Pulling and running NGC containers

# Login (for private/org registries)
docker login nvcr.io
# Username: $oauthtoken
# Password: <your NGC API key>

# Pull a container
docker pull nvcr.io/nvidia/pytorch:24.01-py3

# Run with all GPUs
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:24.01-py3

# Run with a specific GPU
docker run --gpus '"device=0"' -it --rm nvcr.io/nvidia/pytorch:24.01-py3

# Mount a data directory
docker run --gpus all -it --rm \
  -v /data:/workspace/data \
  nvcr.io/nvidia/pytorch:24.01-py3

In Kubernetes:

# Create NGC pull secret
kubectl create secret docker-registry ngc-secret \
  --docker-server=nvcr.io \
  --docker-username='$oauthtoken' \
  --docker-password=<NGC_API_KEY>

spec:
  containers:
  - name: trainer
    image: nvcr.io/nvidia/pytorch:24.01-py3
    resources:
      limits:
        nvidia.com/gpu: 4
  imagePullSecrets:
  - name: ngc-secret

NGC also provides: pre-trained models, NIM inference containers, Helm charts, SDKs (TensorRT, Triton, Riva).