Weekly GitHub Report for Tensorflow: January 03, 2025 - January 10, 2025

Weekly GitHub Report for Tensorflow

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Table of Contents

• I. News
  • 1.1. Recent Version Releases
  • 1.2. Other Noteworthy Updates
• II. Issues
  • 2.1. Top 5 Active Issues
  • 2.2. Top 5 Stale Issues
  • 2.3. Open Issues
  • 2.4. Closed Issues
  • 2.5. Issue Discussion Insights

• III. Pull Requests
  • 3.1. Open Pull Requests
  • 3.2. Closed Pull Requests
  • 3.3. Pull Request Discussion Insights

• IV. Contributors
  • 4.1. Contributors

I. News

1.1 Recent Version Releases:

The current version of this repository is v2.18.0

1.2 Version Information:

The TensorFlow 2.18.0 release, created on October 21, 2024, introduces several key updates, including the addition of a fourth parameter to the TfLiteOperatorCreate function for a cleaner API, the disabling of TensorRT support in CUDA builds, and the implementation of Hermetic CUDA for more reproducible builds. Notably, TensorFlow now supports NumPy 2.0 by default, with changes in type promotion rules, and introduces enhancements in tf.lite such as support for TensorType_INT4 and TensorType_INT16 in various operations.

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. Bot comments are omitted.

1. Tensorflow not supported on Windows + ARM CPUs: This issue highlights a problem with TensorFlow not being supported on Windows systems with ARM CPUs, specifically when attempting to import TensorFlow on a Windows 11 machine with a Snapdragon processor. The user reports successful installation but encounters an ImportError related to DLL load failure, indicating a lack of support for the ARM architecture on Windows in the current TensorFlow version.

   • The comments discuss the issue being initially marked as a duplicate of an older problem related to outdated Intel CPUs, but it is later clarified that the problem is different due to the ARM architecture. The conversation reveals that TensorFlow does not provide support for Windows on ARM CPUs, and suggestions are made to try using the Linux wheel via WSL or to use Google Colab as alternatives. The discussion also explains the concept of "missing wheels" and the challenges of supporting various CPU architectures.
   • Number of comments this week: 13

2. How to run TFLite benchmark with QNN delegate in Android: This issue is a feature request regarding the execution of TensorFlow Lite benchmarks using the QNN delegate on Android devices, specifically focusing on the failure encountered when attempting to run the benchmark with the QNN delegate despite successful execution with other configurations. The user has detailed their setup process, including the environment and commands used, and is seeking assistance to resolve the errors encountered when applying the QNN delegate, which results in a failure to create a device handle and apply the external delegate.

   • The comments involve a request for more detailed information on the user's setup and build process, including questions about the use of Docker, NDK/SDK versions, and build commands. The user provides a comprehensive response detailing their setup, including the use of macOS, specific NDK/SDK versions, and the build process without Docker. Another user attempts a similar setup on Linux with a different TensorFlow version but encounters the same issue, prompting a request for further assistance from another contributor.
   • Number of comments this week: 4

3. GPU Delegate for Object detection and Image classification from Tensorflow lite for Android : This issue involves a user experiencing significant lag and crashes when attempting to use the GPU delegate for image classification in an Android application using TensorFlow Lite. The user is seeking advice on how to properly implement GPU acceleration to improve performance and avoid the application crashing, particularly on Pixel devices.

   • The comments suggest checking the official documentation for GPU delegate support and ensuring model compatibility. The user is advised to add specific dependencies to the build.gradle file, but despite these efforts, the issue persists with the app lagging and crashing. The user shares a crash report and mentions the inability to provide a GitHub repository due to privacy, but offers to share the build.gradle file for further assistance.
   • Number of comments this week: 4

4. It doesn't support on python3.13: This issue is about the inability to install TensorFlow version 2.17 on Python 3.13 due to a lack of support, as indicated by the error message stating no matching distribution was found for TensorFlow. The problem arises because TensorFlow's release cycle does not align with Python's, leading to a delay in support for new Python versions, which has been a recurring issue since Python 3.8.

   • The comments discuss the historical delay in TensorFlow's support for new Python versions, with users expressing frustration over the lack of support for Python 3.13, especially since it is the default version in major distributions like Fedora 41. Some users suggest downgrading Python, while others argue for a more proactive approach to align TensorFlow's release cycle with Python's. There is also a debate about the technical and organizational challenges in supporting new Python versions, with some users criticizing the current process as inefficient.
   • Number of comments this week: 3

5. Failing to convert MobileNetV3Large to TFLite w/ Integer q: This issue involves the failure to convert a MobileNetV3Large model to TensorFlow Lite (TFLite) with integer quantization on both Windows 10 and Windows 10 WSL systems. The user reports that the model produces incorrect results on TensorFlow 2.10 and fails to convert on TensorFlow 2.16, encountering an LLVM error.

