Getting Started
Install Docker
Install the NVIDIA Container Toolkit
Verify your container runtime supports NVIDIA GPUs by running
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
Example output:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.78.01 Driver Version: 525.78.01 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| 41% 30C P8 1W / 260W | 2244MiB / 11264MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
For more information on enumerating multi-GPU systems, please see the NVIDIA Container Toolkit’s GPU Enumeration Docs
NGC (NVIDIA GPU Cloud) Account
Docker log in with your NGC API key using
docker login nvcr.io --username='$oauthtoken' --password=${NGC_CLI_API_KEY}
NGC CLI Tool
Download the NGC CLI tool for your OS.
Use NGC CLI version 3.41.1 or newer. Here is the command to install this on AMD64 Linux in your home directory:
wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.41.3/files/ngccli_linux.zip -O ~/ngccli_linux.zip && \
unzip ~/ngccli_linux.zip -d ~/ngc && \
chmod u+x ~/ngc/ngc-cli/ngc && \
echo "export PATH=\"\$PATH:~/ngc/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile
Set up your NGC CLI Tool locally (You’ll need your API key for this!):
ngc config set
After you enter your API key, you may see multiple options for the org and team. Select as desired or hit enter to accept the default.
Model Specific Requirements
The following are specific requirements for DiffDock NIM.
Hardware
Supported GPU models:
Minimum GPU memory (GB): 16
Once the above requirements have been met, you will use the Quickstart Guide to pull the NIM container and model, perform a health check and then run inference.
Software
Minimum Driver version: 535.104.05
Pull the NIM container.
docker pull nvcr.io/nim/nvidia/bionemo-diffdock:1.2.0
Run container.
The environment variable NGC_API_KEY must be defined and valid in your local environment to ensure that the following command will proceed. For information regarding personal API key setup, please see this website.
docker run --rm -it --name diffdock-nim \
--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 \
--shm-size=2G \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
-e NGC_API_KEY=$NGC_API_KEY \
-p 8000:8000 \
nvcr.io/nim/nvidia/bionemo-diffdock:1.2.0
Open a new terminal, use the following command to check the status of API until it returns
true. This may take a couple of minutes.
curl localhost:8000/v1/health/ready
...
true
Open a new terminal, leaving the current terminal open with the launched service.
Open a new terminal, leaving the current terminal open with the launched service.
The “sed” command is used to convert the multi-line text file into a single line for JSON encoding.
Prepare JSON formatted post-data. This step requires being launched in the most common bash shell environment in Linux. Users can verify if the current session is bash by using the command echo $0. If not, please run the command /bin/bash before this step.
protein_bytes=`curl https://files.rcsb.org/download/8G43.pdb | grep -E '^ATOM' | sed -z 's/\n/\\\n/g'`; \
ligand_bytes=`curl https://files.rcsb.org/ligands/download/ZU6_ideal.sdf | sed -z 's/\n/\\\n/g'`; \
echo "{
\"ligand\": \"${ligand_bytes}\",
\"ligand_file_type\": \"sdf\",
\"protein\": \"${protein_bytes}\",
\"num_poses\": 1,
\"time_divisions\": 20,
\"steps\": 18,
\"save_trajectory\": false,
\"is_staged\": false
}" > diffdock.json
Run Inference and save to output.json.
curl --header "Content-Type: application/json" \
--request POST \
--data @diffdock.json \
--output output.json \
http://localhost:8000/molecular-docking/diffdock/generate
The output file
output.jsonis a JSON formatted content with predicted docking poses (coordinates of ligand atoms) with the structure below:
Field |
Type |
Description |
|---|---|---|
| status | str | report ‘success’ or ‘fail’ for this request |
| ligand_positions | list of str | list of SDF formatted text as the generated poses |
| position_confidence | list of float | list of confidence scores for the generated poses |
| trajectory | list of str | list of PDB formatted text as the diffusion trajectories for the generated poses (optional) |
A simple Python script provided in this section is used to dump the inference results (docked poses of ligands) into a folder named as
output. Create a new blank file, name it asdump_output.pyand copy the content below into it.
import json
import os
import shutil
def dump_one(folder, ligand_positions, position_confidence):
os.makedirs(folder, exist_ok=True)
for i, c in enumerate(position_confidence):
with open('%s/rank%02d_confidence_%0.2f.sdf' % (folder, i+1, c), 'w') as f:
f.write(ligand_positions[i])
shutil.rmtree('output', ignore_errors=True)
os.makedirs('output', exist_ok=True)
with open('output.json') as f:
data = json.load(f)
if type(data['status']) == str:
dump_one('output', data['ligand_positions'], data['position_confidence'])
else:
for i in range(len(data['status'])):
dump_one('output/ligand%d' % (i+1), data['ligand_positions'][i], data['position_confidence'][i])
Run the command below to launch the Python script.
python3 dump_output.py
List the content in the output folder.
$ ls output
rank01_confidence_-0.98.sdf
When you’re done testing the endpoint, you can bring down the container by running docker stop diffdock-nim in a new terminal.