This PyTorch HUD API implementation provides a Python library and MCP server for accessing PyTorch's CI/CD analytics data, enabling developers to investigate build failures and trunk health issues. Built with FastMCP, it offers tools for retrieving workflow and job information, analyzing large log files efficiently, executing ClickHouse queries against CI metrics, and monitoring resource utilization. The server exposes both synchronous and asynchronous functions through a standardized interface, making it valuable for PyTorch contributors debugging CI failures, investigating test flakiness, or analyzing performance trends across the CI infrastructure.
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Highlight AIBasic commit info without jobs.
Aggregated job status counts.
Jobs with filtering by status, workflow, or name.
Failed jobs with detailed failure info.
Status for recent commits with job statistics.
Download logs to local storage.
Find errors, warnings, etc.
Parse test execution results.
Extract specific log sections.
Search across multiple logs.
