nvidia/maisi

Pull and run nvidia/maisi using Docker (this will download the full model and run it in your local environment)

Get API KeyCopy Code
$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Start NIM

1. Export NGC_API_KEY variable.

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export NGC_API_KEY=<your personal NGC key>

2. The NIM container automatically downloads any required models. To save time and bandwidth it is recommended to provide a local cache directory. This way the NIM will be able to reuse any already downloaded models. Execute the following command to setup the cache directory:

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export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p $LOCAL_NIM_CACHE

Note that you may need to run (sudo) chmod -R 777 $LOCAL_NIM_CACHE after the MAISI model is downloaded to avoid permission issues.

3. Run the NIM container with the following commands:

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docker run --rm -it --name maisi \
   --runtime=nvidia -e CUDA_VISIBLE_DEVICES=0 \
   -p 8000:8000 \
   -e NGC_API_KEY=$NGC_API_KEY \
   nvcr.io/nim/nvidia/maisi:latest

This command will start the NIM container and expose port 8000 for the user to interact with the NIM.

4. Open a new terminal, leaving the terminal open with the just launched service. In the new terminal, wait until the health check end point returns {"status":"ready"} before proceeding. This may take a couple of minutes. You can use the following command to query the health check.

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curl -X 'GET' \
    'http://localhost:8000/v1/health/ready' \
    -H 'accept: application/json'

Python Client Example: Generate CT volumes

1. Save following Python example to a file named nim_client.py. Here's a script to generate synthetic images, this script will POST a request to /v1/maisi/run with image generation parameters. It then handles the response, saving ZIP files or displaying JSON messages as appropriate.

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import requests
from datetime import datetime

base_url = "http://localhost:8000"

# Generate synthetic image
payload = {
    "num_output_samples": 1,
    "body_region": ["abdomen"],
    "anatomy_list": ["liver", "spleen"],
    "output_size": [512, 512, 512],
    "spacing": [1.0, 1.0, 1.0],
    "image_output_ext": ".nii.gz",
    "label_output_ext": ".nii.gz",
}

generation_response = requests.post(f"{base_url}/v1/maisi/run", json=payload)

if generation_response.status_code == 200:
    print("Image generation request successful")

    if generation_response.headers.get('Content-Type') == 'application/zip':
        # Save ZIP file with timestamp
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        zip_filename = f"output_{timestamp}.zip"
        with open(zip_filename, "wb") as f:
            f.write(generation_response.content)
        print(f"Output saved as {zip_filename}")

    elif 'application/json' in generation_response.headers.get('Content-Type', ''):
        response_json = generation_response.json()
        print("Response:", response_json.get('message') or response_json.get('error'))

    else:
        print("Unexpected response format")
else:
    print(f"Error {generation_response.status_code}: {generation_response.text}")

2. Execute the example.

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python nim_client.py

3. The resulting CT volume and label files will be returned and written to output.zip.