras/issues/16202) where a similar feature has been proposed and it is still open. Also I request to follow the similar feature which has been proposed to have the updates on the similar issue. Thank you!\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60719\">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/60719\">No</a>\n" ]

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[ "Hi @peratrepic,\r\n\r\nCan you either provide your data + python training code to reproduce the tflite model file or just the tflite model itself. Also it is preferable if you do not redact your input data & labels for now, I can try to reproduce w/o it but it would be helpful to understand the full context.\r\n\r\nThanks!", "Hi @pkgoogle, sure!\r\n\r\nHere is the python code I am using to create the model and convert it to tflite format:\r\n<details><summary>Click to expand!</summary>\r\n\r\n```\r\nimport numpy as np\r\nimport os\r\nimport tensorflow as tf\r\n\r\nIMG_SIZE = 28\r\n\r\nclass Model(tf.Module):\r\n def __init__(self):\r\n self.model = tf.keras.Sequential([\r\n tf.keras.layers.Flatten(input_shape=(IMG_SIZE, IMG_SIZE), name='flatten'),\r\n tf.keras.layers.Dense(\r\n units=10,\r\n kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05),\r\n bias_initializer=tf.keras.initializers.Ones(),\r\n name='dense'\r\n ),\r\n ])\r\n\r\n opt = tf.keras.optimizers.SGD(learning_rate=0.1)\r\n loss_fn = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\r\n self.model.compile(optimizer=opt, loss=loss_fn, metrics=['accuracy'])\r\n\r\n # The `train` function takes a batch of input images and labels.\r\n @tf.function(input_signature=[\r\n tf.TensorSpec([32, IMG_SIZE, IMG_SIZE], tf.float32),\r\n tf.TensorSpec([32, 10], tf.float32),\r\n ])\r\n def train(self, x, y):\r\n with tf.GradientTape() as tape:\r\n prediction = self.model(x)\r\n loss = self.model.loss(y, prediction)\r\n gradients = tape.gradient(loss, self.model.trainable_variables)\r\n self.model.optimizer.apply_gradients(\r\n zip(gradients, self.model.trainable_variables))\r\n result = {\"loss\": loss}\r\n return result\r\n\r\n @tf.function(input_signature=[\r\n tf.TensorSpec([1, IMG_SIZE, IMG_SIZE], tf.float32),\r\n ])\r\n def infer(self, x):\r\n logits = self.model(x)\r\n probabilities = tf.nn.softmax(logits, axis=-1)\r\n return {\r\n \"output\": probabilities,\r\n \"logits\": logits\r\n }\r\n\r\nm = Model()\r\n\r\ndef save_model():\r\n model_path = R\"<insert your path>\"\r\n\r\n tf.saved_model.save(\r\n m,\r\n model_path,\r\n signatures={\r\n 'train':\r\n m.train.get_concrete_function(),\r\n 'infer':\r\n m.infer.get_concrete_function(),\r\n })\r\n\r\n # Convert the model\r\n converter = tf.lite.TFLiteConverter.from_saved_model(model_path)\r\n converter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n ]\r\n converter.experimental_enable_resource_variables = True\r\n tflite_model = converter.convert()\r\n\r\n with open(os.path.join(model_path, \"trained_model.tflite\"), 'wb') as model_writer:\r\n model_writer.write(tflite_model)\r\n\r\nsave_model()\r\n```\r\n\r\n</details>\r\n\r\nAs you can see model is not being trained in python, I am just creating it and saving it to tflite format, with the goal to train it on another device using tflite C++ API. Here is the unredacted code for doing so:\r\n<details><summary>Click to expand!</summary>\r\n\r\n```\r\n#include <fstream>\r\n#include <iostream>\r\n#include <sstream>\r\n#include <string>\r\n#include <vector>\r\n\r\n#include \"tensorflow/lite/interpreter.h\"\r\n#include \"tensorflow/lite/kernels/register.h\"\r\n#include \"tensorflow/lite/logger.h\"\r\n#include \"tensorflow/lite/model.h\"\r\n#include \"tensorflow/lite/signature_runner.h\"\r\n\r\nconst int BATCH_SIZE = 32;\r\nconst int NUM_LABELS = 10;\r\nconst int IMG_SIZE = 28 * 28;\r\n\r\nfloat run_test(tflite::Interpreter* interpreter)\r\n{\r\n tflite::SignatureRunner* inference_runner = interpreter->GetSignatureRunner(\"infer\");\r\n\r\n TfLiteTensor* input_tensor = inference_runner->input_tensor(inference_runner->input_names()[0]);\r\n float* input = input_tensor->data.f;\r\n\r\n int total = 0;\r\n int correct = 0;\r\n\r\n std::ifstream test_data(\"test_set.txt\");\r\n std::string line;\r\n while (std::getline(test_data, line))\r\n {\r\n std::istringstream ss(line);\r\n int label;\r\n ss >> label;\r\n for (std::size_t i = 0; i < IMG_SIZE; ++i)\r\n {\r\n ss >> input[i];\r\n }\r\n\r\n if (inference_runner->Invoke() != kTfLiteOk)\r\n {\r\n std::cout << \"Error invoking inference interpreter signature\" << std::endl;\r\n return 0.f;\r\n }\r\n\r\n const TfLiteTensor* output_tensor = inference_runner->output_tensor(inference_runner->output_names()[0]);\r\n float* output = output_tensor->data.f;\r\n\r\n int predicted = 0;\r\n for (int i = 1; i < NUM_LABELS; ++i)\r\n {\r\n if (output[i] > output[predicted])\r\n {\r\n predicted = i;\r\n }\r\n }\r\n\r\n ++total;\r\n if (label == predicted)\r\n {\r\n ++correct;\r\n }\r\n }\r\n\r\n float accuracy = correct * 100.f / total;\r\n\r\n return accuracy;\r\n}\r\n\r\nvoid run_training(tflite::Interpreter* interpreter)\r\n{\r\n std::cout << \"Test acc before training: \" << run_test(interpreter) << std::endl << std::endl;\r\n\r\n tflite::SignatureRunner* train_runner = interpreter->GetSignatureRunner(\"train\");\r\n\r\n TfLiteTensor* input_data_tensor = train_runner->input_tensor(train_runner->input_names()[0]);\r\n float* input_data = input_data_tensor->data.f;\r\n TfLiteTensor* input_labels_tensor = train_runner->input_tensor(train_runner->input_names()[1]);\r\n float* input_labels = input_labels_tensor->data.f;\r\n\r\n int data_idx = 0;\r\n int num_batches = 0;\r\n std::vector<float> test_accs;\r\n\r\n std::ifstream train_data(\"train_set.txt\");\r\n std::string line;\r\n while (std::getline(train_data, line))\r\n {\r\n std::istringstream ss(line);\r\n int label;\r\n ss >> label;\r\n for (int lbl_idx = 0; lbl_idx < NUM_LABELS; ++lbl_idx)\r\n {\r\n input_labels[data_idx * NUM_LABELS + lbl_idx] = (label == lbl_idx ? 1.f : 0.f);\r\n }\r\n for (int i = 0; i < IMG_SIZE; ++i)\r\n {\r\n ss >> input_data[data_idx * IMG_SIZE + i];\r\n }\r\n ++data_idx;\r\n\r\n if (data_idx == BATCH_SIZE)\r\n {\r\n data_idx = 0;\r\n\r\n if (train_runner->Invoke() != kTfLiteOk)\r\n {\r\n std::cout << \"Error invoking train interpreter signature\" << std::endl;\r\n return;\r\n }\r\n\r\n ++num_batches;\r\n const TfLiteTensor* output_tensor = train_runner->output_tensor(train_runner->output_names()[0]);\r\n float* output = output_tensor->data.f;\r\n std::cout << \"Training of batch \" << num_batches << \" finished with loss: \" << output[0] << std::endl;\r\n\r\n if (num_batches == 100)\r\n {\r\n break;\r\n }\r\n }\r\n }\r\n\r\n std::cout << std::endl << \"Test acc after training: \" << run_test(interpreter) << std::endl;\r\n}\r\n\r\nvoid train_model()\r\n{\r\n tflite::LoggerOptions::SetMinimumLogSeverity(tflite::TFLITE_LOG_WARNING);\r\n\r\n std::unique_ptr<tflite::FlatBufferModel> model =\r\n tflite::FlatBufferModel::BuildFromFile(\"<insert your tflite model path>\");\r\n if (model == nullptr)\r\n {\r\n std::cout << \"Failed to load model\" << std::endl;\r\n return;\r\n }\r\n\r\n tflite::ops::builtin::BuiltinOpResolver resolver;\r\n tflite::InterpreterBuilder builder(*model, resolver);\r\n std::unique_ptr<tflite::Interpreter> interpreter;\r\n builder(&interpreter);\r\n if (interpreter == nullptr)\r\n {\r\n std::cout << \"Failed to create interpreter\" << std::endl;\r\n return;\r\n }\r\n\r\n if (interpreter->AllocateTensors() != kTfLiteOk)\r\n {\r\n std::cout << \"Failed to alocate interpreter tensors\" << std::endl;\r\n return;\r\n }\r\n\r\n run_training(interpreter.get());\r\n}\r\n\r\nint main()\r\n{\r\n train_model();\r\n\r\n return 0;\r\n}\r\n```\r\n\r\n</details>\r\n\r\nModel is being trained on MNIST dataset, I've just reformated it from binary files to txt files: one line per data sample, numbers separated by spaces, first number in line represents label and rest of the numbers represent features. I am attaching first 320 samples of train set in `train_set.txt`, and first 960 samples of test set in `test_set.txt`, since I can only upload limited file size here.\r\n[test_set.txt](https://github.com/tensorflow/tensorflow/files/11630576/test_set.txt)\r\n[train_set.txt](https://github.com/tensorflow/tensorflow/files/11630577/train_set.txt)\r\n", "Hi @peratrepic,\r\n\r\nI tried running your code but I actually run into a seg fault:\r\n```\r\n~/60718_clean/build$ ./main\r\nTest acc before training: 11.3542\r\n\r\nTraining of batch 1 finished with loss: 263.954\r\nSegmentation fault\r\n```\r\n\r\nHere's my setup\r\n[60718.tar.gz](https://github.com/tensorflow/tensorflow/files/11727464/60718.tar.gz)\r\n\r\nunzip it to the same directory that has your tensorflow source then:\r\n```\r\ntar -xzf 60718.tar.gz\r\ncd 60718_clean\r\npython test.py\r\ncd build\r\ncmake ..\r\nmake\r\n./main\r\n```\r\nIs your environment similar? or are you just linking a built .so file? Any additional information about your setup will be helpful.", "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.", "Hey @pkgoogle , sorry for delayed response I am on vacation atm. I cannot run your setup until I return, but from your results it seems that you are already getting abnormal loss after training of first batch (`Training of batch 1 finished with loss: 263.954`), even before you hit the segfault. Shouldn't that be enough for you to debug?\r\n\r\nFor the reference, training of first batch in Python (by calling the \"train\" signature) gives loss around ~2.5.\r\n\r\nRegarding segfault, I was also getting segfault with tflite binaries built from branch r2.10 while accessing the input tensors, but didn't get them with binaries from later branches (r2.12 and r2.13). I think that is also something which should be investigated on your end, because my code is just using snippets of code taken from tflite examples and documentation, it isn't doing anything unusual.\r\n\r\nAnd regarding my setup, I am building and running this on Windows. I am building TFLite from stable tf branches using bazel and linking the built lib/dll in the project which trains the model using tflite C++ API.", "Hi @peratrepic, Thanks for the information it definitely helps, Can you tell me your exact bazel commands and how you're linking the built lib/dll in the project? Are you using these steps? https://www.tensorflow.org/install/source_windows or something else. Are you utilizing WSL at all? Many people have different set ups so its hard to tell exactly what you are doing. In my view, that datapoint is not enough to debug at the moment, we are also dealing with many other issues so let's figure out how to reproduce this correctly first.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Hi @pkgoogle, I've now setup a Linux VM (_Ubuntu 22.04.2 AMD64_) in order to confirm that the issue is not platform dependent. I've confirmed that the issue is still happening and I've also encountered the same segmentation fault as you.\r\n\r\nThe reason that segmentation fault is happening is the same as one I was having with TF 2.10 (but not with TF 2.12 and 2.13) on Windows: reference to the input tensor changes after signature is invoked. I am not sure if that is expected behaviour since it is happening only with certain versions of TF with certain platforms, but anyways, here is a working version of the code without segmentation faults:\r\n<details><summary>Click to expand!</summary>\r\n\r\n```cpp\r\n#include <fstream>\r\n#include <iostream>\r\n#include <sstream>\r\n#include <string>\r\n#include <vector>\r\n\r\n#include \"tensorflow/lite/interpreter.h\"\r\n#include \"tensorflow/lite/kernels/register.h\"\r\n#include \"tensorflow/lite/logger.h\"\r\n#include \"tensorflow/lite/model.h\"\r\n#include \"tensorflow/lite/signature_runner.h\"\r\n\r\nconst int BATCH_SIZE = 32;\r\nconst int NUM_LABELS = 10;\r\nconst int IMG_SIZE = 28 * 28;\r\n\r\nfloat run_test(tflite::Interpreter* interpreter)\r\n{\r\n tflite::SignatureRunner* inference_runner = interpreter->GetSignatureRunner(\"infer\");\r\n\r\n TfLiteTensor* input_tensor = inference_runner->input_tensor(inference_runner->input_names()[0]);\r\n float* input = input_tensor->data.f;\r\n\r\n int total = 0;\r\n int correct = 0;\r\n\r\n std::ifstream test_data(\"test_set.txt\");\r\n std::string line;\r\n while (std::getline(test_data, line))\r\n {\r\n std::istringstream ss(line);\r\n int label;\r\n ss >> label;\r\n for (std::size_t i = 0; i < IMG_SIZE; ++i)\r\n {\r\n ss >> input[i];\r\n }\r\n\r\n if (inference_runner->Invoke() != kTfLiteOk)\r\n {\r\n std::cout << \"Error invoking inference interpreter signature\" << std::endl;\r\n return 0.f;\r\n }\r\n\r\n const TfLiteTensor* output_tensor = inference_runner->output_tensor(inference_runner->output_names()[0]);\r\n float* output = output_tensor->data.f;\r\n\r\n int predicted = 0;\r\n for (int i = 1; i < NUM_LABELS; ++i)\r\n {\r\n if (output[i] > output[predicted])\r\n {\r\n predicted = i;\r\n }\r\n }\r\n\r\n ++total;\r\n if (label == predicted)\r\n {\r\n ++correct;\r\n }\r\n }\r\n\r\n float accuracy = correct * 100.f / total;\r\n\r\n return accuracy;\r\n}\r\n\r\nvoid run_training(tflite::Interpreter* interpreter)\r\n{\r\n std::cout << \"Test acc before training: \" << run_test(interpreter) << std::endl << std::endl;\r\n\r\n tflite::SignatureRunner* train_runner = interpreter->GetSignatureRunner(\"train\");\r\n\r\n TfLiteTensor* input_data_tensor = train_runner->input_tensor(train_runner->input_names()[0]);\r\n TfLiteTensor* input_labels_tensor = train_runner->input_tensor(train_runner->input_names()[1]);\r\n\r\n int data_idx = 0;\r\n int num_batches = 0;\r\n std::vector<float> test_accs;\r\n\r\n std::ifstream train_data(\"train_set.txt\");\r\n std::string line;\r\n while (std::getline(train_data, line))\r\n {\r\n std::istringstream ss(line);\r\n int label;\r\n ss >> label;\r\n float* input_labels = input_labels_tensor->data.f;\r\n float* input_data = input_data_tensor->data.f;\r\n for (int lbl_idx = 0; lbl_idx < NUM_LABELS; ++lbl_idx)\r\n {\r\n input_labels[data_idx * NUM_LABELS + lbl_idx] = (label == lbl_idx ? 1.f : 0.f);\r\n }\r\n for (int i = 0; i < IMG_SIZE; ++i)\r\n {\r\n ss >> input_data[data_idx * IMG_SIZE + i];\r\n }\r\n ++data_idx;\r\n\r\n if (data_idx == BATCH_SIZE)\r\n {\r\n data_idx = 0;\r\n\r\n if (train_runner->Invoke() != kTfLiteOk)\r\n {\r\n std::cout << \"Error invoking train interpreter signature\" << std::endl;\r\n return;\r\n }\r\n\r\n ++num_batches;\r\n const TfLiteTensor* output_tensor = train_runner->output_tensor(train_runner->output_names()[0]);\r\n float* output = output_tensor->data.f;\r\n std::cout << \"Training of batch \" << num_batches << \" finished with loss: \" << output[0] << std::endl;\r\n\r\n if (num_batches == 10)\r\n {\r\n break;\r\n }\r\n }\r\n }\r\n\r\n std::cout << std::endl << \"Test acc after training: \" << run_test(interpreter) << std::endl;\r\n}\r\n\r\nvoid train_model()\r\n{\r\n tflite::LoggerOptions::SetMinimumLogSeverity(tflite::TFLITE_LOG_WARNING);\r\n\r\n std::unique_ptr<tflite::FlatBufferModel> model =\r\n tflite::FlatBufferModel::BuildFromFile(\"<INSERT YOUR MODEL PATH HERE>\");\r\n if (model == nullptr)\r\n {\r\n std::cout << \"Failed to load model\" << std::endl;\r\n return;\r\n }\r\n\r\n tflite::ops::builtin::BuiltinOpResolver resolver;\r\n tflite::InterpreterBuilder builder(*model, resolver);\r\n std::unique_ptr<tflite::Interpreter> interpreter;\r\n builder(&interpreter);\r\n if (interpreter == nullptr)\r\n {\r\n std::cout << \"Failed to create interpreter\" << std::endl;\r\n return;\r\n }\r\n\r\n if (interpreter->AllocateTensors() != kTfLiteOk)\r\n {\r\n std::cout << \"Failed to alocate interpreter tensors\" << std::endl;\r\n return;\r\n }\r\n\r\n run_training(interpreter.get());\r\n}\r\n\r\nint main()\r\n{\r\n train_model();\r\n\r\n return 0;\r\n}\r\n```\r\n\r\n</details>\r\n\r\nHere are the results that I am getting:\r\n```\r\nTest acc before training: 13.4375\r\n\r\nTraining of batch 1 finished with loss: 125.153\r\nTraining of batch 2 finished with loss: 19491.5\r\nTraining of batch 3 finished with loss: 37853.6\r\nTraining of batch 4 finished with loss: 42998.3\r\nTraining of batch 5 finished with loss: 30509.9\r\nTraining of batch 6 finished with loss: 29040.6\r\nTraining of batch 7 finished with loss: 41205.7\r\nTraining of batch 8 finished with loss: 23286.3\r\nTraining of batch 9 finished with loss: 14237.5\r\nTraining of batch 10 finished with loss: 11451.6\r\n\r\nTest acc after training: 55.625\r\n```\r\n\r\nHere is the model that I am using, so you don't have to create it again: [trained_model.tflite.zip](https://github.com/tensorflow/tensorflow/files/11901036/trained_model.tflite.zip)\r\n\r\nAs for the setup, I am building `libtensorflowlite.so` from TF [r2.13 branch](https://github.com/tensorflow/tensorflow/tree/r2.13) using this bazel command: `bazel build -c dbg //tensorflow/lite:libtensorflowlite.so`. Bazel configuration is configured for CPU (no CUDA). Then I am linking the built .so to the cpp project (one cpp file, code given above). Here is the CMakeLists.txt I am using:\r\n<details><summary>Click to expand!</summary>\r\n\r\n```cmake\r\ncmake_minimum_required(VERSION 3.17)\r\nproject(TFLiteCpp)\r\n\r\nset(CMAKE_CXX_STANDARD 14)\r\n\r\n# include has 2 subdirectories: tensorflow and flatbuffers\r\nINCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR}/include/)\r\n\r\n# lib has 1 file: libtensorflowlite.so\r\nADD_LIBRARY(tensorflowlite SHARED IMPORTED)\r\nset_property(TARGET tensorflowlite PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/libs/libtensorflowlite.so)\r\n\r\nadd_executable(TFLiteCpp main.cpp)\r\ntarget_link_libraries(TFLiteCpp tensorflowlite)\r\n```\r\n\r\n</details>", "Hi @peratrepic, I was able to reproduce your results, so currently I do not view this as a \"wrong\" result, loss calculations are not necessarily monotonically decreasing with training if your dataset is variable enough. We also see that batch 7 through 10 we have decreasing loss. This makes sense to me as usually a NN needs some amount of warm-up and as the data changes the loss can increase as the training in the previous batches makes it better at predicting those data, not necessarily the next batch. Usually there is some pattern in the labels and feature set which makes it able to predict the next batch better but it's not guaranteed to be true. Though in the long run and with enough data that is the pattern you will see. The batch sizes here are also relatively small (32 images which are only 28 x 28 pixels large), so it'll take some time to learn. Let me know if you are satisfied with this answer, and if so please feel free to close the issue if you have no more open items.", "Hi @pkgoogle,\r\n\r\nAs I've stated before:\r\n- When you run the **SAME** model, the **SAME** signature (\"train\"), on the **SAME** dataset, with python tf api, the loss starts from around 2.5, jumps up and down a little bit, and then goes steadily down. When you run the same with tflite c++ api, the loss explodes into thousands, which is insanely high for any ML model training. It is just a coincidence in the limited output from above that loss is decreasing in batches 8-10, I've run it for hundreds of batches and it goes erratically up and down by thousands. The numbers themselves are not even important here, the main point here is that the **results with c++ api differ from the results with python api under the same training conditions**.\r\n- The test accuracy is rising batch after batch, up to 80%, which proves that model is being actually trained, it is **NOT** diverging, it is just the reported loss that is faulty. To me that indicates that you are writing some garbage value into the output tensor, otherwise if loss that you were using internally to calculate the gradients was wrong - the model wouldn't be training at all.\r\n\r\nCould you please investigate?", "Hi @peratrepic, do you have the code that shows that \r\n>with python tf api, the loss starts from around 2.5, jumps up and down a little bit, and then goes steadily down\r\n\r\nThe above code only shows construction and conversion in python.\r\n\r\nSo the models are not necessarily exactly the same, conversion actually performs some optimizations and changes (such as op fuses). Additionally are you testing them side by side? (I'm guessing yes but I have to make sure) or are you testing/training the python model, converting, then testing/training the C++ model? It's hard to say w/o more context whether the ML model having a loss of thousands is necessarily incorrect. The type of model, the type of loss, the size of the data, the range of the data, number of classes (and probably more) all have an impact.\r\n\r\n> I've run it for hundreds of batches and it goes erratically up and down by thousands.\r\n\r\nThis is probably better evidence as after hundreds of batches it should stabilize more... but with a batch size of 32 of 28x28 pixels it could potentially still be due to natural variability. If you can show me the data I can take a look.\r\n\r\n```\r\nTraining of batch 1 finished with loss: 172.813\r\nTraining of batch 2 finished with loss: 30406.2\r\nTraining of batch 3 finished with loss: 35372.7\r\nTraining of batch 4 finished with loss: 30955.9\r\nTraining of batch 5 finished with loss: 30645.5\r\nTraining of batch 6 finished with loss: 39069.4\r\nTraining of batch 7 finished with loss: 25181.5\r\nTraining of batch 8 finished with loss: 28106.7\r\nTraining of batch 9 finished with loss: 12969.1\r\nTraining of batch 10 finished with loss: 3079.69\r\nTraining of batch 11 finished with loss: 3693.12\r\nTraining of batch 12 finished with loss: 3314.77\r\nTraining of batch 13 finished with loss: 4591.12\r\nTraining of batch 14 finished with loss: 5880.76\r\nTraining of batch 15 finished with loss: 5654.75\r\nTraining of batch 16 finished with loss: 10133.1\r\nTraining of batch 17 finished with loss: 9301.94\r\nTraining of batch 18 finished with loss: 11654.5\r\nTraining of batch 19 finished with loss: 11827.8\r\nTraining of batch 20 finished with loss: 22028.1\r\nTraining of batch 21 finished with loss: 8553.58\r\n```\r\n\r\nThe natural trend here is still downwards... 21 batches (of size 32 28x28 pixels) is still fairly early so the amount of variability is somewhat expected. Because the accuracy IS increasing stably, tells me that the variability of the loss is still likely due to natural variability rather than a bug.\r\n\r\nLower loss is correlated with accuracy, but not deterministically as you generally do a softmax and pick the most probable class. Then compare the classes... this is not the same calculation as the log difference in predicted and actual probabilities. So that's why they can diverge a bit. Is the accuracy you are quoting overall accuracy or specific batch accuracy? If the loss is not going down long term especially if you increase the batch size (so that the natural variability evens out) then that might be an issue, but I need to see that data first.", "> It's hard to say w/o more context whether the ML model having a loss of thousands is necessarily incorrect\r\n\r\nDo you even know how cross entropy loss is calculated, the math behind? Are you aware how big mistakes model should make to get a loss into thousands, how much the model should diverge instead of giving 87% accuracy on the whole test set? Have you seen a loss larger than two digits in successfull training of any ML model known to mankind?\r\n\r\n\r\nThat aside, you don't find it suspicious that loss is so much smaller just in the first batch?\r\n\r\n> The type of model, the type of loss, the size of the data, the range of the data, number of classes (and probably more) all have an impact.\r\n\r\nHave you heard of MNIST dataset? Are you just ignoring the fact that this is classic MNIST dataset and not some random dataset, that the model is a simple one layer neural network used in all of tensorflow examples with pretty much known expected loss/accuracy results? I've purposely used the simplest model here for the ease of demonstration, but you constantly pretend like we are talking about training GPT... You say \"It's hard to say w/o more context\" when I've given you literally all the context possible, but you still talk in hypotheticals.\r\n\r\n> Is the accuracy you are quoting overall accuracy or specific batch accuracy?\r\n\r\nAccuracy on a whole separate dataset used for testing (MNIST test set). Can you explain how the model can achieve 87% accuracy on a test set but have four digits loss on a training set?\r\n\r\n> Additionally are you testing them side by side? (I'm guessing yes but I have to make sure) or are you testing/training the python model, converting, then testing/training the C++ model?\r\n\r\nI am creating empty model, converting it to tflite, and then I am training it separately side by side in both python and c++, on the same dataset, calling same signature functions. Of course, I am training python model with python and tflite model with c++ tflite api, because how would I otherwise do it - you can't train tflite model in python, and I know you will now say \"got ya, those are different models!\", but more on that below.\r\n\r\n> So the models are not necessarily exactly the same, conversion actually performs some optimizations and changes (such as op fuses).\r\n\r\nYes I am aware of that, I've worked on a couple of ML tools implementations, and I know it should not give exactly the same results, but they should be close to same not differing 1000x times for Christ's sake. I still cannot believe what I am reading, how can someone so confidently ignore the obvious... You gotta be just waiting for me to give up so you can close this issue. The scale of difference in loss values between python and c++ tflite doesn't bother you at all? How do you even test the c++ tflite implementation, what are you comparing it with if not python tensorflow results?", "When I opened this issue I've hoped to get at least one of these things:\r\n- Getting this bug in tflite c++ api fixed.\r\n- Advice on how to better use tflite c++ api, or how to properly use it if I did something incorrectly, because the documentation on these things is almost nonexistent.\r\n- Information about what features and ops are currently available in tflite for backward propagation, this is not documented anywhere.\r\n\r\nInstead, I am getting dragged in circles and being given some trivial ML 101 statements which are not applicable at all in this case. I believe that I've spent more than enough time to convince you that you have a bug, I don't want to spend anymore after this message. At this point it would have been more efficient to me to debug tflite code and fix it myself. Now I think I should better explore some alternative sdks like PyTorch mobile if this is the absolute state of support from TF engineers.", "Hi @peratrepic, apologies for the frustration, we have to check the easy cases and usual failure modes first to make sure it's not a problem elsewhere, that's the reason for all the questions and making sure things are reproducible. We aren't trying to drag you in circles, just trying to get a better understanding of the issue.\r\n\r\nWhile Categorical Cross entropy is technically unbounded, it is highly unlikely to be that high with just 10 output neurons, we'll look deeper into this issue.\r\n\r\nHi @JunyoungLim, can you please take a look? Thanks.", "@peratrepic - try this approach- \r\n const TfLiteTensor* output = train_runner->output_tensor(\"loss\");\r\n float* loss = output->data.f;\r\ncout <<\"Loss : \"<<loss[0]<<endl;\r\n", "@peratrepic can you share code for inference using c++? and do you know any method to merge model.ckpt and model.tflite? " ]

