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PyTorch HUD MCP Server

PyTorch HUD MCP Server

Public
izaitsevfb/claude-pytorch-treehugger

Provides Model Context Protocol (MCP) access to PyTorch CI/CD data, enabling efficient retrieval and analysis of workflows, job statuses, failure details, and large-scale log analytics with integrated ClickHouse queries and resource metrics.

python
0 tools
May 30, 2025
Updated Jun 4, 2025

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MCP Server

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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

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