Weekly GitHub Report for Tensorflow: January 20, 2025 - January 27, 2025
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 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. Notable improvements include default support for NumPy 2.0, enhancements in tf.lite such as support for TensorType_INT4 and TensorType_INT16, and new features in tf.data for improved memory and throughput management.
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.
-
Cannot use GpuDelegate - java.lang.IllegalArgumentException: Internal error: Cannot create interpreter: This issue involves a user encountering a "java.lang.IllegalArgumentException: Internal error: Cannot create interpreter" error when attempting to use the GpuDelegate in a TensorFlow Lite project on an Android device. The user has provided a repository to replicate the issue and is seeking assistance to resolve the problem, which appears to be related to the configuration or compatibility of the GpuDelegate.
- The comments discuss potential configuration issues with the GpuDelegate, suggesting testing with CPU-only execution, which works fine. Suggestions include configuring GpuDelegateFactory.Options for better control and checking compatibility documentation. The user clarifies the use of GpuDelegateFactory for mobile devices and expresses doubt about the helper's ability to resolve the issue, hoping for input from a more experienced contributor.
- Number of comments this week: 10
-
TFnode on TensorflowonSpark 2.2.5: This issue involves a user encountering an error while running code on Microsoft Fabric using TensorFlowOnSpark 2.2.5 with TensorFlow version 2.12, where the user aims to print a "hello world" message to verify if the code runs on different clusters. The problem arises because TensorFlowOnSpark 2.2.5 is not compatible with TensorFlow versions greater than 1.x, leading to compatibility issues with the user's current setup.
- The user initially seeks help due to confusion over the error, and a responder requests a minimal reproducible code snippet for debugging. Another commenter identifies the compatibility issue between TensorFlowOnSpark and TensorFlow 2.x, suggesting using TensorFlow 1.15. The user expresses concern about downgrading TensorFlow due to constraints with Microsoft Fabric, seeking alternative solutions.
- Number of comments this week: 5
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Unable to install TensorFlow: No matching distribution found for TensorFlow!: This issue is about a user who is unable to install TensorFlow due to a "No matching distribution found" error when using Python version 3.13 on Windows 10. The user is attempting to install TensorFlow version 2.8 but encounters compatibility issues with the Python version they are using.
- The comments clarify that TensorFlow 2.8 does not support Python 3.13, and the user is advised to use a compatible Python version, such as 3.8 to 3.11. The user also mentions trying older Python versions like 2.7 and 2.6, which are incompatible with TensorFlow 2.8.
- Number of comments this week: 4
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Tensorflow 2.14.0 installation/run on C++ in visual studio code: This issue involves a user attempting to set up TensorFlow 2.14.0 for C++ development in Visual Studio Code on macOS 14.4, encountering difficulties with building the necessary components using Bazel. The user is specifically facing an error related to the target
//tensorflow/tools/pip_package:pip_packagenot being declared, which is hindering their ability to run a custom model in C++ as they would in Python.- The comments provide guidance on setting up TensorFlow for C++ development, clarifying that the target mentioned is for Python pip packages. Instructions are given for installing the TensorFlow C++ library, including system requirements and step-by-step installation. The user encounters a build failure, and the error details are shared, indicating issues with executing a genrule during the build process.
- Number of comments this week: 4
-
tensorflow takes a long time to prepare before the first iteration: This issue is about a bug in TensorFlow 2.10.0 where the preparation time before the first iteration of training a custom model is excessively long, taking 40-60 minutes even for a small dataset with a model containing only 835,620 parameters. The user is experiencing this delay on a Linux Ubuntu 22.04 system with an Nvidia Tesla K20m GPU, and the model is used for seismic data phase picking.
- The comments suggest that the delay might be due to the time spent converting Python code to TensorFlow's graph representation. Suggestions to speed up the process include writing code in graph mode, using JAX with Keras 3, and optimizing model size, dataset size, and hardware resources. Additionally, using a profiler to identify bottlenecks and adjusting batch size are recommended.
- Number of comments this week: 4
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: 10
Summarized Issues:
- CUDA and GPU Issues: Users frequently encounter problems related to CUDA and GPU configurations when using TensorFlow. These issues include failures to load necessary CUDA shared libraries, errors in CUDA device detection, and TensorFlow not detecting GPUs due to missing libraries. Such problems often arise from incorrect environment setups or compatibility issues with the installed libraries.