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[ "I'm runnig the make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test from the tflite-micro folder in a Ubuntu installed in a WSL machine. I've got some errros about numpy and Pillow but everything solved. Now what I get is something like this:\r\n\r\ntensorflow/lite/micro/tools/make/downloads/flatbuffers already exists, skipping the download.\r\ntensorflow/lite/micro/tools/make/downloads/kissfft already exists, skipping the download.\r\ntensorflow/lite/micro/tools/make/downloads/pigweed already exists, skipping the download.\r\nmake: *** No rule to make target 'test_hello_world_test'. Stop.\r\n\r\nI think it has something to do with the files (make does not find them) so I tried to clone the repository again but nothing...\r\nA bit of help would be appreciated.\r\nAll the best,\r\nJavi", "Hi @JavaldeUB \r\n\r\nI have tried on Ubuntu 20.04 with master branch and I was successfully able to run the `hello world test`.\r\n\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/10c164cd544c2013596b2eaf4428948d/60171.ipynb). \r\n\r\nCould you please try with latest version?\r\n\r\nThanks.", "Hi @pjpratik,\r\nThanks for your time here.\r\nI have the same advances. I have installed Ubuntu 20.04 in the WSL machine and it passed the the test. I do not know if it is because I installed many things to have gnome in the WSL Ubuntu 20.04 distro or because it goes well with the 20.04...\r\n\r\nI have another issue which is related with Warden's TinyML book. I'm following the examples ther but the code in the text doesn't look like the one in the ./examples/hello_world folder. I'm trying to make the test fail and I realized that the input is already set to 1.f. I'm puzzled\r\n\r\nI know these are silly question but I've just landed here and I'm trying to grasp the thing....\r\nThanks for your time.\r\nJ. ", "Hi @JavaldeUB \r\n\r\nThe `hello_world_test.cc` is a fairly small amount of code that creates an interpreter, gets a handle to a model that's been compiled into the program, and then invokes the interpreter with the model and sample inputs.\r\n\r\nAlso, the example has been updated based on the changes made recently. It is suggested to use the latest version and follow the same for other examples.\r\n\r\nPlease check this [hello world](https://www.tensorflow.org/lite/microcontrollers/get_started_low_level#the_hello_world_example) example for the reference.\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/60717\">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/60717\">No</a>\n" ]

