PyTorch HUD

Provides a PyTorch CI/CD analytics API for investigating build failures, analyzing test flakiness, and monitoring performance trends across the PyTorch infrastructure.

Installation Instructions

README: https://github.com/izaitsevfb/claude-pytorch-treehugger

PyTorch HUD API with MCP Support

A Python library and MCP server for interacting with the PyTorch HUD API, providing access to CI/CD data, job logs, and analytics.

Overview

This project provides tools for PyTorch CI/CD analytics including:

• Data access for workflows, jobs, and test runs
• Efficient log analysis for large CI logs
• ClickHouse query integration for analytics
• Resource utilization metrics

Usage (for humans)

# Install from GitHub repository
pip install git+https://github.com/izaitsevfb/claude-pytorch-treehugger.git

claude mcp add hud pytorch-hud

Development

# Install dependencies (if not installing with pip)
pip install -r requirements.txt

# Start MCP server
python -m pytorch_hud

Key Features

Data Access

• get_commit_summary: Basic commit info without jobs
• get_job_summary: Aggregated job status counts
• get_filtered_jobs: Jobs with filtering by status/workflow/name
• get_failure_details: Failed jobs with detailed failure info
• get_recent_commit_status: Status for recent commits with job statistics

Log Analysis

• download_log_to_file: Download logs to local storage
• extract_log_patterns: Find errors, warnings, etc.
• extract_test_results: Parse test execution results
• filter_log_sections: Extract specific log sections
• search_logs: Search across multiple logs

Development

# Run tests
python -m unittest discover test

# Type checking
mypy -p pytorch_hud -p test

# Linting
ruff check pytorch_hud/ test/

Documentation

• CLAUDE.md: Detailed usage, code style, and implementation notes
• mcp-guide.md: General MCP protocol information

License

MIT