Weekly GitHub Report for Tensorflow: November 27, 2024 - December 04, 2024
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 Other Noteworthy Updates:
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.
- It doesn't support on python3.13: This issue is about the inability to install TensorFlow version 2.17 on Python 3.13, as the installation process fails to find a compatible version of TensorFlow for this Python version. The problem arises because TensorFlow's release cycle does not align with the release of new Python versions, leading to a delay in support for the latest Python releases.
- The comments discuss the recurring issue of TensorFlow not supporting new Python versions immediately upon their release, with historical references to similar issues since Python 3.8. Users express frustration and suggest downgrading Python as a temporary solution, while others highlight the importance of supporting Python 3.13 due to its adoption in major distributions like Fedora 41. There is also a discussion about the current supported Python versions, with some discrepancies noted between official documentation and available package versions on PyPI.
- Number of comments this week: None
-
Failed to build
tensorflow_ccin Windows when linking: This issue involves a failure to build thetensorflow_cclibrary on Windows when using LLVM/Clang, where the linking process fails due to missing symbols such asSessionandSavedModelBundleInterface. The problem persists across different versions of LLVM/Clang, indicating that the issue is not related to the compiler version but possibly due to incomplete exported symbol definitions and missing dependencies in the build configuration.- The comments discuss potential causes and solutions for the linking errors, including missing dependencies and incomplete symbol definitions in the build files. Some users have attempted to manually add missing dependencies, but this process is complex and time-consuming. There is a mention of a missing configuration for Windows, which works correctly on Linux, and a team member is investigating a fix. Users are awaiting an official patch to resolve the issue, as manual fixes are not feasible.
- Number of comments this week: None
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Very serious! Using this method will definitely result in memory leaks, I hope you can provide support: This issue reports a memory leak problem when using a specific method in TensorFlow version 2.18, which persists despite attempts to clear memory, as evidenced by a consistent upward trend in memory usage over time. The user has provided a detailed code snippet to reproduce the issue, highlighting that the memory release process cannot keep up, leading to periodic memory recycling but ultimately resulting in increased memory consumption.
- The comments section includes a user expressing interest in contributing to the issue, followed by guidance to raise a pull request and refer to the official TensorFlow contribution documentation. Another comment mentions feedback from Keras, indicating that the issue has been long-standing and originates from TensorFlow, with a request for notification upon resolution. Additionally, there are offers from users to collaborate on resolving the issue.
- Number of comments this week: None
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Unable to build TensorFlow without TensorRT: This issue involves a user encountering an error while attempting to build TensorFlow from source without TensorRT support, despite configuring the build to exclude TensorRT. The error message indicates a failure related to the absence of the
NvInferVersion.hfile, which is associated with TensorRT, suggesting a potential bug in the build configuration process.- The comments discuss various attempts to resolve the issue, including suggestions to explicitly disable TensorRT during configuration, which the original poster found unrecognized. Other users confirmed similar issues with different configurations and attempted workarounds, such as installing TensorRT or using different compiler versions, without success. Some users suggested using older or different TensorFlow versions, like v2.14, which seemed more stable, while others noted that newer versions like v2.18 have disabled TensorRT support in CUDA builds. The discussion highlights ongoing challenges and potential solutions, with a recommendation to try the latest TensorFlow version for improvements.
- Number of comments this week: None
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tf.autodiff.ForwardAccumulator._watch(primal, tangent) erroneously refers to dtype.is_floating which does not exist for a Keras layer.: This issue is about a bug in TensorFlow version 2.17.0, where the method
tf.autodiff.ForwardAccumulator._watch(primal, tangent)incorrectly refers to the attributeprimal.dtype.is_floating, causing a crash becauseprimal.dtypeis a string type variable that does not have the attribute "is_floating". The error occurs when using a Keras layer, and the user has provided standalone code to reproduce the issue, which is based on the example code from the TensorFlow documentation.- The comments discuss the issue with the example code from the TensorFlow documentation, which should work but doesn't due to the bug. A suggestion is made to use
tf-kerasinstead oftf.kerasto avoid the error, and the user confirms that this workaround resolves the issue for them. However, the user points out that the bug still exists intf.kerasand questions whether it should be fixed, as relying on a workaround doesn't seem appropriate. They also suggest that the example code in the documentation should be updated to reflect this workaround. - Number of comments this week: None
- The comments discuss the issue with the example code from the TensorFlow documentation, which should work but doesn't due to the bug. A suggestion is made to use
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: 28
Summarized Issues:
- InvalidArgumentError with MirroredStrategy on GPUs: Users encounter an InvalidArgumentError when using TensorFlow's MirroredStrategy with multiple GPUs, specifically with 2 NVIDIA Tesla V100s. This error does not occur when using tf.distribute.get_strategy(), indicating a potential issue with MirroredStrategy. The user provides standalone code to reproduce the error and expects the code to run without errors under MirroredStrategy as it does with tf.distribute.get_strategy().