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[ "next try with --verbose_failures\r\n\r\n```\r\nruslan@radar-prod1:/sdk4/tensorflow$ bazel build --verbose_failures --config=opt --copt=-march=native --copt=-O3 //tensorflow/tools/pip_package:build_pip_package\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=166\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug\r\nINFO: Found applicable config definition build:short_logs in file /sdk4/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /sdk4/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:opt in file /sdk4/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare\r\nINFO: Found applicable config definition build:linux in file /sdk4/tensorflow/.bazelrc: --define=build_with_onednn_v2=true --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /sdk4/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (0 packages loaded, 0 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /sdk4/tensorflow/tensorflow/cc/BUILD:673:22: Linking tensorflow/cc/ops/user_ops_gen_cc [for tool] failed: (Exit 1): gcc failed: error executing CppLink command (from target //tensorflow/cc:ops/user_ops_gen_cc) \r\n (cd /build/home/ruslan/.cache/bazel/_bazel_ruslan/52d9b56c392034697d983605fa18f4ee/execroot/org_tensorflow && \\\r\n exec env - \\\r\n PATH=/home/ruslan/.local/bin:/home/ruslan/.local/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/local/go/bin \\\r\n PWD=/proc/self/cwd \\\r\n ZERO_AR_DATE=1 \\\r\n /usr/bin/gcc @bazel-out/k8-opt-exec-ST-977fe3d4dab2/bin/tensorflow/cc/ops/user_ops_gen_cc-2.params)\r\n# Configuration: aabe98942a966d02f11cd3e86d39722629ddb362b12f82a6a40652a82b246a2b\r\n# Execution platform: @local_execution_config_platform//:platform\r\nbazel-out/k8-opt-exec-ST-977fe3d4dab2/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Suser_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tsl::table::NewLRUCache(unsigned long)'\r\nbazel-out/k8-opt-exec-ST-977fe3d4dab2/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Suser_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tensorflow::GpuIdManager::TfToPlatformDeviceId(tsl::gtl::IntType<tsl::TfDeviceId_tag_, int>, tsl::gtl::IntType<tsl::PlatformDeviceId_tag_, int>*)'\r\ncollect2: error: ld returned 1 exit status\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nERROR: /sdk4/tensorflow/tensorflow/tools/pip_package/BUILD:293:10 Middleman _middlemen/tensorflow_Stools_Spip_Upackage_Sbuild_Upip_Upackage-runfiles failed: (Exit 1): gcc failed: error executing CppLink command (from target //tensorflow/cc:ops/user_ops_gen_cc) \r\n (cd /build/home/ruslan/.cache/bazel/_bazel_ruslan/52d9b56c392034697d983605fa18f4ee/execroot/org_tensorflow && \\\r\n exec env - \\\r\n PATH=/home/ruslan/.local/bin:/home/ruslan/.local/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/local/go/bin \\\r\n PWD=/proc/self/cwd \\\r\n ZERO_AR_DATE=1 \\\r\n /usr/bin/gcc @bazel-out/k8-opt-exec-ST-977fe3d4dab2/bin/tensorflow/cc/ops/user_ops_gen_cc-2.params)\r\n# Configuration: aabe98942a966d02f11cd3e86d39722629ddb362b12f82a6a40652a82b246a2b\r\n# Execution platform: @local_execution_config_platform//:platform\r\nINFO: Elapsed time: 1.777s, Critical Path: 0.20s\r\nINFO: 34 processes: 34 internal.\r\nERROR: Build did NOT complete successfully\r\nruslan@radar-prod1:/sdk4/tensorflow$ \r\n```\r\n", "Can you also try after a `bazel clean --expunge`?\r\n\r\nIf that still doesn't work, can you post the output of `git show`? It's likely a recent cherrypick broke the build.", "Hi @legale ,\r\n\r\nI have tested the build on Ubuntu-22 VM with default optimization and build is success for me. I have attached the logs for reference along with environment details.I am not sure whether the issue is specific to debian or environment related or whether you have chosen specific optimization during `./configure` step. Please confirm whether you have chosen any particular configuration other than default.\r\n\r\n[60716_logs.txt](https://github.com/tensorflow/tensorflow/files/11592028/60716_logs.txt)\r\n\r\nPlease also try to clean build after executing `bazel clean --expunge` command and let us know the outcome. \r\n\r\nThanks!\r\n\r\n", "> Hi @legale ,\n> \n> I have tested the build on Ubuntu-22 VM with default optimization and build is success for me. I have attached the logs for reference along with environment details.I am not sure whether the issue is specific to debian or environment related or whether you have chosen specific optimization during `./configure` step. Please confirm whether you have chosen any particular configuration other than default.\n> \n> [60716_logs.txt](https://github.com/tensorflow/tensorflow/files/11592028/60716_logs.txt)\n> \n> Please also try to clean build after executing `bazel clean --expunge` command and let us know the outcome. \n> \n> Thanks!\n> \n> \n\nI will try again.\n\nAbout configure:\nNo, there is no special settings. ", "> > Hi @legale ,\r\n> > I have tested the build on Ubuntu-22 VM with default optimization and build is success for me. I have attached the logs for reference along with environment details.I am not sure whether the issue is specific to debian or environment related or whether you have chosen specific optimization during `./configure` step. Please confirm whether you have chosen any particular configuration other than default.\r\n> > [60716_logs.txt](https://github.com/tensorflow/tensorflow/files/11592028/60716_logs.txt)\r\n> > Please also try to clean build after executing `bazel clean --expunge` command and let us know the outcome.\r\n> > Thanks!\r\n> \r\n> I will try again.\r\n> \r\n> About configure: No, there is no special settings.\r\n\r\n\r\nSame problem.\r\n\r\n```\r\n --config=mkl # Build with MKL support.\r\n --config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n --config=monolithic # Config for mostly static monolithic build.\r\n --config=numa # Build with NUMA support.\r\n --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.\r\n --config=v1 # Build with TensorFlow 1 API instead of TF 2 API.\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n --config=nogcp # Disable GCP support.\r\n --config=nonccl # Disable NVIDIA NCCL support.\r\n```\r\n\r\n\r\n```\r\nbazel build //tensorflow/tools/pip_package:build_pip_package\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=222\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /sdk4/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/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,tensorflow/core/tfrt/utils/debug\r\nINFO: Found applicable config definition build:short_logs in file /sdk4/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /sdk4/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:linux in file /sdk4/tensorflow/.bazelrc: --define=build_with_onednn_v2=true --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /sdk4/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (610 packages loaded, 36141 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /sdk4/tensorflow/tensorflow/cc/BUILD:655:22: Linking tensorflow/cc/ops/array_ops_gen_cc [for tool] failed: (Exit 1): gcc failed: error executing CppLink command (from target //tensorflow/cc:ops/array_ops_gen_cc) /usr/bin/gcc @bazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/cc/ops/array_ops_gen_cc-2.params\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Sarray_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tsl::table::NewLRUCache(unsigned long)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/_solib_k8/_U_S_Stensorflow_Scc_Cops_Sarray_Uops_Ugen_Ucc___Utensorflow/libtensorflow_framework.so.2: error: undefined reference to 'tensorflow::GpuIdManager::TfToPlatformDeviceId(tsl::gtl::IntType<tsl::TfDeviceId_tag_, int>, tsl::gtl::IntType<tsl::PlatformDeviceId_tag_, int>*)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/core/ops/_objs/array_ops_op_lib/array_ops.o:array_ops.cc:function tensorflow::register_op101::{lambda(tensorflow::shape_inference::InferenceContext*)#1}::operator()(tensorflow::shape_inference::InferenceContext) const [clone .constprop.0]: error: undefined reference to 'tensorflow::GetWindowedOutputSizeVerbose(long, long, long, tensorflow::Padding, long*, long*, long*)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/core/ops/_objs/array_ops_op_lib/array_ops.o:array_ops.cc:function tensorflow::register_op101::{lambda(tensorflow::shape_inference::InferenceContext*)#1}::operator()(tensorflow::shape_inference::InferenceContext) const [clone .constprop.0]: error: undefined reference to 'tensorflow::GetWindowedOutputSizeVerbose(long, long, long, tensorflow::Padding, long*, long*, long*)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/core/ops/_objs/array_ops_op_lib/array_ops.o:array_ops.cc:function tensorflow::register_op101::{lambda(tensorflow::shape_inference::InferenceContext*)#1}::operator()(tensorflow::shape_inference::InferenceContext) const [clone .constprop.0]: error: undefined reference to 'tensorflow::GetWindowedOutputSizeVerbose(long, long, long, tensorflow::Padding, long*, long*, long*)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/core/ops/_objs/array_ops_op_lib/array_ops.o:array_ops.cc:function tensorflow::register_op100::{lambda(tensorflow::shape_inference::InferenceContext*)#1}::operator()(tensorflow::shape_inference::InferenceContext) const [clone .constprop.0]: error: undefined reference to 'tensorflow::GetWindowedOutputSizeVerbose(long, long, long, tensorflow::Padding, long*, long*, long*)'\r\nbazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/core/ops/_objs/array_ops_op_lib/array_ops.o:array_ops.cc:function __static_initialization_and_destruction_0(int, int) [clone .constprop.0]: error: undefined reference to 'tensorflow::full_type::ReplicateInput[abi:cxx11](int, int)'\r\ncollect2: error: ld returned 1 exit status\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nERROR: /sdk4/tensorflow/tensorflow/tools/pip_package/BUILD:268:10 Middleman _middlemen/tensorflow_Stools_Spip_Upackage_Sbuild_Upip_Upackage-runfiles failed: (Exit 1): gcc failed: error executing CppLink command (from target //tensorflow/cc:ops/array_ops_gen_cc) /usr/bin/gcc @bazel-out/k8-opt-exec-ST-3a2a7d830ca1/bin/tensorflow/cc/ops/array_ops_gen_cc-2.params\r\nINFO: Elapsed time: 2598.484s, Critical Path: 382.03s\r\nINFO: 14560 processes: 1906 internal, 12654 local.\r\nERROR: Build did NOT complete successfully\r\n```\r\n\r\n", "@nitins17 , Could you please have a look into the issue. \r\n\r\nCC- @learning-to-play .\r\n\r\n", "```\r\n\r\nERROR: /var/tmp/portage/sci-libs/tensorflow-2.12.0/work/tensorflow-2.12.0-python3_11/tensorflow/cc/BUILD:673:22: Linking tensorflow/cc/ops/nn_ops_gen_cc [for tool] failed: (Exit 1): gcc failed: error executing CppLink command (from target //tensorflow/cc:ops/nn_ops_gen_cc) \r\n (cd /var/tmp/portage/sci-libs/tensorflow-2.12.0/work/tensorflow-2.12.0-python3_11-bazel-base/execroot/org_tensorflow && \\\r\n exec env - \\\r\n KERAS_HOME=/var/tmp/portage/sci-libs/tensorflow-2.12.0/temp/.keras \\\r\n PATH=/var/tmp/portage/sci-libs/tensorflow-2.12.0/temp/python3.11/bin:/usr/lib/portage/python3.12/ebuild-helpers/xattr:/usr/lib/portage/python3.12/ebuild-helpers:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/bin:/usr/lib/llvm/16/bin \\\r\n PWD=/proc/self/cwd \\\r\n ZERO_AR_DATE=1 \\\r\n /usr/bin/gcc @bazel-out/k8-opt-exec-ST-ba6211b4fe03/bin/tensorflow/cc/ops/nn_ops_gen_cc-2.params)\r\n# Configuration: bf46598e081eb20667309cf6e8fafc6d58b9cd48291dbf068ad4d43d3402041c\r\n# Execution platform: @local_execution_config_platform//:platform\r\nld.lld: error: undefined symbol: tensorflow::GetWindowedOutputSizeVerbose(long, long, long, tensorflow::Padding, long*, long*, long*)\r\n>>> referenced by nn_ops.cc\r\n>>> bazel-out/k8-opt-exec-ST-ba6211b4fe03/bin/tensorflow/core/ops/_objs/nn_ops_op_lib/nn_ops.o:(tensorflow::register_op51::'lambda'(tensorflow::shape_inference::InferenceContext*)::operator()(tensorflow::shape_inference::InferenceContext*) const (.isra.0))\r\n>>> referenced by nn_ops.cc\r\n>>> bazel-out/k8-opt-exec-ST-ba6211b4fe03/bin/tensorflow/core/ops/_objs/nn_ops_op_lib/nn_ops.o:(tensorflow::register_op51::'lambda'(tensorflow::shape_inference::InferenceContext*)::operator()(tensorflow::shape_inference::InferenceContext*) const (.isra.0))\r\ncollect2: error: ld returned 1 exit status\r\n[5,671 / 6,878] 24 actions running\r\n Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc; 53s local\r\n Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc; 47s local\r\n @llvm-project//mlir:LinalgTransforms; 20s local\r\n Compiling tensorflow/compiler/mlir/tfr/ir/tfr_ops[5,691 / 6,878] 4 actions running\r\n Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc; 53s local\r\n Compiling tensorflow/compiler/mlir/tensorflow/ir/INFO: Elapsed time: 766.826s, Critical Path: 104.26s\r\nINFO: 5695 processes: 664 internal, 5031 local.\r\nERROR: Build did NOT complete successfully\r\n```\r\n\r\nsame issue w/ 2.12" ]

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[ "@drewshark,\r\nCould you please confirm whether the above mentioned issue was raised when `tf.math.divide_no_nan and tf.math.reciprocal_no_nan` were trying to use **tf.test.compute_gradient**? or by default it is providing the incorrect gradient? Thank you!", "@tilakrayal,\r\nI think the issue raises when these two APIs were trying to use **tf.test.compute_gradient** to calculate the numerical gradient." ]

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[ "Hi @Janus-Shiau ,\r\n\r\nThanks for reaching us. I have replicated the reported behaviour with TF2.12 and tf-nightly(2.14.0-dev20230528) and attached gists here [TF2.12v](https://colab.research.google.com/gist/SuryanarayanaY/b81d2bc331f6691d9213c931237531aa/60714_2-12.ipynb#scrollTo=y47-_CEf4o3k) and [tf-nightly](https://colab.research.google.com/gist/SuryanarayanaY/be0c3bb90a66cde51eefbc8940c45107/60714_nightly.ipynb).\r\n\r\nWe need to dig more on this to confirm the root cause. Thanks!\r\n\r\n\r\n", "Hi @SuryanarayanaY,\r\n\r\nThank you for your prompt reply and assistance. Look forward to any update and appreciate your efforts in addressing my request.\r\n", "I am new to opensource contributions, can you @SuryanarayanaY @Janus-Shiau explain me the issue so that I can work on it.", "Hi @Bogalakalyani ,\r\n\r\nYou can refer [contributing.md ](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)file for more details if you willing to contribute. Thanks!", "I am facing the same issue. Any progress on it?" ]

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[ "On the nightly version the error output is a bit different, maybe this is helpful:\r\n\r\n```\r\n(tf-nightly) [lnapon@rhel ancient_ai]$ python3 lrnkeras/mnist_conv.py \r\n2023-05-25 19:41:31.002823: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:7704] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-05-25 19:41:31.002868: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-05-25 19:41:31.002884: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1520] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-05-25 19:41:31.847714: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-05-25 19:41:32.672538: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:32.688679: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:32.688911: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:32.689738: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:32.689894: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:32.690034: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:33.135930: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:33.136133: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:33.136290: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-05-25 19:41:33.136425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1318 MB memory: -> device: 0, name: Quadro K620, pci bus id: 0000:01:00.0, compute capability: 5.0\r\nEpoch 1/5\r\n2023-05-25 19:41:34.396173: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:434] Loaded cuDNN version 8600\r\n2023-05-25 19:41:34.653261: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:190] failed to create cublas handle: the resource allocation failed\r\n2023-05-25 19:41:34.653298: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:193] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.\r\n2023-05-25 19:41:34.653625: W tensorflow/core/kernels/conv_ops_gpu.cc:144] None of the algorithms provided by cuDNN frontend heuristics worked; trying fallback algorithms. Conv: batch: 1\r\nin_depths: 1\r\nout_depths: 4\r\nin: 28\r\nin: 28\r\ndata_format: 1\r\nfilter: 3\r\nfilter: 3\r\nfilter: 1\r\ndilation: 1\r\ndilation: 1\r\nstride: 1\r\nstride: 1\r\npadding: 0\r\npadding: 0\r\ndtype: DT_FLOAT\r\ngroup_count: 1\r\ndevice_identifier: \"sm_5.0 with 2099118080B RAM, 3 cores, 1124000KHz clock, 900000KHz mem clock, 2097152B L2$\"\r\nfusion {\r\n activation_mode: kRelu\r\n conv_scale: 1\r\n}\r\nversion: 3\r\n\r\n2023-05-25 19:41:34.660525: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:190] failed to create cublas handle: the resource allocation failed\r\n2023-05-25 19:41:34.660542: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:193] Failure to initialize cublas may be due to OOM (cublas needs some free memory when you initialize it, and your deep-learning framework may have preallocated more than its fair share), or may be because this binary was not built with support for the GPU in your machine.\r\n2023-05-25 19:41:34.660782: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at conv_ops_fused_impl.h:625 : NOT_FOUND: No algorithm worked! Error messages:\r\n Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'\r\n Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'\r\nTraceback (most recent call last):\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 53, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\ntensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:\r\n\r\nDetected at node sequential/conv2d/Relu defined at (most recent call last):\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py\", line 321, in call\r\n return self.activation(outputs)\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py\", line 321, in call\r\n return self.activation(outputs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/activations.py\", line 321, in relu\r\n return backend.relu(\r\n\r\n File \"/home/lnapon/repos/ancient_ai/lrnkeras/mnist_conv.py\", line 25, in <module>\r\n model.fit(train_images, trains_labels, epochs=5, batch_size=1)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1768, in fit\r\n tmp_logs = self.train_function(iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1364, in train_function\r\n return step_function(self, iterator)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1348, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1329, in run_step\r\n outputs = model.train_step(data)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1106, in train_step\r\n y_pred = self(x, training=True)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/training.py\", line 588, in __call__\r\n return super().__call__(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/sequential.py\", line 404, in call\r\n return super().call(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 512, in call\r\n return self._run_internal_graph(inputs, training=training, mask=mask)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/functional.py\", line 669, in _run_internal_graph\r\n outputs = node.layer(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/engine/base_layer.py\", line 1150, in __call__\r\n outputs = call_fn(inputs, *args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n return fn(*args, **kwargs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py\", line 321, in call\r\n return self.activation(outputs)\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/activations.py\", line 321, in relu\r\n return backend.relu(\r\n\r\n File \"/home/lnapon/miniconda3/envs/tf-nightly/lib/python3.10/site-packages/keras/src/backend.py\", line 5397, in relu\r\n x = tf.nn.relu(x)\r\n\r\nNo algorithm worked! Error messages:\r\n Profiling failure on CUDNN engine eng11{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'\r\n Profiling failure on CUDNN engine eng0{}: UNKNOWN: CUDNN_STATUS_ALLOC_FAILED\r\nin tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5333): 'status'\r\n\t [[{{node sequential/conv2d/Relu}}]] [Op:__inference_train_function_1058]\r\n\r\n```", "@O-CLIPE,\r\nI tried to execute the mentioned code by importing the keras from tensorflow and it was executed without any issues on tensorflow v2.12. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/faa9b889f64a401df67da39f8bb716b4/untitled1178.ipynb), [GPU-gist](https://colab.research.google.com/gist/tilakrayal/6fa70e52a2f49c52d3397f1b3da2fcba/untitled1179.ipynb) and below changes.\r\n```\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras.datasets import mnist\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom tensorflow.keras import layers, models\r\n```\r\nThank you!", "@tilakrayal based on the error message, what could this error be about? I did get it is not a global tf issue, it's probably hardware specific. Also, with CPU only the code runs perfectly.", "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/60713\">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/60713\">No</a>\n", "It is hardware specific. Tensorflow drowns my GPU with memory usage and fails itself. To solve this I just limited the GPU memory usage, to lower than what I can expend (I overflows the limit a bit — in my case a bit means 24% of GPU memory).\r\n\r\n2048MiB of memory if just too little for most applications of machine learning.\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/60713\">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/60713\">No</a>\n", "How to limit GPU memory usage: https://stackoverflow.com/a/60069601/20940588\r\n" ]

