tensorflow/tensorflow summary

[jaykchen]

Ops listed in 'experimental_select_user_tf_ops' not being recognized by tf lite converter https://github.com/tensorflow/tensorflow/issues/61662 Issue: Ops listed in 'experimental_select_user_tf_ops' not being recognized by tf lite converter.

Problem: User is trying to convert a model to Tensorflow Lite using the 'experimental_select_user_tf_ops' flag to include a subset of tf_ops. However, the flag is not working and the specified ops are not recognized by the converter.

Suggested solutions:

1. Enable TF kernels fallback using TF Select as mentioned in the error message.
2. Check if the specified ops ('tf.Mul' and 'tf.RandomUniform') are valid custom ops or flex ops.

3. Review the code and conversion process

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[jaykchen]

ERROR: C:/users/ayush/_bazel_ayush/xv6zejqw/external/llvm_openmp/BUILD.bazel:233:34: Compiling external/llvm_openmp/z_Windows_NT-586_asm.S failed: not all outputs were created or valid https://github.com/tensorflow/tensorflow/issues/61661 The central problem discussed in the issue is that the compilation of the file 'z_Windows_NT-586_asm.S' in the external/llvm_openmp folder is failing, resulting in not all outputs being created or valid. The user mentions that everything was going well until this point.

No specific solutions or suggestions are mentioned in the post.

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[jaykchen]

Error in PredictCost() for the op: op: "CropAndResize" attr https://github.com/tensorflow/tensorflow/issues/61659 The central problem discussed in the issue is that after upgrading from TensorFlow 2.4.1 to 2.13.0, the user is encountering an error message related to the op "CropAndResize" in the PredictCost() function. This error seems to be affecting the performance of the user's model.

The main solution proposed by the user is to remove the tf.function decorator, as doing so eliminates the error message. The user suspects that the issue might be related to tracing, but they are unsure why it only occurs in TensorFlow 2.13.0 and not in 2.4.1.

In summary

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[jaykchen]

Unable to serialize VariableSpec https://github.com/tensorflow/tensorflow/issues/61658 The central problem discussed in the issue is the inability to serialize VariableSpec in TensorFlow 2.13.0. The issue is labeled as a bug. The main solutions proposed or agreed upon are not mentioned in the post. The post lacks specific details and relevant information, making it difficult to provide a concise analysis.

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[jaykchen]

W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:689] Error in PredictCost() for the op: op: "Conv2D" attr { key: "T" value { type: DT_FLOAT } } attr { key: "data_format" value { s: "NCHW" } } attr { key: "dilations" value https://github.com/tensorflow/tensorflow/issues/61656 The central problem discussed in the issue is an error message that occurs while loading a model in TensorFlow 2.7.0. The error message specifically mentions an issue with the op "Conv2D" and attributes such as data format and dilations.

The main solutions proposed or agreed upon are not mentioned in the post. The user provides information about the TensorFlow version, custom code, OS platform, Python version, CUDA/cuDNN version, and GPU model and memory. However, there is no standalone code provided to reproduce the issue, and relevant log outputs are also not provided.

In summary, the issue is about an error occurring while

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[jaykchen]

Compiling src/amalgam/gen/neonfp16arith.c failed: (Exit 70): clang failed: error executing command (from target @XNNPACK//:neonfp16arith_amalgam_microkernels) external/androidndk/ndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang '-D__ANDROID_API__=26' -isystemexternal/androidndk/ndk/sysroot/usr/include/arm-linux-androideabi -target ... (remaining 71 arguments skipped) https://github.com/tensorflow/tensorflow/issues/61655 The central problem discussed in this GitHub issue is that the user is facing a compilation error while building "tensorflow-lite-gpu.aar" using the command "bazel build -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain //tensorflow/lite/java:tensorflow-lite-gpu". The error message suggests that the issue is related to the failure of compiling the file 'neonfp16arith.c' with clang.