- Compilation and Build Errors: Several users report difficulties in compiling or building TensorFlow, particularly when using specific compiler flags or building for different platforms. These issues include errors related to undeclared build targets, missing dependency declarations, and suggestions to rebuild with appropriate flags for CPU optimizations.
- Installation Challenges: Users face challenges when installing TensorFlow, especially with package managers like Poetry, where installation candidates for certain versions are not found. These issues highlight the need for guidance on resolving installation errors and ensuring compatibility with the user's system environment.
- Performance and Compatibility Issues: Some users experience performance issues, such as long preparation times before training or compatibility errors when using TensorFlow Lite on specific devices. These problems often require adjustments in configuration or environment to optimize performance and ensure compatibility.
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: 17
Summarized Issues:
- Segmentation Faults and Crashes in TensorFlow Operations: Segmentation faults and crashes have been reported in various TensorFlow operations across different platforms. These issues include a segmentation fault in
tf.raw_ops.TensorArrayV3on Linux Ubuntu 20.04 and crashes in TensorFlow Lite on Apple M1 Pro and Android devices, indicating potential memory and compatibility issues.
- TensorFlow and Keras Compatibility Issues: Compatibility issues between TensorFlow and Keras have been identified, particularly affecting model initialization and distributed strategies. A specific problem was noted with TensorFlow 2.17.1 and Keras 3.7.0, where reverting to an earlier Keras version resolved the issue.
- TensorFlow Lite Performance and Profiling Challenges: Users have faced challenges in profiling TensorFlow Lite models on Android devices, with issues in the Android Studio CPU Profiler not displaying all operators. This problem affects custom models and requires guidance for effective performance tracing.
- TensorFlow Lite Non-Max Suppression Kernel Bug: A bug in the TensorFlow Lite Non-Max Suppression kernel results in inconsistent outputs compared to TensorFlow's NMS, causing slow computation and memory errors on Android devices. A fix is requested to align the outputs of both NMS implementations.
- Compilation and Installation Issues with TensorFlow: Users have reported difficulties compiling and installing TensorFlow on various systems, including issues with Bazel on Linux and pip installation errors. These problems highlight challenges with system compatibility and package dependencies.
- TensorFlow XLA Compiler Bug: A bug in TensorFlow's XLA compiler prevents the compilation of the
tf.keras.layers.Conv2Dlayer withpadding='valid', despite successful execution in eager mode. This issue persists across multiple TensorFlow versions, indicating a need for a fix in the compiler.
- Gradle and TensorFlow Codelab Compatibility: A compatibility issue with Gradle versions was identified during the TensorFlow Digit Classifier Codelab, where downgrading Gradle resolved a dependency resolution error. This suggests a need for compatibility checks with newer Gradle versions.
- Python Version Compatibility with TensorFlow: Users have encountered compatibility issues when installing TensorFlow on newer Python versions, such as Python 3.12, and on Alpine Linux. These issues highlight the importance of adhering to supported Python versions and distributions.
- Documentation and Translation Gaps in TensorFlow: The absence of a compatibility table for TensorFlow 2.18 with CUDA and cuDNN in the Spanish documentation highlights translation gaps. Adding indicators for outdated or missing information is suggested to improve user guidance.
- Miscellaneous Issues and Spam Content: Various other issues include spam content removal and improperly filled issue templates, which were closed due to lack of information. These highlight the need for proper issue reporting and moderation.
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 or closed issues from the past week.
III. Pull Requests
3.1 Open Pull Requests
This section provides a summary of pull requests that were opened in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. All other pull requests are grouped based on similar characteristics for easier analysis.
Pull Requests Opened This Week: 6
Key Open Pull Requests
1. [mlir][tosa] Update Tensorflow to match TOSA v1.0 specification: This pull request aims to update TensorFlow's TOSA (Tensor Operator Set Architecture) implementation to align with the TOSA v1.0 specification by incorporating changes such as adding acc_type to convolution operators, updating the Tile and Pad operations, and ensuring that certain operations have equalized ranks, with references to corresponding LLVM patches for each update.
- URL: pull/85608
- Merged: No
2. numpy copy fix: This pull request addresses an issue in the TensorFlow project by modifying the use of the astype() function to prevent unnecessary data copying by propagating copy=None instead of the default copy=True, thereby allowing the function to only perform a copy when explicitly specified.