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[ "Ah, just a little bit of an embarrassing mistake from my side. The optimizer needs to be called inside strategy.scope() block for it to work.\r\n```\r\n\r\nwith strategy.scope():\r\n model = return_model()\r\n opt = GradientDescent(learning_rate = 0.001)\r\n model.compile(optimizer=opt, loss='mse')\r\n\r\n model(x_train[:2])\r\n model.summary()\r\n model.fit(x_train, y_train, epochs = 20, batch_size = 1024)\r\n opt.temp\r\n```\r\n\r\nNow, I'm just left with my second question only.\r\n\r\n> Apart from that I wish the addition of a new feature - batch size of the data being currently used by the optimizer to compute gradients, as we know is pretty straightforward in a single CPU/GPU, but becomes difficult while executing the same code in distributed systems. If possible, kindly amend my existing standalone code below to add that small tiny feature into my Optimizer.\r\n\r\nI'll close the issue after this.", "Hi @abhaskumarsinha ,\r\n\r\nThe Optimizers by default takes batch_size from model.fit only or from the tf.data.Dataset if set batch explicitly. I am not quite follow through your requirement of batch_size passing to Optimizer here. Normally for multi workers or nodes we can increase the batch_size by the no of GPUs we have.\r\n\r\nSuppose we want `batch_size` of `64` on each of the GPU and total no of GPUs enabled under distribution strategy can be got by `strategy.num_replicas_in_sync`. Please refer the below code.\r\n\r\n```\r\nBATCH_SIZE_PER_REPLICA = 64\r\nBATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync\r\n```\r\nThen BATCH_SIZE shall be passed to model.fit.\r\n\r\nPlease refer to some official tutorials on how to configure distribution training on multiple workers here [link1](https://www.tensorflow.org/tutorials/distribute/keras) and [link2](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl).\r\n\r\nIf you still have any queries please come back and let us know.\r\n\r\nThanks!\r\n", "Reference: https://github.com/tensorflow/tensorflow/issues/60592#issuecomment-1563789462\r\n\r\nHello @SuryanarayanaY \r\n\r\nThank you so much for your response and for bearing me with regarding the issue.\r\nThe thing is that we are working on some sort of special optimizer that works on variable _batch_sizes_ to make it work on some very specific non-convex functions on very large distributed systems.\r\n\r\nSo, I'm left with two requirements for now.\r\n1. Have variable batch size for the optimizer (the optimizer decides the batch size of _$t_n$_ given the optimizer parameters at _$t_n-1$_.\r\n2. We manually specify the upper bound of the batch size, i.e. all the _batch_size at all t_i_ for i in 0, 1, 2, ... should be less than _upper_bound_ i.e. _batch_size at t_i < upper_bound._ In that case, we mask the last entries of our gradients with 0 and calculate the gradient of only the required first _batch_size_ at _t_i_ entries and train the network\r\n\r\nFrom my guess, 2 should be faster and should outperform 1 because the graph wouldn't need to be reconstructed using CUDA API for training the model, unlike case 1.\r\n\r\nSince we need this optimizer to run on distributed training settings on a very large system, I wanted to make sure if any of these features are implementable on Keras or not. (Masking the gradient of certain batch indices with 0).\r\n\r\nThank you in Advance.\r\n", "HI @abhaskumarsinha ,\r\n\r\nThanks for your explanation on your requirement. If you want to customize the training step you need to build custom model class subclassing the `keras.Model` and need to override `train_step`. If you can able to accommodate your requirements in the `train_step` method then may be it can possible to implement your requirement. \r\n\r\nPlease have a look into sample [tutorial](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit) for customizing the train_step of keras model training.\r\n\r\nPlease have a look and let us know if it is helpful. Thanks!", "Hello @SuryanarayanaY \r\n\r\nSorry for the late response. Yes, I've also checked to extend `tf.keras.Model` class and custom training step function. My only concern was whether this whole thing would be applicable to distribution strategies.\r\n\r\nThe second thing is to mask certain batches to 0 while keeping the gradients from other batches. If this gives control over batch-level editing and supports distribution, then this would definitely be helpful for me. Thank you so much.", "\r\n\r\n\r\n@abhaskumarsinha \r\n\r\n> Sorry for the late response. Yes, I've also checked to extend `tf.keras.Model` class and custom training step function. My only concern was whether this whole thing would be applicable to distribution strategies.\r\n\r\nOfcourse custom models also can be used with distribution strategies.Please refer a sample [tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training).\r\n\r\n\r\n\r\n> The second thing is to mask certain batches to 0 while keeping the gradients from other batches. If this gives control over batch-level editing and supports distribution, then this would definitely be helpful for me. Thank you so much.\r\n\r\nI am not quiet follow through you here. AFAIK if you want some batches to be masked Zero then there is no standard way in Tensorflow.This may not possible during training but you can try with [tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) generators and implement your own logic for dataset generation.\r\n\r\nThanks!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60711\">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/60711\">No</a>\n" ]

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[ "`py_function` not only effects `ragged_batch` but also later KerasCV layers in `.map` to operate. According to [this discussion](https://github.com/tensorflow/tensorflow/issues/35108), the return value of `py_function` lost the information of its shape and rank. So the solution can be: 1. to manually set those information by `tensor.set_shape([None, None])`. 2. to return the shape (inside py_function) and assign it to outside tensor. Below are a small demo (the first one solution) showing that it works. However it's definetely a bug. the return value SHOULD know the shape and rank. Or at least this \"feature\" should be written in documentation.\r\n``` python\r\nimport tensorflow as tf\r\n\r\ndef processing(data):\r\n def _processing(data):\r\n arr = [range(data), range(data)]\r\n arr = tf.cast(arr, tf.float32)\r\n print(f\"Inside py_function arr shape: {arr.shape}\")\r\n return arr\r\n \r\n arr = tf.py_function(_processing, [data], tf.float32)\r\n print(f\"Outside py_function arr shape: {arr.shape}\")\r\n arr.set_shape([None, None]) # set rank here\r\n return arr\r\n\r\nlist = [1,2,3,1]\r\n\r\nds = tf.data.Dataset.from_tensor_slices(list)\r\nds = ds.map(processing)\r\nds = ds.ragged_batch(4)\r\n\r\nfor data in ds:\r\n print(\"==========\")\r\n print(data)\r\n```" ]

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[ "Hi @caiotoledo-lunasystems \r\n\r\nThe Hexagon delegates v1.20.0.9 is available in https://github.com/tensorflow/tensorflow/blob/d37fda11945d290f86b85a39f28bbddbbd0f6bee/third_party/hexagon/workspace.bzl#L12\r\n\r\nDownload it [here](https://storage.googleapis.com/mirror.tensorflow.org/storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_headers_v1.20.0.9.tgz).\r\n\r\nThanks.", "@pjpratik,\r\n\r\nThanks for your feedback.\r\n\r\nThe link provided [here](https://storage.googleapis.com/mirror.tensorflow.org/storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_headers_v1.20.0.9.tgz) doesn't have the libraries `libhexagon_nn_skel.so`, `libhexagon_nn_skel_v65.so`, `libhexagon_nn_skel_v66.so`.\r\n\r\nI was expecting a link like the one for [v1.20.0.1](https://storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_skel_v1.20.0.1.run).\r\nI tried to use the link https://storage.cloud.google.com/download.tensorflow.org/tflite/hexagon_nn_skel_v1.20.0.9.run for `v1.20.0.9` but it didn't work.", "It seems the documentation may not be up to date and the .run file isn't there for v1.20.0.9\r\n\r\n@sirakiin Can you take a look? Alternatively is there a script or instructions on how we may compile the .so files manually?\r\n", "@caiotoledo-lunasystems is there any progress/workaround?", "> @caiotoledo-lunasystems is there any progress/workaround?\r\n\r\n@aseok, no we have put this feature on pause on our side for now.\r\nWe couldn't find any workaround." ]

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[ "The program is stuck here and cannot be continued。", "And it will output log like below:\r\n2023-05-25 11:10:57.934361: E tensorflow/core/common_runtime/base_collective_executor.cc:249] BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013057.934031384\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:57.934497: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:737] Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013057.934031384\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:57.934547: F tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:103] Non-OK-status: Stop() status: UNIMPLEMENTED: Clean shutdown is not currently implemented\r\nFatal Python error: Aborted\r\n\r\nCurrent thread 0x00007f2947732740 (most recent call first):\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 623 in ensure_initialized\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 508 in _initialize_multi_worker\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 341 in _initialize_strategy\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 329 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 185 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 252 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py\", line 357 in new_func\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/common/distribute_utils.py\", line 155 in get_distribution_strategy\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 125 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 362 in main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 258 in _run_main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 312 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 369 in <module>\r\n2023-05-25 11:10:57.977399: E tensorflow/core/common_runtime/base_collective_executor.cc:249] BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013057.977083688\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:57.977561: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:737] Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013057.977083688\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:57.977640: F tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:103] Non-OK-status: Stop() status: UNIMPLEMENTED: Clean shutdown is not currently implemented\r\nFatal Python error: Aborted\r\n\r\nCurrent thread 0x00007fa0241df740 (most recent call first):\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 623 in ensure_initialized\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 508 in _initialize_multi_worker\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 341 in _initialize_strategy\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 329 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 185 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 252 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py\", line 357 in new_func\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/common/distribute_utils.py\", line 155 in get_distribution_strategy\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 125 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 362 in main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 258 in _run_main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 312 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 369 in <module>\r\n2023-05-25 11:10:58.273265: E tensorflow/core/common_runtime/base_collective_executor.cc:249] BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013058.272930461\",\"description\":\"Deadels-2.9.2_fp32_4m/official/common/distribute_utils.py\", line 155 in get_distribution_strategy\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 125 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 362 in main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 258 in _run_main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 312 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 369 in <module>\r\n2023-05-25 11:10:58.273265: E tensorflow/core/common_runtime/base_collective_executor.cc:249] BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013058.272930461\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:58.273404: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:737] Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013058.272930461\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:58.273458: F tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:103] Non-OK-status: Stop() status: UNIMPLEMENTED: Clean shutdown is not currently implemented\r\nFatal Python error: Aborted\r\n\r\nCurrent thread 0x00007fb79fed7740 (most recent call first):\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 623 in ensure_initialized\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 508 in _initialize_multi_worker\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 341 in _initialize_strategy\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 329 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 185 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 252 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py\", line 357 in new_func\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/common/distribute_utils.py\", line 155 in get_distribution_strategy\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 125 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 362 in main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 258 in _run_main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 312 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 369 in <module>\r\n2023-05-25 11:10:58.361393: E tensorflow/core/common_runtime/base_collective_executor.cc:249] BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013058.361101419\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:58.361543: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:737] Deadline Exceeded\r\nAdditional GRPC error information from remote target /job:worker/replica:0/task:0:\r\n:{\"created\":\"@1685013058.361101419\",\"description\":\"Deadline Exceeded\",\"file\":\"external/com_github_grpc_grpc/src/core/ext/filters/deadline/deadline_filter.cc\",\"file_line\":69,\"grpc_status\":4}\r\n2023-05-25 11:10:58.361600: F tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:103] Non-OK-status: Stop() status: UNIMPLEMENTED: Clean shutdown is not currently implemented\r\nFatal Python error: Aborted\r\n\r\nCurrent thread 0x00007fd5013c4740 (most recent call first):\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 623 in ensure_initialized\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 508 in _initialize_multi_worker\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 341 in _initialize_strategy\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 329 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 185 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py\", line 252 in __init__\r\n File \"/public/home/qianyj/virtualenv/dtk23.04/tf2.9/venv/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py\", line 357 in new_func\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/common/distribute_utils.py\", line 155 in get_distribution_strategy\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 125 in run\r\n File \"/public/home/qianyj/TF_test/dtk23.04/tf2.9/models-2.9.2_fp32_4m/official/benchmark/models/resnet_imagenet_main.py\", line 362 in main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 258 in _run_main\r\n File \"/public/home/qianyj/.local/lib/python3.8/site-packages/absl/app.py\", line 312 in run", "Hi @south-ocean ,\r\n\r\nCould you please confirm whether you are trying to use multiple systems/servers with each having single or more GPUs ? \r\nAre you using `MirroredStrategy` or `MultiWorkerMirroredStrategy` and I hope you know which to use when.\r\n\r\nPlease submit the steps followed and code you have used for enabling the strategy.\r\n\r\nThe problem seems to be related to communication between different workers. One probable reason might be mentioned below.\r\n\r\n> 'TF_CONFIG' is parsed and TensorFlow's GRPC servers are started at the time you call tf.distribute.MultiWorkerMirroredStrategy. Therefore, you must set the 'TF_CONFIG' environment variable before you instantiate a tf.distribute.Strategy. To save time in this illustrative example, this is not demonstrated in this tutorial, so that servers do not need to start. You can find a full example in the last section of this tutorial.\r\n\r\n\r\nPlease refer some of the documentation tutorials for `MultiWorkerMirroredStrategy` which might be useful. [link1](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl) and [link2](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras)\r\n\r\nThanks!\r\n", "I have also met the simarly issue.\r\nWhen I use 3 workers and each with 8GPUs, it works fine. When 8 workers and each with 8 GPUs, it will also show this kind of error: \"BaseCollectiveExecutor::StartAbort DEADLINE_EXCEEDED: Collective has timed out during execution.\"\r\n\r\nIt seems to be related to the network bandwidth?\r\n", "it related to network settings， but I also solved it by accidental setting，I don't know how to debug this kind of problem about MirroredStrategy or MultiWorkerMirroredStrategy.", "Hi @south-ocean ,\r\n\r\nCould you please confirm whether the issue resolved now. Also it would be helpful for community if you can able to share which settings helped you. \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/60706\">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/60706\">No</a>\n" ]