The user has provided information about the TensorFlow version (2.13

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[jaykchen]

SIGBUS from libc on Android during inference https://github.com/tensorflow/tensorflow/issues/61654 The central problem discussed in the GitHub issue is a SIGBUS error occurring during inference on Android. The issue involves a TFLite file generated on a Linux server using TensorFlow 2.13.0 and interpreted on an Android device running HarmonyOS. The crash log indicates a failure after conversion.

The main solutions proposed or agreed upon are not explicitly mentioned in the provided content. However, potential solutions to address the issue could involve investigating compatibility issues between the TensorFlow library versions used on the server and the Android client, checking for any platform-specific dependencies or configurations, and debugging the code to identify any potential memory access violations or other errors.

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[jaykchen]

Check failed when running tensorflow.python.ops.gen_nn_ops.max_pool_grad_with_argmax https://github.com/tensorflow/tensorflow/issues/61653 The central problem discussed in the issue is a check failure when running the TensorFlow operation "max_pool_grad_with_argmax". The issue is reproducible with TensorFlow Nightly and occurs with a specific input combination. The main solutions proposed or agreed upon are not mentioned in the provided information.

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[jaykchen]

Check failed when running tensorflow.python.ops.gen_nn_ops.max_pool_grad_with_argmax (colab+local) https://github.com/tensorflow/tensorflow/issues/61652 The central problem discussed in the issue is a check failure when running the 'max_pool_grad_with_argmax' operation in TensorFlow 2.13. The issue seems to be caused by a negative large input tensor. No specific solutions are mentioned in the post, but potential fixes could include handling negative large inputs or updating TensorFlow to a newer version.

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[jaykchen]

Tensorflow on Manjaro https://github.com/tensorflow/tensorflow/issues/61651 The central problem discussed in the issue is how to set up and configure TensorFlow on the Manjaro platform. The user has confirmed that they have reproduced the bug with TensorFlow Nightly. The main solution proposed is to follow the steps mentioned in the TensorFlow documentation for setting up the TF and installing the build tools to configure the development environment.

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[jaykchen]

Activation function of a Dense hidden layer not getting invoked. https://github.com/tensorflow/tensorflow/issues/61650 The central problem discussed in the issue is that the activation function (relu) of the encoder layer in an auto-encoder setup is not getting invoked. The user expected the final decoder layer to output values of [0.5, 0.5, 0.5], but this was not the case. The main solution proposed by the user was to downgrade TensorFlow from version 2.13.0 to 2.12.0, which resolved the issue and produced the expected output.

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[jaykchen]

GPU error Tensorflow with 2.1 https://github.com/tensorflow/tensorflow/issues/61649 The central problem discussed in the issue is a ptax error with TensorFlow 2.1 when running a 3D U-net algorithm using the tensorflow-large-model-support. The user is using a Nvidia A100 GPU with 40GB memory. The main solution proposed is to install the right version of CUDA from the official site and use the latest TensorFlow version, as TF 2.1 is an older version and may not receive bug fixes.

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[jaykchen]

Cannot build tensorflow from source https://github.com/tensorflow/tensorflow/issues/61648 The central problem discussed in this issue is that the user is unable to build TensorFlow from source on Debian 12. They have tried multiple times but encountered the same error. The user has searched on Google for a solution but found nothing relevant. The main solutions proposed or agreed upon in the issue are not mentioned in the given information.

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[jaykchen]

docs: spurious favicon in template https://github.com/tensorflow/tensorflow/issues/61647 The central problem discussed in the issue is that the Tensorflow documentation pages contain a spurious favicon link to an image that doesn't exist. This is not causing immediate problems but should be cleaned up. The main solution proposed is to remove the spurious favicon link.

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[jaykchen]

Check failure when running tf.config.experimental_connect_to_host https://github.com/tensorflow/tensorflow/issues/61646 Issue: Check failure when running tf.config.experimental_connect_to_host. The error occurs due to feeding NaN input argument.

Solutions proposed:

1. Rebuild TensorFlow with appropriate compiler flags to enable AVX2 FMA instructions.
2. Install TensorRT to resolve the warning related to TF-TRT.
3. Investigate the negative NUMA node values from SysFS.
4. Check compatibility of CUDA/cuDNN version with the GPU model.
5. Verify the correctness of the host-port pair when using tf.config.experimental_connect_to_host.