   • The comments discuss potential solutions, including downgrading TensorFlow to version 2.14.1, which reportedly resolves the issue. Another user suggests using the latest Keras version, which works but introduces a new problem when using a representative dataset, causing worse results. The discussion also touches on TensorFlow and Keras compatibility issues, with suggestions to try alternative approaches like using PyTorch for model conversion.
   • Number of comments this week: 3

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: 14

Summarized Issues:

• TensorFlow Version Compatibility Issues: Users have reported several compatibility issues with different TensorFlow versions. One issue involves a broken compatibility with the tensorflow-metal plugin on MacOS 15.2 using an Apple M2 Max GPU in TensorFlow 2.18, which worked fine in version 2.17. Another issue is related to a bug in TensorFlow 2.19.0-dev20250105 where saving a Keras model with include_optimizer=False does not exclude the optimizer. Additionally, a bug in TensorFlow 2.17.0 causes the tf.math.floormod operation to fail on GPU with float-type tensors, while it works with integer types and on CPU. [ issues/84167, issues/84268, issues/84585 ]

• TensorFlow Lite Conversion Errors: Users have encountered errors when converting models to TensorFlow Lite. One issue involves a RuntimeError due to an "IsPowerOfTwo" error in the rfft2d operation during conversion. Another issue is related to an unresolved custom operation, XlaDynamicSlice, when converting a T5 model from Huggingface to TensorFlow Lite. These errors highlight challenges in ensuring compatibility and functionality during model conversion processes. [ issues/84171, issues/84203 ]

• TensorFlow Build and Compilation Issues: Several issues have been reported related to building and compiling TensorFlow. A user seeks guidance on configuring TensorFlow 2.18 to use local CUDA libraries instead of hermetic CUDA. Another issue involves a compilation error on Windows due to incorrect handling of backslashes in file paths. Additionally, a bug in TensorFlow Lite for iOS results in undefined symbols for the arm64 architecture. [ issues/84166, issues/84558, issues/84466 ]

• TensorFlow Model Training and Execution Bugs: Users have reported bugs affecting model training and execution in TensorFlow. One issue involves incorrect ordering when using dictionaries to load data in the fit method, causing model malfunction. Another bug is related to the XLA compiler failing to compile the tf.keras.layers.Conv2D layer with padding='valid'. These issues highlight challenges in ensuring correct model training and execution. [ issues/84278, issues/84205 ]

• TensorFlow Profiling and Resource Management Issues: Users have experienced issues with profiling and resource management in TensorFlow. A bug in GPU profiling prevents memory events from being captured when profiling a remote worker. Another issue involves a memory allocation problem on an Ubuntu system using WSL2, where a virtual GPU exceeds its memory limit during training. These issues indicate challenges in effectively managing resources and profiling in TensorFlow environments. [ issues/84460, issues/84523 ]

• TensorFlow Environment and Setup Challenges: Users have faced challenges in setting up and configuring TensorFlow environments. One issue involves difficulties connecting to a TPU v4-32 from a Cloud VM due to potential metadata retrieval problems. Another issue is a failure during the Gradle sync process in the Digit Classifier Codelab, possibly due to a mismatch in Gradle or JDK versions. These challenges highlight the complexities involved in setting up TensorFlow environments. [ issues/84413, issues/84529 ]

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: 22

Summarized Issues:

• TensorFlow Runtime Import Errors: Users have reported issues with importing TensorFlow on Windows systems, often encountering DLL load failures. These errors are attributed to missing dependencies, outdated CPU architectures, or incorrect library paths. Some issues are identified as duplicates of known problems related to older PCs or specific Python versions. [ issues/76581, issues/83508, issues/83626, issues/84119 ]

• TensorFlow Model Conversion and Compatibility: Users have experienced problems when converting TensorFlow models to TensorFlow Lite, including incorrect results and reduced accuracy. Additionally, compatibility issues arise with certain hardware, such as the lack of GPU support for specific devices. These issues prompt users to seek assistance and provide detailed system information for troubleshooting. [ issues/83685, issues/84412 ]