- Build Issues on Windows and CentOS: Users face challenges building TensorFlow from source on different platforms, including Windows and CentOS. On Windows, a misconfiguration with MSYS2 installation paths leads to a failure in executing a command with
bash.exe. On CentOS, a "No such file or directory: 'patchelf'" error occurs despite having installed patchelf, indicating potential path or configuration issues.
- TensorFlow Lite Conversion and Support Issues: Users report problems related to TensorFlow Lite, including incomplete submissions for conversion issues and TypeErrors when importing certain support libraries. These issues highlight the need for detailed system information and potential updates to generated code or package versions to resolve compatibility problems.
- Mixed Precision and Compilation Errors: A bug in TensorFlow 2.18.0 on Google Colab arises due to mixed precision floating point types when combining bilinear interpolation, XLA, and a mixed_float16 policy. This compilation error does not occur if any of these components are used separately, suggesting an issue with their integration. Users seek solutions to address this specific combination of features.
- Docker Image GPU Support Issues: The Docker image 'tensorflow:2.18-gpu-jupyter' lacks GPU support due to missing CUDA libraries, contrary to user expectations based on previous versions and documentation. Users are required to install Hermetic CUDA and modify the
requirements.txtfile to includetensorflow[and-cuda]for proper GPU functionality.
- Feature Requests for TensorFlow Enhancements: Users request new features in TensorFlow, such as int8 support for Unsorted_Segment_X operators and DDR5 memory bandwidth calculation. These enhancements aim to improve performance and compatibility for specific use cases, such as mobile deployment of graph neural networks and accurate memory bandwidth calculations.
- Error Messages and Debugging Challenges: Users encounter unclear error messages, such as "None values not supported," which complicate debugging efforts. Suggestions include improving error messages to reference missing labels or null
y_trueto enhance clarity and assist users in identifying the source of errors.
- XLA Recompilation and Dynamic Dimensions: Frequent recompilations occur when using TensorFlow's XLA on a GPU due to changes in tensor shapes. Users seek guidance on configuring dynamic dimensions to prevent these recompilations and improve performance, highlighting the need for better handling of dynamic shapes in XLA.
- TensorFlow Lite on Windows and ARMv6: Users face difficulties obtaining working TensorFlow Lite libraries for specific platforms, such as x86 on Windows and ARMv6 on Raspberry Pi Zero W. These challenges highlight the need for official releases or guidance to support these architectures.
- Gradient-Based Interpretability Issues: Unexpected behavior occurs when using a gradient-based interpretability method with a pre-trained VGG16 model, where positive gradients are consistently received for filters with negative or zero outputs. Users seek clarification on whether this is due to a coding error, a misunderstanding of gradient computations, or another issue.
- Documentation and Code Clarification Requests: Users request clarification on specific TensorFlow code statements, such as
constant_op.constant(2), due to insufficient documentation. These requests highlight the need for clearer explanations and examples in the official documentation to aid user understanding.
- TensorFlow Installation and Configuration Guidance: Users seek guidance on installing and configuring TensorFlow, including using it with Chrome access and creating Python extension modules with the TensorFlow C API. These requests emphasize the importance of comprehensive installation and configuration documentation.
- Spam and Irrelevant Content: Several issues contain spam or irrelevant content, such as links to viral videos unrelated to the TensorFlow project. These posts highlight the challenges of maintaining relevant and appropriate content on public repositories.
- issues/tensorflow/tensorflow/issues/82068
- issues/tensorflow/tensorflow/issues/82069
- issues/tensorflow/tensorflow/issues/82070
- issues/tensorflow/tensorflow/issues/82071
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- issues/tensorflow/tensorflow/issues/82075
- CPU Performance Optimization: Users report suboptimal CPU performance when running TensorFlow-based applications, such as DeepFace, on different machines. Despite significant differences in processing times and CPU usage percentages, the CPU is not fully utilized, prompting users to seek optimization strategies for better performance.