Summary: The issue involves a check failure caused by feeding NaN input. Proposed solutions include rebuilding

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[jaykchen]

CKPT to TFLite https://github.com/tensorflow/tensorflow/issues/61645 The central problem discussed in the issue is how to convert a .ckpt file to TF Lite when there is no meta-file present. The main solution proposed is to convert the .ckpt file to SavedModel format using tf.saved_model.save, then use tf.lite.TFLiteConverter to convert the SavedModel to TFLite format, and finally save the TFLite model to a .tflite file. Another suggestion is to use concrete functions to convert the checkpoints into a TFLite model.

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[jaykchen]

tf.image.crop_to_bounding_box() assumes tf.int32 arguments, but not documented as such https://github.com/tensorflow/tensorflow/issues/61644 The central problem discussed in the issue is that the tf.image.crop_to_bounding_box() function in TensorFlow assumes that the target width and height arguments are of type tf.int32, but this assumption is not documented anywhere. The issue arises from the use of tf.shape() which has a default dtype of tf.int32 in a stack operation.

The main solution proposed is to update the documentation of tf.image.crop_to_bounding_box() to explicitly state that the target width and height arguments should be of type tf.int32. This would clarify the expected input type for users and prevent any confusion or

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[jaykchen]

Check failure when running tf.keras.layers.RepeatVector https://github.com/tensorflow/tensorflow/issues/61643 The central problem discussed in the issue is a check failure when running tf.keras.layers.RepeatVector due to a very large integer variable as input to the API. The user's session on colab is crashing because of this check failure. The main solution proposed is for the user to report the issue in the proper channel mentioned in the TensorFlow repository.

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[jaykchen]

Check failure when running tf.compat.v1.layers.MaxPooling1D https://github.com/tensorflow/tensorflow/issues/61642 The central problem discussed in the GitHub issue is a check failure when running the tf.compat.v1.layers.MaxPooling1D function in TensorFlow version 2.13. The issue seems to be related to feeding large list elements, causing the session to crash.

The main solutions proposed in the issue include testing the code with TensorFlow versions 2.12 and tf-nightly, which reportedly work fine without any exceptions raised. However, the user who opened the issue states that their session on Colab is still crashing even with TensorFlow version 2.13.

There doesn't seem to be a clear resolution or agreement on the solution to the

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[jaykchen]

Softmax overflow issue with a large tensor https://github.com/tensorflow/tensorflow/issues/61639 The central problem discussed in the issue is an overflow issue with the Softmax function when applied to a large tensor. The error message suggests an invalid configuration argument.

The main solution proposed or agreed upon is not explicitly mentioned in the provided information. However, user 'yufang67' commented that the issue is related to another GitHub issue (#60330), indicating that the problem might be a known issue and being addressed in that thread.

In summary, the issue revolves around a Softmax overflow problem with a large tensor, and it appears to be related to a known issue being discussed in another GitHub thread.

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[jaykchen]

external/XNNPACK/src/qs8-igemm/gen/4x16c8-minmax-avx512skx.c:242:15: error: implicit declaration of function '_kshiftri_mask64' is invalid in C99 https://github.com/tensorflow/tensorflow/issues/61635 The central problem discussed in the issue is an error in the C++ compilation of the XNNPACK library. The error is related to an implicit declaration of the function '_kshiftri_mask64', which is invalid in C99. The main solution proposed or sought after is to find a way to resolve this error and successfully build the target.

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[jaykchen]

libtensorflowlite_jni.so (offset 0x2f3000):signal 11 (SIGSEGV), code 2 (SEGV_ACCERR), fault addr 0xa59fdbc0 https://github.com/tensorflow/tensorflow/issues/61633 The central problem discussed in the issue is a crash in the process caused by the libtensorflowlite_jni.so library. The crash is signaled by a SEGV_ACCERR error with a specific fault address. The user has reproduced the bug with TensorFlow Nightly and has provided the relevant information about the TensorFlow version, OS platform, and device.

