Weekly GitHub Report for Tensorflow - 2024-12-09 12:00:01
Weekly GitHub Report for Tensorflow
Thank you for subscribing to our weekly newsletter! Each week, we deliver a comprehensive summary of your GitHub project's latest activity right to your inbox, including an overview of your project's issues, pull requests, contributors, and commit activity.
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.
-
It doesn't support on python3.13: This issue is about the inability to install TensorFlow version 2.17 on Python 3.13, as the installation process fails to find a compatible version of TensorFlow for this Python release. The problem arises because TensorFlow's release cycle does not align with the release of new Python versions, leading to a delay in support for the latest Python releases.
- The comments discuss the recurring issue of TensorFlow not supporting new Python versions immediately upon their release, with users expressing frustration and suggesting workarounds like downgrading Python. Some users highlight the importance of supporting Python 3.13 due to its adoption in major distributions like Fedora 41. There are suggestions for automating the build process to support new Python versions more quickly, but others point out the complexity of TensorFlow's build system and dependencies as barriers to this approach.
- Number of comments this week: None
-
Very serious! Using this method will definitely result in memory leaks, I hope you can provide support: This issue reports a memory leak problem when using a specific method in TensorFlow version 2.18, which persists despite attempts to clear memory and garbage collection, as evidenced by the increasing memory usage over time. The problem has been reproduced with TensorFlow Nightly and involves custom code running on Ubuntu 2.2 or Mac M1 with Python 3.11.
- The comments discuss potential contributions to resolving the issue, with users expressing interest in collaborating on a solution. A suggestion to use a memory clearing function was made, but it was noted that this approach had already been tried without success. There is also a reference to feedback from Keras indicating that the issue has been long-standing and originates from TensorFlow.
- Number of comments this week: None
-
TPU not support TensorFlow 2.18 and 2.17.1: This issue reports a bug where importing TensorFlow versions 2.18 and 2.17.1 on a TPU results in a segmentation fault, indicating a compatibility problem with these specific versions. The problem does not occur with TensorFlow versions lower than 2.17.0, suggesting a regression or change in the newer versions that affects TPU support.
- The comments discuss the issue of TensorFlow versions 2.18 and 2.17.1 not working on TPU, with users confirming that lower versions work fine. Suggestions are made to ensure proper TPU setup and to try different TPU configurations, but the original poster clarifies that the error occurs at the import statement itself, making it difficult to test different configurations. The discussion includes advice to avoid using Colab and instead use specific TPU-VM Pod images, but the issue persists with the recommended setups.
- Number of comments this week: None
-
Failed to build
tensorflow_ccin Windows when linking: This issue involves a failure to build thetensorflow_cclibrary on Windows when using LLVM/Clang, where the linking process fails due to missing symbols such asSessionandSavedModelBundleInterface. The problem persists across multiple versions of LLVM/Clang, indicating that the issue is not related to the compiler version but rather to missing dependencies or configurations in the build process.- The comments discuss potential causes for the linking errors, including incomplete exported symbol definitions and missing dependencies in the build configuration. Some users have attempted to manually add missing dependencies, but this process is complex and time-consuming. A contributor mentions that the issue is due to a missing configuration for Windows, which works fine on Linux, and they are working on a fix. Other users express their need for a solution and are waiting for an official patch.
- Number of comments this week: None
-
Textfile initializer sharing bug: This issue involves a bug in TensorFlow's
TextFileInitializerwhere the logic for sharing does not account for key/value data types, leading to a crash when two initializers are created for the same vocabulary file but with different data types. The proposed fix is a minor change to include data types in the shared name to prevent conflicts.- The comments discuss the need for thorough testing of any modifications to ensure correct functionality and address potential edge cases. The original poster agrees that modifying the logic to include data types in shared names would be a good fix and plans to make a small pull request. Another commenter suggests trying the latest TensorFlow version as the framework is constantly being improved, but the original poster confirms that the specific code causing the issue has not changed and the problem persists.
- 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: 23
Summarized Issues:
- TensorFlow Compilation and Installation Issues: Users frequently encounter challenges when compiling or installing TensorFlow on various platforms, such as CentOS, Ubuntu, and Raspberry Pi. These issues often involve missing dependencies, unsupported architectures, or errors during the build process. Users seek guidance on resolving these problems to successfully install or compile TensorFlow.
