Weekly GitHub Report for Tensorflow: November 15, 2024 - November 22, 2024
Weekly GitHub Report for Tensorflow
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Table of Contents
I. News
1.1 Recent Version Releases:
The current version of this repository is v2.18.0
1.2 Other Noteworthy Updates:
II. Issues
2.1 Top 5 Active Issues:
We consider active issues to be issues that that have been commented on most frequently within the last week.
- Tensorflow Import Not working: This issue involves a user experiencing difficulties importing TensorFlow on a Windows 11 system, despite following various troubleshooting steps such as reinstalling Python and TensorFlow, and trying different Python versions. The user consistently encounters a DLL load failure error, indicating a problem with the dynamic link library initialization when attempting to import TensorFlow.
- The comment section reveals a series of troubleshooting attempts, including suggestions to use different TensorFlow versions, creating new virtual environments, and reinstalling packages. Despite these efforts, the user continues to face the same import error, leading to discussions about potential machine-specific issues, such as processor compatibility. The conversation also includes requests for sharing the user's environment setup to further diagnose the problem, with the user eventually sharing a zip file of their virtual environment for further investigation.
- Number of comments this week: None
- GPU MaxPool gradient ops do not yet have a deterministic XLA implementation: This issue is about the lack of a deterministic XLA implementation for GPU MaxPool gradient operations in TensorFlow, which causes a runtime exception when attempting to use deterministic settings with MaxPooling2D. The problem persists across different versions of TensorFlow and Keras, and users are seeking a solution to achieve reproducible results without encountering this error.
- The comments discuss the need for a reproducible code example, with multiple users reporting the same error when using deterministic settings in TensorFlow. Suggestions include disabling XLA by setting
jit_compile=Falsein the compile method, which resolves the issue for some users. However, others encounter additional errors, such asAttributeError, when attempting to implement this solution. The conversation includes clarifications on where to apply thejit_compile=Falsesetting, ultimately leading to successful resolution for some participants.- Number of comments this week: None
- JIT compliation failed: This issue is about a bug in TensorFlow 2.16.1 where JIT compilation fails when running custom code on a GPU, specifically a Radeon 7900XT, while it works fine on a CPU. The problem seems to be related to the use of ROCm, as the error does not occur when running the code in environments like Colab that do not use ROCm.
- The comments discuss troubleshooting steps and attempts to replicate the issue, with suggestions to provide a Colab gist for easier debugging. The user reports that the issue persists even with simple TensorFlow code from the ROCm configuration guide, but running the code via the terminal works, suggesting a potential issue with PyCharm's run configuration. Another user confirms a similar problem with a different setup, and there is a suggestion to try newer TensorFlow versions, though the user notes compatibility limitations with their current ROCm version.
- Number of comments this week: None
-
TensorFlow Stable Delegate Python API: This issue is about the lack of support for running stable delegates using the TensorFlow Python API, as the user is seeking workarounds or future plans for this feature. The user has provided a code snippet to illustrate the problem and is looking for guidance on whether the feature will be supported in the future.
- The comments discuss the current lack of support for stable delegates in the TensorFlow Python API, with references to the C++ API as an alternative. Various contributors confirm the absence of support and mention that the stable delegate API is no longer experimental but is only available for certain languages. There is a suggestion that adding support for the Python API would be beneficial, and one contributor expresses interest in contributing to this development.
- Number of comments this week: None
-
tflite int8 export is twice as large as saved_model.pb: This issue is about a discrepancy in file size when exporting a TensorFlow Lite model in int8 format, which results in a file that is twice as large as the original saved_model.pb. The user provides system information, code snippets, and a screenshot to illustrate the problem, seeking assistance to understand and resolve the size difference.
- The comments involve a request for additional resources to replicate the issue, including the saved model and a Google Colab notebook. The user responds by sharing links to the necessary files and code, but access issues arise, prompting a request for permission adjustments, which the user then resolves by setting the file access to anyone with the link.
- Number of comments this week: None
2.2 Top 5 Stale Issues:
We consider stale issues to be issues that has had no activity within the last 30 days. The team should work together to get these issues resolved and closed as soon as possible.
As of our latest update, there are no stale issues for the project this week.
2.3 Open Issues
This section lists, groups, and then summarizes issues that were created within the last week in the repository.