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[ "I have the same problem on Samsung S21. Prediction errors will occur randomly when hardwareAccelerated is enabled.", "Update some new information.\r\n\r\nI have discovered that not only can animations in the app cause errors, but if there are operations in other apps simultaneously with the model's prediction, they can also impact the predicted results of the model.", "Hi @JeremyJian,\r\n\r\nCan you please fill out the issue template?: https://github.com/tensorflow/tensorflow/blob/master/ISSUE_TEMPLATE.md\r\n\r\nAdditionally for this use case are you using the Task Library API? The Interpreter API? Are you using a real device or an emulator in Android Studio? Is the same behavior happening in both?\r\n\r\nAny additional information will be great. Thanks!", "Hi, in my project, I use the Interpreter API provided by Google Play Services. We have found this issue only on the Samsung S21, which is a real device. We have also run the same code on other devices such as the Pixel 5, Pixel 6, and Samsung S10, and everything works fine. To further clarify the issue, we have written a simple code using a simple model (MNIST model) to observe the behavior. The video is shown below.\r\n\r\nhttps://drive.google.com/file/d/1CUb6fIeuX-Xg1KA4p0lKM5umjmjb5wJS/view\r\n\r\nIn the sample app, we perform inference with the same input(handwritten 7) 100 times for each scenario and display the accuracy on the user interface. As you can see in the video, when we run the inference and the circular progress bar simultaneously, the accuracy drops to 94%. However, we are using the exact same input (a bitmap of a 7-digit number), so the accuracy should always be 100%.\r\n\r\n\r\nThe summary of simple minst model:\r\n_________________________________________________________________\r\n Layer (type) Output Shape Param # \r\n\r\n conv2d (Conv2D) (None, 26, 26, 16) 160 \r\n \r\n max_pooling2d (MaxPooling2D) (None, 13, 13, 16) 0 \r\n \r\n \r\n conv2d_1 (Conv2D) (None, 11, 11, 16) 2320 \r\n \r\n max_pooling2d_1 (MaxPooling2D) (None, 5, 5, 16) 0 \r\n \r\n \r\n flatten (Flatten) (None, 400) 0 \r\n \r\n dense (Dense) (None, 64) 25664 \r\n \r\n dense_1 (Dense) (None, 10) 650 \r\n \r\n=================================================================\r\nTotal params: 28,794\r\nTrainable params: 28,794\r\nNon-trainable params: 0", "Hi @JeremyJian, can you try on a Samsung S21 emulator as well and let us know the results? I'm trying to determine if we can replicate it on emulator here as well (this will help us immensely as that would make it reproducible on our end) otherwise we would have to procure a real device. Additionally, it seems your drive link is going to the github issues page instead, I can copy and paste the info to get there but if you can edit it so that the community doesn't get confused, that'll be great.", "Sorry, I can't find the emulator of Samsung S21 (SM-G9910) from Android Studio or Firebase TestLab. On the other hand, our issue only occurs in scenarios where both the GPU and Google Play Services (com.google.android.gms:play-services-tflite-gpu:16.2.0) are used simultaneously. It seems that reproducing this issue on a simulator may not be suitable. As described in the initial question, using the CPU or the standalone version of TensorFlow Lite (org.tensorflow:tensorflow-lite-gpu:2.9.0) does not encounter this problem.\r\n\r\nThank you for your reminder. The video link has been corrected.", "Thanks for the info, @sirakiin, can you please take a look at this? Thanks.", "Hi, Is there any new information or update regarding this issue? \r\nThanks", "We have recently encountered similar issues with both the Samsung S22 Ultra and S23 Ultra devices. Including the previously mentioned S21 series, are equipped with Qualcomm processors. While we're uncertain whether the processor is directly linked to these problems, we are eager to receive any updates or new information regarding this issue. Please keep us informed if you have any new insights." ]

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[ "Hi N-Harish,\r\n\r\nIt seems problem with CoreML converter as per my understanding. Please see the error below.\r\n\r\n`InvalidArgumentError: Input 0 of node Model1/FPN/FPN1/bn/AssignNewValue was passed float from Model1/FPN/FPN1/bn/FusedBatchNormV3/ReadVariableOp/resource:0 incompatible with expected resource.`\r\n\r\nI am not sure how your model was built. But saved model will contain parameters which are floats and may be CoreML converter may be expecting some other data type? I am not much aware of CoreML APIs may be you can dig more there.\r\n\r\nAlso can you have a look into TFlite [documentation](https://www.tensorflow.org/lite/models/convert) which is suitable for converting TF model into TFlite model for faster inferences.\r\n\r\n\r\n\r\n", "Ok the model was getting converted in tflite and I could do inference with it as well but I wanted to use m1 gpus so i wanted to convert it into coreml format. Is there any way in which i can make tflite model to use m1 gpus/metal framework using python ?", "Hi @N-Harish ,\r\n\r\nFor Apple M1/M2 (Apple silicon, Arm Architecture) Tensorflow package is different which was built and maintained by Apple itself. To make it M1 compatible maybe you need to use `tensorflow-macos` not `tensorflow`.\r\n\r\nI think you might have built the model using `tensorflow` package and hence it may not be compatible for Mac M1.You can ask at CoreML repo also for confirmation.\r\n\r\nTo built model specific for Apple Metal M1/M2 you have to follow the instructions of Apple metal plugin as mentioned [here](https://developer.apple.com/metal/tensorflow-plugin/).\r\n\r\nThanks!\r\n\r\n", "@SuryanarayanaY I have installed tensorflow-macos but when converting to tflite it won't use Apple Metal M1/M2. Do I need to install something else ?\r\n", "@N-Harish ,\r\n\r\nTFlite can convert `tensorflow` models but I doubt `tensorflow-macos` models may not be convertible. Have you checked the instructions [here](https://www.tensorflow.org/lite/models/convert/convert_models#convert_a_savedmodel_recommended_) for converting tensorflow model to TFlite ? What error exactly you are getting ? Have you trained your model with `tensorflow-macos` ?", "@SuryanarayanaY tensorflow-macos model is also getting converted into tflite but it's not using coreml delegates. I'm running inference in python. Is there any way to make tflite use coreml delegates in python inference ?", "@SuryanarayanaY I also opened this issue on coremltools repo by Apple. Turns out it is the error with tensorflow. Coreml actually loads the tensorflow model and covert it into frozen graph using convert_variables_into_constants_v2 function in tensorflow. When this model is tried to convert into frozen graph using the above function, the above reported error occurs. I also tried to convert the model into frozen graph using the same function and got the same error reported here. I'm ready to share the model with you so that you can have a look at it and suggest some changes so that I can convert the model into frozen graph.", "Hi @N-Harish ,\r\n\r\nPlease share your model code if Ok to you, which might also be helpful for us to debug.\r\n\r\nThanks!", "@SuryanarayanaY here is the repo that we are using for training our model\r\n\r\n[https://github.com/peteryuX/retinaface-tf2](https://github.com/peteryuX/retinaface-tf2\r\n)\r\n\r\nthe training script is as follows [training scripts for model](https://github.com/peteryuX/retinaface-tf2/blob/master/train.py). We just retrained this model (the one with MobilenetV2 backbone) on our own data\r\n", "I am new to tensorflow can someone help me to solve the issues", "@sachinprasadhs here is the repo that we are using for training our model\n\nhttps://github.com/peteryuX/retinaface-tf2\n\nthe training script is as follows training scripts for model. We just retrained this model (the one with MobilenetV2 backbone) on our own data. I'm sharing this again for your reference ", "Okay\nThanks!\n\nOn Wed, 31 May, 2023, 7:34 am Harish Natarajan, ***@***.***>\nwrote:\n\n> @sachinprasadhs <https://github.com/sachinprasadhs> here is the repo that\n> we are using for training our model\n>\n> https://github.com/peteryuX/retinaface-tf2\n>\n> the training script is as follows training scripts for model. We just\n> retrained this model (the one with MobilenetV2 backbone) on our own data.\n> I'm sharing this again for your reference\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60703#issuecomment-1569382439>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/A3UGGNTZ4SGPWNEOC3EVC4LXI2RJNANCNFSM6AAAAAAYOLPZMA>\n> .\n> You are receiving this because you commented.Message ID:\n> ***@***.***>\n>\n", "@sachinprasadhs, here is the repo that we are using for training our model\r\n\r\nhttps://github.com/peteryuX/retinaface-tf2\r\n\r\nthe training script is as follows [training scripts](https://github.com/peteryuX/retinaface-tf2/blob/master/train.py) We just retrained this model (the one with MobilenetV2 backbone) on our own data. I'm sharing this again for your reference", "Hi @N-Harish,\r\n\r\nCan you please fill out the Issue Template: https://github.com/tensorflow/tensorflow/blob/master/ISSUE_TEMPLATE.md\r\n\r\nAdditionally for running the training scripts, it seems like there is an implicit assumption that it is running on linux with a gpu, one of the packages in their environment packages is tensorflow-gpu. What is The environment which is producing the model (\"./temp_with_weights_model.h5\")? Alternatively, if you can upload a model that reproduces this issue we can just use that as well. From the above conversation it looks like you are running the script that runs into the issue on an M1 Mac?\r\n\r\nThanks for your help.\r\n", "------------------------\r\n\r\n### System information\r\n\r\n- **Have I written custom code (as opposed to using a stock example script\r\n provided in TensorFlow)**: yes\r\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Ubuntu 20.04\r\n- **TensorFlow installed from (source or binary)**: No\r\n- **TensorFlow version (use command below)**: 2.5.0\r\n- **Python version**: 3.8.16\r\n\r\n\r\n### Describe the problem\r\nWhen I try to run the below code for converting the defined model to coreml it won't convert for dynamic shape inputs. I am sharing the layer causing the issue as well as the stacktrace below for converting model to coreml. \r\n\r\n### Source code / logs\r\n#### source code\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import Model\r\nfrom tensorflow.keras.applications import MobileNetV2, ResNet50\r\nfrom tensorflow.keras.layers import Input, Conv2D, ReLU, LeakyReLU\r\nimport coremltools as ct\r\n\r\n\r\n\r\ndef _regularizer(weights_decay):\r\n \"\"\"l2 regularizer\"\"\"\r\n return tf.keras.regularizers.l2(weights_decay)\r\n\r\n\r\ndef _kernel_init(scale=1.0, seed=None):\r\n \"\"\"He normal initializer\"\"\"\r\n return tf.keras.initializers.he_normal()\r\n\r\n\r\nout_ch = 64\r\nwd = 0.0005\r\n\r\n\r\nclass ConvUnit(tf.keras.layers.Layer):\r\n \"\"\"Conv + BN + Act\"\"\"\r\n def __init__(self, f=None, k=None, s=None, wd=None, act=None, name='ConvBN', **kwargs):\r\n super(ConvUnit, self).__init__(name=name, **kwargs)\r\n self.f = f\r\n self.k = k\r\n self.s = s\r\n self.wd = wd\r\n self.act = act\r\n# self.name = name\r\n self.conv = Conv2D(filters=f, kernel_size=k, strides=s, padding='same',\r\n kernel_initializer=_kernel_init(),\r\n kernel_regularizer=_regularizer(wd),\r\n use_bias=False, name='conv')\r\n\r\n # self.bn = BatchNormalization(name='bn')\r\n self.bn = tf.keras.layers.BatchNormalization(name='bn')\r\n\r\n if act is None:\r\n self.act_fn = tf.identity\r\n elif act == 'relu':\r\n self.act_fn = ReLU()\r\n elif act == 'lrelu':\r\n self.act_fn = LeakyReLU(0.1)\r\n else:\r\n raise NotImplementedError(\r\n 'Activation function type {} is not recognized.'.format(act))\r\n\r\n\r\n def call(self, x):\r\n return self.act_fn(self.bn(self.conv(x)))\r\n # return self.bn(self.conv(x))\r\n # return self.act_fn(self.conv(x))\r\n\r\n def get_config(self):\r\n config = super().get_config()\r\n temp = {\r\n 'f': self.f,\r\n 'k': self.k,\r\n 's': self.s,\r\n 'wd': self.wd,\r\n 'act': self.act,\r\n# 'convunit_name': self.name,\r\n# 'conv': self.conv,\r\n# 'bn': self.bn,\r\n# 'act_fn': self.act_fn,\r\n }\r\n config.update(temp)\r\n return config\r\n\r\n\r\nclass FPN(tf.keras.layers.Layer):\r\n \"\"\"Feature Pyramid Network\"\"\"\r\n def __init__(self, out_ch, wd, name='FPN', **kwargs):\r\n super(FPN, self).__init__(name=name, **kwargs)\r\n self.act = 'relu'\r\n self.out_ch = out_ch\r\n# self.name = name\r\n self.wd = wd\r\n if (out_ch <= 64):\r\n self.act = 'lrelu'\r\n\r\n self.output1 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=self.act, name=name+str(1))\r\n self.output2 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=self.act, name=name+str(2))\r\n self.output3 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=self.act, name=name+str(3))\r\n self.merge1 = ConvUnit(f=out_ch, k=3, s=1, wd=wd, act=self.act, name=name+str(4))\r\n self.merge2 = ConvUnit(f=out_ch, k=3, s=1, wd=wd, act=self.act, name=name+str(5))\r\n\r\n def call(self, x):\r\n print(f\"x[0] :- {x[0].shape}\")\r\n print(f\"x[1] :- {x[1].shape}\")\r\n print(f\"x[2] :- {x[2].shape}\")\r\n \r\n print(f\"Running output1 conv layer\")\r\n output1 = self.output1(x[0]) # [80, 80, out_ch]\r\n print(f\"Output of output1 conv :- {output1.shape} \\n\")\r\n \r\n print(f\"Running output2 conv layer\")\r\n output2 = self.output2(x[1]) # [40, 40, out_ch]\r\n print(f\"Output of output2 conv :- {output2.shape} \\n\")\r\n \r\n print(f\"Running output3 conv layer\")\r\n output3 = self.output3(x[2]) # [20, 20, out_ch]\r\n print(f\"Output of output3 conv :- {output3.shape} \\n\")\r\n\r\n print(f\"Running resize of output3 \\n\")\r\n up_h, up_w = tf.shape(output2)[1], tf.shape(output2)[2]\r\n \r\n # up_h, up_w = output2.shape[1], output2.shape[2]\r\n print(output2.shape[1], output2.shape[2])\r\n \r\n up3 = tf.image.resize(output3, [up_h, up_w], method='nearest')\r\n print(f\"Resized output3 to :- {tf.shape(up3)} {up3.shape}\\n\")\r\n \r\n print(f\"Adding output2 to resized output3 \\n\")\r\n # print(f\"Shape of tf.concat output2 ,up3 :- {tf.add(output2, up3).shape}\")\r\n \r\n # output2 = output2 + up3\r\n output2 = tf.add(output2, up3)\r\n print(f\"Output2 shape :- {tf.shape(output2)} {output2.shape}\\n\")\r\n \r\n print(f\"passing output2 to merge2 conv \\n\")\r\n output2 = self.merge2(output2)\r\n print(f\"Output of output2 :- {output2.shape} \\n\")\r\n \r\n print(f\"Running resize of output2 w.r.t. output1 \\n\")\r\n up_h, up_w = tf.shape(output1)[1], tf.shape(output1)[2]\r\n up2 = tf.image.resize(output2, [up_h, up_w], method='nearest')\r\n print(f\"Resized output2 to :- {tf.shape(up2)} {up2.shape}\\n\")\r\n \r\n print(f\"Adding output1 to resized output2 \\n\")\r\n \r\n # output1 = output1 + up2\r\n output1 = tf.add(output1, up2)\r\n print(f\"Output1 shape :- {tf.shape(output1)} {output1.shape}\\n\")\r\n \r\n print(f\"Running merge1 conv on output1 \\n\")\r\n output1 = self.merge1(output1)\r\n print(f\"Shape of output1 :- {output1.shape} \\n\")\r\n\r\n return output1, output2, output3\r\n\r\n def get_config(self):\r\n config = super().get_config()\r\n temp = {\r\n# 'fpn_act': self.act,\r\n 'out_ch': self.out_ch,\r\n# 'fpn_name': self.name,\r\n 'wd': self.wd,\r\n# 'fpn_conv1': self.output1,\r\n# 'fpn_conv2': self.output2,\r\n# 'fpn_conv3': self.output3,\r\n# 'fpn_merge1': self.merge1,\r\n# 'fpn_merge2': self.merge2,\r\n }\r\n config.update(temp)\r\n return config\r\n\r\n\r\n\r\nx = (Input(shape= (None, None, 192)), Input(shape= (None, None, 576)), Input(shape= (None, None, 960)))\r\n\r\n# x = (Input(shape= (100, 100, 192)), Input(shape= (50, 50, 576)), Input(shape= (25, 25, 960)))\r\n\r\n\r\n# name = \"CU\"\r\n# output1 = ConvUnit(f=out_ch, k=1, s=1, wd=wd, act=\"relu\", name=name+str(1))\r\n# output1(x[0])\r\n\r\nfpn = FPN(out_ch=out_ch, wd=wd)\r\n# fpn(x)\r\n\r\nfpn_mdl = Model(inputs=x, outputs=fpn(x))\r\nfpn_mdl.summary()\r\n\r\n\r\n\r\n\r\ninput_name = fpn_mdl.inputs[0].name\r\ninput_name1 = fpn_mdl.inputs[1].name\r\ninput_name2 = fpn_mdl.inputs[2].name\r\n\r\nheight = 200\r\nwidth = 200\r\n\r\n\r\ninput_shape1 = ct.Shape(shape=(ct.RangeDim(lower_bound=1, upper_bound=-1),\r\n ct.RangeDim(lower_bound=height, upper_bound=-1),\r\n ct.RangeDim(lower_bound=width, upper_bound=-1),\r\n 192))\r\n\r\ninput_shape2 = ct.Shape(shape=(ct.RangeDim(lower_bound=1, upper_bound=-1),\r\n ct.RangeDim(lower_bound=height, upper_bound=-1),\r\n ct.RangeDim(lower_bound=width, upper_bound=-1),\r\n 576))\r\n\r\ninput_shape3 = ct.Shape(shape=(ct.RangeDim(lower_bound=1, upper_bound=-1),\r\n ct.RangeDim(lower_bound=height, upper_bound=-1),\r\n ct.RangeDim(lower_bound=width, upper_bound=-1),\r\n 960))\r\n\r\n\r\n\r\nfpn_model = ct.convert(fpn_mdl, inputs=[ct.TensorType(shape=input_shape1, name=input_name), ct.TensorType(shape=input_shape2, name=input_name1), ct.TensorType(shape=input_shape3, name=input_name2)], source='tensorflow')\r\n```\r\n\r\n#### issue\r\n```\r\n2023-06-07 07:12:53.914456: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0\r\n2023-06-07 07:12:53.914593: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session\r\n2023-06-07 07:12:53.917406: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize\r\n function_optimizer: function_optimizer did nothing. time = 0.007ms.\r\n function_optimizer: function_optimizer did nothing. time = 0.001ms.\r\n\r\n2023-06-07 07:12:53.994451: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0\r\n2023-06-07 07:12:53.994538: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session\r\n2023-06-07 07:12:54.005123: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize\r\n constant_folding: Graph size after: 72 nodes (-25), 150 edges (-25), time = 3.705ms.\r\n dependency_optimizer: Graph size after: 72 nodes (0), 75 edges (-75), time = 0.72ms.\r\n debug_stripper: debug_stripper did nothing. time = 0.07ms.\r\n constant_folding: Graph size after: 72 nodes (0), 75 edges (0), time = 1.29ms.\r\n dependency_optimizer: Graph size after: 72 nodes (0), 75 edges (0), time = 0.576ms.\r\n debug_stripper: debug_stripper did nothing. time = 0.058ms.\r\n\r\nRunning TensorFlow Graph Passes: 100%|██████████| 6/6 [00:00<00:00, 38.40 passes/s]\r\nConverting TF Frontend ==> MIL Ops: 83%|████████▎ | 60/72 [00:00<00:00, 2437.44 ops/s]\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[35], line 36\r\n 19 input_shape3 = ct.Shape(shape=(ct.RangeDim(lower_bound=1, upper_bound=-1),\r\n 20 ct.RangeDim(lower_bound=height, upper_bound=-1),\r\n 21 ct.RangeDim(lower_bound=width, upper_bound=-1),\r\n 22 960))\r\n 27 # c_model = ct.convert(model, inputs=[ct.TensorType(shape=input_shape, name=input_name)], pass_pipeline=pipeline, source='tensorflow')\r\n 28 \r\n 29 \r\n (...)\r\n 34 \r\n 35 # c_model = ct.convert(model, inputs=[ct.TensorType(shape=input_shape, name=input_name)], pass_pipeline=pipeline, source='tensorflow')\r\n---> 36 fpn_model = ct.convert(fpn_mdl, inputs=[ct.TensorType(shape=input_shape1, name=input_name), ct.TensorType(shape=input_shape2, name=input_name1), ct.TensorType(shape=input_shape3, name=input_name2)], source='tensorflow')\r\n 39 # # out = fpn\r\n 40 \r\n 41 # features = [SSH(out_ch=out_ch, wd=wd, name=f'SSH_{i}')(f)\r\n (...)\r\n 70 \r\n 71 # model_after_extractor = Model(fpn, out)\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/_converters_entry.py:492, in convert(model, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, compute_units, package_dir, debug, pass_pipeline)\r\n 489 if specification_version is None:\r\n 490 specification_version = _set_default_specification_version(exact_target)\r\n--> 492 mlmodel = mil_convert(\r\n 493 model,\r\n 494 convert_from=exact_source,\r\n 495 convert_to=exact_target,\r\n 496 inputs=inputs,\r\n 497 outputs=outputs_as_tensor_or_image_types, # None or list[ct.ImageType/ct.TensorType]\r\n 498 classifier_config=classifier_config,\r\n 499 skip_model_load=skip_model_load,\r\n 500 compute_units=compute_units,\r\n 501 package_dir=package_dir,\r\n 502 debug=debug,\r\n 503 specification_version=specification_version,\r\n 504 main_pipeline=pass_pipeline,\r\n 505 )\r\n 507 if exact_target == 'milinternal':\r\n 508 return mlmodel # Returns the MIL program\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:188, in mil_convert(model, convert_from, convert_to, compute_units, **kwargs)\r\n 149 @_profile\r\n 150 def mil_convert(\r\n 151 model,\r\n (...)\r\n 155 **kwargs\r\n 156 ):\r\n 157 \"\"\"\r\n 158 Convert model from a specified frontend `convert_from` to a specified\r\n 159 converter backend `convert_to`.\r\n (...)\r\n 186 See `coremltools.converters.convert`\r\n 187 \"\"\"\r\n--> 188 return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:212, in _mil_convert(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)\r\n 209 weights_dir = _tempfile.TemporaryDirectory()\r\n 210 kwargs[\"weights_dir\"] = weights_dir.name\r\n--> 212 proto, mil_program = mil_convert_to_proto(\r\n 213 model,\r\n 214 convert_from,\r\n 215 convert_to,\r\n 216 registry,\r\n 217 **kwargs\r\n 218 )\r\n 220 _reset_conversion_state()\r\n 222 if convert_to == 'milinternal':\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:285, in mil_convert_to_proto(model, convert_from, convert_to, converter_registry, main_pipeline, **kwargs)\r\n 280 frontend_pipeline, backend_pipeline = _construct_other_pipelines(\r\n 281 main_pipeline, convert_from, convert_to\r\n 282 )\r\n 284 frontend_converter = frontend_converter_type()\r\n--> 285 prog = frontend_converter(model, **kwargs)\r\n 286 PipelineManager.apply_pipeline(prog, frontend_pipeline)\r\n 288 PipelineManager.apply_pipeline(prog, main_pipeline)\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/converter.py:98, in TensorFlow2Frontend.__call__(self, *args, **kwargs)\r\n 95 from .frontend.tensorflow2.load import TF2Loader\r\n 97 tf2_loader = TF2Loader(*args, **kwargs)\r\n---> 98 return tf2_loader.load()\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/load.py:82, in TFLoader.load(self)\r\n 75 dot_string = self._tf_ssa.get_dot_string(\r\n 76 annotation=True, name_and_op_style=True, highlight_debug_nodes=[]\r\n 77 )\r\n 78 graphviz.Source(dot_string).view(\r\n 79 filename=\"/tmp/ssa_before_tf_passes\", cleanup=True\r\n 80 )\r\n---> 82 program = self._program_from_tf_ssa()\r\n 83 logger.debug(\"program:\\n{}\".format(program))\r\n 84 return program\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow2/load.py:210, in TF2Loader._program_from_tf_ssa(self)\r\n 203 self._run_tf_ssa_passes()\r\n 204 converter = TF2Converter(\r\n 205 tfssa=self._tf_ssa,\r\n 206 inputs=self.kwargs[\"inputs\"],\r\n 207 outputs=self.kwargs[\"outputs\"],\r\n 208 opset_version=self.kwargs[\"specification_version\"],\r\n 209 )\r\n--> 210 return converter.convert()\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/converter.py:465, in TFConverter.convert(self)\r\n 463 for g_name in self.graph_stack[1:]:\r\n 464 self.context.add_graph(g_name, self.tfssa.functions[g_name].graph)\r\n--> 465 self.convert_main_graph(prog, graph)\r\n 466 return prog\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/converter.py:389, in TFConverter.convert_main_graph(self, prog, graph)\r\n 387 input_var = mb.cast(x=input_var, dtype=\"fp32\", name=name)\r\n 388 self.context.add(name, input_var)\r\n--> 389 outputs = convert_graph(self.context, graph, self.output_names)\r\n 390 ssa_func.set_outputs(outputs)\r\n 391 prog.add_function(\"main\", ssa_func)\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/convert_utils.py:191, in convert_graph(context, graph, outputs)\r\n 187 msg = \"Conversion for TF op '{0}' not implemented.\\n \\n{1}\".format(\r\n 188 node.op, node.original_node\r\n 189 )\r\n 190 raise NotImplementedError(msg)\r\n--> 191 add_op(context, node)\r\n 193 if len(node.outputs) > 0:\r\n 194 # set_global / get_global / NoOp has no direct consumer / outputs\r\n 195 x = context[node.name]\r\n\r\nFile ~/SageMaker/envs/coreml_env/lib64/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/ops.py:2584, in ResizeNearestNeighbor(context, node)\r\n 2580 Hout, Wout = None, None\r\n 2581 if context[node.inputs[1]].val is None:\r\n 2582 # for the dynamic input shape case,\r\n 2583 # context[node.inputs[1]] is a mul(x=input_shape, y=scaling_factor) op.\r\n-> 2584 scaling_factor_h = context[node.inputs[1]].op.y.val[0]\r\n 2585 scaling_factor_w = context[node.inputs[1]].op.y.val[1]\r\n 2586 else:\r\n\r\nAttributeError: 'concat' object has no attribute 'y'\r\n```", "Hi, @N-Harish, thanks for giving us more information and context, this definitely helps. In your example, \"ConvUnit\" seems to be defined outside of this script -- as such I'm running into that issue instead. How are you defining ConvUnit? it doesn't seem to be part of the imports.", "Hi @pkgoogle Sorry I forgot to add Code for ConvUnit. I've added it now in the same form above", "@N-Harish, just realized you provided the answer above... TFLite doesn't support dynamic input shapes currently, this is because it is hard to support simultaneously while being very efficient in memory and latency, which runs counter to TFLite's design principles. As such we do not plan to support it in the near future. If you can change your use case to just use static input shapes it should work fine. (This might require standardizing input shapes depending on your dataset). Apologies this is probably not the resolution you are looking for. Please feel free to close the issue as not planned if you have no more open items regarding this.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60703\">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/60703\">No</a>\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60703\">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/60703\">No</a>\n" ]

