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cli-inference

cli-inference

Run, script, and troubleshoot Chai-1 folding with the chai-lab fold CLI and chai_lab.chai1.run_inference API.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: cli-inference description: "Run, script, and troubleshoot Chai-1 folding with the chai-lab fold CLI and chai_lab.chai1.run_inference API." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Chai-1 CLI and Inference

Use this sub-skill when the task is to run, template, script, or debug Chai-1 molecular structure prediction through chai-lab fold or chai_lab.chai1.run_inference.

Use This For

  • Constructing chai-lab fold commands, including device, seed, recycle, sample, and output settings.
  • Writing Python inference scripts around chai_lab.chai1.run_inference and reading StructureCandidates outputs.
  • Diagnosing inference setup failures: non-empty output directories, CUDA/device problems, model downloads, and long-running jobs.
  • Routing advanced custom-context work to make_all_atom_feature_context plus run_folding_on_context.

Route Elsewhere

Quick Start

Install a pinned public package build or a Git build in a Linux Python 3.10+ environment:

pip install chai_lab==0.6.1
# or: pip install git+https://github.com/chaidiscovery/chai-lab.git

Run a minimal ESM-only fold, writing to a new or empty directory:

chai-lab fold input.fasta outputs/chai-run --seed 42 --device cuda:0

Print citation metadata without running inference:

chai-lab citation

Generate a configurable Python script template without starting a GPU job:

python scripts/write_inference_template.py \
  --fasta input.fasta \
  --output-dir outputs/chai-run \
  --script-out run_chai_inference.py \
  --device cuda:0 \
  --seed 42

Runtime Expectations

  • Chai-1 inference is intended for Linux, Python >=3.10, and a CUDA GPU with bfloat16 support; do not promise practical CPU inference.
  • The default device is effectively cuda:0 if device is not provided.
  • Model and helper assets download automatically on first use. Set CHAI_DOWNLOADS_DIR=/path/to/cache when the default package-local download location is unsuitable.
  • Folding can be slow and memory-intensive. Reduce --num-diffn-samples, --num-diffn-timesteps, or input size for faster/cheaper exploratory runs.
  • output_dir must not contain existing files; create a fresh run directory or deliberately clean it before invoking inference.

Reference Map

Gespeicherte Skills