Weekly GitHub Report for Tensorflow: November 27, 2024 - December 04, 2024
Weekly GitHub Report for Tensorflow
Thank you for subscribing to our weekly newsletter! Each week, we deliver a comprehensive summary of your GitHub project's latest activity right to your inbox, including an overview of your project's issues, pull requests, contributors, and commit activity.
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 users expressing frustration and suggesting downgrading Python as a temporary solution. There is a particular emphasis on the importance of supporting Python 3.13 due to its adoption in major distributions like Fedora 41. Some users inquire about the support status of Python 3.12, with responses indicating that it should be supported, though there is some discrepancy between official documentation and available package versions.
- 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 using LLVM/Clang, where the linking process fails due to missing symbols such asSessionandSavedModelBundleInterface. The problem persists across various LLVM/Clang versions, 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 for the linking errors, including missing dependencies and incomplete symbol definitions in the build files. Users share their experiences with similar issues and express frustration over the complexity of manually adding dependencies. A contributor mentions that the issue is specific to Windows and is being investigated, with a promise of a forthcoming solution. Meanwhile, users are seeking temporary workarounds, but no viable solution is provided in the comments.
- Number of comments this week: None
-
Unable to build TensorFlow without TensorRT: This issue involves a user attempting to build TensorFlow from source without TensorRT support on a Linux Ubuntu 22.04 system, but encountering an error related to TensorRT despite configuring the build to exclude it. The user provides detailed steps and configurations used during the build process, including the use of CUDA 12.0.1, cuDNN 8.8, and Clang 16, and highlights the absence of the
NvInferVersion.hfile, which is expected to be part of TensorRT.
- The comments section includes suggestions to explicitly disable TensorRT during configuration, but the user reports that the suggested command is unrecognized and expresses difficulty in modifying the build configuration due to the complexity of the Bazel files. Other users confirm experiencing similar issues with different versions of CUDA and cuDNN, and some attempt workarounds like installing TensorRT or using different TensorFlow versions. A user suggests using an older, more stable TensorFlow version with specific configurations, while another user notes that TensorRT support is disabled in the latest TensorFlow version, potentially resolving the issue.
- Number of comments this week: None
-
tf.autodiff.ForwardAccumulator._watch(primal, tangent) erroneously refers to dtype.is_floating which does not exist for a Keras layer.: This issue describes a bug in TensorFlow version 2.17.0, where the method
tf.autodiff.ForwardAccumulator._watch(primal, tangent)incorrectly references the attributeprimal.dtype.is_floating, causing a crash becauseprimal.dtypeis a string type variable that lacks theis_floatingattribute. The error occurs when using a Keras layer, and the user provides standalone code to reproduce the issue, highlighting that the example code from the TensorFlow documentation also fails due to this bug.- The comments discuss the reproduction of the issue using example code from TensorFlow's documentation, and a suggested workaround involves using
tf-kerasinstead oftf.keras. The user confirms that the workaround resolves the issue but points out that the bug persists intf.keras, suggesting that the example code and the underlying TensorFlow code should be updated to handle string dtypes properly. The conversation includes a request not to close the issue until a permanent fix is implemented. - Number of comments this week: None
- The comments discuss the reproduction of the issue using example code from TensorFlow's documentation, and a suggested workaround involves using
-
When using
tf.math.log1pand NumPy'snp.log1pwith the same complex input, the outputs are inconsistent.: This issue highlights a discrepancy between TensorFlow'stf.math.log1pand NumPy'snp.log1pfunctions when handling complex inputs that include infinity, resulting in different outputs. The problem is observed on Ubuntu 22.04.3 with TensorFlow version 2.17.1 and Python 3.11, where TensorFlow returns[inf+0.j, nan+nanj, nan+nanj]while NumPy returns[inf+0.j, inf+1.57079633j, inf+0.78539816j].- A user confirmed the issue by testing the provided code on multiple TensorFlow versions, including the nightly build, and shared a Colab gist for reference. Another user inquired about any plans to address the inconsistency.
- Number of comments this week: None
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: 27
Summarized Issues:
- Build Issues on Windows and CentOS: Users are experiencing difficulties building TensorFlow from source on both Windows and CentOS environments. On Windows, the issue is related to the
CreateProcessWfunction not locatingbash.exefrom MSYS2, while on CentOS, a "No such file or directory: 'patchelf'" error occurs despite the tool being installed. Both issues suggest potential solutions such as updating TensorFlow, cleaning and syncing with Bazel, or verifying installation paths.
