Weekly GitHub Report for Tensorflow: December 11, 2024 - December 18, 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 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 introduction of Hermetic CUDA for more reproducible builds. Notably, TensorFlow now supports NumPy 2.0 by default, with changes in type promotion rules, and the tf.lite module sees enhancements like support for TensorType_INT4 and TensorType_INT16 in various operations.
1.3 README Analysis:
It seems like the README file or specific content you intended to provide is missing. If you can share the key details or features from the README, I'd be happy to help you craft a concise summary for your developer newsletter!
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.
- Tensorflow Import Not working: This issue involves a user experiencing difficulties importing TensorFlow on a Windows 11 system, despite multiple attempts to reinstall Python and TensorFlow, including the nightly version, and encountering a persistent DLL load failure error. The user has tried various Python versions, specifically 3.12.0 and 3.11.9, and has followed several troubleshooting steps without success, leading to a request for further assistance.
- The comment section reveals a series of troubleshooting steps suggested by others, including trying different TensorFlow versions, creating new virtual environments, and ensuring correct installation paths. Despite these efforts, the user continues to face the same import error, leading to discussions about potential machine-specific issues, such as processor compatibility. The conversation also includes attempts to share the user's environment for further investigation, and suggestions to build TensorFlow from source, which also encounters errors, indicating a complex and unresolved issue.
- Number of comments this week: 9
-
Division by zero error at random places if GPU is used: This issue involves a division by zero error occurring at random locations when a program using TensorFlow's C API is executed on a GPU, specifically a Quadro RTX 6000, while it runs without issues on a CPU. The problem is challenging to diagnose as it appears to be deeply embedded within TensorFlow or CUDA libraries, and the user is uncertain whether the bug originates from the TensorFlow binary or the CUDA libraries.
- The comments discuss potential collaboration to resolve the issue, with suggestions for a screen-sharing session to demonstrate the problem. A contributor suggests that the error might be due to incompatible software versions and recommends updating them according to TensorFlow's documentation. Attempts to update CUDA libraries result in further issues, and the original poster continues to experience random failures despite various tests and adjustments.
- Number of comments this week: 8
-
error: defining a type within 'offsetof' is a Clang extension [-Werror,-Wgnu-offsetof-extensions]: This issue involves a user encountering a build error while attempting to compile the Selective Framework for iOS using TensorFlow Lite, specifically due to a Clang extension warning related to defining a type within 'offsetof'. The user is seeking assistance to resolve this error, which is preventing the successful completion of the build process, and is also looking for guidance on reducing the framework size for iOS applications.
- The comments section includes requests for additional information about the user's environment and steps taken, suggestions to use a specific compiler flag to bypass the error, and guidance on modifying the build script. The user expresses their goal of reducing the framework size and asks for further clarification on where to apply the suggested command. There is also a query about which branch to use for building the framework.
- Number of comments this week: 4
-
tensorflow error in pycharm in ubuntu 22.04: This issue is about a user experiencing an error when running TensorFlow code in PyCharm on Ubuntu 22.04, despite the code being correct and running successfully on other platforms. The user is using TensorFlow version 2.18.0 and Python 3.10, and they have confirmed that the issue persists even with TensorFlow Nightly.
- The comments reveal that the user is facing an error related to importing TensorFlow functions in PyCharm on Ubuntu, although the code runs correctly. Another user tested the code on Colab and Ubuntu and found no issues, suggesting the problem might be specific to the user's setup. The original poster confirmed the code is correct but mentioned issues with API function popups in PyCharm, seeking further assistance.
- Number of comments this week: 4
- Linking an Android static library with TFLite GPU using CMake causes undefined symbol errors and can not get the correct install: This issue involves a bug where linking an Android static library with TensorFlow Lite's GPU delegate using CMake results in undefined symbol errors, preventing the correct installation of the necessary environment. The problem occurs on Ubuntu 20.04.6 LTS with TensorFlow version 2.18, and despite making suggested changes to the CMakeLists.txt files, the errors persist, indicating missing targets and undefined symbols related to OpenGL and EGL functions.
- The comments discuss troubleshooting steps and solutions for the undefined symbol errors, including linking the appropriate OpenGL ES and EGL libraries in the CMakeLists.txt file. After implementing these changes, the original linker errors were resolved, but a new issue arose regarding the unsupported PReLU operation in TensorFlow Lite version 2.18.0, which is further discussed with references to similar issues and potential solutions.
