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NVIDIA GPU Cloud (NGC) offers pre-built containers

They have both general-purpose and domain-specific offerings for machine learning and deep learning workloads on NVIDIA GPUs. Domino enhances NGC containers for use in Domino and for general data science work. Add Domino compatibility
Domino automatically manages code and data versioning as part of the container lifecycle and as a result we require specific additional software in the container. Add Data Source drivers
Domino adds drivers for Data Sources such as Snowflake, Oracle, and Microsoft SQL to make it easy to connect to a Data Source without needing to manually add drivers. Add Workspaces
When an NGC container doesn’t include an interactive notebook, Domino adds Jupyter to the image so you can interactively develop in the image. When there’s a notebook already included, Domino configures the existing notebook to work. Domino builds the latest versions of NGC containers with a preference for Ubuntu base images. If there’s an Environment that you’re looking for which is not available, request it from ngc-request@dominodatalab.com.

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Add an NGC image to Domino

  1. Choose the NGC container from the list of available containers.
  2. Create a new Compute Environment in Domino.
  3. Use the link from the container you selected as the base compute Environment (for example, quay.io/domino/ngc-pytorch:20.12-py3)
  4. Enter the following in the Workspace Definition area: 
jupyter:
 title: "Jupyter (Python, R, Julia)"
 iconUrl: "/assets/images/workspace-logos/Jupyter.svg"
 start: [ "/opt/domino/workspaces/jupyter/start" ]
 httpProxy:
   port: 8888
   rewrite: false
   internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{   requireSubdomain: false
jupyterlab:
 title: "JupyterLab"
 iconUrl: "/assets/images/workspace-logos/jupyterlab.svg"
 start: [  "/opt/domino/workspaces/jupyterlab/start" ]
 httpProxy:
   internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{   port: 8888
   rewrite: false
   requireSubdomain: false

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Available containers

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RAPIDS

https://ngc.nvidia.com/catalog/containers/nvidia:rapidsai:rapidsai
Domino registry path:quay.io/domino/ngc-rapids:0.18-cuda11.0-runtime-ubuntu20.04-py3.8
Versions:RAPIDS 0.18, Ubuntu 18.04, CUDA 11
Base Image:docker pull nvcr.io/nvidia/rapidsai/rapidsai:0.18-cuda11.0-base-ubuntu18.04
Notes:N/A

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PyTorch

https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
Domino registry path:quay.io/domino/ngc-pytorch:20.12-py3
Versions:Ubuntu 18.04, CUDA 11
Base Image:docker pull nvcr.io/nvidia/pytorch:21.02-py3
Notes:N/A

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TensorFlow

https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow
Domino registry path:quay.io/domino/ngc-tensorflow:20.12-tf1-py3
Versions:TensorFlow v21.03, Ubuntu 18.04, CUDA 11
Base Image:docker pull nvcr.io/nvidia/tensorflow:21.03-tf1-py3
Notes:N/A

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Clara Train

https://ngc.nvidia.com/catalog/containers/nvidia:clara-train-sdk
Domino registry path:quay.io/domino/ngc-clara-train:v3.1.01
Versions:Clara v3.1.01, Ubuntu 18.04, CUDA 11
Base Image:docker pull nvcr.io/nvidia/clara-train-sdk:v3.1.01
Notes:N/A

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MXNet

https://ngc.nvidia.com/catalog/containers/nvidia:mxnet
Domino registry path:quay.io/domino/ngc-mxnet:20.12-py3
Versions:MXNet v21.03, Ubuntu 18.04, CUDA 11
Base Image:docker pull nvcr.io/nvidia/mxnet:21.03-py3
Notes:N/A