Weekly GitHub Report for Tensorflow: September 29, 2025 - October 06, 2025 (12:04:23)

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 v2.19.0

1.2 Version Information:

Released on March 5, 2025, TensorFlow version 2.19.0 introduces breaking changes to the tf.lite API, including the deprecation of tf.lite.Interpreter in favor of ai_edge_litert.interpreter and changes to certain C++ constants for improved API flexibility. Key updates also include runtime support for the bfloat16 data type in the tfl.Cast operation and the discontinuation of standalone libtensorflow package publishing, while still allowing unpacking from 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.

1. tensorFlow runtime issue: This issue describes a problem where the user encounters a failure to load the native TensorFlow runtime on a Windows 11 system with an Intel UHD Graphics 600 GPU and Python 3.13, resulting in a DLL import error during TensorFlow initialization. The user reports installing TensorFlow 2.18 via pip for a game project but faces compatibility issues likely related to missing dependencies or hardware instruction support.

   • The comments include a request for more environment details and possible causes such as missing MSVC redistributables or CPU instruction support, followed by the user providing their processor and installation commands, and finally sharing a photo after reinstalling TensorFlow, indicating ongoing troubleshooting efforts.
   • Number of comments this week: 3

2. Footer Responsiveness on Mobile: This issue addresses the problem of the footer on mobile devices being displayed as a single vertical column, which results in an unnecessarily long footer. The reporter suggests improving the footer layout by arranging the footer items into two columns on mobile screens to create a more compact, visually balanced, and user-friendly design.

   • The first comment requests to be assigned the issue, and the second comment provides a solution involving dividing the footer into two sections using
     elements with column classes for mobile view, along with fixed font sizes to maintain consistency across device sizes.
   • Number of comments this week: 2

3. Transfer Learning - MobileNetV3Large/EfficientNetV2L CategoricalCrossentropy keep getting negative predictions: This issue describes a problem encountered when using transfer learning with the MobileNetV3Large or EfficientNetV2L models combined with a CategoricalCrossentropy loss function, where the user consistently receives negative prediction values. The user shares their model architecture and preprocessing steps, noting that despite scaling input images correctly, the model outputs logits with unexpected negative values and seeks guidance on what might be missing.

   • The single comment suggests adding a softmax activation function to the final Dense layer, which appears to resolve the issue, implying that the negative predictions were due to the absence of an activation function converting logits to probabilities.
   • Number of comments this week: 1

4. An exception caused by a lack of null check in the SimpleMemoryArena::Commit function: This issue reports a crash caused by the removal of a null check in the SimpleMemoryArena::Commit function after upgrading TensorFlow from version 2.6.0 to 2.19.0, which leads to an unhandled exception when memory allocation fails despite the allocation function returning success. The user is seeking clarification on whether this change was intentional and requests the possibility of reinstating the null check to prevent such crashes.

   • The commenter observed that the null check removal might be related to a scenario where a buffer pointer is null due to a zero high_water_mark_, and they are asking if there is a better solution or a specific reason behind this change.
   • Number of comments this week: 1

5. Fix the Doc of input_min and input_max in tf.raw_ops.QuantizeAndDequantizeV4Grad(): This issue addresses a documentation bug in the TensorFlow function tf.raw_ops.QuantizeAndDequantizeV4Grad(), specifically concerning the descriptions of the input_min and input_max parameters. The current docstring does not clarify that these inputs must have a rank of 1 when an axis is specified, which leads to an error during execution that should be explicitly noted in the documentation.

   • A pull request has been opened to fix the documentation, acknowledging the problem and aiming to clarify the requirements for input_min and input_max in the function’s docstring.
   • Number of comments this week: 1

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.