The main solution proposed is to upgrade to the latest TensorFlow version, as older versions are not actively supported. Additionally, the user 'sushreebarsa' suggests that keeping extra lines in label.txt could also be a potential issue for the crash.

In summary, the issue revolves around a crash caused

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[jaykchen]

Colab session crashes for unknown reasons when when running tf.raw_ops.ResizeBilinear on colab https://github.com/tensorflow/tensorflow/issues/61618 The central problem discussed in the issue is that a Colab session crashes when running the tf.raw_ops.ResizeBilinear operation. The issue includes logs indicating memory allocation errors and warnings related to TensorFlow and TensorRT. No specific solutions or agreements are mentioned in the issue.

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[jaykchen]

Overflow bug when running tf.clip_by_value on colab https://github.com/tensorflow/tensorflow/issues/61617 Issue: Overflow bug when running tf.clip_by_value on Colab. TensorFlow version: 2.13.0. Current behavior: Due to a large tensor, an overflow occurs when multiplying two values. No information provided on OS, Python version, and other details. No standalone code provided. The issue seems to be related to memory allocation and optimization. No proposed solutions or agreements mentioned in the issue.

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[jaykchen]

Test_on_batch() gives the same loss output on different batches in a single run https://github.com/tensorflow/tensorflow/issues/61616 The central problem discussed in the GitHub issue is that the test_on_batch() function in Keras is giving the same loss output for different batches in a single run. The user is using the sequential model and has observed that while train_on_batch() gives converging losses, test_on_batch() consistently gives the same loss value for all batches in a single run.

The main solutions proposed or agreed upon in the issue are not explicitly mentioned in the post. However, based on the information provided, potential solutions could involve checking the following:

1. Verify the correctness of the test data and labels used in the test_on_batch() function.

2.                 this result is generated by flows.network. Triggered by @jaykchen

[jaykchen]

Issue with nightly-gpu docker image https://github.com/tensorflow/tensorflow/issues/61607 The central problem discussed in the issue is that when using the tensorflow/tensorflow:nightly-gpu docker image, the user is getting an error stating that the "DNN library is not found". However, when the user changes the base image to tensorflow/tensorflow:latest-gpu, the code works fine. The main suggested solution is to try using TensorFlow v2.13 instead of the nightly version, as it might be unstable.

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[jaykchen]

Crash when running tf.keras.layers.MaxPool2D on colab https://github.com/tensorflow/tensorflow/issues/61605 The central problem discussed in the issue is a crash that occurs when running tf.keras.layers.MaxPool2D on Colab. The user provided code that triggers the crash. One potential solution suggested by another user is to change the input arguments to tuples instead of individual values. However, it is unclear if this solution resolves the issue.

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[jaykchen]

Integer overflow when running tf.compat.v1.matrix_diag on colab https://github.com/tensorflow/tensorflow/issues/61604 Issue: 'Integer overflow when running tf.compat.v1.matrix_diag on colab'

Problem: The issue is that there is an integer overflow when multiplying large elements in the input lists while running the tf.compat.v1.matrix_diag function.

Solutions: The user 'SuryanarayanaY' replicated the behavior using TensorFlow 2.13 and tf-nightly versions and provided code examples for reference. No further solutions or agreements were mentioned in the comments.

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[jaykchen]

Crash when running tf.compat.v1.keras.layers.MaxPool2D on colab https://github.com/tensorflow/tensorflow/issues/61603 The central problem discussed in the issue is that running tf.compat.v1.keras.layers.MaxPool2D on Colab crashes due to large elements in input lists. The main solution proposed is to use TensorFlow v2.13 or tf-nightly, which seems to resolve the issue for some users. However, the issue still persists for others, suggesting that it may be related to calling a CUDA kernel.

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[jaykchen]

Crash when running tf.keras.layers.MaxPooling2D https://github.com/tensorflow/tensorflow/issues/61601 The central problem discussed in the issue is a crash when running tf.keras.layers.MaxPooling2D in TensorFlow version 2.13. The issue occurs due to an overflow error when performing certain calculations.

