Weekly GitHub Report for Tensorflow: July 07, 2025 - July 14, 2025 (12:03:04)
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 v2.19.0
1.2 Version Information:
The TensorFlow 2.19.0 release, created on March 5, 2025, introduces breaking changes in the LiteRT
(a.k.a. tf.lite
) C++ and Python APIs, including the transition of tf.lite.Interpreter
to a new location ahead of its removal in version 2.20. Additionally, the update enhances the tfl.Cast
operation to support bfloat16
in the runtime kernel and discontinues the publication of libtensorflow
packages, though they remain accessible via PyPI.
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.
-
Build Error While Compiling TensorFlow Lite Using CMake: This issue involves a build error encountered while compiling TensorFlow Lite using CMake on Ubuntu 22.04.2 LTS, where the error messages indicate that several TensorFlow version macros (TF_MAJOR_VERSION, TF_MINOR_VERSION, TF_PATCH_VERSION, and TF_VERSION_SUFFIX) are not defined. The problem persists despite attempts to reproduce it with TensorFlow Nightly and involves custom code, suggesting a potential misconfiguration or missing definitions in the build setup.
- The comments discuss various compatibility issues related to upgrading or downgrading TensorFlow versions, particularly with dependencies like
protobuf
andnumpy
, and suggest using specific CMake commands to define TensorFlow version macros to resolve the build error. Users share their experiences with similar issues and seek advice on dependency management and version conflict resolution, with a suggestion to follow updated official documentation for a potential fix. - Number of comments this week: 8
- The comments discuss various compatibility issues related to upgrading or downgrading TensorFlow versions, particularly with dependencies like
-
Fail to build libtensorflow_framework.so.2.20.0: This issue involves a failure to build the
libtensorflow_framework.so.2.20.0
using Bazel on Ubuntu 22.04.1 LTS, where the build command that previously succeeded now results in an error indicating that the target is not declared in the package. The problem appears to be linked to a specific commit, as the build succeeds on a previous commit but fails on a newer one, suggesting a potential issue with recent changes in the codebase.- The comments discuss compatibility issues related to TensorFlow upgrades and downgrades, particularly focusing on dependency conflicts with packages like
protobuf
andnumpy
. Users share their experiences with version conflicts and seek advice on resolving these issues, including recommended pip commands and dependency management strategies. There are also mentions of off-topic comments that are not relevant to the issue at hand. - Number of comments this week: 6
- The comments discuss compatibility issues related to TensorFlow upgrades and downgrades, particularly focusing on dependency conflicts with packages like
-
lib new version not support 16kb pages in android: This issue involves a compatibility problem with the
libtensorflowlite_jni.so
library from TensorFlow Lite version 2.17.0, which does not support the required 16 KB page size for Android devices targeting SDK 35+. The user is seeking guidance on whether there is an officially supported build or a method to compile TensorFlow Lite with 16 KB alignment using CMake or Bazel.- The comments discuss various compatibility issues when upgrading or downgrading TensorFlow, particularly with dependencies like
protobuf
andnumpy
. Users share their experiences and seek advice on resolving these conflicts, while a TensorFlow team member acknowledges the 16 KB alignment issue and suggests building from source or using LiteRT as a workaround. - Number of comments this week: 4
- The comments discuss various compatibility issues when upgrading or downgrading TensorFlow, particularly with dependencies like
-
GPU Not Detected by TensorFlow Despite Proper System Setup: This issue involves TensorFlow's failure to detect or utilize available NVIDIA GPUs despite the system being correctly configured with the necessary hardware, drivers, CUDA, and cuDNN versions. The problem significantly impacts model training performance and efficiency, as TensorFlow does not throw explicit errors, making diagnosis difficult.
- The comments discuss various version compatibility issues between TensorFlow, CUDA, and cuDNN, with users sharing their experiences and troubleshooting steps, such as creating new virtual environments and adjusting dependencies. There is a request for community input on best practices for resolving these conflicts, and a reminder that TensorFlow versions support specific NVIDIA devices. Additionally, there is a comment addressing spam in the issue thread.
