Each run and workspace in Domino operates in its own Docker container. These Docker containers are defined by Domino compute environments. Environments can be shared and customized, and they are automatically versioned by Domino.

New installations of Domino come with a standard environment known as the Domino Analytics Distribution (DAD). Periodically, Domino publishes a new set of standard environments with updated libraries and packages. These environments include some common data science packages and libraries pre-configured for use in Domino.

We also make available a minimal environment (known as the Domino Minimal Distribution) which includes only the necessary packages required to work with in Domino. These are a good option for a user who wants to build a Domino-compatible environment from scratch, which helps speed up environment build times and execution start times.

Domino Analytics Distribution (DAD)

The Domino Analytics Distributions can handle most of what a typical data science workflow needs out of the box. They include the most common Python and R packages along with an installation of CUDA that is required for using NVIDIA GPUs.

See the available Dockerfile and descriptions here: Domino Base Images.

quay.io/domino/base hosts the built images unless otherwise stated in the READMEs for the corresponding image. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

Domino Minimal Distribution (DMD)

While the DAD includes most of what a data scientist needs to do their work, the DMD includes only the bare necessities required to work in Domino.

The DMD’s objective is to provide an image which allow you to:

Open Jupyter and Jupyterlab.
Batch run Python and R jobs.
Host a Shiny web app.
Publish a Python and R Model API.
Use Domino’s Git integration.
Install Python and R packages.

To shrink the DMD, remove workspaces that you won’t use or remove Python or R.

You can review the Dockerfile and descriptions here: Domino Base Images.

The built images are hosted on quay.io/domino/base unless otherwise stated in the READMEs for the corresponding image. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

By default, the DSE includes fewer packages than the DAD, giving it a smaller footprint and making it faster and easier to work with. See Managing Environments for more information about how to add packages to the DSE. The DSE also comes with a gpu flavor that includes CUDA support and common packages for taking advantage of gpus.

Warning

Go to quay.io/domino/standard-environment to access the available Domino Standard Environment images. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

Domino Minimal Environment (DME)

The Domino Minimal Environment is a revised version of the Domino Minimal Distribution. It includes Jupyter and JupyterLab workspace support, but leaves out several of the packages that are included in the DSE. Domino recommends using the DME if you will be doing several custom installations on top of a base environment image, because its smaller size dramatically speeds up build times and avoids conflicting dependencies. See Managing Environments for more information about how to add packages to the DME.

Go to quay.io/domino/minimal-environment for the Domino Minimal Environment images. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

Note

Domino Spark Environment

The Domino Spark Environment is an environment built specifically for workspaces that control a Spark cluster. It includes Scala and Spark on top of the typical DSE functionality. This environment is best used alongside a Spark cluster environment. To ensure compatibility between the Spark compute environment and Spark cluster environment, the Spark and Python versions must match across environment images.

Go to quay.io/domino/spark-environment for the available Domino Spark Environment images. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

See Spark Cluster Environments.

Domino Ray Environment

The Domino Ray Environment is an environment built specifically for workspaces that control a Ray cluster. It includes Ray on top of the typical DSE functionality. This environment is best used alongside a Ray cluster environment. To ensure compatibility between the Ray compute environment and Ray cluster environment, the Ray and Python versions must match across environment images.

Go to quay.io/domino/ray-environment for the available Domino Ray Environment images. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

See Ray Cluster Environments.

Domino Dask Environment

The Domino Dask Environment is an environment built specifically for workspaces that control a Dask cluster. It includes Dask on top of the typical DSE functionality. This environment is best used alongside a Dask cluster environment. To ensure compatibility between the Dask compute environment and Dask cluster environment, the Dask and Python versions must match across environment images.

Go to quay.io/domino/dask-environment for the Domino Dask Environment images. The version of the Domino environment indicates the version of Domino that the environment is shipped with, but typically any image will work on any version of Domino.

GPU environment flavors

The Domino Standard Environment also includes a GPU version with support for CUDA and common GPU specific libraries like Torch and Tensorflow. These GPU-enabled environment images are larger, so Domino recommends that you use them only if you are using a GPU-enabled hardware tier.