There are no specific solutions proposed or agreed upon in the issue thread.

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[jaykchen]

Integer overflow when running tf.linalg.diag https://github.com/tensorflow/tensorflow/issues/61600 The central problem discussed in the issue is an integer overflow error when running the tf.linalg.diag function in TensorFlow version 2.13. The error is caused by multiplying large numbers, resulting in an overflow and a negative result.

One potential solution suggested is to upgrade to TensorFlow Nightly, as the user 'SuryanarayanaY' replicated the issue with both TensorFlow 2.13 and the nightly version. The user provided gists for reference.

In summary, the issue revolves around an integer overflow error in tf.linalg.diag in TensorFlow 2.13, and upgrading to TensorFlow Nightly may potentially solve the

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[jaykchen]

AttributeError: module 'tensorflow.python.pywrap_mlir' has no attribute 'experimental_convert_saved_model_v1' https://github.com/tensorflow/tensorflow/issues/61598 The central problem discussed in the issue is an AttributeError in TensorFlow, specifically in module 'tensorflow.python.pywrap_mlir', where the attribute 'experimental_convert_saved_model_v1' is missing. The user suggests that the function name is a typo and proposes a solution by changing the function call to 'experimental_convert_saved_model_v1_to_mlir'.

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[jaykchen]

Could not find a version that satisfies the requirement tensorflow-compression~=2.12.0 (from versions: none) https://github.com/tensorflow/tensorflow/issues/61596 The central problem discussed in the GitHub issue is that the user is unable to find a compatible version of tensorflow-compression (specifically version 2.12.0) for their TensorFlow installation. They tried to install tensorflow-compression from source but encountered an error. One solution suggested by SuryanarayanaY is to import version 2.13 of tensorflow-compression, as binaries are available only from version 2.13 onwards. However, the user encountered an error when trying to install version 2.13. SuryanarayanaY then suggested using "pip install tensorflow-compression" to install the latest

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[jaykchen]

TFLite GPU delegate: Broadcast output incorrect https://github.com/tensorflow/tensorflow/issues/61594 The central problem discussed in the issue is that the TFLite GPU delegate produces incorrect output for a model with a broadcast operation. The issue is confirmed to be reproducible with TensorFlow Nightly version. The user has also provided a TFLite model file and the expected output. The incorrect output is produced only when using the GPU delegate, while the correct output is obtained with the CPU. The user is concerned that this could be a security issue.

One potential solution suggested by a commenter is to be more explicit in restructuring the data for broadcasting on the GPU delegate. They reference a similar issue and suggest checking if it helps. However,

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[jaykchen]

Build: Protobuf fails with "File already exists in database" https://github.com/tensorflow/tensorflow/issues/61593 The central problem discussed in the issue is that when building TensorFlow from source with the TF_SYSTEMLIBS version of protobuf, a "File already exists in database" error occurs. The user traced the issue to a specific import statement in a Python file.

The main solution proposed by another user is to confirm the sequence of steps and ensure that the correct version of protobuf (>=3.20) is installed, as TensorFlow 2.13 supports protobuf >=3.20. It is implied that the user currently has protobuf version 3.19 installed.

In summary, the issue revolves around a protobuf-related error during TensorFlow build, and

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[jaykchen]

Train Simple Audio Recognition - TinyML https://github.com/tensorflow/tensorflow/issues/61590 Issue: The user is encountering errors while trying to train a simple audio recognition model using TensorFlow 2.12.0 in Google Colab. The errors occur during the installation of dependencies and when running the training script.

Potential Solution: Other users suggest that the errors might be due to the code provided in the book being intended for TensorFlow 1.15, which is no longer supported. They recommend migrating the code to TensorFlow 2.x by replacing the deprecated tensorflow.contrib module with TF Slim symbols. They also provide a link to the official migration guide for reference.

