tensorboard-plugin-profile
XProf Profiler Plugin
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires internet access to load Google Chart library; some visualizations may be unavailable offline or behind corporate firewalls.
- Python 3.10+ required.
- Low friction installation via pip with a single runtime dependency.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 570 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 871,596 downloads/mo, #4,845 on PyPI
Alternatives
Verify before relying
pip install tensorboard-plugin-profile
# Standalone usage:
xprof --logdir=profiler/demo --port=6006
# Or with TensorBoard:
tensorboard --logdir=profiler/demo- Whether the package works with all major ML frameworks or if framework-specific setup is needed.
- Performance impact of running the profiler on large-scale distributed training jobs.
- Whether gRPC distributed processing mode is production-ready or experimental.
- Actual monthly download volume and user base size.
What it is and 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.
You 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
tensorboard-plugin-profile on PyPI
Before you install
Low friction installation via pip with a single runtime dependency. Actively maintained with a recent release and no known vulnerabilities. Requires Python 3.10 or later; Python 3.12+ users may need to downgrade setuptools if pkg_resources import fails.
Requires internet access to load Google Chart library; some visualizations may be unavailable offline or behind corporate firewalls. Python 3.10+ required.
License in practice
Licensed under Apache 2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
Quickstart
pip install tensorboard-plugin-profile
# Standalone usage:
xprof --logdir=profiler/demo --port=6006
# Or with TensorBoard:
tensorboard --logdir=profiler/demoVerify before relying
- Whether the package works with all major ML frameworks or if framework-specific setup is needed.
- Performance impact of running the profiler on large-scale distributed training jobs.
- Whether gRPC distributed processing mode is production-ready or experimental.
- Actual monthly download volume and user base size.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagexprof |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 871,596 / month, #4,845 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorboard_plugin_profile-2.23.1-py3-none-any.whl
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See also torch-tb-profiler · xprof · google-cloud-mldiagnostics · tensorboard · tensorboard-data-server · tb-nightly · tensorboard-plugin-wit · tuna · tbparse · visualdl