---
id: tensorboard-plugin-profile
version: "2.23.1"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# tensorboard-plugin-profile — XProf Profiler Plugin

License: permissive · Maintenance: active · Downloads: 871.6K/mo

## 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 above — verify before relying.

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.

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

pip install tensorboard-plugin-profile
uv add tensorboard-plugin-profile
poetry add tensorboard-plugin-profile

## Installing tensorboard-plugin-profile

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.

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/demo

Requires internet access to load Google Chart library; some visualizations may be unavailable offline or behind corporate firewalls. Python 3.10+ required.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 871.6K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ml model profiling visualization, tensorboard profiler plugin, device performance analysis, execution timeline viewer, memory profiling ml, xprof profiler, distributed training profiling, ml-profiling, performance-analysis, distributed-computing

[View on SkillFed](https://skillfed.io/packages/tensorboard-plugin-profile) · [View on PyPI](https://pypi.org/project/tensorboard-plugin-profile/)