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 jobsget_job_summary
: Aggregated job status countsget_filtered_jobs
: Jobs with filtering by status/workflow/nameget_failure_details
: Failed jobs with detailed failure infoget_recent_commit_status
: Status for recent commits with job statistics
Log Analysis
download_log_to_file
: Download logs to local storageextract_log_patterns
: Find errors, warnings, etc.extract_test_results
: Parse test execution resultsfilter_log_sections
: Extract specific log sectionssearch_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