{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/12"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Integrates PyTorch profiling data with TensorBoard, providing GPU timeline tracing and performance diagnostics for ML workloads through a TensorBoard plugin interface.","skillfed_tags":["profiling","pytorch-ecosystem","performance-analysis"],"use_cases":["Visualize GPU kernel execution timelines and identify which operations consume the most compute time.","Profile data loading, model forward/backward passes, and synchronization overhead in training loops.","Analyze memory usage patterns and detect GPU memory bottlenecks during model execution.","Compare profiling results across different hardware or model configurations using TensorBoard's interface.","Generate performance recommendations for common ML training issues like GPU underutilization."],"what_it_does":"torch-tb-profiler is a TensorBoard plugin that integrates Libkineto, PyTorch's profiling library, to visualize and analyze ML workload performance. It focuses on low-overhead GPU timeline tracing and provides actionable recommendations for common performance bottlenecks in deep learning models. The plugin runs as an in-process profiler within PyTorch and surfaces results directly in TensorBoard's interface.\n\nThe package depends on pandas for data handling and tensorboard for visualization. It's designed for developers and researchers who need to diagnose performance issues in PyTorch models, particularly GPU-related bottlenecks. Installation is straightforward with no compiled dependencies beyond what PyTorch itself requires.","worth_installing":"Yes, if you actively profile PyTorch models and use TensorBoard. The low install friction, permissive license, and active maintenance make it a straightforward addition to a profiling workflow. However, the last release was in October 2023; verify compatibility with your current PyTorch version before relying on it for new projects."},"id":"torch-tb-profiler","links":{"html":"https://skillfed.io/packages/torch-tb-profiler","md":"https://skillfed.io/packages/torch-tb-profiler.md","pypi":"https://pypi.org/project/torch-tb-profiler/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2023-10-06","license_spdx":null,"license_treatment":"permissive","name":"torch-tb-profiler","python_support":"supports_current","summary":"PyTorch Profiler TensorBoard Plugin"},"popularity":{"monthly_downloads":376115,"position":7131,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.3"}