evo2-nim

name	evo2-nim
description	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.
license	Apache-2.0 AND CC-BY-4.0
compatibility	requests>=2.28; numpy>=1.24
allowed-tools	Bash, Read, Write, AskUserQuestion

Evo 2 NIM

Use Evo 2 for DNA generation and, locally, layer-output extraction. Use this SKILL.md for basic hosted/local use; 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.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker Evo 2 NIM?

• Hosted generation: https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate
• Local generation: http://localhost:8000/biology/arc/evo2/generate
• Local forward/layer outputs: http://localhost:8000/biology/arc/evo2/forward

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.

Local Docker Requirements

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.

• Default 40B: 2x H100 80 GB or 1x H200 141 GB. Use NIM_TEST_GPUS=0,1 for 2x H100, or NIM_TEST_GPUS=0 for one H200.
• 7B fallback: set NIM_VARIANT=7b; supported GPUs include H100, H200, RTX 6000 Ada, and L40S.
• Approximate disk: 110 GB for 40B, 50 GB for 7B.

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 777 "${LOCAL_NIM_CACHE}"

# 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:

until curl -sf http://localhost:8000/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.

DNA Generation

Normalize prompts before sending. Use A/C/G/T unless ambiguous bases are a deliberate modeling choice and clearly reported.

import json
import os
from pathlib import Path
import requests

HOSTED = True

def clean_dna(value: str) -> str:
    seq = "".join(value.upper().split())
    invalid = sorted(set(seq) - set("ACGT"))
    if invalid:
        raise ValueError(f"Unexpected DNA characters: {''.join(invalid)}")
    return seq

prompt = clean_dna("ACTGACTGACTGACTG")
url = (
    "https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate"
    if HOSTED else "http://localhost:8000/biology/arc/evo2/generate"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"

payload = {
    "sequence": prompt,
    "num_tokens": 64,
    "temperature": 0.7,
    "top_k": 3,
    "top_p": 0.0,
    "random_seed": 1,
    "enable_sampled_probs": True,
    "enable_elapsed_ms_per_token": True,
}
response = requests.post(url, headers=headers, json=payload, timeout=180)
response.raise_for_status()
result = response.json()
seq = result["sequence"]
if sorted(set(seq.upper()) - set("ACGT")):
    raise ValueError("Generated sequence contains unexpected non-ACGT bases")

Path("evo2_generation.json").write_text(json.dumps(result, indent=2) + "\n")
Path("evo2_generated.fa").write_text(f">evo2_generated\n{seq}\n")
print(f"Generated {len(seq)} bases in {result.get('elapsed_ms')} ms")

Only request enable_logits when needed; logits can make responses large. random_seed supports development reproducibility, not biological certainty.

Local Forward Pass

Forward returns base64-encoded NPZ tensors.

import base64
import io
import numpy as np
import requests

payload = {
    "sequence": clean_dna("ACTGACTGACTG"),
    "output_layers": ["output_layer", "decoder.layers.3.self_attention"],
}
response = requests.post(
    "http://localhost:8000/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()))

Validate And Report

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.

Troubleshooting

• 401/403: hosted key missing/expired or not sent as Bearer token.
• 422: wrong field names such as max_tokens instead of num_tokens.
• Local auth confusion: do not send Authorization to localhost.
• Local startup: first run downloads model assets; wait on /v1/health/ready.
• FP8 failure: use hosted, 7B on a supported FP8 GPU, or documented 40B GPUs.