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[ "Hi @AnwarsaeedDMU ,\r\n\r\nCurrently we are not supporting TF 1.x versions. Could you please migrate to TF2.x versions using the migration [guide](https://www.tensorflow.org/guide/migrate) and then report the issue with the changed code.\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/60702\">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/60702\">No</a>\n" ]

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[ "reworked per review comment, and rebased.", "Hi @Tai78641 Can you please resolve conflicts? Thank you!", "> Hi @Tai78641 Can you please resolve conflicts? Thank you!\r\n\r\nresolved merge conflicts", "resolved merge conflicts", "Hi @Tai78641 Can you please resolve conflicts? Thank you!", "rebased to resolve conflicts", "Hi @rsuderman, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "Hi @jpienaar, Can you please review this PR ? Thank you!", "@NatashaKnk would you review this please?", "Hi @Tai78641 Can you please resolve conflicts? Thank you!", "> Hi @Tai78641 Can you please resolve conflicts? Thank you!\r\n\r\nrebased and resolved conflicts", "Hi @NatashaKnk Can you please review this PR? Thank you!", "Hi @NatashaKnk Can you please review this PR? Thank you!" ]

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[ "@Tessil FYI", "Hi @jpienaar and @rsuderman, please could you help to review when you have time.\r\nThank you!", "Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!", "Force-pushed the PR feature branch after resolving merge conflicts caused by rebasing.", "> Could you add a test for these changes. Other than that the PR looks good.\r\n\r\nI have add new mlir test for quantized reduce_mean." ]

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[ "Hi @wenchenvincent Can you please resolve conflicts? Thank you!", "> Hi @wenchenvincent Can you please resolve conflicts? Thank you!\r\n\r\nConflicts resolved. Thanks!" ]

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[ "Hi @drewshark ,\r\n\r\nThanks for reporting this. I have replicated the reported behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/d16754b4df71e7e3242a78a43d11a51e/60965.ipynb) for reference.\r\n\r\nIt seems when 'x2' having '0' then only problem arising. If there is no '0' in 'x2' then the result is expected output only. This behaviour is bit strange for me and needs to be analysed.\r\n\r\nThanks!" ]

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[ "Could you point me to where //tensorflow/compiler/tests:matrix_solve_op_test_cpu and //tensorflow/compiler/tests:matrix_solve_op_test_cpu_mlir_bridge_test need/import tensorflow.python.platform.sysconfig?\r\n\r\nFrom https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/tests/matrix_solve_op_test.py I don't see any imports of sysconfig\r\n", "> Could you point me to where //tensorflow/compiler/tests:matrix_solve_op_test_cpu and //tensorflow/compiler/tests:matrix_solve_op_test_cpu_mlir_bridge_test need/import tensorflow.python.platform.sysconfig?\r\n> \r\n> From https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/tests/matrix_solve_op_test.py I don't see any imports of sysconfig\r\n\r\nSorry for the confusion. The context was that the recent upstream refactoring made the //tensorflow/compiler/tests:matrix_solve_op_test_cpu test in our ROCm fork fail (https://github.com/ROCmSoftwarePlatform/tensorflow-upstream) due to missing dependency. So I added the fix and would like to upstream. However, I didn't realize that the previous change in this test to disable cublasLt for ROCm (made last year) was never upstreamed. Now I have just added that commit in this PR as well.", "Thanks for the context. Now that this is modifying XLA flags, I don't think that I am the right reviewer. @gbaned Could you find another reviewer for this PR?", "Hi @wenchenvincent Can you please take look on below internal errors. Thank you!\r\n\r\nERROR: Strict deps violations: //tensorflow/compiler/tests:matrix_solve_op_test_cpu\r\n In third_party/tensorflow/compiler/tests/matrix_solve_op_test.py, no direct deps found for imports:\r\n line 26: from tensorflow.python.platform import sysconfig: Module \"tensorflow.python.platform\" not provided by a direct dep\r\n\r\n\r\nUse --define=PYDEPS=<OFF|WARN> to temporarily change how strict deps are enforced.\r\n\r\n\r\n*** Please fix target dependencies. ***\r\n\r\nbuild_cleaner //third_party/tensorflow/compiler/tests:matrix_solve_op_test_cpu" ]

