{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/11"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"XProf is a profiler for ML workloads that visualizes performance across multiple devices, showing execution timelines, memory usage, and operation-level details through a web interface.","skillfed_tags":["ml-profiling","performance-analysis","distributed-computing"],"use_cases":["Analyze step-time performance and identify bottlenecks in multi-device ML training runs.","Visualize operation-level execution timelines to understand which ops run on host vs. device and inter-device communication.","Monitor memory usage patterns during model training to detect memory leaks or inefficient allocations.","Debug distributed training by viewing aggregated performance metrics across all devices and hosts.","Integrate profiling into TensorBoard workflows for unified experiment tracking and analysis."],"what_it_does":"XProf is an open-source profiler designed for modern ML workloads, providing a web-based interface to analyze and visualize model performance across multiple devices. It integrates with TensorBoard as a plugin or runs standalone, offering tools like an overview page showing step-time breakdowns, a trace viewer displaying operation-level execution timelines, memory profiling, and HLO graph visualization. The package depends on 13 runtime libraries including protobuf, gcsfs, google-cloud-storage, and werkzeug, indicating support for both local and cloud-based profile storage and serving.\n\nThe profiler is actively maintained (latest release 9 days old, first released May 2025) and requires Python 3.10 or later. It has no known security vulnerabilities. Installation is straightforward via pip, though the dependency footprint is moderate. The tool is intended for developers, researchers, and educators working with JAX, PyTorch, TensorFlow, and XLA\u2014frameworks commonly used in ML research and production.","worth_installing":"Yes, if you train ML models on multiple devices or use JAX, PyTorch, TensorFlow, or XLA. The package is actively maintained, has no security issues, and provides essential multi-device profiling tools. Medium install friction is acceptable for the capability gained. Not necessary for single-device or non-ML workloads."},"id":"xprof","links":{"html":"https://skillfed.io/packages/xprof","md":"https://skillfed.io/packages/xprof.md","pypi":"https://pypi.org/project/xprof/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":null,"license_treatment":"permissive","name":"xprof","python_support":"supports_current","summary":"XProf Profiler Plugin"},"popularity":{"monthly_downloads":473640,"position":6462,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.23.1"}