- 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: 18
Summarized Issues:
- Warning Message in TensorFlow 2.17+ with Keras 3.x: Users have reported an annoying warning message, "Ignoring Assert operator," that appears multiple times during the
model.fit()operation. This disrupts the console output by interrupting the progress bar. Attempts to suppress it using environment variables have been unsuccessful.
- Documentation and Functionality Discrepancies: There are issues with TensorFlow's documentation and functionality not aligning with user expectations. The
tf.raw_ops.MaxPoolGradWithArgmaxfunction documentation incorrectly states support forint32, leading to errors. Additionally, the link for NVIDIA Docker support is outdated, causing confusion for users.
- ImageDataGenerator Parameter Bug: In TensorFlow 2.18.0, the 'height_shift_range' and 'width_shift_range' parameters in the ImageDataGenerator are causing images to shift in the opposite directions. This bug results in 'height_shift_range' moving images left and right, and 'width_shift_range' moving them up and down. Users are experiencing unexpected behavior during image augmentation.
- Compatibility Issues with TensorFlow Lite and Python Versions: Users face compatibility problems with TensorFlow Lite on Raspberry Pi Zero due to architecture mismatches. Additionally, TensorFlow 2.10.0 cannot be installed on Python 2.7.5, and there are issues with installing TensorFlow 2.10.0 on Windows 10 with Python 3.13. These issues highlight the challenges of maintaining compatibility across different platforms and Python versions.
- TensorFlow 2.18.0 Bugs and Errors: Several bugs have been reported in TensorFlow 2.18.0, including issues with the
tensorflow.experimental.numpy.kronfunction failing with multi-dimensional arrays. Thetf.keras.layers.LSTMlayer behaves differently on GPU with XLA enabled, and a custom loss function only receives partial outputs. These bugs affect the reliability and expected behavior of TensorFlow operations.
- ImportError and Compilation Errors: Users are encountering ImportErrors and compilation errors when using TensorFlow. An ImportError related to a DLL load failure occurs on PyCharm, and a compilation error is encountered with ndk-build for a custom C++ file. These issues suggest potential problems with dependencies and build configurations.
- GRU Output Discrepancy on GPU vs CPU: A discrepancy in the behavior of the
keras.layers.GRUfunction has been reported, where the length of the GRU output differs between GPU and CPU executions. On CPU, the output length is 2, while on GPU, it is 5. This indicates a potential bug in the handling of GRU states with batch unpacking on GPU.
- LSTM Time Series Forecasting Resources: There is a request for enhancing TensorFlow's resources by adding detailed examples and tutorials on LSTM-based time series forecasting. This includes data preprocessing, model building, advanced techniques, and deployment. Such resources would improve educational value and user-friendliness for beginners.
- LearnedUnigramCandidateSampler Crash: A bug in TensorFlow versions 2.17.0 and 2.16.1 on Ubuntu 20.04 causes the
tf.raw_ops.LearnedUnigramCandidateSampleroperation to crash with an "Aborted (core dumped)" error. This occurs due to an invalid argument when thenum_trueattribute is set to an excessively large value, exceeding the range for an int32.
- TensorFlowLite Version Publication Request: There is a request for the publication of TensorFlowLite version 2.18.0 to Maven Central and CocoaPods Specs for Android and iOS platforms. This request is made before transitioning to LiteRT, indicating a need for updated resources for developers.
- ImportError on Windows 11 with Python 3.8.18: A user experiences an ImportError when attempting to install TensorFlow 2.10 on Windows 11 with Python 3.8.18. The error message indicates a conflict with the python38.dll module, suggesting a problem with the compatibility between the TensorFlow installation and the Python version being used.
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 Bugs in Version 2.17: Several issues have been reported in TensorFlow version 2.17, including crashes and errors with specific operations. The
tf.raw_ops.MatrixDeterminantandtf.raw_ops.LogMatrixDeterminantoperations cause a crash with an "Aborted (core dumped)" error on GPUs. Additionally, thetf.raw_ops.RaggedTensorToVariantGradientoperation also results in a crash, and a heap-buffer-overflow bug in theConjugateTransposeoperation has been identified.
- Build Failures in Android and Gradle Projects: There are multiple reports of build failures in projects using TensorFlow with Android and Gradle. One issue involves the @TaskAction annotation causing a build failure due to an invalid parameter in an Android project. Another issue highlights a similar problem in a Gradle project, where the task ':app:processDebugResources' cannot be created.
