Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Predict biomolecular structures and optional ligand affinity. Use this guide for first-pass hosted/local usage; load supplemental files only when needed:
references/api.md: exact endpoints, schemas, Docker flags, response fields.references/science.md: purpose, strengths, limitations, and handoffs.references/parameters.md: prediction, sampling, MSA, template, affinity tuning.references/validation.md: mmCIF, confidence, affinity, and chemistry checks.references/examples.md: compact hosted/local payload patterns.Read credentials from the environment only when needed. Check presence with
bool(os.getenv("NGC_API_KEY")); keep key values and Authorization headers out of
terminal output, logs, saved artifacts, and the final response. Avoid environment
dumps when diagnosing authentication. If the hosted key is absent, report the
missing variable before submitting a request.
Ask only when context is unclear:
Hosted NVIDIA API or local Docker NIM?
https://health.api.nvidia.com/v1/biology/mit/boltz2/predicthttp://localhost:8000/biology/mit/boltz2/predictHosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker
startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for
registry login, entitlement checks, and first-run model downloads; pass it
into the container with -e NGC_API_KEY. Local inference requests use no
auth header after readiness. Warm-cache key-free startup varies by
image/version and should not be assumed.
For local setup answers, copy the preflight below before docker login,
docker run, readiness, and the no-auth local request. Do not invent a cache
default or drop the .env load or NVIDIA_API_KEY fallback.
set -a
[ -f .env ] && . ./.env
set +a
if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 755 "${LOCAL_NIM_CACHE}"
docker run --rm --name boltz2 --gpus all \
--shm-size=16G \
-e NGC_API_KEY \
-v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
-p 8000:8000 \
nvcr.io/nim/mit/boltz2:1.6.0
Readiness:
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done
First startup downloads about 30 GB of model weights.
import os
import requests
HOSTED = True
url = (
"https://health.api.nvidia.com/v1/biology/mit/boltz2/predict"
if HOSTED else "http://localhost:8000/biology/mit/boltz2/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
api_key = os.getenv("NGC_API_KEY")
if not api_key:
raise SystemExit("NGC_API_KEY is required for the hosted API")
headers["Authorization"] = f"Bearer {api_key}"
payload = {
"polymers": [{
"id": "A",
"molecule_type": "protein",
"sequence": "MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPT",
}],
"recycling_steps": 3,
"sampling_steps": 50,
"diffusion_samples": 1,
"step_scale": 1.638,
"output_format": "mmcif",
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()
Payload essentials:
{"molecule_type": "protein", "sequence": "..."}.molecule_type "dna" or "rna".{"id": "L1", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"}.{"id": "L1", "ccd": "ATP"}."predict_affinity": True on exactly one ligand; report
affinity_pic50, affinity_pred_value, and affinity_probability_binary.alignment, format, and rank; do not use a stale data field.protein_with_msa = {
"id": "A",
"molecule_type": "protein",
"sequence": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
"msa": {"msa_search": {"a3m": {
"alignment": ">query\nMTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
"format": "a3m",
"rank": 0,
}}},
}
Save every .cif artifact and read the confidence/affinity fields using the
snippet in references/examples.md under Save
Structures And Affinity. Visualize in PyMOL, ChimeraX, or UCSF Chimera. For
confidence/affinity sanity checks, read references/validation.md.
422: invalid sequence, invalid CCD/SMILES, malformed MSA, or multiple
affinity ligands./v1/health/ready.--gpus all, --shm-size=16G, and the /opt/nim/.cache mount.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
日本語の概要は準備中です。原文の説明を表示しています。
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
日本語の概要は準備中です。原文の説明を表示しています。
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
日本語の概要は準備中です。原文の説明を表示しています。