- TensorFlow Lite and Conversion Issues: Users are facing challenges with TensorFlow Lite, including incomplete submissions for conversion assistance and import errors related to flatbuffers and metadata. These issues highlight the need for detailed system information and potential version adjustments to resolve TypeErrors.
- TensorFlow 2.18.0 Bugs: Several bugs have been reported in TensorFlow 2.18.0, including compilation errors with XLA and mixed_float16 policy, unspecific error messages, and missing symbols in the Metal plugin on macOS. These issues affect model performance and usability, prompting users to seek clearer error messages and compatibility fixes.
- Docker and GPU Support Issues: The Docker image 'tensorflow:2.18-gpu-jupyter' lacks GPU support due to missing CUDA libraries, contrary to user expectations. Users are advised to install Hermetic CUDA and modify the
requirements.txtfile to includetensorflow[and-cuda]for proper functionality.
- Feature Requests and Support: Users are requesting new features and support, such as int8 support for Unsorted_Segment_X operators and accurate DDR5 bandwidth calculation. These requests aim to enhance TensorFlow's capabilities for specific use cases like mobile deployment and memory performance.
- XLA and Dynamic Dimensions: A user is experiencing frequent recompilations when using TensorFlow's XLA on a GPU due to changes in dynamic dimensions. They are seeking guidance on configuring XLA to avoid recompilation when the shape changes, which affects performance.
- Spam and Irrelevant Content: The TensorFlow GitHub repository has been targeted by spam posts promoting leaked videos of Maryam Faisal and Samra Chaudhry. These posts are unrelated to the project's purpose and highlight the misuse of the platform for spreading sensational content.
- Performance Optimization: A user is seeking optimization strategies for better CPU utilization when running a TensorFlow-based DeepFace application. Despite a significant speedup on a more powerful CPU, neither system fully utilizes its CPU capacity, prompting the need for performance improvements.
- Installation Challenges on ARMv6: Users are facing difficulties installing TensorFlow version 2.10.1 on a Raspberry Pi Zero W with ARMv6 architecture. The lack of a compatible version prompts users to seek guidance or an official release for ARMv6 support.
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 the correct "manylinux_2_31_x86_64." This necessitates either a renaming of the wheels or a rebuild in a compatible environment.
- TensorFlow Lite CMake build issues on Windows: A bug causes the TensorFlow Lite CMake build to fail on Windows 11 using Visual Studio 2022. The errors are related to 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: There is 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.
- Conversion issues with TensorFlow Lite ConcreteFunctions: The TensorFlow Lite converter fails to convert ConcreteFunctions with int8 quantization during the calibration step. 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, disrupting automated model generation pipelines. Additionally, a crash occurs during the model conversion process due to a runtime error related to tensor feeding and gather index out of bounds.
- Lack of support for
tf.float64in TensorFlow Lite: TensorFlow Lite does not support thetf.float64data type in C++ inference, resulting in segmentation faults during high-precision inference. This contrasts with Python inference, which handlestf.float64successfully, and raises a ValueError during model conversion when specifyingtf.float64as input and output types.
- Performance and compatibility challenges with TensorFlow Lite LSTM models: Users face challenges when 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, with discussions around potential solutions and performance benchmarking.
- Incorrect inference results with TensorFlow Lite FP16 model on iOS: The TensorFlow Lite FP16 model using the Core ML Delegate on iOS 17.2 produces incorrect inference results. There is no performance improvement compared to the FP32 model, despite attempts to implement the model in an iOS application using the MiDaS example.
- Build errors with TensorFlow Lite GPU and NNAPI delegates on Android: Users encounter build errors due to an undefined symbol 'TfLiteGpuDelegateV2Create' when integrating GPU and NNAPI delegate support into an Android app. This is suspected to be related to model compatibility with the delegate, and users 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. This impacts model performance when deployed on microcontrollers, and users seek solutions to address this conversion challenge.
- 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, prompting queries on how to convert these dynamic tensors to static or ensure CPU fallback.
- Feature requests for TensorFlow Lite selective builds and quantization: Users request TensorFlow Lite to support selective builds using TensorFlow operations (flex delegate) for embedded Linux on aarch64 architecture. Additionally, there is a request for the TensorFlow Lite Converter to allow users to exclude specific operations from quantization to prevent significant accuracy drops in models.