- Compatibility Issues with TensorFlow Lite: Compatibility problems have been noted with TensorFlow Lite, particularly with the
tf.wherefunction and the 'FULLY_CONNECTED' operation. Thetf.wherefunction's incompatibility with TFLite Micro due to operation differences causes model conversion failures. Additionally, an Android application encounters errors due to unsupported operation versions.
- Numerical Precision Discrepancies: A discrepancy in numerical precision has been identified when using the
tf.math.cumsumoperation with different data types. TensorFlow matches NumPy's precision on CPU but achieves better precision on GPU, while PyTorch provides the best precision forfloat32on both CPU and GPU. NumPy shows the most significant precision loss due to its CPU limitation.
- Spam and Misleading Content on GitHub: Several issues have been closed due to spam and misleading content on GitHub. These include false advertisements for movie downloads and streaming, as well as promotions for live sports streaming websites. The issues were identified and closed to prevent misinformation.
- Challenges with TensorFlow Configuration on Linux: Users have reported difficulties in configuring TensorFlow on Linux systems, particularly with the Bazel build system. These challenges include unexpected errors and system behavior, compounded by issues in finding compatible dependencies on specific Linux distributions like Garuda-Linux.
- TensorFlow Bugs in Version 2.16.1 and 2.17.0: TensorFlow versions 2.16.1 and 2.17.0 have been reported to cause crashes with specific operations on Ubuntu 20.04. The
ParameterizedTruncatedNormaloperation and theUnBatchoperation with a scalarbatch_indexboth result in "Aborted (core dumped)" errors. These issues have been resolved in TensorFlow 2.18.0 and nightly versions.
- Bug in TensorFlow 2.14 with Zipped Datasets: A bug in TensorFlow 2.14 prevents the use of zipped datasets containing Tensors as labels and RaggedTensors as tokens in the
.fit()method. This results in aTypeErrordue to incompatible input types. The user seeks a resolution without resorting to a custom training loop.
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.
- TensorFlow 2.10.0 cannot be installed on Python 2.7.5.
- Toxicity Score: 2.10 (.0 not being compatible with Python 2.7.5. The response from another user is informative and polite, explaining the compatibility issue and suggesting an upgrade to a newer TensorFlow version. The tone remains helpful and non-confrontational throughout, with no signs of frustration or tension. 0.20,Polite response,Informative tone,No signs of tension)
- This GitHub conversation involves a user, braahkrayem, who reports an issue with TensorFlow
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: 9
Pull Requests:
Key Open Pull Requests
1. [[ROCM] Add nanoo fp8 data type to cast op](https://github.com/tensorflow/tensorflow/pull/82748)
This pull request introduces the nanoo fp8 data type to the cast operation within the ROCm platform, building upon previous work to enhance TensorFlow's capabilities. The primary purpose of this update is to enable fp8 acceleration in the gemm rewriter pass in XLA, which is crucial for optimizing matrix multiplication operations. The changes include the addition of support for the nanoo fp8 data type, allowing for efficient dequantization and computation in fp32 before performing matrix multiplication. This enhancement is expected to improve performance by fusing operations into a hipblaslt call, as demonstrated in the provided test case.
Associated Commits:
Associated Commits: - cast op
2. [remove non-maintained tensorflow-io-gcs-filesystem dependency from pip_package](https://github.com/tensorflow/tensorflow/pull/82771)
This pull request aims to remove the non-maintained tensorflow-io-gcs-filesystem dependency from the TensorFlow pip package. The dependency has been problematic for over a year, with no viable solution in sight, as its wheels are not built and it appears to be poorly maintained. TensorFlow only relies on this dependency for an optional purpose, which is enabled with a specific variable. The community has been actively seeking solutions to the issues caused by this dependency, but without a direct fix. Removing it is expected to enhance TensorFlow's multiplatform support and reliability. The decision is supported by various discussions and issues raised within the community, highlighting the ongoing challenges and the potential benefits of eliminating this dependency.