Issues Opened This Week: 27
Summarized Issues:
- ImportError and DLL Load Failures: Users are experiencing ImportErrors related to DLL load failures when running TensorFlow on Python 3.11, which may be due to version incompatibilities. These issues suggest checking version compatibility and providing more information about the OS platform for effective troubleshooting. The problems persist even after downgrading Python versions and following installation instructions.
- TensorFlow Lite Compilation and Installation Issues: Users are facing challenges with TensorFlow Lite, including installation failures and compilation problems due to incorrect regular expressions in CMakeLists.txt. These issues lead to linking problems and prevent the use of certain functions, such as evaluating a BERT QA model. Users are advised to check for updates and known issues for solutions.
- Build and Compilation Failures on Specific Platforms: There are reports of build failures when compiling TensorFlow with CUDA support on specific hardware, such as Jetson Orin Nano. These failures occur despite trying different compilers and are attributed to version compatibility issues. Users are encouraged to refer to related known issues for potential solutions.
- TensorFlow Java and Model Loading Errors: A user encountered an error when loading a SavedModelBundle in Java, despite the model working in Python. This suggests potential issues with variable initialization or deletion in the TensorFlow Java environment. The error message indicates a missing variable, which requires further investigation.
- TensorBoard and Performance Discrepancies: Users have reported discrepancies in TensorBoard's Trace Viewer, with blank waiting times on ARM machines compared to continuous computations on x86 machines. These differences may be due to asynchronous and synchronous computation variations between oneDNN and the default library. Understanding and resolving these discrepancies is crucial for accurate performance analysis.
- Documentation and Usability Improvements: There is a need to enhance TensorFlow's installation documentation for Windows, focusing on troubleshooting PATH conflicts and managing CUDA and cuDNN version mismatches. Additionally, improving code readability in Keras models by breaking down large functions is suggested to aid new users in navigating the setup process.
- Bugs and Crashes in TensorFlow Operations: Several bugs in TensorFlow version 2.17 cause crashes with "Aborted (core dumped)" errors when operations like
MatrixDeterminant,MatrixInverse, andTridiagonalSolveare executed with empty inputs on a GPU. These issues persist even with TensorFlow Nightly, indicating a need for bug fixes.
- Performance Issues on TPUs: The
tf.gatherfunction is significantly slower on TPUs compared to GPUs during DeBERTa model training. Users are seeking solutions to enhance the performance of thetf.one_hotandtf.einsumworkaround to improve training efficiency on TPUs. Addressing these performance issues is critical for optimizing model training.
- TensorFlow Lite Model Size Discrepancies: A user reported that a TensorFlow Lite int8 model is unexpectedly twice as large as the original saved_model.pb. Understanding and resolving this discrepancy is important for efficient model deployment and storage management.
- Documentation and Implementation Discrepancies: There are discrepancies between TensorFlow documentation and actual implementation, such as the
tfl.quantizeoperation's support for QI4 data types and thenum_threadssetting in TensorFlow Lite Interpreter. These inconsistencies can lead to unexpected behavior and require clarification.
- Compiler Warnings and Build Issues: A compiler warning in TensorFlow Lite's
graph_info.hfile due to a multi-line comment can be resolved by modifying the comment style. Additionally, users face difficulties in building and preloading thetfrt_sessioncomponent due to missing symbols, requiring guidance on correct compilation.
- GPU Detection and Compatibility Issues: Users are experiencing difficulties with TensorFlow not detecting a GPU when using the
tensorflow/tensorflow:latest-gpuDocker image on Ubuntu 20.04. These issues are related to CUDA compatibility and version mismatches, which need to be addressed for successful GPU utilization.
2.4 Closed Issues
This section lists, groups, and then summarizes issues that were closed within the last week in the repository. This section also links the associated pull requests if applicable.
Issues Closed This Week: 8
Summarized Issues:
- GPU Utilization and Compatibility Issues: Users face challenges in utilizing GPUs for TensorFlow models due to compatibility issues with Bazel and TensorFlow versions. Despite having a GPU available, models default to CPU usage, causing inefficiencies. These issues often require troubleshooting and adjustments to software configurations to resolve.
- TensorFlow Version Compatibility: Installing additional TensorFlow packages can lead to version rollbacks due to dependency restrictions. Users must ensure compatibility between TensorFlow and its extensions to maintain functionality. This often involves managing package versions carefully to avoid conflicts.