- Compilation and quantization issues in TensorFlow Lite: Compiling the TensorFlow Lite benchmark tool using CMake on macOS 13.5.2 fails due to undefined symbols related to the Core ML delegate. Additionally, there is a discrepancy in TensorFlow Lite's quantization processes, where full integer quantization of models with multiple signatures results in subgraphs of significantly different sizes.
- Need for Python 3.11 compatible tflite-runtime wheels for MacOS: There is a need to build and release Python 3.11 compatible tflite-runtime wheels for MacOS on PyPI. The current availability is limited to Linux, causing installation errors for users attempting to run automated jobs on MacOS with this configuration.
- Lack of SVDF layer implementation in TensorFlow's Keras library: There is no dedicated SVDF layer implementation in TensorFlow's Keras library that can be recognized and fused as an SVDF operator when converting models to TFLite. This prompts discussions on the correct sequence of operations needed for this conversion and attempts to resolve the issue using various approaches.
- Adding GPU model to TensorFlow Lite compatibility database: A user seeks guidance on how to add the GPU model "samsung_xclipse_940" for the Samsung S24 to the TensorFlow Lite compatibility database. This follows the recent addition of the "samsung_xclipse_920" model, and the issue is redirected to the LiteRT project for further support.
- Performance discrepancies in TensorFlow Lite benchmarking on Android: There is a discrepancy in performance results when using TensorFlow Lite's benchmark script and benchmark APK on Android devices. The GPU performance measured by the APK is nearly twice as fast as that measured by the script, prompting questions about the differences between these benchmarking methods.
- Feature request for TensorFlow C API to support multiple delegates: A feature request is made for the TensorFlow C API to support multiple TensorFlow Lite delegates simultaneously. This is similar to the behavior of the benchmark_model tool and aims to improve model execution efficiency by allowing operations unsupported by one delegate to be handled by another.
- Challenges in model conversion to INT4 precision in TensorFlow: There is a lack of support for quantizing models to INT4 using the TFLite converter and the new quantization API. Users seek guidance from the TensorFlow team on the direction and development needed to enable INT4 post-training quantization (PTQ).
- Efforts to make TensorFlow Lite compatible with Windows on Arm: Efforts are underway to make TensorFlow Lite compatible with the Windows on Arm platform, with a current patch available on a specific GitHub branch. Discussions include integrating the XNNPACK patch into the mainstream and considering the transition to LiteRT for enhanced support.
- Build failures with TensorFlow Lite using Docker and CMake: A build failure occurs when attempting to compile TensorFlow Lite using Docker due to the nnapi_delegate requiring C++20 extensions. Additionally, linker errors related to the GPU delegate occur when building TensorFlow Lite version 2.16.1 for Android using CMake and Android NDK r25b.
- Limitations with TensorFlow Lite Java Interpreter API on Android: A user encounters limitations when running a TensorFlow Lite model on an Android device's GPU using the Java Interpreter API. The absence of the GpuDelegateV2 class prevents specifying inference priority options, and a solution or workaround is sought without resorting to the Native C++ API.
- Performance discrepancies in TensorFlow Lite inference on Android: There is a significant performance discrepancy in inference times when using the TensorFlow Lite Interpreter API on Android devices, specifically a Google Pixel 4a. Initial inference runs are 10–50 times slower compared to the same model running on iOS devices like the iPhone 12.
- Inefficiencies in TensorFlow Lite's GPU delegation policy: TensorFlow Lite's GPU delegation policy, specifically the FirstNLargestPartitions logic, prioritizes partitions with more layers for GPU acceleration. This potentially overlooks partitions with fewer but more computationally intensive layers, prompting suggestions for improvements.
- Segmentation fault with TensorFlow Lite 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, specifically the Pixel 7 and Pixel 8. Other devices like the Samsung Galaxy S23 and Xiaomi Redmi Note 10 5G do not experience this problem.
- Difficulty in force-loading TensorFlowLiteSelectTfOps.framework on iOS: A developer faces difficulty 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.
- Request to enhance TensorFlowLite format schema with new data types: A request is made to enhance 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-compiling TensorFlow Lite C library for ARMv7 architecture: Users face difficulties in cross-compiling the TensorFlow Lite C library using CMake for an ARMv7 architecture on a BeagleBone Black device. Errors occur during the build process 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 generated from the Google AutoML GitHub repository, fail to produce valid outputs when run on the Hexagon delegate. They function correctly on CPU and GPU delegates, with additional reports of similar failures on the Mediatek neuron delegate.