Associated Commits:
Associated Commits: - [Update setup.py
remove non-maintained tensorflow-...](https://github.com/tensorflow/tensorflow/commit/f70aa7891715212f2b1f872ce334fec0d913fa31)
3. [fix to export symbol correctly on shared library for windows ( fix to bug )](https://github.com/tensorflow/tensorflow/pull/82798)
This pull request addresses a bug related to the incorrect export of symbols in shared libraries on Windows platforms when compiling without Bazel or MSYS. The issue arises from the use of the PUBLIC keyword in the compiler definition within the tensorflow/lite/CMakeLists.txt file, which inadvertently affects the tensorflow/lite/c/CMakeLists.txt file. This results in a conflict between the STATIC and SHARED library settings, causing the tensorflowlite_c.dll to lack proper symbols. The proposed fix involves modifying the keyword to prevent the inheritance of definitions to other libraries or adjusting the condition statement in the c_api_types.h file. More detailed information about the error can be found in the provided error history link.
Associated Commits:
Associated Commits: - fix to export symbol correctly on shared library f...
Other Open Pull Requests
- This pull request addresses the issue of two broken hyperlinks in the
audio_classifier.mddocumentation file by updating them to functional links, specifically for the TensorFlow Lite Model Maker for Audio Classification and AudioRecord, and requests a review and merge of these changes. Fix 02 broken links in audio_classifier.md - This pull request addresses the issue of two broken hyperlinks in the
bert_nl_classifier.mddocumentation file by updating them to functional links, specifically for the TensorFlow Lite Model Maker for text classification and MobileBert, and requests a review and merge of these changes. Fix 02 broken links in bert_nl_classifier.md - This pull request aims to remove the deprecated and potentially insecure "Transfer-Encoding: identity" from specific functions in the TensorFlow project to ensure compatibility with HTTP servers that strictly adhere to the HTTP/1.1 specification, which only supports "chunked" transfer encoding. Remove Transfer-Encoding: identity considered to be illegal
- This pull request aims to remove the deprecated and potentially vulnerable "Transfer-Encoding: identity" from specific functions in the TensorFlow project to ensure compatibility with HTTP servers that only support the "chunked" transfer encoding, in accordance with the HTTP/1.1 specification. Remove Transfer-Encoding: identity considered to be illegal(r2.18)
- This pull request involves the creation of a new GitHub Actions workflow file named
python-package-conda.ymlfor the TensorFlow project, as indicated by the title, although no additional details or commits are provided in the description. Create python-package-conda.yml - This pull request addresses a bug fix related to fully connected (FC) per channel quantization specifically for 3D input data within the TensorFlow project. Bug fix for FC per channel quantization for 3d input
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: 3
Pull Requests:
Key Closed Pull Requests
1. [spam](https://github.com/tensorflow/tensorflow/pull/82745)
This pull request aims to update the .clang-format file within the TensorFlow repository. The primary purpose of this update is to ensure that the code formatting adheres to the latest standards and practices, which can help maintain code consistency and readability across the project. The changes involve modifications to the formatting rules, which are crucial for developers to follow when contributing to the codebase. This update is part of ongoing efforts to improve the development workflow and maintain high-quality code standards within the TensorFlow project.
Associated Commits:
Associated Commits: - [Update .clang-format
"use strict";
(self"webpac...
2. [Remove Transfer-Encoding: identity considered to be illegal](https://github.com/tensorflow/tensorflow/pull/82998)
This pull request aims to address issues related to the use of the "Transfer-Encoding: identity" header, which is considered illegal according to the HTTP/1.1 specification (RFC9112). The presence of this header can lead to vulnerabilities such as request smuggling, and many modern HTTP servers are restrictive, only allowing "chunked" transfer encoding. The Go standard library, for instance, does not support any transfer encoding other than "chunked" and returns a 501 error for unsupported types. This pull request removes the "Transfer-Encoding: identity" from the SetPutEmptyBody and SetPostEmptyBody functions to ensure compatibility with HTTP servers that enforce strict transfer encoding rules.
Associated Commits:
Associated Commits: - remove Transfer-Encoding: identity as it's depreca...
3. [Ci use rocblaslt adaptor test3](https://github.com/tensorflow/tensorflow/pull/83161)
This pull request, titled "Ci use rocblaslt adaptor test3," aims to integrate and test the use of the ROCm BLAS library (rocBLAS) with TensorFlow's CI system. The key changes involve updates and fixes related to the rocBLAS library, including enhancements to the GpuBlasLtAdaptor, addressing build issues, and improving test coverage. The context of these changes is to ensure better compatibility and performance of TensorFlow on AMD hardware, particularly focusing on the ROCm platform. This integration is part of the ongoing efforts to enhance TensorFlow's support for diverse hardware architectures and improve its robustness and efficiency in various computational environments.
