{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/8"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Provides a TensorBoard plugin and standalone server for profiling and visualizing ML model performance across multiple devices, showing execution timelines, memory usage, and computational graphs.","skillfed_tags":["ml-profiling","performance-analysis","distributed-computing"],"use_cases":["Identify performance bottlenecks in multi-device training by examining per-operation execution timelines and device utilization.","Monitor memory consumption patterns during model training to detect memory leaks or inefficient allocations.","Visualize communication overhead between distributed devices to optimize collective operations.","Debug step-time variability by comparing individual step profiles against the aggregated performance summary.","Analyze HLO graph structure to understand compiler optimizations and identify suboptimal computation patterns."],"what_it_does":"tensorboard-plugin-profile is a TensorBoard plugin that integrates XProf, an open profiler for modern ML workloads. It runs as a web-based UI via TensorBoard or standalone to visualize model execution across multiple devices. The plugin displays performance summaries, step-time breakdowns, execution timelines showing which device ran each operation, memory usage patterns, and HLO graph structures. It depends on xprof as its core runtime dependency.\n\nYou can launch it standalone with xprof command-line tools pointing to a directory of profile data, or integrate it into TensorBoard for a unified debugging environment. The server supports distributed profiling via gRPC workers and offers command-line flags to customize ports, hide UI elements, and link source code. Internet access is required for chart rendering; offline or firewalled environments may see incomplete visualizations.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with minimal friction, and is in the top 5000 on PyPI. It solves a real need for ML practitioners debugging performance. The Apache 2.0 license is permissive. The main gotcha is the internet requirement for full UI rendering; if you profile in offline environments, verify that limitation first."},"id":"tensorboard-plugin-profile","links":{"html":"https://skillfed.io/packages/tensorboard-plugin-profile","md":"https://skillfed.io/packages/tensorboard-plugin-profile.md","pypi":"https://pypi.org/project/tensorboard-plugin-profile/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":null,"license_treatment":"permissive","name":"tensorboard-plugin-profile","python_support":"supports_current","summary":"XProf Profiler Plugin"},"popularity":{"monthly_downloads":871596,"position":4845,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.23.1"}