- Metal Plugin Support on macOS: A bug in TensorFlow 2.18.0 on macOS 15.1.1 results in an error due to a missing symbol in the Metal plugin's shared library. This issue prevents the import of TensorFlow with Metal plugin support in a virtual environment, highlighting compatibility challenges with macOS updates.
- Restoring Partial Parameters from Checkpoints: Users seek guidance on restoring partial parameters from a saved checkpoint of a Keras model to fine-tune it on a new dataset with a smaller vocabulary size. This process involves addressing shape mismatches in layers such as the embedding and output layers, which can be challenging.
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: 210
Summarized Issues:
- Non-compliance with PEP600 standards for tflite_runtime wheels: The pre-built x86_64 wheels for tflite_runtime 2.14 on PyPI are not compliant with PEP600 standards. They incorrectly use the platform tag "manylinux2014_x86_64" instead of "manylinux_2_31_x86_64". This necessitates either renaming the wheels or rebuilding them in a compatible environment.
- TensorFlow Lite CMake build issues on Windows: There is a bug where the TensorFlow Lite CMake build fails on Windows 11 using Visual Studio 2022. Errors indicate missing include files such as 'sys/mman.h', 'Python.h', and 'unistd.h'. This occurs despite following the default build instructions and having installed the necessary workloads and dependencies.
- Inference time discrepancies in TensorFlow Lite models on Android: Users have reported a significant increase in inference time for TensorFlow Lite models on Android devices when using bundled versions 2.12.0 and later. This is compared to earlier versions and the Google Play Services version. Users are seeking insights into the cause of this performance discrepancy.
- TensorFlow Lite converter issues with int8 quantization: The TensorFlow Lite converter fails during the calibration step when converting ConcreteFunctions with int8 quantization. This is due to the generation of a saved model without any signatures. A workaround using
tf.lite.TFLiteConverter.from_keras_model()is available, but users seek a fix for the dedicated ConcreteFunction conversion pipeline.
- Crashes during TensorFlow Lite inference and conversion: TensorFlow Lite inference crashes when using the
tf.reversefunction with an empty axis list, identified as a non-op causing program crashes. Additionally, a crash occurs during model conversion at theconverter.convert()step due to a "gather index out of bounds" error, with suggestions for debugging involving C++ knowledge.
- Lack of support for
tf.float64in TensorFlow Lite: TensorFlow Lite does not support thetf.float64data type in C++ inference, resulting in a segmentation fault during high-precision inference. Python inference handlestf.float64successfully, but model conversion raises a ValueError when specifyingtf.float64as input and output types, highlighting a need for higher precision support.
- Performance and compatibility challenges with LSTM models in TensorFlow Lite: Users face challenges converting Keras LSTM models to TensorFlow Lite format, particularly with "While" operations on target platforms. They seek guidance on optimizing LSTM operators to retain hidden states efficiently, focusing on better performance using the "UnidirectionalSequenceLSTM" operator.
- Incorrect results with TensorFlow Lite FP16 model on iOS: A TensorFlow Lite FP16 model using the Core ML Delegate on iOS 17.2 produces incorrect results. It does not achieve any speed improvement compared to the FP32 model, as reported by a user implementing it in an iOS application.
- Build errors with GPU and NNAPI delegate support in Android apps: Users encounter build errors due to an undefined symbol 'TfLiteGpuDelegateV2Create' when integrating GPU and NNAPI delegate support into Android apps using TensorFlow Lite models. They seek clarification on why the model does not fallback to CPU execution as expected.
- Non-converted operations in TensorFlow Lite model conversion: Users question why certain operations remain unconverted when converting a simple TensorFlow model to TensorFlow Lite. They seek solutions to address this conversion challenge and its impact on model performance on microcontrollers.
- Runtime errors with TensorFlow Lite GPU delegate: A runtime error occurs when executing a TensorFlow Lite model on a GPU delegate without CPU fallback due to unsupported operations with dynamic sized tensors. Specifically, the 'WHILE' operation does not automatically revert to CPU execution as expected.