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[ "Hi @Tai78641 Can you please take a look on below internal errors. Thank you!\r\n\r\nTensorFlow crashed, please file a bug on https://github.com/tensorflow/tensorflow/issues with the trace below.\r\n #0 0x0000562cb79c0f6e llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) ()\r\n #1 0x0000562cb79bee29 llvm::sys::RunSignalHandlers() \r\n#29 0x00007f49cbc1d69f clone (/usr/grte/v5/lib64/libc.so.6+0x13969f)\r\nFileCheck error: '<stdin>' is empty.\r\nFileCheck command line: FileCheck /tensorflow/compiler/mlir/tosa/tests/tf-to-tosa-pipeline.mlir\r\n ", "@gbaned I rebased and double checked. tf-to-tosa-pipeline.mlir passes for me.", "@jpienaar I cannot figure out why the Py+CPP Test Suite failures. ", "rebased to use dyn_cast(...) style", "@rsuderman please have a look at this again. thanks", "Hi @Tai78641 Can you please check @jpienaar's comments and keep us posted ? Thank you!", "I had to make some modifications to land. I believe it should still be good but you may need to validate that things are working as intended." ]

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[ "@fwyzard,\r\nThe latest tensorflow stable version 2.12 is compatible with CUDA 11.8 and cuDNN 8.6. Please take a look at the official document for the tested build [configurations](https://www.tensorflow.org/install/source).\r\n\r\n Going forward for the upcoming releases, tensorflow might move to CUDA 12.0 or higher version. We will announce that information in the mentioned official doc once the next release happens. Thank you!", "> Going forward for the upcoming releases, tensorflow might move to CUDA 12.0 or higher version. We will announce that information in the mentioned official doc once the next release happens.\r\n\r\nHi @tilakrayal, thanks for the answer, even though it's not very helpful for planning ahead :-)", "@fwyzard,\r\nThe Tensorflow nightly moved to CUDA 12 a few days ago. Could you please take a look at this commit [3de4416 2](https://github.com/tensorflow/tensorflow/commit/3de44168950a5972ba4cfa7e3c6cbf4cffa67fe6).\r\n\r\nYou can try to use the latest tf-nightly for immediate use-case and it will be made available from TF 2.15.\r\n\r\nThank you!", "Thank you @tilakrayal , we will start testing it.", "@fwyzard,\r\nThank you and Could you please confirm whether the issue can be closed as the required feature is available in tf-nightly. 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/60691\">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/60691\">No</a>\n" ]

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[ "@Pranil51,\r\nCould you please provide the complete reproducible code or the colab gist to analyse the issue in an effective way. 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/60690\">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/60690\">No</a>\n" ]

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[ "Hi @ujjwalnur ,\r\n\r\nRequest you to confirm the below things.\r\n\r\n1. Command used for the build\r\n2. Can you confirm whether you have followed the instructions [here](https://www.tensorflow.org/install/pip#step-by-step_instructions) mentioned in documentation for enabling GPU setup.\r\n3. Also for Tf 2.12 version recommended CUDA is 11.8, cuDNN is 8.6. You can find the recommended and tested configurations [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\nAlso please test by adding the option `--per_file_copt=//tensorflow/.*\\.cc@-g,-O0` and let us know if it works.\r\n\r\nProviding exact build command and steps followed may help us if above also won't work.\r\n\r\nThanks!", "> Hi @ujjwalnur ,\r\n> \r\n> Request you to confirm the below things.\r\n> \r\n> 1. Command used for the build\r\n> \r\n> 2. Can you confirm whether you have followed the instructions [here](https://www.tensorflow.org/install/pip#step-by-step_instructions) mentioned in documentation for enabling GPU setup.\r\n> \r\n> 3. Also for Tf 2.12 version recommended CUDA is 11.8, cuDNN is 8.6. You can find the recommended and tested configurations [here](https://www.tensorflow.org/install/source#gpu).\r\n> \r\n> \r\n> Also please test by adding the option `--per_file_copt=//tensorflow/.*\\.cc@-g,-O0` and let us know if it works.\r\n> \r\n> Providing exact build command and steps followed may help us if above also won't work.\r\n> \r\n> Thanks!\r\n\r\nCommand used for build : `bazel build --config=opt --config=cuda -k //tensorflow/tools/pip_package:build_pip_package`\r\n\r\nI confirm that all the instructions were followed for enabling GPU setup.\r\n\r\nAfter enabling the option` --per_file_copt=//tensorflow/.*\\.cc@-g,-O0` the code still does not link and gives the following error -\r\n```\r\nERROR: /tmp/tensorflow/tensorflow/BUILD:1219:21: Linking tensorflow/libtensorflow_cc.so.2.12.0 failed: (E\r\nxit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command external/local_config_cuda/cr\r\nosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc @bazel-out/k8-opt/bin/tensorflow/libtensorflow_cc.s\r\no.2.12.0-2.params\r\n/usr/bin/ld: bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/cuda/libcuda_graph.pic.a(cuda_g\r\nraph.pic.o): in function `stream_executor::gpu::CaptureCudaGraph(stream_executor::Stream*, absl::lts_2022\r\n0623::AnyInvocable<tsl::Status ()>, cudaStreamCaptureMode)':\r\n/proc/self/cwd/tensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc:136: undefined reference to `cu\r\ndaGraphDebugDotPrint'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(propagate_device_id_to_function_args.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(restore_shape_inference.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(set_default_sharding.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(sparse_expansion.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(spmd_expansion.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(tpu_add_resource_device_attribute.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(tpu_integration.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/libdtensor_mlir_passes.pic.lo(undo_merge_const_across_mesh.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/cc/liblayout_to_xla_sharding.pic.a(layout_to_xla_sharding.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/utils/libdtensor_mlir_passes_internal.pic.a(collective_lowering.pic.o):(.debug_aranges+0x6): relocation truncated to fit: R_X86_64_32 against `.debug_info'\r\nbazel-out/k8-opt/bin/tensorflow/dtensor/mlir/utils/libdtensor_mlir_passes_internal.pic.a(dtensor_mlir_passes_internal.pic.o):(.debug_aranges+0x6): additional relocation overflows omitted from the output\r\ncollect2: error: ld returned 1 exit status\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 1338.070s, Critical Path: 819.66s\r\nINFO: 15964 processes: 247 internal, 15717 local.\r\nFAILED: Build did NOT complete successfully\r\n```\r\n\r\n\r\n", "I think that the following commit is responsible for the failed build : \r\n\r\n[de4440c2f8d0a84774afdef180d3ae4acab5398a](https://github.com/tensorflow/tensorflow/commit/de4440c2f8d0a84774afdef180d3ae4acab5398a) (For v2.12 branch)\r\n\r\nThe later commit [aae3467d8c5c8ae0a18dbfedecce7b50f2ac5ce7](https://github.com/tensorflow/tensorflow/commit/aae3467d8c5c8ae0a18dbfedecce7b50f2ac5ce7) adds a check for `CUDA version>=12000` but has not been put as part of v2.12 branch\r\n\r\nCan you confirm that these changes need to be merged into v2.12 branch for a proper build ? \r\n\r\nI can confirm that I was able to build v2.11 successfully on CUDA 11.5", "@ujjwalnur ,\r\n\r\nThanks for the information.We have observed build failures with `--config=cuda` and team already working on it.\r\n\r\nI request you to provide the steps on how you have configured the CUDA and cuDNN paths.\r\n", "@SuryanarayanaY \r\nCUDA and cudnn paths were given as base paths to the custom installed paths.\r\n\r\nThese paths were setup using `configure.py` \r\n\r\nThese paths are properly setup. As I have already said, for v2.11 I used the same paths without error as inall the versions since v2. 3", "@SuryanarayanaY Any updates yet on this ? Even some technical explanations would be helpful because then I can take a look into this problem. Since I am a newbie to TF source code, a little help could go a long way.", "@ujjwalnur @SuryanarayanaY \r\nI have the same problem.\r\ntensorflow_version:2.12.0\r\ncuda_version:11.6\r\n[tensorflow-2.12.0/tensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc](https://github.com/tensorflow/tensorflow/blob/v2.12.0/tensorflow/compiler/xla/stream_executor/cuda/cuda_graph.cc)\r\n", "Hi @ujjwalnur ,\r\n\r\nApologies for the delayed response. Could you please confirm the path setting instructions followed by you to enable CUDA and cuDNN paths. Could you please check the Nvidia instructions [here](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html#ubuntu) for same and try the build and let us know the outcome. \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/60689\">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/60689\">No</a>\n" ]

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[ "The ARM_CI tests are all failing due to this issue at the moment so it would be good if it could be addressed soon.\r\nThe failing checks, PyLint and Code Check, are pre-existing in the file so are not related to this PR." ]

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[ "@elfringham,\r\nThe related PR #60688 was merged and as requested the '**typing_extensions >= 3.6.6**', are changed to\r\n '**typing_extensions>=3.6.6,<4.6.0**' with the mentioned merged PR. \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py#L100.\r\n\r\nCould you please confirm whether changes are reflected as mentioned. Thank you!", "The rough cause of error is [here](https://github.com/tensorflow/tensorflow/blob/f8066222ad6b2edbf1ee359fda66f2b43473dbe7/tensorflow/python/trackable/data_structures.py#L812). The current getattribute for dictwrapper can return TypeError. When an attribute is not found it's instead expected to return AttributeError. Finding place that produces TypeError and replacing it with AttributeError should fix typing extensions incompatibility. typing 4.6 uses inspect.getattr_static which expects that failed attribute access using getattribute only triggers attribute error.\r\n\r\nThis particular error will also be an error if you try to run tensorflow on python 3.12. The typing extensions change was motivated from [here](https://github.com/python/cpython/pull/103034).\r\n\r\nedit: Minimal version of this error is,\r\n\r\n```python\r\nfrom tensorflow.python.trackable.data_structures import _DictWrapper\r\n\r\nt = _DictWrapper()\r\nobject.__getattribute__(t, \"__dict__\")\r\n```\r\nThe last line raises TypeError. On other hand `t.__dict__` properly raises AttributeError. DictWrapper's custom getattribute is rough area that needs bug fix.\r\n\r\nThe error message comes from [here](https://github.com/python/cpython/blob/f90d3f68db720bd6d0deda8cc0030339ccd43858/Objects/typeobject.c#L2871). I've cross posted in cpython side as unsure whether this tensorflow bug or cpython bug.", "FYI folks @AlexWaygood over on typing-extensions thinks this is an issue with wrapt, from https://github.com/python/typing_extensions/issues/216#issuecomment-1574225352 here's @AlexWaygood's message:\r\n\r\nLooks to me like it's probably an issue with wrapt rather than typing_extensions or TensorFlow, actually. We discussed this a bit in https://github.com/python/cpython/issues/105134.\r\n\r\nWrapt raises TypeError if you do object.__getattribute__(<some_wrapt_ObjectProxy_instance, '__dict__'). I know of no way to construct a class using pure Python that has this behaviour (wrapt achieves this behaviour by using a C extension), and it breaks some fundamental assumptions that the stdlib function inspect.getattr_static makes. That, in turn, breaks typing_extensions.", "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 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 is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "A consequence of this is that Tensorflow is not compatible with Pydantic V2 due to the restraints on typing_extensions\r\nIs there any roadmap to loosen this?", "The root issue comes more from wrapt then tensorflow and is this [one](https://github.com/GrahamDumpleton/wrapt/issues/231). I think downgrading wrapt version should be enough to work around this until that issue is closed. Specifically I think only wrapt 1.15 (or maybe 1.14+) causes an issue while tensorflow requirement is >1.11. Add an upper bound and we should be fine.\r\n\r\nThis is not ideal long term fix and that is still resolve wrapt issue (or remove wrapt dependency).\r\n\r\nSecond solution is set `WRAPT_DISABLE_EXTENSIONS=true`. wrapt has both c extension and pure python implementation. The former causes the error, while latter works fine.", "Is anything being done to loosen the upper bound on typing-extensions here? This continues to cause issues on macOS.\r\n\r\nI have a Pipfile which includes `pydantic`, `pydantic-settings`, and `tensorflow` as packages. Locking packages with a pre-release flag using pipenv works fine with pre-release tag `2.13.0-rc1`. However I cannot include tensorflow in this project without using the pre-release. Here is the output from pipenv when manually installing the latest pydantic-settings, which indicates that `tensorflow-macos` is the problem which seems to be related to the upperbound of typing-extensions versions here. Everything installs just fine on a Windows OS with the most recent versions of packages and without a pre-release flag. \r\n\r\n```\r\n% pipenv run pip show pydantic-settings \r\n\r\nName: pydantic-settings\r\nVersion: 1.99\r\nSummary: Settings management using Pydantic\r\nHome-page: \r\nAuthor: \r\nAuthor-email: Samuel Colvin <[email protected]>, Eric Jolibois <[email protected]>, Hasan Ramezani <[email protected]>\r\nLicense: The MIT License (MIT)\r\n\r\nCopyright (c) 2022 Samuel Colvin and other contributors\r\n\r\nPermission is hereby granted, free of charge, to any person obtaining a copy\r\nof this software and associated documentation files (the \"Software\"), to deal\r\nin the Software without restriction, including without limitation the rights\r\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\ncopies of the Software, and to permit persons to whom the Software is\r\nfurnished to do so, subject to the following conditions:\r\n\r\nThe above copyright notice and this permission notice shall be included in all\r\ncopies or substantial portions of the Software.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\nSOFTWARE.\r\nLocation: /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages\r\nRequires: pydantic, python-dotenv\r\nRequired-by: \r\n```\r\n\r\n```\r\n% pipenv run pip install -v pydantic-settings==2.0.2\r\n\r\nUsing pip 23.2.1 from /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pip (python 3.11)\r\nCollecting pydantic-settings==2.0.2\r\n Obtaining dependency information for pydantic-settings==2.0.2 from https://files.pythonhosted.org/packages/9c/3e/3311eeab406db6116e7f2d4ed9b05c2811282143efa55feb12cc513a8b84/pydantic_settings-2.0.2-py3-none-any.whl.metadata\r\n Downloading pydantic_settings-2.0.2-py3-none-any.whl.metadata (2.9 kB)\r\nCollecting pydantic>=2.0.1 (from pydantic-settings==2.0.2)\r\n Obtaining dependency information for pydantic>=2.0.1 from https://files.pythonhosted.org/packages/87/80/52770e747e4bee5012e60b2684db36c8fdf010f8dadb4ded0efec808b07d/pydantic-2.1.1-py3-none-any.whl.metadata\r\n Downloading pydantic-2.1.1-py3-none-any.whl.metadata (136 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 136.5/136.5 kB 3.1 MB/s eta 0:00:00\r\nRequirement already satisfied: python-dotenv>=0.21.0 in /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages (from pydantic-settings==2.0.2) (1.0.0)\r\nRequirement already satisfied: annotated-types>=0.4.0 in /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages (from pydantic>=2.0.1->pydantic-settings==2.0.2) (0.5.0)\r\nCollecting pydantic-core==2.4.0 (from pydantic>=2.0.1->pydantic-settings==2.0.2)\r\n Obtaining dependency information for pydantic-core==2.4.0 from https://files.pythonhosted.org/packages/32/68/324013cf826ad1f09bce46b01259c2d88607d137e297d213fe7ef225a91f/pydantic_core-2.4.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\r\n Downloading pydantic_core-2.4.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (6.5 kB)\r\nCollecting typing-extensions>=4.6.1 (from pydantic>=2.0.1->pydantic-settings==2.0.2)\r\n Obtaining dependency information for typing-extensions>=4.6.1 from https://files.pythonhosted.org/packages/ec/6b/63cc3df74987c36fe26157ee12e09e8f9db4de771e0f3404263117e75b95/typing_extensions-4.7.1-py3-none-any.whl.metadata\r\n Downloading typing_extensions-4.7.1-py3-none-any.whl.metadata (3.1 kB)\r\nDownloading pydantic_settings-2.0.2-py3-none-any.whl (11 kB)\r\nDownloading pydantic-2.1.1-py3-none-any.whl (370 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 370.9/370.9 kB 11.4 MB/s eta 0:00:00\r\nDownloading pydantic_core-2.4.0-cp311-cp311-macosx_11_0_arm64.whl (1.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 31.1 MB/s eta 0:00:00\r\nDownloading typing_extensions-4.7.1-py3-none-any.whl (33 kB)\r\nInstalling collected packages: typing-extensions, pydantic-core, pydantic, pydantic-settings\r\n Attempting uninstall: typing-extensions\r\n Found existing installation: typing_extensions 4.5.0\r\n Uninstalling typing_extensions-4.5.0:\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/__pycache__/typing_extensions.cpython-311.pyc\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/typing_extensions-4.5.0.dist-info/\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/typing_extensions.py\r\n Successfully uninstalled typing_extensions-4.5.0\r\n Attempting uninstall: pydantic-core\r\n Found existing installation: pydantic_core 0.25.0\r\n Uninstalling pydantic_core-0.25.0:\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic_core-0.25.0.dist-info/\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic_core/\r\n Successfully uninstalled pydantic_core-0.25.0\r\n Attempting uninstall: pydantic\r\n Found existing installation: pydantic 2.0a3\r\n Uninstalling pydantic-2.0a3:\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic-2.0a3.dist-info/\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic/\r\n Successfully uninstalled pydantic-2.0a3\r\n Attempting uninstall: pydantic-settings\r\n Found existing installation: pydantic-settings 1.99\r\n Uninstalling pydantic-settings-1.99:\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic_settings-1.99.dist-info/\r\n Removing file or directory /Users/user/.local/share/virtualenvs/app-B-pslT81/lib/python3.11/site-packages/pydantic_settings/\r\n Successfully uninstalled pydantic-settings-1.99\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ntensorflow-macos 2.13.0 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.7.1 which is incompatible.\r\nSuccessfully installed pydantic-2.1.1 pydantic-core-2.4.0 pydantic-settings-2.0.2 typing-extensions-4.7.1\r\n```", "@tommyzieba You can see from https://github.com/tensorflow/tensorflow/pull/61387 that there is no longer an upper limit on typing-extensions in git HEAD and that this will be picked up by the next release of TensorFlow which will be 2.14.0.", "Is this issue still active? It looks like it is about to cause an issue for [keras-core](https://github.com/keras-team/keras-core) with the 2.14 release, probably other things affected as well.\r\n\r\nhttps://colab.research.google.com/gist/mattdangerw/019d1ebcec746d6f7424248911cf16ae/tf-2-14-bug.ipynb\r\n\r\nMore directly, with keras out of the picture, I think the minimal repro below will cause failures in the tensorflow 2.14 docker images (which have a more recent `typing_extensions`).\r\n\r\n```\r\nfrom tensorflow.python.trackable.data_structures import _DictWrapper\r\n\r\nt = _DictWrapper()\r\nobject.__getattribute__(t, \"__dict__\")\r\n```" ]

