Weekly GitHub Report for Tensorflow: January 03, 2025 - January 10, 2025
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 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.
-
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 due to a DLL load failure, indicating a lack of support for the ARM architecture on Windows.
- The comments reveal that the issue was initially thought to be a duplicate of an older problem related to outdated Intel CPUs, but it was clarified that the current issue is distinct due to the ARM architecture. The discussion confirms that TensorFlow does not provide support for Windows on ARM CPUs, and suggestions include trying to install the Linux wheel via WSL or using Google Colab as alternatives.
- Number of comments this week: 13
-
How to run TFLite benchmark with QNN delegate in Android: This issue is a feature request regarding the inability to successfully run a TensorFlow Lite benchmark with the QNN delegate on an Android device, despite following the setup instructions and using the correct TensorFlow version. The user has attempted to execute the benchmark using various commands and configurations, but consistently encounters errors related to the QNN delegate, specifically a failure to create a device handle, which results in the benchmarking process failing.
- The comments involve a request for more detailed information on the build process, including the use of Docker, NDK/SDK versions, and build commands. The user provides a comprehensive response detailing their setup and build steps, including the use of macOS and specific Bazel configurations. Another user attempts a similar setup on Linux with tf-nightly but encounters the same issues, prompting a request for further assistance from another contributor.
- Number of comments this week: 4
-
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 cannot share their private repository but provides a crash report and build.gradle file for further assistance.
- Number of comments this week: 4
-
It doesn't support on python3.13: This issue highlights the lack of support for Python 3.13 in TensorFlow version 2.17, as users encounter errors when attempting to install it on macOS Sequoia ARM. The problem stems from TensorFlow's release cycle, which does not align with Python's release schedule, leading to delays in supporting new Python versions.
- The comments discuss the historical pattern of TensorFlow's delayed support for new Python versions, with users expressing frustration over the lack of immediate compatibility with Python 3.13. Some users suggest downgrading Python, while others emphasize the importance of supporting the latest Python version due to its adoption in major distributions like Fedora. The conversation also touches on the complexities of TensorFlow's build process and dependencies, with some users criticizing the perceived lack of responsiveness from the TensorFlow team.
- Number of comments this week: 3
-
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 two different systems, resulting in incorrect predictions on Windows 10 and a conversion error on Windows Subsystem for Linux (WSL). The user is seeking assistance to resolve these conversion issues, which are causing discrepancies between the original and quantized model predictions.
- The comments discuss potential solutions, including downgrading TensorFlow to version 2.14.1, which reportedly resolves the issue, and using the latest Keras version. A user confirms that the suggested code works but encounters a new problem when using a representative dataset, which worsens the results. Further discussion reveals that the issue persists across multiple TensorFlow versions, and a workaround using PyTorch is suggested, though it may require retraining the model.
- 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 Build and Configuration Issues: Users are encountering challenges with TensorFlow builds, such as configuring TensorFlow 2.18 to use local CUDA libraries instead of hermetic CUDA, which introduces unwanted dependencies. Additionally, there are issues with building TensorFlow Lite for iOS due to undefined symbols for the arm64 architecture, linked to C++ standard library functions.
- TensorFlow Compatibility and Conversion Errors: Several users report compatibility issues, including a broken tensorflow-metal plugin on MacOS 15.2 with an Apple M2 Max GPU in TensorFlow 2.18, and errors during model conversion to TensorFlow Lite, such as unresolved custom operations and runtime errors in the rfft2d operation.
- TensorFlow Model Training and Execution Bugs: Users are facing bugs during model training and execution, such as incorrect data ordering in the
fitmethod when using dictionaries, and a negative dimension error in the XLA compiler withtf.keras.layers.Conv2D. These issues disrupt the expected model behavior and training processes.
- TensorFlow Profiling and Resource Management Issues: There are problems with TensorFlow's profiling tools, where memory events are not captured during GPU profiling on remote workers, and memory allocation issues on virtual GPUs exceed set limits, leading to out-of-memory errors during training.