- Feature requests for TensorFlow Lite selective builds and quantization: Users request TensorFlow Lite support for selective builds using TensorFlow operations (flex delegate) for embedded Linux on aarch64 architecture to reduce binary size. Additionally, they request the ability to exclude specific operations from quantization to prevent accuracy drops in models.
- Compilation and build issues with TensorFlow Lite on various platforms: Users face compilation errors when building TensorFlow Lite benchmark tools on macOS due to undefined symbols related to the Core ML delegate. Additionally, build failures occur when compiling TensorFlow Lite using Docker due to C++20 extensions required by the nnapi_delegate component.
- Discrepancies in TensorFlow Lite quantization and model conversion: Users report discrepancies in TensorFlow Lite's full integer quantization process, where models with multiple signatures are treated differently compared to dynamic range quantization. This results in varied subgraph sizes and raises concerns about handling aliased signatures and computational graph segments.
- Need for Python 3.11 compatible tflite-runtime wheels for MacOS: Users request the building and release of Python 3.11 compatible tflite-runtime wheels for MacOS on PyPI. The current available wheels only support Linux, causing installation errors for users attempting to run automated jobs on MacOS with Python 3.11.
- Lack of SVDF layer implementation in TensorFlow's Keras library: Users report the absence of a dedicated SVDF layer implementation in TensorFlow's Keras library that can be recognized as an SVDF operator when converted to TFLite. Discussions focus on the correct sequence of operations needed for such recognition and attempts to resolve the issue through various implementations.
- GPU model compatibility in TensorFlow Lite: Users seek guidance on adding the GPU model "samsung_xclipse_940" from the Samsung S24 to TensorFlow Lite's compatibility database. They inquire about the process or possibility of submitting pull requests for this purpose.
- Performance discrepancies in TensorFlow Lite model benchmarks on Android: Users report a significant performance discrepancy in inference times when using the TensorFlow Lite Interpreter API on Android devices. The inference process is 10–50 times slower on a Google Pixel 4a running Android 13 compared to an iPhone 12 using the same TensorFlow Lite model.
- Inefficiencies in TensorFlow Lite's GPU delegation policy: Users criticize TensorFlow Lite's GPU delegation policy, specifically the FirstNLargestPartitions logic for prioritizing partitions with more layers rather than those with higher computational demands. They suggest improvements such as a FLOPs-based partition selection policy to optimize inference performance.
- Segmentation fault with TensorFlow Lite's GPUv2 delegate on Pixel devices: A segmentation fault occurs when running a split-head attention CLIP model using TensorFlow Lite's GPUv2 delegate on Google Pixel devices. The same model runs successfully on other devices like the Samsung Galaxy S23 and Xiaomi Redmi Note 10 5G.
- Challenges with TensorFlowLiteSelectTfOps.framework on iOS: Developers face difficulties in force-loading the TensorFlowLiteSelectTfOps.framework, created with a selective build for iOS using Xcode 15 and TensorFlow version r2.9. This results in numerous undefined symbol errors related to the Google protobuf library during the build process.
- Feature request for new data types in TensorFlowLite format schema: Users request enhancements to the TensorFlowLite format schema by adding new data types, such as a custom floating point format with configurable bits for exponent and mantissa. This is to support custom accelerators pertinent for proprietary tools relying on the TFLite format.
- Cross-compilation issues with TensorFlow Lite C library for ARM devices: Users encounter errors during the cross-compilation of the TensorFlow Lite C library using CMake for a BeagleBone Black device with an ARMv7 processor. This occurs despite following the official TensorFlow Lite guide and using the specified toolchain.
- EfficientDet models failing on Hexagon delegate: EfficientDet models, specifically the normal and lite int8 versions, fail to produce valid outputs when run on the Hexagon delegate. They function correctly on CPU and GPU delegates, with the problem highlighted by invalid output values and a request for crash logs to investigate the discrepancy.
- TensorFlow Lite converter's constant folding pass issues: The TFLite converter's constant folding pass prevents storing packed int8 tensors and instead stores dequantized float32 tensors. This results in a larger model size than expected, and users seek a way to prevent constant folding for specific nodes to ensure efficient storage of packed weights.
- Bounding box errors in Android object detection application: A bug in an Android object detection application using TensorFlow causes bounding boxes for detected objects to be incorrectly drawn and shift to the right when using the front camera. Attempts to correct the mirrored coordinates by flipping them horizontally in the code have been unsuccessful.
