NVIDIA RTX PRO 6000 Blackwell Server Edition으로 단백질 구조 추론 100배 이상 가속

# See https://build.nvidia.com/openfold/openfold2/deploy for
# instructions to configure your docker login, NGC API Key, and
# environment for running the OpenFold NIM on your local system.
 
# Run this in a shell, providing the username below and your NGC API Key
$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>
 
export NGC_API_KEY=<your personal NGC key>
 
# Configure local NIM cache directory so the NIM model download can be reused
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
sudo chmod 0777 -R "$LOCAL_NIM_CACHE"
 
# Then launch the NIM container, in this case using GPU device ID 0.
docker run -it \
    --runtime=nvidia \
    --gpus='"device=0"' \
    -p 8000:8000 \
    -e NGC_API_KEY \
    -v "$LOCAL_NIM_CACHE":/opt/nim/.cache \
    nvcr.io/nim/openfold/openfold2:latest
 
# It can take some time to download all model assets on the initial run.
# You can check the status using the built-in health check.  This will
# return {"status": "ready"} when the NIM endpoint is ready for inference.
curl http://localhost:8000/v1/health/ready

#!/usr/bin/env python3
 
import requests
import os
import json
from pathlib import Path
 
# ----------------------------
# parameters
# ----------------------------
output_file = Path("output1.json")
selected_models = [1, 2]
 
# SARS-CoV-2 proteome example
# Spike protein (1273 residues) — critical for vaccine development
sequence = (
"MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFAST
EKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNL
REFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVD
CALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLC
FTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCY
FPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITP
CSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARS
VASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQV
KQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWT
FGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLD
KVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHD
GKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQK
EIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT"
)
 
data = {
    "sequence": sequence,
    "selected_models": [1, 2],
    "relax_prediction": False,
}
print(data)
 
# ---------------------------------------------------------
# Submit
# ---------------------------------------------------------
url = "http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template"
print("Making request...")
response = requests.post(url=url, json=data)
 
# ---------------------------------------------------------
# View response
# ---------------------------------------------------------
if response.status_code == 200:
    output_file.write_text(response.text)
    print(f"Response output to file: {output_file}")
 
else:
    print(f"Unexpected HTTP status: {response.status_code}")
    print(f"Response: {response.text}")