1. TF-TRT Warning: Could not find TensorRT: This issue describes a problem where TensorFlow on Ubuntu 22.04 cannot detect TensorRT despite having an NVIDIA RTX 3050 Ti GPU with the appropriate CUDA 12.4 and NVIDIA driver 535 installed. The user reports difficulties with driver compatibility and repeated installation attempts, resulting in a persistent warning "TF-TRT Warning: Could not find TensorRT," which is hindering their ability to proceed with machine learning development.
2. SystemError in tf.ensure_shape and tf.compat.v1.ensure_shape when dtype of shape is tf.uint64 and its value is too large.: This issue reports a bug in TensorFlow where using tf.ensure_shape or tf.compat.v1.ensure_shape with a shape tensor of type tf.uint64 containing very large values close to 2^64 causes a SystemError and OverflowError. Specifically, when such large values are passed in eager execution mode, the functions fail with an error related to the built-in isinstance function, indicating improper handling of large unsigned 64-bit integers in shape validation.
3. Feature Request: Integrate different Digital Signal Processing into tf.signal: This issue is a feature request proposing the integration of advanced digital signal processing (DSP) functionalities, similar to those found in the julius library, into TensorFlow's tf.signal module. The goal is to enhance TensorFlow's native capabilities for audio data augmentation, enabling researchers and developers to perform complex audio preprocessing and augmentation within the TensorFlow ecosystem without relying on external libraries.
4. [DOCS] Missing complex input for Round op: This issue highlights a documentation bug in TensorFlow where the Round operation is described as supporting complex tensor inputs, but in practice, attempting to use a complex tensor with this operation results in an error. The user reports that they must manually apply the Round operation separately to the real and imaginary parts of the complex tensor to achieve the expected behavior, indicating a discrepancy between the official documentation and the actual functionality.
5. tf.raw_ops.Unbatch aborts with "Check failed: d < dims()": This issue reports a bug in TensorFlow version 2.17 where the operation tf.raw_ops.Unbatch aborts with a fatal check failure error "Check failed: d < dims()" when invoked with certain tensor inputs. The user has reproduced the problem on Linux Ubuntu 20.04.3 using Python 3.11.8, providing a minimal code snippet that triggers the crash, which results in the program aborting with a core dump due to a tensor shape dimension check failure.

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: 16

Summarized Issues:

• TensorFlow function bugs and inconsistencies: Several issues report bugs or inconsistent behavior in TensorFlow functions, such as tf.image.resize with antialias=True producing unexpected results, tf.nn.weighted_moments() causing a TypeError when keepdims=False, and tf.keras.applications.EfficientNetV2L including an unexpected top_activation layer despite include_top=False. These problems highlight the need for clearer implementation and error handling to align with user expectations and avoid runtime errors.
• [issues/101265, issues/101499, issues/101580]

• Transfer learning and model output issues: There are problems related to transfer learning with specific models like MobileNetV3Large and EfficientNetV2L, where using a CategoricalCrossentropy loss configured for logits results in negative prediction values due to missing softmax activation. This indicates a mismatch between model architecture and loss function expectations that can lead to incorrect training behavior.
• [issues/101296]

• Import and runtime initialization failures: Users experience ImportErrors caused by failed DLL loads on Windows 11 with TensorFlow 2.18 and Python 3.13, preventing the native TensorFlow runtime from initializing properly. This issue blocks TensorFlow from running and requires attention to compatibility and runtime loading mechanisms.
• [issues/101307]

• Memory allocation and null check regressions: A bug was introduced in TensorFlow 2.19.0 due to the removal of a null check in SimpleMemoryArena::Commit, causing unhandled exceptions during memory allocation failures. The reporter requests clarification on whether this change was intentional and if the null check can be restored to prevent crashes.
• [issues/101361]

• TensorFlow Lite and security fix incompleteness: A potential incompleteness in the CVE-2022-23559 fix for TensorFlow Lite is reported, where some variables related to embedding offsets were not updated from int to size_t following a commit. This inconsistency could lead to errors or security issues if not addressed properly.
• [issues/101397]