- Support for dilation parameter in Conv2dTranspose operation: There is a need for TensorFlow Lite to support the dilation parameter in the Conv2dTranspose operation to align with the Web Neural Network API specifications. This feature is necessary for implementing the WebNN API in the Chromium browser on various platforms.
- Build issues with TensorFlow Lite GPU Delegate on macOS: Developers face difficulties building the TensorFlow Lite GPU Delegate for Android on macOS Sonoma 14.5. The build process incorrectly attempts to link macOS-specific frameworks like CoreFoundation, causing linker errors and highlighting the need for improved documentation and compatibility guidance.
- Crashes with TensorFlow Lite model on Android GPU: A TensorFlow Lite model crashes on Android devices when using the GPU delegate due to a 'null pointer dereference'. The problem is specific to certain devices and potentially related to the OpenGL implementation, as indicated by the error logs and discussions among developers.
- Concerns about removal of GPU support for TensorFlow on Windows: A visually impaired data scientist raises concerns about the removal of GPU support for TensorFlow on Windows, arguing it is discriminatory as it limits accessibility for users who rely on Windows for its screen reader support. They request the re-enabling of GPU support on Windows and Mac systems.
- Documentation bug in TensorFlow's
tf.keras.Inputlayer: A documentation bug in TensorFlow highlights that thetf.keras.Inputlayer performs implicit data conversion when the data type is not specified. This leads to potential unexpected behavior, performance overhead, and loss of data semantics, suggesting the need for explicit type checks and error messages.
- Warnings during TensorFlow build on Linux Ubuntu: Users encounter numerous warnings while building TensorFlow version 2.18.0 from source on a Linux Ubuntu 24.04 system. These warnings are related to passing
input_shape/input_dimarguments to layers and registration conflicts with cuFFT, cuDNN, and cuBLAS plugins, prompting concerns about potential build corruption.
- Error handling discrepancies between CPU and GPU in TensorFlow: A discrepancy in error handling is reported between CPU and GPU when using
tf.gatherin TensorFlow. The CPU correctly raises anINVALID_ARGUMENTerror for out-of-range indices, but the GPU fails to do so, leading to unexpected outputs and potential silent data corruption.
- Inference issues with EfficientNet v2 b0 model in TensorFlow Lite: Users experience significantly slower inference times and incorrect output classes when using an EfficientNet v2 b0 model in TensorFlow Lite format compared to ONNX. This is particularly challenging when running inference in Kotlin and facing compatibility issues with GPU and NNAPI on mobile devices.
- Installation issues with GPU-optimized TensorFlow on Windows 11: Users face difficulties installing the GPU-optimized version of TensorFlow on a Windows 11 Home system. The installation process defaults to downloading the Intel-optimized version, preventing the use of an Nvidia 4060 laptop GPU for model training, despite having CUDA and cuDNN configured.
- Loss of custom methods in subclassed models with ModelCheckpoint: Users report a problem where custom methods and training configurations in a subclassed model using TensorFlow 2.12.0 are not preserved when saved with the ModelCheckpoint callback. This results in the loss of overridden methods and training configurations upon loading the model.
- Compilation errors with TensorFlow Lite delegates using ARM toolchain: Users encounter a compilation error when building TensorFlow Lite delegates with the ARM toolchain. The error is due to an 'asm' operand having impossible constraints in the file
streams_arm_32.h, occurring when executing a Bazel build command with specific configurations for an ARMv7 architecture.
- Spam and privacy breach issues on TensorFlow GitHub project: The TensorFlow GitHub project has been plagued with spam posts promoting unauthorized content, including viral videos and live streaming links. Additionally, there are numerous privacy breach issues involving the unauthorized leak and viral spread of explicit videos of Pakistani TikTokers, raising significant concerns about privacy and security.
- issues/tensorflow/tensorflow/issues/81151
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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 issues from the past week.
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: 8
Pull Requests:
This pull request aims to enhance the readability and accuracy of the TensorFlow documentation by fixing several typographical errors across multiple documentation strings. The changes are primarily focused on correcting spelling mistakes and improving the clarity of the documentation, which is crucial for developers and users who rely on these resources for guidance. This update is part of ongoing efforts to maintain high-quality documentation and ensure that users have access to clear and accurate information.