- Number of comments this week: 4
-
How do we silence noisy messages?: This issue is about a user experiencing excessive and unhelpful error messages from TensorFlow's C++ backend, specifically from the
check_numerics_op.cc
file, which are hindering their debugging process. The user is seeking a method to suppress these messages, ideally by printing a single line indicating the frequency of the issue, rather than the verbose output currently being generated.- The comments discuss potential solutions to suppress the error messages, including redirecting standard error output to null, which silences all errors but may hide important logs. The user prefers a more targeted approach, such as using an environment variable or a logger flag, and shares a custom solution they developed to address the issue.
- 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.
- TF-TRT Warning: Could not find TensorRT: This issue involves a user experiencing difficulties with TensorFlow on an Ubuntu 22.04 system, where the TensorRT library cannot be found despite multiple installation attempts. The user suspects that the problem may be related to the NVIDIA driver version, as they are using the 535 driver instead of the automatically installed 550 driver, and they are seeking assistance to resolve this issue to focus on their Machine Learning coursework.
SystemError
intf.ensure_shape
andtf.compat.v1.ensure_shape
whendtype
ofshape
istf.uint64
and its value is too large.: This issue describes a bug in TensorFlow where usingtf.ensure_shape
ortf.compat.v1.ensure_shape
with ashape
ofdtype
tf.uint64
and a value close to 2^64 results in aSystemError
andOverflowError
. The problem occurs specifically in eager mode, as demonstrated by the exampleshape = tf.constant([18446743219011059112, 1], dtype=tf.uint64)
, and has been reproduced with TensorFlow version 2.15 on a Linux Ubuntu 20.04 system.- Feature Request: Integrate different Digital Signal Processing into tf.signal: This issue is a feature request to integrate advanced Digital Signal Processing (DSP) functionalities into TensorFlow's
tf.signal
module, similar to those available in the PyTorch ecosystem, particularly from the julius library. The integration aims to enhance TensorFlow's capabilities in audio data augmentation, providing researchers and developers with native tools for complex audio processing, thereby improving workflow efficiency and model generalization without relying on external libraries. - [DOCS] Missing complex input for Round op: This issue highlights a documentation bug in TensorFlow's
Round
operation, where the official documentation incorrectly states that a complex tensor can be used as input, but in practice, this does not work as expected. The user reports that they must apply theRound
operation separately to the real and imaginary parts of the tensor to achieve the desired result, indicating a discrepancy between the documented and actual behavior of the function. - tf.raw_ops.Unbatch aborts with "Check failed: d < dims()": This issue pertains to a bug in TensorFlow version 2.17, where the
tf.raw_ops.Unbatch
operation aborts unexpectedly with an error message indicating a failed check on tensor dimensions. The problem has been reproduced using TensorFlow Nightly on a Linux Ubuntu 20.04.3 LTS system with Python 3.11.8, and involves a specific standalone code snippet that triggers the error.
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: 7
Summarized Issues:
- Error Message Filtering in TensorFlow: Users are experiencing issues with repetitive and unhelpful error messages from TensorFlow's C++ backend, specifically from
check_numerics_op.cc
. These messages clutter the debugging process, and users are seeking a solution to filter these messages without suppressing all error logs.
- Build and Compatibility Issues with TensorFlow: Several users are encountering build and compatibility issues with TensorFlow on different platforms. One issue involves a failure to build
libtensorflow_framework.so.2.20.0
using Bazel on Ubuntu 22.04.1 LTS due to a potential compatibility problem between Bazel and TensorFlow versions. Another issue concerns a build error with TensorFlow Lite using CMake on Ubuntu 22.04.2 LTS, where undefined version macros lead to a failure in the build process.
- TensorFlow Lite Library Alignment Issue: The
libtensorflowlite_jni.so
library from TensorFlow Lite version 2.17.0 is incompatible with Android devices requiring a 16 KB page size, as it is currently aligned to a 4 KB page size. Users are requesting either an official build supporting 16 KB alignment or guidance on compiling it with the necessary alignment using CMake or Bazel.
- Dependency Conflicts in TensorFlow Upgrades: Users face compatibility problems when upgrading TensorFlow from version 2.x to 2.15.0 due to dependency conflicts with
protobuf
andnumpy
. Similar errors occur when attempting to downgrade to an earlier 2.x release, prompting requests for solutions or strategies to resolve these version conflicts.
- GPU Detection Issues in TensorFlow: TensorFlow fails to detect and utilize available NVIDIA GPUs despite proper system configuration, impacting model training performance and efficiency. The issue is characterized by
tf.config.list_physical_devices('GPU')
returning an empty list whilenvidia-smi
detects the GPU correctly, with no explicit TensorFlow errors thrown, complicating diagnosis.