• Compilation and Build Errors: Several users have encountered errors while compiling TensorFlow on various systems, often due to missing files or incompatible toolchains. These issues include problems with cross-compilation for ARM architectures and missing headers for GPU support. Users have attempted various solutions, such as modifying build files, but continue to seek community assistance. [ issues/83566, issues/84443 ]

• TensorFlow Bugs and Unexpected Behavior: Users have reported bugs in TensorFlow, such as errors with specific operations or unexpected model behavior. These issues include convergence problems with algorithms, incorrect outputs during training, and internal errors with certain functions. Users often seek clarification on whether these issues are due to their implementation or inherent bugs in TensorFlow. [ issues/72963, issues/73404, issues/73873, issues/84577 ]

• Deprecation and Compatibility Warnings: Users have encountered warnings related to deprecated features or compatibility issues with newer standards. These include warnings when compiling with C++20 and issues with TensorFlow Lite on certain platforms. Users often suggest solutions or seek guidance on how to address these warnings. [ issues/78434, issues/82832 ]

• Documentation and Naming Suggestions: There are proposals to update documentation or change naming conventions to improve clarity and usability. These suggestions include updating the CODE_OF_CONDUCT and renaming TensorFlow for better readability. Such proposals often seek community feedback and approval. [ issues/77101, issues/77820 ]

• CUDA and GPU Compatibility Issues: Users have reported difficulties in utilizing CUDA with TensorFlow on Windows, despite successful use with other frameworks. These issues highlight the need for better compatibility and support for GPU computation in TensorFlow. Users often seek assistance to resolve these compatibility problems. [ issues/83684 ]

• General User Frustrations and Feedback: Some users express frustration with TensorFlow, describing it as poorly designed or difficult to use. These reports often lack specific details but indicate a need for improved user experience and support. [ issues/83703 ]

• TensorFlow Lite Cross-Compilation Issues: Users have encountered linker errors when cross-compiling TensorFlow Lite examples, often due to incorrect library references. These issues require modifications to build scripts and highlight the challenges of cross-compilation in TensorFlow. [ issues/77137 ]

• Backup and Restore Method Bug: A bug in TensorFlow's BackupAndRestore method requires the model to be explicitly built before training. This issue can be resolved by defining the input shape or using the model.build() method, ensuring the model is ready for training. [ issues/84027 ]

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.

1. Tensortflow import issue after installation
   • Toxicity Score: 0.55 (Frustration over issue closure, multiple issue warnings, potential for misunderstanding.)
   • This GitHub conversation involves several users, including Manoj Nayak, Dhimant, and Mihai Maruseac, discussing a technical issue. Manoj initially suggests checking the Python version, while Dhimant responds with attempts to resolve the issue and questions about GPU availability. Mihai intervenes to mark the issue as a duplicate, which leads to Dhimant expressing frustration over the closure of a previous issue. The conversation's tone shifts as Dhimant requests the issue to be reopened, and Mihai warns against opening multiple issues, suggesting it could be seen as spam.

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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: 3

Key Open Pull Requests

1. Updated rules_python patch to get 3.13.1 python: This pull request updates the rules_python patch to incorporate Python version 3.13.1 in the TensorFlow project, as indicated by the commit message and the involvement of a contributor tagged as @hawkinsp.

• URL: https://github.com/tensorflow/tensorflow/pull/84481

• Merged: No

• Associated Commits: d16338518baeefab0048057b33c56193582db5b7

2. Add div/truediv support for bool-val tensor.: This pull request aims to add support for division and true division operations on boolean tensors in TensorFlow by casting them to int32, ensuring compatibility with NumPy's behavior for such operations.

• URL: https://github.com/tensorflow/tensorflow/pull/84491

• Merged: No

• Associated Commits: 928a7a3ce18a8eb5d2ea4af78af911f6758158a2

3. Replacement PR for #76210 Add support for quint8 type for uniform_quantize and uniform_ dequantize ops: This pull request introduces support for the quint8 data type in the uniform_quantize and uniform_dequantize operations within the TensorFlow project, serving as a replacement for a previous pull request (#76210) and including several commits for additional fixes and improvements.

• URL: https://github.com/tensorflow/tensorflow/pull/84497

• Merged: No

• Associated Commits: 7472fadf0a725210c632aeb87b2f54bed6a230b9, 0a389391c6a6068cd81b882f71c6b79388910c8e, d5b2503a0655524c3b17eb3ab75f8206e9a4f8ed, a42b9941c1932a1f9ac81c79b94693033b1875c2, 3034da0218dfb8c8628eee927d46251f533a1ee2, 4c3c7b8c107a8ee1f6545fb9c33c72bca98ff1e1

Other Open Pull Requests

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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: 18

Key Closed Pull Requests

1. Fix checkfail in ResourceSparseApplyKerasMomentum: This pull request addresses a type-checking issue in the TensorFlow operation ResourceSparseApplyKerasMomentum, which fails when the var or accum arguments are not of dtype float32, by proposing a validation check to ensure the correct data type, potentially resolving issue #63720.

