rosettacommons/rfd4-proteina/rfd4-proteina-setup
Claude Code and agent skill by rosettacommons/rfd4-proteina
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Prepare a machine to run RFD4-Proteina (RFD4-Proteina) — the Baker Lab x NVIDIA flow-matching protein design model. Reach for this skill for "set up rfproteina", "install rfproteina", "environment for rfproteina", "download the rfproteina checkpoints", "DATA_PATH not found", "atomworks not found", "which checkpoint do I use", "run rfproteina on a cluster", or before any generate/evaluate/search run. It installs the native Python environment and AtomWorks, wires DATA_PATH and runtime settings, d…
Install
npx vskill@latest install rosettacommons/rfd4-proteina/rfd4-proteina-setupSKILL.md
RFD4-Proteina Setup
Get a host ready to run RFD4-Proteina inference: environment, model weights, and a green preflight. The two failure modes this skill exists to prevent are (1) importing the model before its environment and AtomWorks are installed, and (2) launching an hours-long run only to have it die because DATA_PATH or a checkpoint is missing. Preflight both up front.
Run everything from the repository root (the directory containing .project-root).
Step 1 — Install the runtime
From the repository root:
Native setup creates .venv with the CUDA 13 model dependencies and installs the public AtomWorks version pinned in pyproject.toml; private setup retains its AtomWorks development submodule. It creates .env.local only if neither .env.local nor .env exists, preserves existing files, and does not edit shell startup files.
Step 2 — Check data paths and optional refolding
Setup downloads the configured native checkpoint pair to DATA_PATH, or caches an enabled Hugging Face bundle under HF_HOME. DATA_PATH defaults to ./data_path. Exported shell settings take precedence over the environment file. Use bash setup.sh --skip-weights for an environment-only installation.
configs/inference.yaml selects native checkpoints or a pinned model repository. Use HF_TOKEN or hf auth login while private; public downloads use the same code. To add OpenFold3 on a host with Apptainer:
Pass your configured DATA_PATH instead if it differs from ./data_path. Set the printed oracle paths in .env.local and use metrics/refolding_oracles=of3. See docs/installation.rst for the separate host oracle.
Step 3 — Preflight
This uses the same runtime dispatcher as model entry points and checks imports, CUDA visibility, and cached model files. Resolve failures before sampling.
Step 4 — Smoke test
A generated CIF under runs/smoke/inference_samples/ confirms the inference path. Hand off to rfd4-proteina-generate for real designs.
Optional host tools
Setup always creates .venv for SLURM configuration resolution. bash setup.sh --venv adds notebooks and development dependencies. Use the installed rfd4-* commands from the activated environment.
Troubleshooting
- Import failures: rerun setup or the pip installation in your selected environment.
- Missing model files: check
HF_HOMEand rerun setup. Optional data usesDATA_PATH. - Missing GPU: verify the NVIDIA driver and the selected runtime's GPU support.
- Refolding failures: check the separate oracle image, weights, cache, and host Apptainer.
- SLURM host tooling missing: rerun setup, then configure
scripts/.user_info_local.
For checkpoint details, see checkpoints.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/rfd4-proteina-setup/SKILL.md
Popularity
- Repository stars
- 496
- Repository forks
- 59
- 7-day trend score
- 0
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