{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "google-deepmind-alphafold3--split-the-alphafold-3-data-pipeline-from-inference",
 "repo": "google-deepmind/alphafold3",
 "repo_url": "https://github.com/google-deepmind/alphafold3",
 "repo_slug": "google-deepmind-alphafold3",
 "title": "Run the CPU search and the GPU fold on different machines",
 "page_title": "How to run the CPU search and the GPU fold on different machines with Alphafold3",
 "summary": "Use the run flags to do genetic and template search on a CPU box and inference on a GPU box.",
 "who": "A lab sharing one GPU across many predictions.",
 "problem": "The data pipeline is slow and does not need the GPU, so it wastes GPU hours.",
 "outcome": "A split workflow where the expensive search runs where it is cheap.",
 "steps": [
  "Install AlphaFold 3 as described in docs/installation.md",
  "Run `python run_alphafold.py --help` to list all the available flags",
  "Run the data pipeline alone with `--run_data_pipeline` set to true and `--run_inference` set to false on a machine without a GPU",
  "Run inference alone on the GPU machine with `--run_data_pipeline` false and `--run_inference` true, reading docs/performance.md for tuning"
 ],
 "agent_prompt": "With google-deepmind/alphafold3, show me how to run the data pipeline on my CPU server and inference on my GPU server using --run_data_pipeline and --run_inference, and what to copy between them.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Advanced",
 "time": "3 hours",
 "basis": "readme",
 "caveat": "The data pipeline needs the genetic and template databases, which are large.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "",
 "requirements": [
  "The model parameters, which you may only use if received directly from Google, under the weights terms of use",
  "Docker and an NVIDIA GPU for the inference step",
  "The genetic and template databases for the data pipeline, mounted into the container",
  "A fold_input.json describing your sequences, model seeds, dialect and version"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "credentials",
   "notice": "Keep the model parameters and their terms of use in mind when copying files between machines."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-deepmind-alphafold3/split-the-alphafold-3-data-pipeline-from-inference/",
 "md": "https://dedupedai.com/use-cases/google-deepmind-alphafold3/split-the-alphafold-3-data-pipeline-from-inference/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/google-deepmind-alphafold3--split-the-alphafold-3-data-pipeline-from-inference.json",
 "api_version": "v1"
}