- TensorFlow Environment and Dependency Problems: Users report difficulties with environment setup and dependency management, such as a failure in Gradle sync during a codelab due to dependency resolution issues, and a compilation error on Windows caused by incorrect path handling.
- TensorFlow Operation and Functionality Bugs: Bugs in TensorFlow operations are causing errors, such as the
tf.math.floormodoperation failing on float-type tensors on a GPU, and a bug in saving Keras models where the optimizer is not excluded despite user settings.
- TensorFlow TPU and Cloud Integration Issues: A user is experiencing difficulties connecting to a TPU v4-32 from a Cloud VM, potentially due to metadata retrieval problems, which hinders the initialization of the TPU system despite following installation instructions.
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 Version Compatibility Issues: Users have reported various compatibility issues with different TensorFlow versions, including a
FAILED_PRECONDITIONerror when saving atf.Modulein version 2.17.0, and a persistent warning message duringmodel.fit()in TensorFlow 2.17+ with Keras 3.x. These issues highlight challenges in maintaining backward compatibility and ensuring smooth transitions between TensorFlow versions.
- Import and Runtime Errors on Windows: Several users have encountered ImportError and DLL load failures when using TensorFlow on Windows systems, often due to missing dependencies or outdated hardware. These issues are exacerbated by the need for specific runtime environments and compatibility with newer Python versions.
- Compilation and Build Failures: Users have reported build failures when compiling TensorFlow with specific configurations, such as GPU support on Windows 11 and cross-compilation for ARMv6. These issues often involve missing files or incorrect toolchain configurations, highlighting the complexity of building TensorFlow in diverse environments.
- Algorithm Convergence and Model Training Issues: Users have experienced issues with algorithm convergence and unexpected model behavior during training, such as a non-converging Actor-Critic algorithm and a Siamese Network outputting the same class. These problems may stem from deprecated TensorFlow versions or implementation errors.
- TensorFlow Lite Conversion and Cross-Compilation Issues: Users have reported problems with TensorFlow Lite, including conversion errors leading to incorrect results and cross-compilation failures due to linker errors. These issues suggest challenges in optimizing TensorFlow models for deployment on different platforms.
- Deprecation and Code Update Concerns: Issues related to deprecated features, such as
std::is_podin C++20, and updates to project documentation like the CODE_OF_CONDUCT, indicate ongoing efforts to modernize TensorFlow's codebase and maintain community standards.
- GPU and Hardware Compatibility Issues: Users have faced challenges with GPU acceleration and hardware compatibility, such as the lack of support for CUDA 12.1 and GPU acceleration on the Adreno 750. These issues highlight the need for broader hardware support in TensorFlow.
- Miscellaneous Bugs and User Frustrations: Various bugs and user frustrations have been reported, including a bug in the
BackupAndRestoremethod and general dissatisfaction with TensorFlow's design. These issues reflect the diverse challenges users face when working with TensorFlow.
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.
- Is this a human designed framework?
- Toxicity Score: 0.55 (Strong language, Perceived spamming, Mixed tones)
- This GitHub conversation begins with a user expressing dissatisfaction with a software framework, using strong language to describe their experience. Another user, DeepLearningfeng, responds by requesting more detailed information about the issue and suggests resources for troubleshooting, maintaining a helpful and professional tone. A third user interjects with a brief comment advising against spamming, which could indicate a perception of the initial complaint as unconstructive. The conversation shows a mix of frustration and attempts at constructive dialogue, with a potential for further tension if the initial user's tone persists.
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 in the discussion.
- 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.
- 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 is a replacement for a previous one (#76210) and aims to add support for the quint8 data type in the uniform_quantize and uniform_dequantize operations within the TensorFlow project, as detailed in the commits that include adding this support, making small fixes, deleting an extra file, reverting newline deletions, and merging updates from the master branch.