• TensorFlow Lite usage concerns in Play Services: There is a request to avoid using TensorFlow within Play Services when possible, indicating concerns about its integration or performance in that context.
• [issues/101358]

• TensorFlow installation and package availability issues: Users are unable to install a specific old version of the TensorFlow nightly package (tf_nightly==2.20.0.dev20250619) required by tensorflow-text-nightly because it has been removed from PyPI. Guidance is sought on how to access or install this unavailable version.
• [issues/101391]

• Documentation bugs and inaccuracies: Multiple documentation issues are reported, including incorrect wording in tf.keras.layer.Attention, incomplete parameter descriptions in tf.raw_ops.QuantizeAndDequantizeV4Grad(), invalid characters in docstrings for tf.raw_ops.ThreadPoolHandle() and tf.raw_ops.StatsAggregatorHandleV2(), and missing notes about GPU limitations in tf.raw_ops.DenseBincount(). These inaccuracies can mislead users and cause runtime errors if not corrected.
• [issues/101445, issues/101516, issues/101521, issues/101578]

• Model runtime errors and debugging difficulties: Using Keras models with Conv3D followed by MaxPool3D layers can cause fatal cuDNN aborts due to zero-sized spatial dimensions in pooling, instead of raising clear Python errors. This makes debugging difficult and indicates a need for better error reporting in such cases.
• [issues/101409]

• UI layout improvements for mobile devices: The footer on mobile devices is currently displayed as a single vertical column, and an improvement is proposed to redesign it into two columns for a more compact, balanced, and user-friendly layout.
• [issues/101472]

• Tensorboard Profiler failures on specific hardware: The Tensorboard Profiler fails to work with TensorFlow 2.14 on Linux systems using NVIDIA Tesla P100 GPUs, with errors related to the CUPTI interface and the profiler tab not appearing despite matching software versions. This issue affects profiling capabilities on certain hardware configurations.
• [issues/101590]

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: 0

Summarized Issues:

As of our latest update, there were no issues closed in the project this week.

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.

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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: 5

Key Open Pull Requests

1. Fix the docstring in tf.raw_ops.ThreadPoolHandle() and tf.raw_ops.StatsAggregatorHandleV2(): This pull request aims to correct and improve the docstrings in the TensorFlow raw operations tf.raw_ops.ThreadPoolHandle() and tf.raw_ops.StatsAggregatorHandleV2(), addressing issue #101521.

• URL: pull/101522

• Merged: No

• Associated Commits: d60d5, 0fa3c

2. fix: add explicit length check in ShapeEquals: This pull request addresses a bug in the ShapeEquals function by adding an explicit length check to ensure that shapes are only considered equal if they have the same rank, preventing incorrect equality results when shapes share a prefix but differ in overall dimensions.

• URL: pull/101287

• Merged: No

• Associated Commits: 3a608

3. Update api_def_QuantizeAndDequantizeV4Grad.pbtxt to fix the docstring: This pull request proposes an update to the api_def_QuantizeAndDequantizeV4Grad.pbtxt file to correct the docstring, addressing issue #101516.

• URL: pull/101517

• Merged: No

• Associated Commits: 488af

Other Open Pull Requests

• Documentation improvements: These pull requests focus on enhancing the clarity and correctness of TensorFlow documentation. One adds a note about the UnimplementedError in tf.raw_ops.DenseBincount(), while the other fixes a grammatical typo in the docstring of tf.keras.layers.Attention.call to improve readability in IDEs and on tensorflow.org.
• pull/101579, pull/101603

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: 0

As of our latest update, there are no closed pull requests for the project this week.

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.

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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
ILCSFNO	14	6	13	9
mihaimaruseac	0	0	0	14
khteh	0	0	7	3
Ma-gi-cian	4	2	0	3
apach301	2	2	2	0
kadirb4rut	3	2	0	1
LiSsHhUuAaIi	0	0	6	0
KamranBaig1122	2	2	0	1
tinywisdom	0	0	5	0
gzmkl	2	2	0	0