- TensorFlow Lite converter's constant folding pass issues: The TensorFlow Lite converter's constant folding pass prevents the storage of packed int8 tensors by storing dequantized float32 tensors instead. This results in a larger model size, and users seek a method to prevent constant folding for specific nodes.
- Bounding box errors in TensorFlow Android object detection app: A bug in an Android object detection application using TensorFlow causes bounding boxes for detected objects to be incorrectly drawn. They shift to the right when an object is moved towards the front camera, despite attempts to correct the mirrored coordinates by flipping them horizontally in the code.
- Need for TensorFlow Lite to support dilation parameter in Conv2dTranspose: 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 difficulties with TensorFlow Lite GPU Delegate on macOS: Developers face difficulties in 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.
- TensorFlow Lite model crashes on Android GPU delegate: A TensorFlow Lite model crashes on Android devices when using the GPU delegate due to a 'null pointer dereference', specifically when the OpenGL backend is used. It runs successfully on the CPU and on devices using the OpenCL backend, indicating a potential device-specific or backend-specific problem.
- 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. This creates accessibility challenges for users who rely on Windows for its screen reader support, and there is a request to re-enable GPU support on Windows and Mac systems.
- Documentation bug in TensorFlow's
tf.keras.Inputlayer: A documentation bug in TensorFlow involves thetf.keras.Inputlayer performing implicit data conversion when the data type (dtype) is not specified. This leads to potential unexpected behavior, performance overhead, and loss of data semantics, suggesting that explicit type checks and error messages should be introduced.
- Warnings and compatibility issues when building TensorFlow from source: Numerous warnings are encountered when building TensorFlow version 2.18.0 from source, particularly related to the improper passing of
input_shape/input_dimarguments to layers. Concerns include compatibility with CUDA and cuDNN versions and potential configuration errors during the build process.
- Error handling discrepancies between CPU and GPU in TensorFlow: There is a discrepancy in error handling 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: The EfficientNet v2 b0 model's inference runs significantly slower than the ONNX format on a Windows 11 system when converted to TensorFlow Lite format. It produces incorrect outputs with low probabilities when executed in Kotlin, despite being seemingly compatible with GPU delegates.
- Installation issues with TensorFlow GPU version on Windows 11: A user encounters a problem where the installation of the TensorFlow GPU version on a Windows 11 Home system defaults to the Intel-optimized version. This prevents the use of an NVIDIA 4060 laptop's GPU for model training, despite having CUDA and cuDNN configured.
- Loss of custom methods in TensorFlow subclassed model with ModelCheckpoint: 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, despite attempts to resolve it using suggested fixes.
- Compilation error with TensorFlow Lite Flex delegates on ARM architecture: A compilation error occurs while building TensorFlow Lite with Flex delegates on an ARM architecture. This is due to an 'asm' operand having impossible constraints, which occurs during the execution of a Bazel build command.
- Spam and inappropriate content on TensorFlow GitHub project: Multiple spam entries on the TensorFlow GitHub project inappropriately advertise support phone numbers for various services, including Gemini and DeFi wallets. These entries are unrelated to the project's purpose and have been closed as spam.
- Spam posts promoting unauthorized live streaming links: Several spam posts on the TensorFlow GitHub project promote unauthorized live streaming links for various sports events, including soccer matches and WWE events. These posts are unrelated to the project's purpose and have been closed as spam.
- issues/tensorflow/tensorflow/issues/81412
- issues/tensorflow/tensorflow/issues/81413
- issues/tensorflow/tensorflow/issues/81419
- issues/tensorflow/tensorflow/issues/81421
- issues/tensorflow/tensorflow/issues/81423
- issues/tensorflow/tensorflow/issues/81425
- issues/tensorflow/tensorflow/issues/81427
- issues/tensorflow/tensorflow/issues/81429
- issues/tensorflow/tensorflow/issues/81431
- issues/tensorflow/tensorflow/issues/81433
- issues/tensorflow/tensorflow/issues/81435
- issues/tensorflow/tensorflow/issues/81437
- issues/tensorflow/tensorflow/issues/81439
- issues/tensorflow/tensorflow/issues/81444
- issues/tensorflow/tensorflow/issues/81446
- issues/tensorflow/tensorflow/issues/81448
- issues/tensorflow/tensorflow/issues/81450
- issues/tensorflow/tensorflow/issues/81452
- issues/tensorflow/tensorflow/issues/81454
- issues/tensorflow/tensorflow/issues/81456
- issues/tensorflow/tensorflow/issues/81458
- issues/tensorflow/tensorflow/issues/81460
- Privacy breaches and viral video leaks of Pakistani TikTokers: Multiple issues report privacy breaches where explicit videos of Pakistani TikTokers were leaked and went viral on social media. This led to intense trolling and the deactivation of their social media accounts, raising significant concerns about the privacy and security of social media influencers.
