undefined

Readiness:

```bash
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"
mkdir -p "${LOCAL_NIM_CACHE}"; chmod 755 "${LOCAL_NIM_CACHE}"
docker run --rm --name msa-search \
  --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_PROFILE=<hash-from-list-model-profiles> \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

docker run --rm --entrypoint list-model-profiles nvcr.io/nim/colabfold/msa-search:2

docker run --rm --name msa-search \
  --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_PROFILE=<hash-from-list-model-profiles> \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

curl -s localhost:8000/v1/metadata | jq

ngc registry model download-version nim/colabfold/msa-search:uniref30_2302-m18v1

`NIM_MODEL_NAME` **replaces** the profile databases entirely — the NIM uses only what is
under that directory (discovered by scanning for `**/*.idx`). Mount multiple databases under
one parent to combine them. NGC-downloaded databases are pre-indexed for GPU Server; custom
databases must be indexed with `mmseqs createindex` first. Individually downloadable NGC model
versions: `uniref30_2302-m18v1`, `colabfold_envdb_202108-m18v1`, `pdb70_220313-m18v1`,
`pdb100_230517-m18v1`, `pdb_20251028_zip-m18v1`.

undefined

For **all databases** (equivalent to `databases:all`), repeat step 1 for each individual DB
version and download them into sibling directories under one parent, then point
`NIM_MODEL_NAME` at that parent — the NIM discovers every DB by scanning `**/*.idx`:

```bash

The per-connection CDN throttle is the same for every database, so parallel download helps
the full set proportionally — the more you download, the more absolute time it saves.

Notes:

- The presigned URLs expire (typically within a day) — build the aria2 input and start the
  download promptly after fetching `files.json`.
- Keep the downloaded directory's internal layout intact (e.g. `uniref30_2302/…`); the
  `filepath` values already encode it. The NIM needs the `.idx` file plus its companion files
  and the small `.UNIREF30_READY` / `*.tar.gz.unpacked` markers.
- The bottleneck is the CDN's per-connection cap, not local disk or CPU — a fast NVMe volume
  writes far faster than the network delivers. Raising `--split` / `--max-connection-per-server`
  helps only up to the node's aggregate egress ceiling.
- Best of all: download the profile once, then **persist the cache volume** (or this
  `fast-db` directory) and mount it on future nodes for a ~20 s warm start with no re-download.

import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/predict"
    if HOSTED else "http://localhost:8000/biology/colabfold/msa-search/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "sequence": "SGSMKTAISLPDETFDRVSRRASELGMSRSEFFTKAAQR",
    "databases": ["Uniref30_2302", "colabfold_envdb_202108"],
    "e_value": 0.0001,
    "output_alignment_formats": ["a3m"],
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

url = (
    "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/paired/predict"
    if HOSTED else "http://localhost:8000/biology/colabfold/msa-search/paired/predict"
)
payload = {
    "sequences": [chain_a_sequence, chain_b_sequence],
    "e_value": 0.0001,
    "output_alignment_formats": ["a3m"],
}

url = "http://localhost:8000/biology/colabfold/msa-search/structure-templates/predict"
headers = {"Content-Type": "application/json"}
payload = {
    "sequence": "VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVA",
    "structural_template_databases": ["pdb70_220313"],
    "max_structures": 20,
    "max_msa_sequences": 500,
}

undefined

A3M output can feed OpenFold3, AlphaFold2, or RoseTTAFold. For alignment depth,
template, and sequence sanity checks, read `references/validation.md`.