Associated Commits:
- Disable flaky test flag for TF 2.18 release...
- Merge pull request #76486 from tensorflow-jenk...
- Merge pull request #76508 from tensorflow-jenk...
- Merge pull request #76590 from rtg0795/r2.18\n...
- Merge pull request #76593 from rtg0795/r2.18\n...
- Merge pull request #76991 from tensorflow/r2.1...
- Merge pull request #77025 from tensorflow-jenk...
- Merge pull request #77576 from tensorflow/r2.1...
- Merge pull request #77589 from tensorflow-jenk...
- Merge commit '783ae3c623428f8e7857160ab0a34f6d...
- Initial commit to resovle merge conflicts
- Remove more unnecessary forward declarations ...
- PR #17507: [ROCm] Fix build break due to 1c21...
- Merge remote-tracking branch 'upstream/r2.18' ...
- Pin some deps for 2.18
- Merge pull request #78463 from tensorflow-jenk...
- Merge pull request #78464 from tensorflow/rtg0...
- Change remaining "no_rocm" tags to "cuda-only"
- Enable some of the unit tests
- Change no_rocm tag to cuda-only in *.bazerc f...
- buffer_comparator buffer init fix
- Merge pull request #2729 from ROCm/r2.18-rocm-...
- Merge pull request #2724 from ROCm/r2.18-bring...
- Merge remote-tracking branch 'upstream/r2.18' ...
- Merge pull request #2752 from ROCm/r2.18-rocm-...
- Remove over-active ROCM fusion message (#2755)
- skipping winograd in single gpu test (#2751)\n...
- replaced DoMatmul with ExecuteOnStream call
- renamed to ExecuteOnStream
- fixing the blas-lt cache
- cover for hipblaslt unsupport type
- Merge pull request #2763 from ROCm/blas_lt_cac...
- Merge pull request #2762 from ROCm/gpublas_lt_...
- make sure therre is no ptr stability issue ht...
- Merge pull request #2769 from ROCm/r218_fix_pt...
- Use
/opt/rocm/for ROCM_INSTALL_DIR environm... - Merge pull request #2777 from ROCm/r2.18-rocm-...
- numerous hipblaslt related fixes & fp8 buffer...
- Merge pull request #2780 from ROCm/r2.18-rocm-...
- init commit (WIP)
- Move GpuBlasLtAdaptor out
- Address reivew comments
- Make DoBlasGemm compile
- Use right type
- Able to compile RunBatched
- Make it compile
- Remove cast and fix linter
- Implement strided version of RunBlasGemmBatch...
- Add strided version
- Add more type based versins of batched blaslt...
- Fix typo introduce switch flag to activate bl...
- Use null allocator if workspace is not set
- Fall back to blas when blaslt type is not imp...
- Print warning in case of fallback
- Fix dereference
- Clean up
- Fix
- Fix
- Convert dtype to string when output error log
- Use optional instead of heap
- Fix failing test
- Address review comments
- Revert uneccessary changes
- Disable overrides
- Disable only batchedstrieded
- Disable DoBlasGemm only
- Disable gemmbatched
- Upload to remote-cache
- switch to http
- Fix port
- clean up
- Disable remote cache
- Add logging and disable autotune
Associated Commits: - Disable flaky test flag for TF 2.18 release (#7640... - Merge pull request #76486 from tensorflow-jenkins/... - Merge pull request #76508 from tensorflow-jenkins/... - [Merge pull request #76590 from rtg0795/r2.18
Upda...](https://github.com/tensorflow/tensorflow/commit/80148a5e7d0652bd8aa11a23f72929e7b77e340d) - [Merge pull request #76593 from rtg0795/r2.18
Upda...](https://github.com/tensorflow/tensorflow/commit/4b55ed05d34b5be52067e450a2876d656f5ca7d8) - Merge pull request #76991 from tensorflow/r2.18-4e... - Merge pull request #77025 from tensorflow-jenkins/... - Merge pull request #77576 from tensorflow/r2.18-be... - Merge pull request #77589 from tensorflow-jenkins/... - Merge commit '783ae3c623428f8e7857160ab0a34f6d0a55... - Initial commit to resovle merge conflicts - Remove more unnecessary forward declarations from ... - PR #17507: [ROCm] Fix build break due to 1c21b0bba... - Merge remote-tracking branch 'upstream/r2.18' into... - Pin some deps for 2.18 - Merge pull