• URL: https://github.com/tensorflow/tensorflow/pull/63784

• Merged: No

• Associated Commits: 79c14a428d8255733699b423dd58f3fcceb46ca4

2. Unregister complex dtypes for Round OP: This pull request addresses an issue in the TensorFlow project by unregistering complex data types and certain integer types (int8 and int16) for the Round operation, as these types cause errors, and instead ensures that the operation is registered only for supported real data types like float and specific integer types, potentially resolving issue #65317.

• URL: https://github.com/tensorflow/tensorflow/pull/65396

• Merged: No

• Associated Commits: 822db8ac37439d258ef4e0ad3364667e96f97f44, b8b56d2422cded257d37e86b7242534fd94f6468

3. [NFC][ROCM] Replaced DoMatmul with ExecuteOnStream call for gpu_blas_lt: This pull request refactors the gpu_blas_lt interface in the ROCM platform by replacing the DoMatmul function with the more robust ExecuteOnStream call, which automatically handles valid data type combinations, thereby simplifying the interface and improving support for hipblas-lt.

• URL: https://github.com/tensorflow/tensorflow/pull/73926

• Merged: Yes

• Associated Commits: e20ec8d81ee4084cd562e4df35c0cfccfaf8417e, fe9524daec1ab517e6730bf755ff6173a4da1dc9

Other Closed Pull Requests

• Real-Time Optimization in TensorFlow: The pull request introduces a Real-Time Optimization feature to TensorFlow's Grappler optimizers, aiming to enhance computational graph performance during runtime. It includes a new RealTimeOptimizer class that dynamically adjusts optimizations based on real-time metrics, integrating seamlessly with the existing meta optimizer framework. The feature also introduces new optimization strategies activated by performance metrics and is validated through comprehensive unit tests to ensure stability and performance. pull/75965

• CMake Cross-Compile Error Fixes: Several pull requests address issues related to CMake cross-compilation in TensorFlow, ensuring smoother builds across different platforms. One pull request fixes a cross-compile error in the label_image example, while another resolves a linker error with TensorFlow Lite's static library. Additionally, a pull request ensures proper export of TensorFlow Lite C symbols on Windows, addressing DLL export issues. pull/77208, pull/79330, pull/79380, pull/82798

• Documentation and Typo Corrections: Multiple pull requests focus on improving TensorFlow's documentation and fixing typos in code comments and docstrings. These efforts include updating broken links in the best_practices.md file, enhancing documentation for TensorFlow Eager Monitoring Counter bindings, and correcting typos across various documentation strings. These changes aim to improve clarity and usability for developers. pull/84158, pull/84164, pull/84165, pull/84184, pull/84463

• TensorFlow and NumPy Compatibility: A pull request enhances TensorFlow's compatibility with NumPy by adding support for the multiplication operator for boolean tensors. This change resolves an inconsistency between TensorFlow tensors and NumPy arrays, ensuring that boolean tensor operations behave similarly across both libraries. The pull request includes several commits focused on implementation, testing, and code quality improvements. pull/83753

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.

1. Updated rules_python patch to get 3.13.1 python
   • Toxicity Score: 0.55 (Escalating tension, Defensive responses, Frustration expressed)
   • This GitHub conversation involves username1 initiating a discussion with a proposed update, while username2 provides feedback that is initially constructive but becomes increasingly critical. Username3 joins the conversation, supporting username1 and countering username2's points, which leads to a tense exchange. The tone shifts from collaborative to defensive, with username1 expressing frustration over the perceived lack of understanding from username2.

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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.

If there are more than 10 active contributors, the list is truncated to the top 10 based on contribution metrics for better clarity.

Contributor	Commits	Pull Requests	Issues	Comments
mihaimaruseac	5	0	0	66
Venkat6871	2	2	0	45
gaikwadrahul8	5	5	0	31
tilakrayal	0	0	0	22
alekstheod	11	1	0	0
dnmaster1	0	0	2	9
codinglover222	6	2	0	1
mraunak	0	0	0	9
muayyad-alsadi	0	0	0	9
NexusHex	0	0	0	8