- URL: pull/85408
- Merged: No
- Associated Commits: 35115
3. Fix typos in documentation strings: This pull request addresses the correction of typographical errors in the documentation strings of the TensorFlow project, as indicated by the commit with the message "Fix typos in documentation strings" and is currently not merged.
- URL: pull/85639
- Merged: No
- Associated Commits: 4dafe
Other Open Pull Requests
- Typographical Error Corrections: This pull request focuses on fixing minor typographical errors in the TensorFlow project. The commit message "fix typo" indicates the nature of the changes, and the pull request is currently not merged.
- QNN Type Wrappers in LiteRt: The pull request introduces basic wrappers for QNN types in the LiteRt project. It emphasizes managing dynamic resources throughout the lifecycle of instances and handling various parameters such as scalar, tensor dimensions, and scale offsets for axis tensors.
- Compiler Component Replacement with Qualcomm Implementations: This pull request involves replacing the compiler component with Qualcomm implementations in the TensorFlow project. It includes commits from three related pull requests and provides instructions for testing the changes, while noting that certain models are disabled due to unavailability and one test fails due to a bug in the simple_slice_op.mlir file.
3.2 Closed Pull Requests
This section provides a summary of pull requests that were closed in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. All other pull requests are grouped based on similar characteristics for easier analysis.
Pull Requests Closed This Week: 11
Key Closed Pull Requests
1. [RFC] rocprof insights for rocprof data: This pull request proposes the development of a Python package named "rocprof insights" to enhance the analysis and visualization of AMD hardware performance data collected by ROCm/HIP profiling tools, offering functionalities such as data loading, statistical analysis, and visualization through various plots, with potential features like overlaying latency on computational graphs and tracing input/output values for accuracy checks.
- URL: pull/85559
- Merged: No
- Associated Commits: 4ac7a, c2332, 7f4f9, bff30, 1fedf, a1cfa, 3644d, e0aef, be1cc, 07b32, dcac5, f5c6f, ec39c, eeccd, 27d0e, cfe44, f8021, 1ce2d, 32069, 9c01e, 56f62, 8d06b, 84d52, 6af30, cf09f, 4da4f, 47368, cc4a2, b3fcb, b1a42, a3440, 82c8c, c3a1f, 50990, bfe7f, a8220, bca3b, 70706, 1cfeb, 9c905, 50a78, 16101, aae89, ab856, 74698, 60a2d, 18778, 60fc5, fa3a0, abaaa, 184dc, b0af0, c4679, 6d4a5, dc439, f8a8e, 285cc, 096d7, e9be6, 5d5d0, 6a78b, 075b5, 60b8a, 7b52e, 8b7fc, 150bb, f1d1a, 7219d, 426c0, d88d5, 1c607, 0d724, 90403, b7c50, b00c7, 78a7e, 5c652, b488d, ba815, 2dba9, 34d80, ea53c, 40e3e, f65e5, 5a71c, 83713, 70b4d, abb24, 642ae, b6821, 15640, c4958, b6b66, 6d19b, 4c853, d1038, 54e1f, d8198, d860d, c0b36, 18d5c, 7946a, 24c78, 0c992, 2bab5, 89810, 4cc12, 6aa74, 63752, 57188, dc9fb, a91e4, 75ee5, 1e3c6, d40d9, 31d9c, ec39e, 94d31, f42a7, 94f61, 71a8f, d2e43, 1e3ef, ef2d4, 5bad4, 8f1cb, 37d76, e8844, 4556d, cf094, a2036, 33542, 4717f, b9bf3, 3ed87, cd4b0, f40da, a26fb, 038a6, 52994, 79309, 84c13, 4396a, 3c61e, 06702, 6e9f2, da5df, ff760, de1e4, b4e5e, ce1b2, 874c4, 451d1, 8fc12, c1526, 5dbad, 5c1aa, f7991, d377c, 7c927, f5f98, b5400, 1537a, cece9, 7ae16, e624e, 09335, 2211b, 2a929, 6c915, 07cab, 4d444, 575fd, 67b5d, a2bad, 468cf, 7ccb1, 58fc0, db5ae, 9e21f, da24c, 3515d, 8cf48, 09b52, ccced, a382c, 532df, b854f, 58d80, f4770, 3c370, e5dd7, 9f051, 1afb6, f854d, 554f8, 68c43, 2baac, 33a41, e554c, 1c5d7, 4d453, d7f29, 4ca78, 3ac1e, ed3e4, 5788e, 75503, 04c3e, e2acb, 188d1, 1d388, 87930, c1375, 66ca7, b0d33, 713b6, 09053, b6971, d9163, 76f24, 252a9, 7ec4c, 2464a, 8aedd, 071ee, 3a90e, 67d8f, ac4cb, 02686, 25619, e60b7, 70057, fd54d, b74d5, 72a9f, d71ec, f22b6, 8df41, 22772, b1e81, 34e1c, b45da, f8452, 03daf, 2e1af, 09a2b, 6afd0, 88c6b, fff74
2. Branch for Pack and DUS op: This pull request involves multiple updates to the TensorFlow project, including the addition of LiteRt Qualcomm wrappers, modifications to the TensorWrapper to return data sizes in bytes, implementation of a TensorPool for managing TensorWrappers, support for cloning static tensors with different data types, the introduction of op builders, and support for Pack and DUS operations, although it was ultimately not merged.