Conclusion: The central problem discussed in the issue is the

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[jaykchen]

Crash when running tensorflow.python.ops.nn_ops.fractional_max_pool https://github.com/tensorflow/tensorflow/issues/61580 Issue: User 'dmc1778' reported a crash while running tensorflow.python.ops.nn_ops.fractional_max_pool in TensorFlow 2.10. The crash occurs due to feeding a large integer value.

Solution: User 'Varsha-anjanappa' suggested testing the code with TensorFlow 2.12 and tf-nightly, where the issue is resolved. They also advised reporting security-related issues through the proper channel and provided instructions for patching older versions of TensorFlow.

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[jaykchen]

Abort when running tensorflow.python.ops.nn_ops.pool https://github.com/tensorflow/tensorflow/issues/61579 The central problem discussed in the issue is an abort error occurring when running tensorflow.python.ops.nn_ops.pool. The issue is likely caused by a large list element. The main solution proposed is to rebuild TensorFlow with appropriate compiler flags. Additionally, the user is advised to report potential security vulnerabilities through proper channels and create a patch to resolve such issues.

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[jaykchen]

Abort when running tensorflow.python.ops.nn_ops.max_pool https://github.com/tensorflow/tensorflow/issues/61578 Issue: 'Abort when running tensorflow.python.ops.nn_ops.max_pool'

The central problem discussed in the issue is that when running the 'max_pool' operation in TensorFlow version 2.10, an error occurs due to an invalid list element. The error is specifically related to the 'cudnnSetPoolingNdDescriptor' function in the 'cuda_dnn.cc' file.

Potential solutions proposed or suggested in the issue posts include:

• The user 'SuryanarayanaY' suggests reporting the issue through the proper channel mentioned in the 'SECURITY.md' file.

• The user 'mihaimaruseac'

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[jaykchen]

Crash when running tensorflow.python.eager.context.check_alive https://github.com/tensorflow/tensorflow/issues/61577 The central problem discussed in the GitHub issue is a crash when running tensorflow.python.eager.context.check_alive. The crash is likely caused by an invalid string argument. The user has provided information about their environment, including TensorFlow version, OS platform, Python version, and CUDA/cuDNN version.

One potential solution suggested by user 'sushreebarsa' is to refer to the provided Colab notebook that reproduces the issue with different TensorFlow versions. This can help identify if the crash is specific to certain versions. Additionally, 'sushreebarsa' recommends consulting the TensorFlow security guidelines and reporting the issue through the proper channels if

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[jaykchen]

Crash when running tensorflow.python.ops.gen_image_ops.resize_area https://github.com/tensorflow/tensorflow/issues/61576 The central problem discussed in the issue is a crash that occurs when running the tensorflow.python.ops.gen_image_ops.resize_area function. The crash is caused by a negative large tensor. The main solutions proposed include running the code on a different machine or environment to determine if the issue is specific to the setup, and reporting the issue through proper channels to address potential security vulnerabilities.

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[jaykchen]

Abort when running tensorflow.python.ops.gen_ctc_ops.ctc_loss https://github.com/tensorflow/tensorflow/issues/61575 The central problem discussed in the issue is an abort when running tensorflow.python.ops.gen_ctc_ops.ctc_loss due to a large input tensor. The user has provided details about their environment and the error message. Another user has reproduced the issue and suggested reading the SECURITY.md file for reporting potential security vulnerabilities and creating a patch to resolve the issue.

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[jaykchen]

Crash when running tensorflow.python.framework.kernels.get_registered_kernels_for_op https://github.com/tensorflow/tensorflow/issues/61572 The central problem discussed in the issue is a crash occurring when running tensorflow.python.framework.kernels.get_registered_kernels_for_op. The crash is caused by feeding a None argument. The issue has been reproduced on TensorFlow Nightly version 2.10.0. One potential solution suggested is to make sure that the missing TensorRT libraries are installed properly. A colleague was able to reproduce the issue on Colab using TensorFlow v2.13 and provided a code gist. It is also mentioned that segmentation faults can be considered potential security vulnerabilities and should be reported through the proper channels.

