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.
-
Choose the NGC container from the list of available containers.
-
Create a new Compute Environment in Domino.
-
Use the link from the container you selected as the base compute Environment (for example,
quay.io/domino/ngc-pytorch:20.12-py3) -
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: falseRAPIDS
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
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
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
Clara Train
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
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