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[ "Hi @0-chan-kor, I believe the answer is in the comments actually:\r\n\r\nin https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/toco/graph_transformations/convert_squeeze_to_reshape.cc\r\n```\r\n// Replaces a tf.squeeze operator with a reshape.\r\n// Squeeze removes dimensions == 1 (if in the list of squeeze_dims). This\r\n// means that the data layout will never change with this op, just the shape.\r\n// By converting these to reshapes once we have run shape propagation we allow <--\r\n// standard reshape optimization transforms to do their magic. <--\r\n::tensorflow::Status ConvertSqueezeToReshape::Run(Model* model,\r\n std::size_t op_index,\r\n bool* modified) {\r\n```\r\n\r\nTo paraphrase, \"we are converting them to reshapes so that we can use standard reshape optimization transforms\"\r\n\r\nPlease close if this satiates your curiosity", "The behavior you mentioned, where tf.squeeze() is converted to a reshape operator when using the TOCO (TensorFlow Lite Optimizing Converter) tool, is related to the optimization process performed by TOCO.\r\n\r\nTOCO aims to optimize TensorFlow models for deployment on mobile and embedded devices using TensorFlow Lite. During the conversion process, TOCO applies a set of optimizations to reduce the model size, improve inference speed, and ensure compatibility with the target platform.\r\n\r\nWhen TOCO encounters a tf.squeeze() operation during the conversion, it analyzes the operation and its inputs to determine if the operation can be equivalently represented using a reshape operation. In many cases, the removal of singleton dimensions can be achieved through a reshape operation without changing the behavior of the model.\r\n\r\nBy converting tf.squeeze() to a reshape operator, TOCO can potentially simplify the resulting model graph and eliminate the need for explicit squeeze operations, leading to improved performance or reduced memory requirements in the optimized TensorFlow Lite model.\r\n\r\nIt's important to note that the conversion behavior may vary depending on the specific versions of TensorFlow and TOCO being used. The optimization strategies employed by TOCO can evolve over time as new versions are released.", "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/60686\">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/60686\">No</a>\n" ]

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[ "@FaisalQarah,\r\nAs mentioned above I tried to execute the code on tensorflow v2.12 GPU, https://keras.io/guides/distributed_training/ and it was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/7017286ec14e573b4d02a99e7be69bbc/distributed_training.ipynb).\r\n\r\nAlso I request to try to run the basic model and test whether it is executing as expected or not and moreover it doesn't look like the tensorflow issue. Thank you!", "keras code worked (it didn't stuck before training like last time??) and it shows the number of devices is 2. but when i execute the code, nvidia-smi shows that only the the first gpu is being utilized. Also, after the code is finished execution, it doesn't free the gpus vram from data.\r\nWith bert code, when i change the number of gpus flag to 2 it shows that both gpus are 100% utilized but it gets stuck before starting the training at the messages:\r\nINFO:tensorflow:batch_all_reduce: 134 all-reduces with algorithm = nccl, num_packs = 1\r\nI0525 02:47:56.682721 140068259058752 cross_device_ops.py:897] batch_all_reduce: 134 all-reduces with algorithm = nccl, num_packs = 1\r\nINFO:tensorflow:batch_all_reduce: 134 all-reduces with algorithm = nccl, num_packs = 1\r\nI0525 02:47:58.796215 140068259058752 cross_device_ops.py:897] batch_all_reduce: 134 all-reduces with algorithm = nccl, num_packs = 1\r\nalso, there are this weird green tiles shown on my screen even though im using the cpu for displaying ", "@FaisalQarah,\r\n\r\nGenerally a model needs to be big enough in order to profit from GPU acceleration.\r\n\r\nIf you would like a particular operation to run on a device of your choice, you can use `tf.device `to create a device context. For more details please refer to [Use a GPU](https://www.tensorflow.org/guide/gpu)\r\n\r\nAlso to find out which devices your operations and tensors are assigned to, put [tf.debugging.set_log_device_placement](https://www.tensorflow.org/api_docs/python/tf/debugging/set_log_device_placement) as the first statement of your program.\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/60685\">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/60685\">No</a>\n", "How did you solve it?", "i didn't. I still use each GPU individually. I tried different APIs and\r\napproaches but none of them worked. but on vast.ai i saw people rent their\r\nGPUs and you can see some of them have setup with dual 4090s but not sure\r\nif it work. probably if you use the same docker image that the vast ai\r\nprovide maybe you can use both GPUs with ease. Whenever i need a setup with\r\nmore than 24GB vram, i go and rent an A100-80GB for a couple of weeks and\r\ncontinue my work on my local setup\r\n\r\nOn Mon, Apr 8, 2024 at 11:49 AM JavaZero ***@***.***> wrote:\r\n\r\n> How did you solve it?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/60685#issuecomment-2042203412>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AD23YMYSV44DYSB4S4CUE7LY4JKZRAVCNFSM6AAAAAAYMRKH5OVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANBSGIYDGNBRGI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[ "please help", "i beg\r\n", "Hi @akulthota ,\r\n\r\nIt seems the issue is related to tensorflow models. For models we have separate repo. Can you also post the issue [here](https://github.com/tensorflow/models/issues) where our team can have a look into it.\r\n\r\nCould you please confirm the tensorflow version you have used.Also please check whether protobuf is installed and which version it is.You can find the required packages [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) needed for TF. \r\n\r\nPlease note that 1.x versions are not supported now. Request you to migrate to 2.x versions and preferably latest versions.\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/60684\">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/60684\">No</a>\n" ]

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[ "@branko-fathom,\r\nTensorFlow 2 is fundamentally different from TF1.x in several ways. You can still run unmodified TF1.x code ([except for contrib](https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md)) against TF2 binary installations like so.\r\n\r\nAlso please take a look at this official tf1.x to tf2.x migration document for reference.\r\nhttps://www.tensorflow.org/guide/migrate\r\nhttps://www.tensorflow.org/guide/migrate/migrate_tf2\r\n\r\nThank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60683\">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/60683\">No</a>\n" ]

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[ "@Floppa2003,\r\nI was facing a different issue while executing the mentioned code on tensorflow v2.12. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/994769068436f60d1fa7f990e16190db/untitled1174.ipynb) and provide the dependencies. \r\n\r\nGenerally the error typically indicates that the system is not in state to execute the operation and requires preconditions to be met before successfully executing current operation.\r\nhttps://www.tensorflow.org/api_docs/python/tf/errors/FailedPreconditionError\r\n\r\n\r\nThank you!", "> @Floppa2003, I was facing a different issue while executing the mentioned code on tensorflow v2.12. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/994769068436f60d1fa7f990e16190db/untitled1174.ipynb) and provide the dependencies. Thank you!\r\n\r\nSorry, I solved the problem already. Turns out that tensorflow doesn't support non-english letters in directory paths.", "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/60682\">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/60682\">No</a>\n" ]

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[]

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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/60680/checks?check_run_id=13702908276) 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.", "can anyone tell me how many days it will take to merge my pull request.\r\nif i did anything wrong please let me know. I am new to open source. kindly help", "First, this PR has been open for only 19 hours, so https://github.com/tensorflow/tensorflow/pull/60680#issuecomment-1560429577 is early. It mentions \"days\" but it was sent when PR was only 9 hours old.\r\n\r\nSecond, is there an issue this PR tries to fix? I think the badge is displaying correctly.\r\n\r\nThird, please consult [CODE_OF_CONDUCT.md](https://github.com/tensorflow/tensorflow/blob/master/CODE_OF_CONDUCT.md) and [CONTRIBUTING.md](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)\r\n\r\nFinally, because this PR is not bringing anything useful, we will just close it.\r\n\r\n" ]

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[ "Hi @elfringham ,\r\n\r\nI have tested the build on Ubuntu Aarch64 VM and the build failed.Logs are attached below.\r\n\r\n[60679_logs(Aarch build).txt](https://github.com/tensorflow/tensorflow/files/11578566/60679_logs.Aarch.build.txt)\r\n", "@nitins17 , @TensorFlow-MKL \r\n CC- @learning-to-play ", "@SuryanarayanaY Your build environment was not valid which is why it failed. You cannot use a generic Ubuntu AARCH64 VM." ]

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[ "Hi @LukeBoyer Can you please review this PR ? Thank you!\r\n", "looks good to me" ]

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[]

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[ "@dv-ai,\r\nI tried to execute the code on tensorflow v2.12 & tf-nightly, and it was executed without an infinite loop as mentioned. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/ddea7e0534f153e96e81eebbeb18cc58/untitled1173.ipynb) and also please have a look at an issue with the similar error which is under the developer priority list. Thank you!\r\nhttps://github.com/tensorflow/tensorflow/issues/57343", "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/60676\">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/60676\">No</a>\n" ]

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[ "@dv-ai,\r\nI tried to execute the code on tensorflow v2.12 & tf-nightly, and it was executed without an infinite loop as mentioned. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/ddea7e0534f153e96e81eebbeb18cc58/untitled1173.ipynb) and also please have a look at an issue with the similar error which is under the developer priority list. Thank you!\r\nhttps://github.com/tensorflow/tensorflow/issues/57343", "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/60675\">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/60675\">No</a>\n" ]

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[ "Hi @MrAchraf10 ,\r\n\r\nRequest you to fill the all details mentioned in template. Also I see there is no code snippet to debug the issue. \r\n\r\nI see the error arises during model.fit, which indicates may be the inputs or labels you are feeding to the model might not be compatible which model was expecting.There are also other probable reasons. But without the code snippet we can't confirm the root cause. Request you to provide same for looking into this issue.\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/60674\">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/60674\">No</a>\n" ]

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[ "@sbmenary,\r\nI tried to execute the code on tensorflow 2.12 and tf-nightly on colab, ubuntu and macos. Observed that the code got executed without any errors on both colab and ubuntu environments. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/e6467c53f8e5fee5dec8c2f6bb8a5bd8/untitled1175.ipynb) and below screenshot for the reference\r\n<img width=\"1440\" alt=\"Screenshot 2023-05-24 at 3 29 01 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/e9fa2a30-42c2-49b8-a4a0-9b50879fed9f\">\r\n\r\nBut whereas on tensorflow-macos, it is failing with the mentioned error.\r\n<img width=\"1397\" alt=\"image (11)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/94699f00-ae27-4081-b33f-e5fc0b82b82c\">\r\n\r\nAs it is failing only on tensorflow-macos, we request to raise the concern on the macos-apple [forum](https://developer.apple.com/forums/) for the quick resolution. Thank you!\r\n\r\n", "Hi @tilakrayal, thanks for reproducing the error and your response. Sure - I'm happy to raise this on the macos forum instead. Does this mean that tensorflow-macos is not maintained here but externally, and so I should raise future issues on the apple forum if they are problems with tensorflow-macos and not tensorflow?\r\n\r\nThanks, Ste", "@sbmenary,\r\nYeah, as suggested it would be better, if you raise the issue on apple macos platform. If it is the tensorflow related issue, you can raise the issue here in this repo. As the issue is only on the tensorflow-macos, it is suggested to raise the issue in that forum. 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.", "Okay - thanks for the information - doing this now and happy to close the issue, and will raise future tf-macos problems there as well - thanks for your help!", "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/60673\">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/60673\">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/60672/checks?check_run_id=13691562286) 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.", "Can we use the GitHub action instead of a custom script?", "> Can we use the GitHub action instead of a custom script?\r\nHi @mihaimaruseac, \r\nWe use the stale action workflow to manage stale issues. However, this workflow does not close issues that have received comments from users other than the author after the issue has been marked as stale. This issue [discusses](https://github.com/actions/stale/issues/470) the same problem we are experiencing. So to resolve this problem we are using custom script. Thank You !", "I think you only need to remove `remove-stale-when-updated: false`.\r\n\r\nIf the issue receives comments it should not be marked as stale, there are people that still care about it!", "Thank you for your suggestion @mihaimaruseac. I have made the necessary changes to the workflow accordingly.", "PS: Note that your git config is wrong, your email gets quoted in the commit (https://github.com/tensorflow/tensorflow/pull/60672/commits/6140088e0eb5fd9d759f237493a1bffb490d9026) and that required forcing the CLA to pass.", "Hi @mihaimaruseac, \r\nI have modified my GitHub configuration. I also made a change so that if a user comments, it will also remove the stale dependent labels. This will eliminate the need for Probot to remove the labels.\r\nThank You!" ]

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[ "Can you see what version of portpicker is installed? ", "@MichaelHudgins It should be 1.4.0 for this one as that is what should be installed in the docker container I think. But the log does not show it. https://source.cloud.google.com/results/invocations/3be5d51d-0531-4b39-8813-703e6e84c608/log", "You are right, the place i am thinking of it ended up getting removed from the docker files when the pip tests were changed. Lets try and update and see if it helps. Will give that a try shortly ", "Updated portpicker in https://github.com/tensorflow/tensorflow/commit/948361e05e8bbe0efe9e5f79867d17331f085eaf . The updates have some improvements to the port picking logic so lets see if that helps out. ", "The ARM jobs are now failing due to a permissions issue that was shown up by the update to portpicker. This PR should resolve that https://github.com/tensorflow/tensorflow/pull/60705 so it would be good if that could get merged.", "The above mentioned PR https://github.com/tensorflow/tensorflow/pull/60705 is merged now, could you please let us know if that solves your 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/60671\">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/60671\">No</a>\n" ]

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[ "I tried the build and it's failed. Please refer the attached logs below.\r\n\r\n[60670_logs.txt](https://github.com/tensorflow/tensorflow/files/11589108/60670_logs.txt)\r\n\r\n@TensorFlow-MKL \r\n\r\nCC- @nitins17 , @learning-to-play \r\n", "x86 reproduction log\r\nhttps://source.cloud.google.com/results/invocations/1b8efc29-4d7b-44e2-a66e-56071b03a3a0/log\r\nAARCH64 reproduction log\r\nhttps://github.com/tensorflow/tensorflow/actions/runs/5395781377/jobs/9798627617#step:5:7398", "@rishikasinha-tf flaky test" ]

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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/60669\">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/60669\">No</a>\n" ]