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[jaykchen]

Crash when running tensorflow.python.framework.importer._PopulateTFImportGraphDefOptions https://github.com/tensorflow/tensorflow/issues/61571 The central problem discussed in the issue is a crash occurring when running tensorflow.python.framework.importer._PopulateTFImportGraphDefOptions due to a None argument. The main solution proposed is to follow the patching guidelines mentioned in the TensorFlow README.md to create a custom patch and resolve the issue.

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[jaykchen]

Crash when running tensorflow.python.framework.importer._GatherReturnElements https://github.com/tensorflow/tensorflow/issues/61570 Issue: User 'dmc1778' encountered a crash when running a specific code in TensorFlow version 2.10.0. The crash is caused by a None argument.

Solutions:

• User 'SuryanarayanaY' suggests testing the code with the latest version of TensorFlow (tf-nightly) and reports that it also fails with a segmentation fault. They advise reporting security-related issues through the proper channel mentioned in the SECURITY.md file.

• User 'mihaimaruseac' recommends checking with the latest version of TensorFlow and advises against mentioning security issues in public.

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[jaykchen]

Internal Assertion Failure when running tensorflow.python.eager.remote.connect_to_remote_host https://github.com/tensorflow/tensorflow/issues/61568 The central problem discussed in the issue is an internal assertion failure occurring when running the tensorflow.python.eager.remote.connect_to_remote_host function. The error is triggered by feeding NaN inputs. The user provided information about their TensorFlow version (2.10.0), Python version (3.9), and CUDA/cuDNN version (nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0).

A potential solution suggested by user 'sushreebarsa' is to create a patch to resolve the issue by following the patching guidelines mentioned in the TensorFlow

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[jaykchen]

Crash when running tensorflow.python.ops.gen_list_ops.tensor_list_reserve https://github.com/tensorflow/tensorflow/issues/61563 Issue: User 'dmc1778' reported a bug titled 'Crash when running tensorflow.python.ops.gen_list_ops.tensor_list_reserve' on TensorFlow version 2.10.0. The crash is caused by a large integer value.

Solution: User 'sushreebarsa' reproduced the issue on colab using TensorFlow versions 2.11, 2.13, and tf-nightly. They suggested that segmentation faults, like the one observed, can be potential security vulnerabilities and should be reported through the proper channels mentioned in the SECURITY.md file. They also recommended creating a custom patch to resolve such issues if the user wants

                    this result is generated by flows.network. Triggered by @jaykchen

[jaykchen]

Floating point exception when running tensorflow.python.ops.gen_array_ops.depth_to_space https://github.com/tensorflow/tensorflow/issues/61562 The central problem discussed in the issue is a floating point exception occurring when running the tensorflow.python.ops.gen_array_ops.depth_to_space function. The issue seems to be related to a large integer value. The main solution proposed is to update to TensorFlow version 2.13.0, which dmc1778 mentioned as resolving the issue.

                    this result is generated by flows.network. Triggered by @jaykchen

[jaykchen]

Crash when running tensorflow.python.ops.gen_math_ops._histogram_fixed_width https://github.com/tensorflow/tensorflow/issues/61560 The central problem discussed in the issue is that when running the specific TensorFlow operation tensorflow.python.ops.gen_math_ops._histogram_fixed_width, the code crashes due to negative float values. The user provided information about their TensorFlow version (2.10.0), operating system (Ubuntu 22.04), and CUDA/cuDNN version (nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0).

One potential solution suggested by the user 'SuryanarayanaY' is to test the code with TensorFlow Nightly, which they did and encountered a

                    this result is generated by flows.network. Triggered by @jaykchen

[jaykchen]

Crash when running tensorflow.python.ops.gen_data_flow_ops.record_input https://github.com/tensorflow/tensorflow/issues/61558 The central problem discussed in the issue is a crash occurring when running tensorflow.python.ops.gen_data_flow_ops.record_input due to very large integer values. The main solution proposed is to test with the latest version of TensorFlow (2.13.0) and report the issue through the proper channels outlined in the SECURITY.md file, as segmentation faults can be considered potential security vulnerabilities.

                    this result is generated by flows.network. Triggered by @jaykchen