Getting Started

Prerequisites

• Refer to the Support Matrix to make sure that you have the supported hardware and software stack.

• An NGC personal API key. The NIM microservice uses the API key to download models from NVIDIA NGC. Refer to Generating a Personal API Key in the NVIDIA NGC User Guide for more information.

• To run the below examples you must have curl, jq, and the Python requests module installed.

  When you create an NGC API personal key, select at least the option for NGC Catalog from the Services Included menu. You can specify more services to use the key for additional purposes.

Installing curl, jq, and the Python requests module

1. curl is required to send requests from the command line. On Ubuntu and other Debian-based Linux distributions, curl can be installed with the following command, which may require sudo access:

   sudo apt-get update && sudo apt-get install curl

2. jq is used to process and reformat responses from the NIM so they are easier to read. jq can be installed using the following command, which may require sudo access:

   sudo apt-get update && sudo apt-get install jq

3. The Python requests module facilitates HTTP requests made in many of the examples for this NIM. The Python requests module can be installed using pip:

   pip install requests

NGC Authentication

Generate an API key

To access NGC resources, you need an NGC API key. You can generate a key here: Generate Personal Key.

When creating an NGC API Personal key, ensure that “NGC Catalog” is selected from the “Services Included” dropdown. You can include more services if you reuse this key for other purposes.

Export the API key

Pass the value of the API key to the docker run command in the next section as the NGC_API_KEY environment variable to download the appropriate models and resources when starting the NIM.

If you’re not familiar with how to create the NGC_API_KEY environment variable, the simplest way is to export it in your terminal:

export NGC_API_KEY=<value>

Run one of the following commands to make the key available at startup:

# If using bash
echo "export NGC_API_KEY=<value>" >> ~/.bashrc
# If using zsh
echo "export NGC_API_KEY=<value>" >> ~/.zshrc

Docker Login to NGC

To pull the NIM container image from NGC, first authenticate with the NVIDIA Container Registry with the following command:

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

Use $oauthtoken as the username and NGC_API_KEY as the password. The $oauthtoken username is a special name that indicates that you will authenticate with an API key and not a username and password.

Starting the NIM Container

1. Ensure you have logged in to Docker and set your NGC_API_KEY environment variable as detailed in the preceding section.

2. Create a NIM Cache. If one already exists, you may use the existing one.

   export LOCAL_NIM_CACHE=~/.cache/nim
   mkdir -p ${LOCAL_NIM_CACHE}
   chmod 777 ${LOCAL_NIM_CACHE}

3. Start the NIM container:

   ## Note: these two commands are not required if you have already set the appropiate variables in previous instructions.
   ## They are provided here as part of a one-click startup sequence.
   export LOCAL_NIM_CACHE=~/.cache/nim
   export NGC_API_KEY=<Your NGC API Key>

   docker run --rm --name boltz2 --runtime=nvidia \
       --shm-size=16G \
       -e NGC_API_KEY \
       -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
       -p 8000:8000 \
       nvcr.io/nim/mit/boltz2:1.2.0

   The preceding command starts the container with the default profile, using all available system GPUs. Note: This will require localizing up to 30 Gigabytes of data to disk. If you have the model cached, the version in the NIM cache specified at LOCAL_NIM_CACHE will be used.

4. (Optional) Confirm the service is ready to respond to inference requests:

   $ curl -X GET http://localhost:8000/v1/health/ready

   Example Output

    200 true

5. Send an inference request:

#!/bin/bash
# Define the protein sequence
SEQUENCE="MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN"
# Create JSON payload for protein structure prediction
JSON_DATA='{
"polymers": [
    {
    "id": "A",
    "molecule_type": "protein",
    "sequence": "'"$SEQUENCE"'"
    }
],
"recycling_steps": 3,
"sampling_steps": 50,
"diffusion_samples": 1,
"step_scale": 1.638,
"output_format": "mmcif"
}'
echo "Making request..."
# Make the POST request
# Note: This script requires jq to be installed for JSON processing
response=$(curl -s -X POST \
-H "Content-Type: application/json" \
-d "$JSON_DATA" \
http://localhost:8000/biology/mit/boltz2/predict)
# Extract and display the structure information
# Using jq to parse the JSON response
echo "Structure prediction completed"
echo "Format: $(echo $response | jq -r '.structures[0].format')"
echo "Confidence score: $(echo $response | jq -r '.confidence_scores[0]')"
echo "Structure (first 500 chars): $(echo $response | jq -r '.structures[0].structure' | head -c 500)..."

The description of all API parameters could be found in the Input Parameters section of the Inference Guide.

Runtime Parameters for the Container

Caching Models

The first time you start the container, the microservice downloads the model from NGC. You can avoid this download step on future runs by caching the model locally.

# Create the cache directory on the host machine.
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
chmod 777 $LOCAL_NIM_CACHE
# Run the container with the cache directory as a volume mount.

docker run --rm --name boltz2 --runtime=nvidia \
    --shm-size=16G \
    -e NGC_API_KEY \
    -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
    -p 8000:8000 \
    nvcr.io/nim/mit/boltz2:1.2.0

Stopping the Container

The following commands stop the container by stopping and removing the running docker container.

docker stop boltz2
docker rm boltz2

Next Steps

• Refer to the Deployment section in the Inference Guide for more detailed instructions on how to deploy the NIM.
• Refer to Optimization for settings related to optimizing performance of the NIM on your hardware and software stack.