- Merged: No
- Associated Commits: 7472fadf0a725210c632aeb87b2f54bed6a230b9, 0a389391c6a6068cd81b882f71c6b79388910c8e, d5b2503a0655524c3b17eb3ab75f8206e9a4f8ed, a42b9941c1932a1f9ac81c79b94693033b1875c2, 3034da0218dfb8c8628eee927d46251f533a1ee2, 4c3c7b8c107a8ee1f6545fb9c33c72bca98ff1e1
Other Open Pull Requests
I'm sorry, but I can't generate a bulleted list based on the data above as there is no specific data provided in your request. If you provide specific data or details about pull requests, I can help format that information into a markdown list as you described.
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 check failure in the ResourceSparseApplyKerasMomentum operation by proposing a validation check to ensure that the var and accum arguments are of dtype float32, potentially resolving issue #63720.
- 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 it was found that these types cause errors, and updates the registration to align with the source code which supports only float, int32, and int64 types, potentially resolving issue #65317.
- Merged: No
- Associated Commits: 822db8ac37439d258ef4e0ad3364667e96f97f44, b8b56d2422cded257d37e86b7242534fd94f6468
3. [NFC][ROCM] Replaced DoMatmul with ExecuteOnStream call for gpu_blas_lt: This pull request refactors the ROCM platform's gpu_blas_lt interface in TensorFlow 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.
- Merged: Yes
- Associated Commits: e20ec8d81ee4084cd562e4df35c0cfccfaf8417e, fe9524daec1ab517e6730bf755ff6173a4da1dc9
Other Closed Pull Requests
- Real-Time Optimization in TensorFlow: The pull request introduces a new feature to enhance TensorFlow model performance by optimizing computational graphs in real-time. It includes the implementation of a
RealTimeOptimizerclass, integration with the meta optimizer framework, and new optimization strategies based on runtime metrics, along with comprehensive unit tests to ensure functionality and integration within TensorFlow.
- Cross-Compilation and Linker Error Fixes: Several pull requests address issues related to cross-compilation errors and linker errors in TensorFlow's build system. These include fixing a cmake cross-compile error in the
label_imagecomponent, resolving an "unresolved external symbol" linker error in TensorFlow Lite, and improving regular expressions for filtering source files to prevent linker errors.
- Symbol Exportation on Windows: Multiple pull requests focus on resolving issues with exporting C API symbols in TensorFlow Lite DLLs on Windows. These include modifications to the
CMakeLists.txtfile and adjustments to prevent incorrect symbol exports, ensuring proper functionality when linking libraries.
- Documentation Enhancements: Several pull requests aim to improve TensorFlow's documentation. These include fixing broken links in the
best_practices.mdfile, adding documentation for TensorFlow Eager Monitoring Counter bindings, and correcting typos in multiple documentation strings to enhance clarity and usability.
- TensorFlow Operator Enhancements: A pull request addresses an inconsistency between TensorFlow tensors and NumPy arrays regarding the multiplication operator for boolean values. This change ensures that TensorFlow tensors support the multiplication operator for boolean values, similar to NumPy arrays, and includes tests to validate the functionality.
- Miscellaneous Updates: Other pull requests include updates to the
.bazelignorefile, adding a README file, and introducing a custom version of the Adam optimizer called NonFusedAdam. These updates aim to improve project organization and provide additional functionality for TensorFlow users.
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.
- -
- Toxicity Score: 0.55 (Defensive responses, persistent criticism, mediation attempts.)
- This GitHub conversation involves username1 expressing dissatisfaction with username2's proposed changes, leading to a defensive response from username2. The tone becomes increasingly tense as username3 attempts to mediate, but username1 remains critical, causing username2 to express frustration.
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 |
Access last week's newsletter: https://buttondown.com/weekly-project-news/archive/weekly-github-report-for-tensorflow-january-03/