- TensorFlow Performance and Optimization: Users report suboptimal performance when running TensorFlow applications, particularly on CPUs and GPUs. These issues include underutilization of hardware resources and unexpected behavior during execution. Users are looking for optimization techniques to enhance performance.
- TensorFlow and Keras Bugs: Several bugs have been identified in TensorFlow and Keras, affecting functionalities such as checkpoint restoration, model initialization, and gradient computations. These bugs lead to errors or unexpected results, prompting users to seek fixes or workarounds.
- TensorFlow Security Concerns: A potential security vulnerability in TensorFlow's Keras library has been reported, where custom layers can execute malicious code. This poses a significant risk, as attackers could exploit this to run harmful commands on a victim's system. Users are concerned about the implications and seek solutions to mitigate this risk.
- TensorFlow XLA and GPU Issues: Users experience issues with TensorFlow's XLA compiler and GPU integration, such as recompilation problems and inconsistent outputs. These issues affect the reliability and performance of models, leading users to seek guidance on configuration and troubleshooting.
- TensorFlow Compatibility and Environment Issues: Compatibility issues arise when using TensorFlow with different versions of Python, operating systems, or hardware. These issues often result in import errors or crashes, and users seek advice on ensuring compatibility and resolving these errors.
- TensorFlow Model Conversion and Layer Configuration: Users face challenges when converting models to TensorFlow Lite or configuring specific layers, such as preventing layer fusion. These issues require detailed guidance to achieve the desired model structure and functionality.
- TensorFlow API and Documentation Clarifications: Users seek clarification on TensorFlow API usage and documentation, particularly when the existing documentation is insufficient. These requests often involve understanding specific functions or statements within the TensorFlow codebase.
- TensorFlow Memory and Resource Management: Issues related to memory management, such as bandwidth calculation for DDR5 memory, are reported by users. These issues require updates to the codebase to ensure accurate resource utilization and performance.
- TensorFlow Training and Model Behavior: Users encounter warnings and errors during model training, such as input structure mismatches and kernel crashes. These issues affect the training process and require troubleshooting to ensure smooth execution.
- TensorFlow JIT Compilation Issues: Enabling JIT compilation in TensorFlow can lead to unexpected results, such as NaN values in function outputs. Users report these issues and seek solutions to maintain correct functionality with JIT compilation enabled.
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: 20
Summarized Issues:
- TensorFlow Version Compatibility Issues: Users have reported various issues related to TensorFlow version compatibility, including discrepancies in prediction results when using LSTM models across different TensorFlow versions and errors when compiling TensorFlow from source. These issues often require troubleshooting steps such as using compatibility modes, downgrading dependencies, or upgrading to newer versions. Ensuring consistent behavior across versions is crucial for maintaining model performance and avoiding unexpected errors.
- TensorFlow Installation and Build Challenges: Several users have faced challenges during the installation and build processes of TensorFlow, particularly on Windows and ARM architectures. These issues include ImportErrors, build failures due to missing libraries or incompatible versions, and difficulties with GPU setup. Addressing these challenges often involves checking version compatibility, ensuring proper environment configurations, and following detailed troubleshooting steps.
- TensorFlow Documentation and Accessibility Concerns: Users have raised concerns about the accessibility and accuracy of TensorFlow's documentation, particularly regarding GPU support and library builds. These issues highlight the need for clear and inclusive documentation to support diverse user needs, including those of visually impaired developers. Ensuring up-to-date and comprehensive documentation is essential for facilitating smooth user experiences and promoting inclusivity.
- TensorFlow Model and Code Functionality Issues: Users have encountered various functionality issues with TensorFlow models and code, such as errors in loading models across different languages, unexpected behavior with specific layers, and challenges in code readability. These issues often require code refactoring, debugging, and seeking guidance from the community to resolve. Addressing these functionality issues is crucial for ensuring reliable model performance and maintainable codebases.
- TensorFlow Lite and Model Conversion Issues: Users have reported issues related to TensorFlow Lite, including errors during model conversion and build failures with specific configurations. These challenges often involve troubleshooting build commands, checking for compatibility with hardware and software environments, and exploring alternative methods for model evaluation. Resolving these issues is important for leveraging TensorFlow Lite's capabilities in resource-constrained environments.