---

name: running-predictions description: "Plan and run AlphaFold 3 predictions via Docker or local Python, including full runs, data-pipeline-only/inference-only splits, database/model setup, hardware and performance flags, and runtime diagnostics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Running AlphaFold 3 Predictions

Use this sub-skill when a user needs to execute or debug AlphaFold 3 prediction workflows. It covers command shape, setup prerequisites, stage splitting, database/model paths, HMMER tools, GPU/runtime tuning, and safe preflight checks.

Route First

• For creating or validating AlphaFold 3 JSON input entities, use ../input-preparation/.
• For interpreting ranking scores, confidence JSON, structures, embeddings, or distograms, use ../output-interpretation/.
• For calling folding_input, DataPipeline, ModelRunner, or process_fold_input directly from Python, use ../python-apis/.
• For CLI/Docker/local execution, database/model setup, hardware, and performance flags, stay here.

Minimum Run Shape

AlphaFold 3 runs through run_alphafold.py. A full prediction needs exactly one of --json_path or --input_dir, an --output_dir, model parameters via --model_dir, and database paths via --db_dir or explicit database flags when the data pipeline runs.

Typical Docker command shape:

docker run --rm -it \
  --volume "$AF_INPUT:/root/af_input:ro" \
  --volume "$AF_OUTPUT:/root/af_output" \
  --volume "$AF_MODEL_DIR:/root/models:ro" \
  --volume "$AF_DB_DIR:/root/public_databases:ro" \
  --gpus all \
  alphafold3 \
  python run_alphafold.py \
  --json_path=/root/af_input/fold_input.json \
  --model_dir=/root/models \
  --db_dir=/root/public_databases \
  --output_dir=/root/af_output

Local Python command shape:

python run_alphafold.py \
  --json_path=fold_input.json \
  --model_dir=/path/to/model_parameters \
  --db_dir=/path/to/public_databases \
  --output_dir=/path/to/af_output

Use scripts/build_run_command.py to print a command without executing it. Use scripts/check_runtime_requirements.py to check imports, generated CCD resources, HMMER binaries, and optional model/database paths without downloading or mutating anything.

Stage Selection

• Full prediction: keep both defaults, --run_data_pipeline=true and --run_inference=true.
• CPU data pipeline only: set --run_inference=false; this writes JSON augmented with MSAs/templates for later inference.
• GPU inference only: set --run_data_pipeline=false; input JSON must already contain precomputed MSA/template fields or explicitly empty values where appropriate.
• Reusing one output directory across split stages often needs --force_output_dir=true on the second stage.

Operational Caveats

• Model parameters are external to the code package and must be obtained directly under the AlphaFold 3 model-parameter terms; do not assume they are bundled.
• Full public databases are external, large, and slow to download; plan hundreds of GB compressed and more when unpacked, preferably on SSD.
• Inference requires a CUDA-capable GPU in practice; A100/H100 80 GB are the best-supported high-throughput targets, while older GPUs need conservative settings.
• python run_alphafold.py --help can render flags but still exit non-zero because --output_dir is marked required; treat visible help text as useful even if the exit code is 1.
• Outputs are theoretical predictions and model/output terms apply; do not present results as experimentally validated or clinically approved.

References

• references/cli-reference.md groups the runtime flags by purpose.
• references/setup-and-performance.md explains setup, Docker/local execution, split workflows, sharding, GPU choices, and performance knobs.
• references/troubleshooting.md maps common runtime failures to concrete checks and fixes.