- issues/tensorflow/tensorflow/issues/81537
- issues/tensorflow/tensorflow/issues/81538
- issues/tensorflow/tensorflow/issues/81540
- issues/tensorflow/tensorflow/issues/81542
- issues/tensorflow/tensorflow/issues/81550
- issues/tensorflow/tensorflow/issues/81555
- issues/tensorflow/tensorflow/issues/81565
- issues/tensorflow/tensorflow/issues/81567
- issues/tensorflow/tensorflow/issues/81569
- issues/tensorflow/tensorflow/issues/81571
- issues/tensorflow/tensorflow/issues/81573
- issues/tensorflow/tensorflow/issues/81575
- issues/tensorflow/tensorflow/issues/81577
- issues/tensorflow/tensorflow/issues/81579
- issues/tensorflow/tensorflow/issues/81581
- issues/tensorflow/tensorflow/issues/81583
- issues/tensorflow/tensorflow/issues/81587
- issues/tensorflow/tensorflow/issues/81589
- issues/tensorflow/tensorflow/issues/81591
- issues/tensorflow/tensorflow/issues/81603
- issues/tensorflow/tensorflow/issues/81606
- issues/tensorflow/tensorflow/issues/81608
- issues/tensorflow/tensorflow/issues/81610
- issues/tensorflow/tensorflow/issues/81612
- issues/tensorflow/tensorflow/issues/81614
- issues/tensorflow/tensorflow/issues/81616
- issues/tensorflow/tensorflow/issues/81618
- issues/tensorflow/tensorflow/issues/81620
- issues/tensorflow/tensorflow/issues/81622
- issues/tensorflow/tensorflow/issues/81625
- issues/tensorflow/tensorflow/issues/81627
- issues/tensorflow/tensorflow/issues/81629
- issues/tensorflow/tensorflow/issues/81631
- issues/tensorflow/tensorflow/issues/81633
- issues/tensorflow/tensorflow/issues/81706
- issues/tensorflow/tensorflow/issues/81724
- issues/tensorflow/tensorflow/issues/81732
- issues/tensorflow/tensorflow/issues/81736
- issues/tensorflow/tensorflow/issues/81744
- issues/tensorflow/tensorflow/issues/81758
- issues/tensorflow/tensorflow/issues/81767
- issues/tensorflow/tensorflow/issues/81774
- issues/tensorflow/tensorflow/issues/81781
- issues/tensorflow/tensorflow/issues/81783
- issues/tensorflow/tensorflow/issues/81854
- issues/tensorflow/tensorflow/issues/81856
- issues/tensorflow/tensorflow/issues/81858
- issues/tensorflow/tensorflow/issues/81860
- issues/tensorflow/tensorflow/issues/81862
- issues/tensorflow/tensorflow/issues/81864
- issues/tensorflow/tensorflow/issues/81866
- issues/tensorflow/tensorflow/issues/81868
- issues/tensorflow/tensorflow/issues/81870
- issues/tensorflow/tensorflow/issues/81872
- issues/tensorflow/tensorflow/issues/82094
- issues/tensorflow/tensorflow/issues/82139
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.
-
- Toxicity Score: 0.70 (Spam content, Lack of moderation, Potential for user frustration.)
- This GitHub conversation involves a user posting spam links repeatedly, which could potentially frustrate other users and disrupt the discussion. The tone is impersonal and automated, with no direct interaction between users. The primary trigger of tension is the presence of irrelevant and potentially harmful content.
-
TFLite conversion (w/ int8 quantization) from ConcreteFunction is broken
- Toxicity Score: 0.65 (Frustration expressed, Miscommunication, Unrelated suggestions, Escalating tone.)
- This GitHub conversation involves several users, including DLumi, sushreebarsa, LakshmiKalaKadali, pkgoogle, and gaikwadrahul8, discussing a technical issue. DLumi expresses frustration and confusion over the proposed solutions and the lack of clarity in the documentation. LakshmiKalaKadali attempts to provide a workaround, which DLumi critiques as inconsistent with expected behavior. The tone escalates when gaikwadrahul8 suggests an unrelated solution, prompting DLumi to express dissatisfaction with the direction of the project. The conversation is marked by a mix of collaborative problem-solving and growing tension due to perceived miscommunication and inadequate solutions.