Associated Commits:
This pull request aims to address and correct typographical errors found in the cumulative_logsumexp function within the TensorFlow repository. The purpose of these changes is to enhance the readability and accuracy of the code documentation and comments, ensuring that developers can better understand and utilize the function without confusion caused by these minor errors. This update is part of ongoing efforts to maintain high-quality code standards in the project.
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This pull request addresses a test case failure in the TensorFlow project related to the constant_loading_test for the s390x platform. The issue arises from the way data is interpreted in the TensorBuffer class, which is affected by system endianness. The code changes involve modifying the initialization of the data variable to ensure that the buffer interprets the values correctly on big-endian systems like s390x. This is achieved by swapping the bits of the data pointer values, allowing the buffer to correctly cast the uint8/int8 data back to the desired uint4/int4 type using reinterpret_cast.
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The pull request titled "Fix issue #70730" addresses a problem with the regular expression used for filtering neon and sse related sources in the TensorFlow project. The purpose of this update is to enhance the regular expression to prevent missing files when the absolute path includes the terms neon or sse, which previously led to linker errors. This improvement resolves the issue #70730, ensuring that all relevant files are correctly identified and included during the build process.
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This pull request introduces GPU configuration options to TensorFlow's Go bindings, enhancing the library's flexibility and alignment with its Python counterpart. The key changes include the addition of a new GPUOptions struct, which allows users to configure GPU-specific settings such as enabling memory growth and specifying a GPU allocator type. To ensure these options integrate seamlessly with existing functionality, unit tests have been added. Additionally, the session creation API has been updated to accept and apply GPUOptions during initialization, further aligning the Go API with TensorFlow’s Python API.
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This pull request aims to enhance TensorFlow's build configuration by adding support for the ppc64le architecture and improving the handling of linker version detection. The key changes include updates to configure.py to better detect linker versions by accommodating varied output formats and implementing fallback logic for version parsing. Additionally, the build configuration in tensorflow/tools/pip_package/BUILD has been updated to support the ppc64le architecture. These modifications have been successfully validated locally using Bazel build and tests, with no regressions observed in existing functionality. Overall, these changes enhance TensorFlow's compatibility with diverse environments and architectures.
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This pull request addresses the issue of broken links in the text classification overview documentation within the TensorFlow repository. The purpose of this update is to ensure that all links in the overview.md file are functional, thereby improving the accessibility and usability of the documentation. The author identified four broken links and has replaced them with correct, working URLs. This change is crucial for maintaining the quality and reliability of the documentation, which is an essential resource for developers and users of TensorFlow. The team is requested to review these changes and merge them as appropriate.
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This pull request addresses an issue in the TensorFlow documentation where a broken link was identified in the video classification overview.md file. The link to the TensorFlow Lite Support Library was not functioning correctly, and this update replaces it with a working hyperlink. This change ensures that users can access the necessary resources without encountering dead links, thereby improving the overall usability and reliability of the documentation. The update is straightforward and focuses solely on correcting the hyperlink to enhance the user experience.
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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: 4
Summarized Pull Requests:
This pull request aims to update the ByteSwapTFLiteModel function to address a specific failure encountered on the s390x architecture when using clang. The issue arises due to clang's strict checks for s390x, particularly concerning the initialization of a Variable Length Array (VLA), which necessitates runtime memory allocation. To resolve this, the VLA has been replaced with a std::vector, ensuring compatibility and preventing the error. This change is crucial for maintaining the functionality and reliability of the TensorFlow model on s390x systems.
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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 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.
| Contributor | Commits | Pull Requests | Issues | Comments |
|---|---|---|---|---|
| ghost | 0 | 0 | 147 | 2 |
| gaikwadrahul8 | 16 | 13 | 1 | 93 |
| redfref | 0 | 0 | 0 | 103 |
| A. Unique TensorFlower | 91 | 0 | 0 | 0 |
| Venkat6871 | 3 | 3 | 0 | 59 |
| tilakrayal | 0 | 0 | 0 | 61 |
| medityt | 0 | 0 | 0 | 22 |
| pkgoogle | 0 | 0 | 0 | 14 |
| dopardrop | 0 | 0 | 0 | 14 |
| Luke Boyer | 12 | 0 | 0 | 0 |