- URL: pull/85635
- Merged: No
- Associated Commits: 509ec, 7564f, 5a3f2, 451fb, f82a5, c01f6, 94763, 4400f, 21bff, cbc17, 10fcc, a90ee
3. Extend tensorflow restore kernel op to support type casting upon reading.: This pull request aims to enhance the TensorFlow restore kernel operation by adding support for type casting during the reading process, as evidenced by multiple commits including extending the restore function for tensor casting and implementing unit tests and optimizations.
- URL: pull/85263
- Merged: No
Other Closed Pull Requests
- Layer Utilities Optimization: This pull request focuses on optimizing the layer utilities module by enhancing runtime performance, memory efficiency, and code readability. It introduces constants for validation, combines validation steps, improves error handling, and implements lazy evaluation, although it was ultimately not merged.
- TensorFlow Installation Fixes: This pull request addresses issue #85385 by making necessary updates to the BUILD file, setup script, and manifest. These changes aim to fix the installation and running of TensorFlow 2.14.0 on C++ in Visual Studio Code, although it was not merged.
- Typographical Error Corrections: This pull request addresses the correction of typographical errors across multiple files in the TensorFlow project. The commits update specific files such as
full_type_util.handtensorflow/core/protobuf/config.proto, and it has been successfully merged into the main codebase.
- GPU Tracing Bug Fix: This pull request addresses a bug in TensorFlow's GPU tracing by converting relative timestamps in
XSpaceto absolute timestamps inStepStats. This ensures compatibility withRunMetadataand the correctness of analysis tools liketimeline.Timelinein TensorFlow v1.
- Matrix Multiplication Bug Fix: This pull request addresses a bug in the TensorFlow library where the implementation of vectorized matrix multiplication (
matmul) incorrectly used the adjoint operation instead of the transpose. It provides a fix by swapping the transpose and adjoint arguments in thetf.matmulfunction to ensure accuracy in computations involving complex data types.
- Broken Links Correction: This pull request addresses the issue of broken links in the
config.protofile within the TensorFlow project. The changes have been successfully merged, with the modifications encapsulated in a single commit.
- Support for Quint8 Type: This pull request adds support for the quint8 type to the uniform_quantize and uniform_dequantize operations. It updates the TF2XLA bridge for proper conversion and involves moving certain operation definitions to comply with a specific comment, with contributions from @mahmoud-abuzaina and unit tests added by @nhatleSummer22.
- Tosa Conv Ops Enhancement: This pull request aims to enhance the Tosa Conv Ops in the TensorFlow project by adding an 'acc_type' attribute. It also adjusts the TileOp to accommodate multiples as input, although it was ultimately not merged.
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 or closed 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.
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 | 6 | 0 | 0 | 68 |
| Venkat6871 | 2 | 2 | 0 | 32 |
| gaikwadrahul8 | 1 | 1 | 0 | 25 |
| tilakrayal | 3 | 2 | 0 | 18 |
| weilhuan-quic | 11 | 3 | 0 | 0 |
| codinglover222 | 6 | 2 | 2 | 4 |
| arzoo0511 | 0 | 0 | 0 | 14 |
| LongZE666 | 0 | 0 | 12 | 1 |
| dnmaster1 | 0 | 0 | 2 | 10 |
| pkgoogle | 0 | 0 | 0 | 11 |