-
Build/release Python 3.11 tflite-runtime MacOS wheels to PyPI
- Toxicity Score: 3.11 (tflite-runtime on MacOS. User feranick offers a temporary solution with a third-party repository, while chatnord and tilakrayal have a disagreement over the relevance of a linked issue. The tone is generally constructive, though there is some frustration over the lack of official support and updates. The conversation remains focused on finding a resolution, with users like terryheo being tagged for further assistance. 0.45, Frustration over lack of support, Disagreement on issue relevance, Constructive tone)
- This GitHub conversation involves multiple users discussing the lack of support for Python
-
[18+NEW~VIRAL@S.E.X@VIDEOS]*Divya Prabha Video Original Video Link Divya Prabha Video Viral On Social Media X Trending Now #269 #1878 #81538](https://github.com/tensorflow/tensorflow/issues/81548)
- Toxicity Score: 0.70 (Spam content, Lack of constructive engagement, Potential for user frustration.)
- This GitHub conversation involves multiple users posting repetitive and irrelevant content, primarily consisting of links and images related to a viral video. The tone is spammy and lacks any constructive discussion or engagement with the issue at hand. There is no direct interaction between users, but the nature of the content could lead to frustration among legitimate contributors.
-
[18+NEW~S.E.X@VIDEOS]*One Girl one Frog Video Link Short Clip One Girl one Frog Video Viral On Social Media X Twitter Trending](https://github.com/tensorflow/tensorflow/issues/81555)
- Toxicity Score: 0.70 (High volume of spam, lack of genuine interaction, potential for user frustration.)
- This GitHub conversation involves multiple users posting spam content, including links to external sites and repeated phrases, without any meaningful interaction or discussion. The tone is impersonal and automated, with no genuine engagement or exchange of ideas. The conversation is dominated by spammy content, which could trigger frustration among legitimate users.
-
- Toxicity Score: 0.70 (Spam content, Lack of moderation, Potential for user frustration.)
- This GitHub conversation involves a user posting multiple spam links and images, which disrupts the usual flow of discussion. The tone is impersonal and automated, lacking any genuine interaction or engagement from other users. The conversation is marked by a lack of meaningful content, with the primary trigger of tension being the presence of unsolicited and irrelevant material.
-
- Toxicity Score: 0.70 (Spam content, Lack of genuine interaction, Potential for user frustration.)
- This GitHub conversation involves a user posting multiple spam links and images, which disrupts the usual flow of discussion. The tone is impersonal and automated, lacking any genuine interaction or engagement with other users. The presence of spam content is likely to trigger frustration among other participants, potentially leading to negative sentiments if not addressed promptly.
-
- Toxicity Score: 0.70 (Spam content, Lack of genuine interaction, Potential for user frustration.)
- This GitHub conversation involves a user posting multiple spam links and images, which disrupts the usual flow of discussion. The tone is impersonal and automated, lacking any genuine interaction or engagement from other users. The presence of spam content is the primary trigger of tension, as it detracts from the platform's intended use for collaborative problem-solving and discussion.
-
Contact us - DeFi Wallet Customer Service | Live Chat and Call
- Toxicity Score: 0.70 (Spam content, Lack of constructive engagement, Potential for user frustration.)
- This GitHub conversation involves a single user posting repetitive and irrelevant content, which appears to be spam, as it includes multiple links to external sites and images without any context or contribution to the discussion. The tone is neutral but disruptive due to the nature of the content.
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 correcting several typographical errors found in the documentation strings. The changes are focused on improving the clarity of the documentation, which is crucial for developers and users who rely on accurate information for effective use of the library. The updates include fixing typos across multiple files, ensuring that the documentation is both professional and precise. This effort is part of ongoing maintenance to uphold the quality of the TensorFlow project.
Associated Commits:
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 addresses a specific issue encountered on the s390x architecture when using clang, which enforces strict checks. The problem arises from the use of a Variable Length Array (VLA) that requires runtime memory allocation, leading to failures. To resolve this, the VLA has been replaced with a std::vector, ensuring compatibility and stability on s390x. This change is crucial for maintaining the functionality of the ByteSwapTFLiteModel function on this architecture.
Associated Commits:
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 |
Don't miss what's next. Subscribe to Weekly Project News: