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。
Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.
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。
Use Evo 2 for DNA generation and, locally, layer-output extraction. Load supplemental files only when needed:
references/api.md: exact schemas, layer names, Docker flags, hardware notes.references/science.md: genomic use cases, limits, and interpretation.references/parameters.md: generation/forward parameter effects.references/validation.md: DNA, probability, timing, and tensor checks.references/examples.md: compact hosted/local request patterns.For generation, use scripts/generate.py to execute the request, validate the
response, and save its artifacts. Resolve the script path relative to this
skill's directory and choose an output directory in the user's workspace.
Use the user's sequence and requested parameters; the example below is only
a smoke test.
elapsed_ms, sampled-probability summary, seed, and artifact paths from the
successful run. Read the saved response or metrics if any result is unclear.If the request or validation fails, report the actual failure and any diagnostic files. Do not replace an unavailable API response with example values. For a code-only request, provide the command without making an inference call.
Honor NIM_API_MODE when it is set. Accepted values are hosted and local.
If it is unset, treat an explicit EVO2_NIM_URL as local; otherwise ask when
the requested mode is unclear:
Hosted NVIDIA API or local Docker Evo 2 NIM?
https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate$EVO2_NIM_URL, falling back to http://localhost:8000$EVO2_NIM_URL/biology/arc/evo2/generate$EVO2_NIM_URL/biology/arc/evo2/forwardAlways resolve local health and inference routes from EVO2_NIM_URL when it
is present. localhost works only when the caller and NIM share a network
namespace; a caller in a separate container usually needs a service URL such
as http://evo2-nim:8000. Do not silently switch modes when the selected
endpoint is unavailable. Report the failed endpoint and fix its configuration.
The hosted docs expose generation. /forward is documented for local Docker;
do not invent a hosted /forward endpoint. Hosted 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.
Normalize prompts before sending. Use A/C/G/T unless ambiguous bases are a deliberate modeling choice and clearly reported.
For a hosted generation request, run the bundled client with the user's inputs (the script path below is relative to the skill directory):
python scripts/generate.py \
--mode hosted \
--sequence ACTGACTGACTGACTG \
--num-tokens 64 --seed 1 \
--temperature 0.7 --top-k 3 --top-p 0.0 \
--output-dir /path/to/workspace/evo2-output
For an already-ready local NIM, use --mode local; the client resolves
EVO2_NIM_URL and sends no Authorization header. It never switches endpoints
after a failed request. Set --timeout for a longer read if the user requests
a larger generation; failed requests are not automatically resubmitted.
The client saves request.json, the actual response.json, generated.fasta,
and metrics.json in the chosen output directory. It also saves the exact
response body in response.raw before checking HTTP status or parsing JSON,
so diagnostics survive malformed JSON and non-finite probability/timing values.
It validates the requested number of generated bases, A/C/G/T alphabet, finite sampled probabilities in
[0, 1], and nonnegative timing before printing a successful summary. Existing
directories are never reused, even if empty. Choose an output directory that
does not exist; the client creates it atomically so concurrent runs cannot
overwrite each other's artifacts.
The FASTA contains generated bases only, not the input prompt prepended again.
sampled_probs is requested by the client and summarized with count/min/max/mean;
the full values stay in the saved response. A missing or malformed probability
array is a validation failure, not permission to invent confidence values.
Only request enable_logits in a custom request when needed; logits can make
responses large. See references/api.md for custom payloads.
random_seed supports development reproducibility, not biological certainty.
Evo 2 local deployment requires FP8-capable GPUs. Do not present A100 as compatible; A100 can pull the image but fails warmup because FP8 requires compute capability 8.9 or higher.
NIM_TEST_GPUS=0,1 for
2x H100, or NIM_TEST_GPUS=0 for one H200.NIM_VARIANT=7b; supported GPUs include H100, H200,
RTX 6000 Ada, and L40S.Use shell env first; source repo-root .env only if present. Do not invent a
cache default or drop the 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
# 40B default: 0,1 for 2x H100; set 0 for a single H200.
export NIM_TEST_GPUS="${NIM_TEST_GPUS:-0,1}"
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 700 "${LOCAL_NIM_CACHE}" # owner-only; if the NIM runs as a different UID, add -u "$(id -u)" to docker run
# For 7B: export NIM_VARIANT=7b; export NIM_TEST_GPUS="${NIM_TEST_GPUS:-0}"
docker run --rm -it --name evo2-nim \
--runtime=nvidia \
--gpus "\"device=${NIM_TEST_GPUS}\"" \
-e NGC_API_KEY \
-e NIM_VARIANT \
-v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
-p 8000:8000 \
nvcr.io/nim/arc/evo2:2
Readiness:
evo2_nim_url="${EVO2_NIM_URL:-http://localhost:8000}"
until curl -sf "${evo2_nim_url%/}/v1/health/ready"; do sleep 10; done
If RTX PRO 6000 Blackwell Workstation fails with no Transformer Engine attention backend, treat it as outside the current validated matrix and rerun on a documented GPU/runtime.
Forward returns base64-encoded NPZ tensors.
import base64
import io
import os
import numpy as np
import requests
mode = os.getenv("NIM_API_MODE", "local")
if mode != "local":
raise RuntimeError("Evo 2 /forward is available only in local mode")
nim_url = os.getenv("EVO2_NIM_URL", "http://localhost:8000").rstrip("/")
sequence = "ACTGACTGACTG" # Replace with the user's DNA sequence.
sequence = "".join(sequence.upper().split())
if not sequence or set(sequence) - set("ACGT"):
raise ValueError("Expected nonempty A/C/G/T DNA")
payload = {
"sequence": sequence,
"output_layers": ["output_layer", "decoder.layers.3.self_attention"],
}
response = requests.post(
f"{nim_url}/biology/arc/evo2/forward",
headers={"Content-Type": "application/json"},
json=payload,
timeout=300,
)
response.raise_for_status()
npz_bytes = base64.b64decode(response.json()["data"])
with open("evo2_forward_outputs.npz", "wb") as handle:
handle.write(npz_bytes)
arrays = np.load(io.BytesIO(npz_bytes), allow_pickle=False)
for name in arrays.files:
arr = arrays[name]
print(name, arr.shape, arr.dtype, bool(np.isfinite(arr).all()), float(arr.mean()))
Save request/response JSON, generated FASTA, and a metrics JSON with sequence
length, GC fraction, ambiguous-base fraction, homopolymer length, sampled-prob
checks, and elapsed timing. Treat invalid schema or alphabet as hard failures;
treat extreme GC, low complexity, duplicates, and missing motifs as warnings.
For deeper checks, read references/validation.md.
Key fields: sequence, num_tokens, temperature, top_k (0-6), top_p
(0-1), random_seed, enable_sampled_probs, enable_elapsed_ms_per_token,
and optional enable_logits.
401/403: hosted key missing/expired or not sent as Bearer token.422: wrong field names such as max_tokens instead of num_tokens.NIM_API_MODE and EVO2_NIM_URL; do not
replace a configured service URL with localhost.Authorization to local inference.$EVO2_NIM_URL/v1/health/ready before inference.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。