- Outdated TensorFlow Java Documentation: The TensorFlow Java documentation on the official website is significantly outdated, with installation instructions still referencing version 0.3.3 instead of the current version 1.1.0. Despite a pull request being submitted to address this, there has been no response from the documentation maintainers, prompting further attention through an issue report.
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: 15
Summarized Issues:
- TensorFlow Lite Task Audio library compliance: The TensorFlow Lite Task Audio library needs to support 16KB page sizes by the Google Play deadline of November 1, 2025. The current version 0.4.4 does not meet this requirement, prompting an urgent request for an update to ensure compliance.
- Code quality and spam issues: Discussions about TensorFlow's code quality have been marred by spam issues, leading to the closure of some discussions. Specific concerns include unclear API documentation and the need for more discussions on best practices.
- Google RBE API reference removal: A reference to the Google RBE API in the
.bazelrc
file of the TensorFlow project needs to be removed because the API is no longer publicly accessible.
- Version compatibility and dependency conflicts: Users face challenges with version compatibility and dependency conflicts when upgrading or downgrading TensorFlow, particularly with dependencies like
protobuf
,numpy
, andkeras
. These issues prompt requests for solutions such as reliable pip command sequences and tools for managing these transitions across different environments.
- Import errors on Windows systems: Import errors occur when attempting to use TensorFlow 2.19 on Windows systems, with failures in loading the
_pywrap_tensorflow_internal
DLL. These issues may be due to missing dependencies or compatibility problems with the system's configuration, such as the absence of the MSVC 2019 redistributable or mismatched architecture.
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. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.
Pull Requests Opened This Week: 4
Key Open Pull Requests
1. fix(dtensor): guard against nullptr
from TF_TensorData
in ExtractSmallTensorValue
: This pull request introduces a defensive check in the ExtractSmallTensorValue
function to handle cases where TF_TensorData()
might return a nullptr
, preventing potential segmentation faults with invalid or uninitialized tensors, and includes a new C++ unit test to validate this handling, ensuring robustness across all environments without requiring specific hardware.
- URL: pull/96866
- Merged: No
2. Alessio win: This pull request involves adding documentation files to the TensorFlow project, as indicated by the commits titled "build_tensorflow_howto_hiistory.txt" and "Add files via upload\ndocs," but it has not yet been merged.
- URL: pull/96867
- Merged: No
3. Fixes L2Pool implementation to not average pooling region squares: This pull request addresses the correction of the L2Pool implementation in TensorFlow by ensuring it does not average the squares of the pooling regions, and it aims to reintroduce changes from a previous pull request (PR-74079) after discussions in issue #73742.
- URL: pull/96599
- Merged: No
- Associated Commits: 5d42d
Other Open Pull Requests
- 16KB Page Size Alignment for TensorFlow Lite Libraries: This pull request addresses a warning in the application by enabling 16KB page size alignment for
libtensorflowlite_gpu_gl.so
. It also suggests a similar update forlibtensorflowlite_gpu_delegate.so
to ensure consistency across TensorFlow Lite libraries.
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. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.
Pull Requests Closed This Week: 1
Key Closed Pull Requests
1. Remove reference to deprecated Google RBE API in .bazelrc (#96600): This pull request addresses the removal of an outdated reference to the deprecated Google Remote Build Execution API endpoint from the .bazelrc file in the TensorFlow project to prevent build issues, as the endpoint is no longer publicly accessible.
- URL: pull/96656
- Merged: No
- Associated Commits: b57df
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 |
---|---|---|---|---|
CloudSmallInsect | 0 | 0 | 8 | 27 |
Venkat6871 | 2 | 0 | 0 | 15 |
mihaimaruseac | 2 | 0 | 0 | 10 |
gaikwadrahul8 | 0 | 0 | 0 | 8 |
Tai78641 | 4 | 2 | 0 | 0 |
xin486946 | 0 | 0 | 2 | 3 |
hitbuyi | 0 | 0 | 1 | 3 |
dyoung23 | 0 | 0 | 4 | 0 |
mohiuddin-khan-shiam | 2 | 1 | 0 | 0 |
shubhk18 | 3 | 0 | 0 | 0 |