- TensorFlow Lite and Flex Delegate Issues: Users inquire about TensorFlow Lite's capabilities, such as int8 quantization and enabling Flex delegates. These inquiries often involve understanding the limitations and available APIs for specific functionalities. Proper guidance and code examples are necessary to implement these features effectively.
- TensorFlow Convolution and Algorithm Support: Bugs in TensorFlow can cause convolution operations to fail under specific conditions, such as certain batch sizes and feature group counts. These failures are often due to unsupported algorithms in cuDNN, requiring users to adjust parameters to avoid crashes. Understanding these limitations is crucial for successful model execution.
2.5 Issue Discussion Insights
This section will analyze the tone and sentiment of discussions within this project's open and closed issues that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.
Based on our analysis, there are no instances of toxic discussions in the project's open issues from the past week.
III. Pull Requests
3.1 Open Pull Requests
This section lists and summarizes pull requests that were created within the last week in the repository.
Pull Requests Opened This Week: 4
Pull Requests:
- Documentation Improvements: Several pull requests focus on enhancing the documentation of the TensorFlow project. One pull request adds code examples with outputs to the docstrings of all functions in
tf.queue.FIFOQueue, aiming to clarify their usage and address user confusion. Another pull request corrects typographical errors in the documentation strings, ensuring that the documentation is clear and free of mistakes. These efforts collectively improve the readability and usability of the TensorFlow documentation for developers.
- Build System Enhancements: A pull request proposes changes to the CMakeLists.txt file to refine the regular expression pattern used for filename matching. This modification aims to prevent unintended directory name matches that can cause issues with file compilation and library linking. By ensuring strict filename matching, the pull request addresses potential build errors, especially in directories with complex names.
- Compiler Warning Fixes: A pull request addresses a specific compiler warning issue in the
graph_info.hfile of TensorFlow Lite. The issue arises from multi-line comments that trigger warnings when compiling with GCC 11.3.0. By modifying these comments, the pull request ensures smoother compilation processes, particularly for users compiling the library for embedded devices.
3.2 Closed Pull Requests
This section lists and summarizes pull requests that were closed within the last week in the repository. Similar pull requests are grouped, and associated commits are linked if applicable.
Pull Requests Closed This Week: 5
Summarized Pull Requests:
- Cherrypick Operations in TensorFlow: Several pull requests involve cherrypick operations to integrate changes from previous commits into the TensorFlow project. One such operation includes merging an upgrade patch for the curl library, ensuring the project remains up-to-date with security and functionality improvements. Another cherrypick adds a deletion warning to the
tf.lite.interpreter, guiding users towards theai-edge-litert.interpreterfor future use.
- ROCm Dependency Management for CI: A pull request focuses on implementing a hermetic ROCm dependency baseline for TensorFlow's continuous integration system. This involves addressing issues like divergence between local and upstream XLA, enabling ROCm nightly builds, and updating ROCm versions. The changes also include fixing build errors and managing test configurations to ensure smooth integration and testing processes.
- Documentation Hyperlink Fixes: Two pull requests address broken hyperlink issues in TensorFlow's documentation. One fixes a non-functional link in the TensorFlow Lite documentation, specifically for the Model Maker's question-answering model. The other updates a broken link in the
jax_conversion/overview.mddocumentation to the correct Orbax Export page URL, ensuring users have access to accurate resources.
3.3 Pull Request Discussion Insights
This section will analyze the tone and sentiment of discussions within this project's open and closed pull requests that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.
Based on our analysis, there are no instances of toxic discussions in the project's open pull requests from the past week.
IV. Contributors
4.1 Contributors
Active Contributors:
We consider an active contributor in this project to be any contributor who has made at least 1 commit, opened at least 1 issue, created at least 1 pull request, or made more than 2 comments in the last month.
| Contributor | Commits | Pull Requests | Issues | Comments |
|---|---|---|---|---|
| A. Unique TensorFlower | 91 | 0 | 0 | 0 |
| gaikwadrahul8 | 12 | 10 | 0 | 49 |
| tilakrayal | 0 | 0 | 0 | 53 |
| Venkat6871 | 2 | 2 | 0 | 42 |
| pkgoogle | 0 | 0 | 0 | 34 |
| NexusHex | 0 | 0 | 0 | 14 |
| Luke Boyer | 12 | 0 | 0 | 0 |
| Kyle Lucke | 11 | 0 | 0 | 0 |
| scxfjiang | 10 | 0 | 0 | 0 |
| NeilPandya | 0 | 0 | 1 | 8 |
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