Extend sleap-rtc doctor With Worker Pre-Flight Checks#71
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…p-nn checks Add four new diagnostic checks to the existing doctor command: - Account key: shows key source (env var or credentials file) with fix instructions if missing - GPU: detects via torch.cuda, shows model/memory/CUDA version from WorkerCapabilities. Warns (no fail) if unavailable. - Training dependencies: checks sleap-nn import and version - Data mounts & working directory: validates configured mount paths and working directory exist on disk Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
WorkerCapabilities. Warns but doesn't fail if unavailable.sleap-nnimport and versionMotivation
Workers are now deployable on GPU clusters via Docker (PR #70). Before starting a worker, users need a quick way to verify their setup is correct — is the account key set? Is the GPU detected? Are the data mounts accessible? The existing
sleap-rtc doctorcommand checked Python, dependencies, network, and credentials, but missed these worker-specific checks. This PR fills that gap so users can runsleap-rtc doctorinside a container or on a remote machine and immediately see what's working and what needs fixing.Test plan
tests/test_doctor.py— all passingsleap-rtc doctoron a GPU machine and verify output🤖 Generated with Claude Code