FUSE environment flavors

The DSE environment also has a version that includes FileSystem in Userspace (FUSE) binaries to enable Goofys and SSHFS support. You can add these commands to your environment Dockerfile to enable FUSE functionality:

USER root

# Goofys
ADD https://github.com/kahing/goofys/releases/download/v0.24.0/goofys  /usr/bin/
RUN chmod a+x /usr/bin/goofys

# SSHFS
RUN apt-get update && apt-get install -y sshfs && \
    sed -i "s/^#user_allow_other/user_allow_other/" /etc/fuse.conf

USER ubuntu

Example: creating a new Domino environment

Although Domino comes with only the Domino Standard Environment installed, you can create a new Domino Environment using the environment images referenced previously. This section describes how to create a new Domino Environment or to customize one.

Select an environment, choosing the correct Python and R version. Typically, you’ll want to choose the environment from the latest release of Domino.

Note

Note

Find the appropriate name, description, image URI and pluggable notebook properties for your environment.

Note

For example, for the Domino Standard Environment you might use:

Title

Domino Standard Environment Py 3.8 R 4.1

URI

quay.io/domino/standard-environment:ubuntu18-py3.8-r4.1-domino4.6

Description

Ubuntu 18.04
Mini-conda py38_4.9.2
Python 3.8
R 4.1
Jupyter, Jupyterlab, VSCode, Rstudio

Pluggable Workspace Tools (decoded from environment metadata)

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}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    requireSubdomain: false
  supportedFileExtensions: [ ".ipynb" ]
jupyterlab:
  title: "JupyterLab"
  iconUrl: "/assets/images/workspace-logos/jupyterlab.svg"
  start: [  /opt/domino/workspaces/jupyterlab/start ]
  httpProxy:
    internalPath: "/{{ownerUsername}}/{{projectName}}/{{sessionPathComponent}}/{{runId}}/{{#if pathToOpen}}tree/{{pathToOpen}}{{/if}}"
    port: 8888
    rewrite: false
    requireSubdomain: false
vscode:
  title: "vscode"
  iconUrl: "/assets/images/workspace-logos/vscode.svg"
  start: [ "/opt/domino/workspaces/vscode/start" ]
  httpProxy:
    port: 8888
    requireSubdomain: false
rstudio:
  title: "RStudio"
  iconUrl: "/assets/images/workspace-logos/Rstudio.svg"
  start: [ "/opt/domino/workspaces/rstudio/start" ]
  httpProxy:
    port: 8888
    requireSubdomain: false

Create a new Domino Compute environment.
- See Compute Environment Management for how to create and manage environments. This sections also includes information about how to customize your environment with Docker commands or pre-run scripts.
Update your Domino AMI (not required for non-cloud).
- After you’ve created a compute environment with a new base image, work with your administrator to update your Domino’s AMI (or if not on AWS, the GCP or Azure equivalent) by caching the new image. As Domino spins up and down new executors, if your new image is not in the AMI, it must pull that image onto the executor the first time it starts up. This can cause a ~10 minute delay for starting workspaces on new executors. See here for the procedure to snap and update your AMI.

FAQ

How can I tell which image I’m currently using?: The URI for the image will be listed on your compute environment’s overview page. If your environment is built on top of another environment, you may need to click through to the parent environment before seeing the underlying docker image.
I have a third-party Docker image, can I use that in Domino?: Maybe, but not likely without some customization. The DSE and DME are tested and configured to meet the Domino platform requirements and conventions. For example, by convention Domino uses /mnt as the default working directory. It is much easier to use the DME as your base environment to build on top of than it is to try to get a 3rd party environment to work directly in Domino.

However, this is not the case for environments for compute cluster worker nodes. In most cases, these environments can be plugged directly into Domino with no modifications, as they do not need to support the same workflows as the Domino Compute Environments.
How can I learn about new versions of the DSE and make feature requests?: See the Compute Environment Catalog for the list of environments. To make feature requests, submit a request to Domino support.

Domino Standard Environments