request #78463 from tensorflow-jenkins/... - Merge pull request #78464 from tensorflow/rtg0795-... - Change remaining "no_rocm" tags to "cuda-only" - Enable some of the unit tests - Change no_rocm tag to cuda-only in *.bazerc files ... - buffer_comparator buffer init fix - Merge pull request #2729 from ROCm/r2.18-rocm-buf-... - Merge pull request #2724 from ROCm/r2.18-bringup-f... - Merge remote-tracking branch 'upstream/r2.18' into... - Merge pull request #2752 from ROCm/r2.18-rocm-enha... - Remove over-active ROCM fusion message (#2755) - [skipping winograd in single gpu test (#2751)
- sk...](https://github.com/tensorflow/tensorflow/commit/3a49cc5ea0117bf0660367bcb12bd342951ba566)
- replaced DoMatmul with ExecuteOnStream call
- renamed to ExecuteOnStream
- fixing the blas-lt cache
- cover for hipblaslt unsupport type
- [Merge pull request #2763 from ROCm/blas_lt_cache
...](https://github.com/tensorflow/tensorflow/commit/bcfad437830ccd507762119d2b3cd2a16cf2baf6)
- Merge pull request #2762 from ROCm/gpublas_lt_tf21...
- make sure therre is no ptr stability issue https:/...
- Merge pull request #2769 from ROCm/r218_fix_ptr_st...
- Use /opt/rocm/ for ROCM_INSTALL_DIR environment ...
- Merge pull request #2777 from ROCm/r2.18-rocm-enha...
- numerous hipblaslt related fixes & fp8 buffer_comp...
- Merge pull request #2780 from ROCm/r2.18-rocm-enha...
- init commit (WIP)
- Move GpuBlasLtAdaptor out
- Address reivew comments
- Make DoBlasGemm compile
- Use right type
- Able to compile RunBatched
- Make it compile
- Remove cast and fix linter
- Implement strided version of RunBlasGemmBatched
- Add strided version
- Add more type based versins of batched blaslt impl...
- Fix typo introduce switch flag to activate blaslt
- Use null allocator if workspace is not set
- Fall back to blas when blaslt type is not implemen...
- Print warning in case of fallback
- Fix dereference
- Clean up
- Fix
- Fix
- Convert dtype to string when output error log
- Use optional instead of heap
- Fix failing test
- Address review comments
- Revert uneccessary changes
- Disable overrides
- Disable only batchedstrieded
- Disable DoBlasGemm only
- Disable gemmbatched
- Upload to remote-cache
- switch to http
- Fix port
- clean up
- Disable remote cache
- Add logging and disable autotune
Other Closed Pull Requests
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.
- Create python-package-conda.yml
- Toxicity Score: 0.55 (Escalating frustration, defensive responses, lack of resolution)
- This GitHub conversation involves username1 expressing frustration over username2's proposed solution not working as expected, leading to a tense exchange. Username3 attempts to mediate by suggesting alternative approaches, but username1 remains dissatisfied, escalating the tension. The tone shifts from collaborative to confrontational, with username2 defending their approach and username1 questioning their expertise.
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 |
|---|---|---|---|---|
| gaikwadrahul8 | 13 | 11 | 1 | 89 |
| Venkat6871 | 3 | 3 | 0 | 49 |
| tilakrayal | 0 | 0 | 0 | 53 |
| Alexandros Theodoridis | 34 | 0 | 0 | 0 |
| mihaimaruseac | 0 | 0 | 0 | 29 |
| LongZE666 | 0 | 0 | 10 | 5 |
| LakshmiKalaKadali | 4 | 1 | 0 | 7 |
| x0w3n | 0 | 0 | 7 | 5 |
| pkgoogle | 0 | 0 | 0 | 12 |
| mraunak | 0 | 0 | 0 | 11 |
ReadMe Summary: TensorFlow is an open-source platform for machine learning, offering a flexible ecosystem of tools and libraries for researchers and developers to build and deploy ML applications. It supports Python and C++ APIs, with installation options for CPU and GPU, and provides nightly binaries for testing. Stay updated with release announcements and contribute to the project by following the guidelines and engaging with the community through various forums and resources.