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Domino Standard Environments

Note

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 Standard Environment (DSE). 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 Environment) which includes only the necessary packages required to work with in Domino. These would be an appropriate 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 Standard Environment (DSE)

The Domino Standard Environment is a revised and slimmed down version of the old Domino Analytics Distribution. It is designed to handle common data science workflows out of the box, and includes a handful of the most common Python and R packages.

Note

The DSE includes far fewer packages by default than the DAD, giving it a much smaller footprint and making it much faster and easier to work with. You can find more information on how to add packages to the DSE in Managing Environments. The DSE also comes with a gpu flavor that includes CUDA support and common packages for taking advantage of gpu’s.

Warning

You can find the available Domino Standard Environment images at quay.io/domino/standard-environment.

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 many of the packages that are included in the DSE. We recommend using the DME if you will be doing many custom installations on top of a base environment image, as its smaller size dramatically speeds up build times and helps you avoid conflicting dependencies. You can find more information on how to add packages to the DME in Managing Environments .

You can find the available Domino Minimal Environment images at quay.io/domino/minimal-environment.

Note

Domino Spark Environment

The Domino Spark Environment is an environment built specifically to be the environment 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 used must match across environment images.

You can find the available Domino Spark Environment images at quay.io/domino/spark-environment.

See here for more information on Spark Cluster Environments.

Domino Ray Environment

The Domino Ray Environment is an environment built specifically to be the environment 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 used must match across environment images.

You can find the available Domino Ray Environment images at quay.io/domino/ray-environment.

See here for more information on Ray Cluster Environments.

Domino Dask Environment

The Domino Dask Environment is an environment built specifically to be the environment 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 used must match across environment images.

You can find the available Domino Dask Environment images at quay.io/domino/dask-environment.

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 much larger, so we recommend avoiding using them if you are not taking advantage of 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

While Domino comes with only the Domino Standard Environment installed, you can easily create a new Domino Environment using the environment images referenced above. This section explains how to go about creating a new Domino Environment for a use case or to customize for specific user needs.

  1. Select an environment from those available, choosing the python and R version you desire. Typically, you’ll always want to choose the environment from the latest release of Domino.

    Note
    Note

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

    Note

    For example, for the Domino Standard Environment we 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

  3. Create a new Domino Compute environment

    • See Compute Environment Management for an overview of how to create and manage environments. This sections also includes info on how customize your new environment with additional Docker commands or pre-run scripts.

  4. Update your Domino AMI (not required for non-cloud)

    • After you’ve created a compute environment with a new base image, you’ll want to work with your admin 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 will need to 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

  1. 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.

  2. 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.

  3. How can I learn about new versions of the DSE and make feature requests?

    Check out the Domino community forum for news and updates.

5.0 Environment Changes

Stability and security are improved in the Domino Environments for 5.0. Here are the key changes in the 5.0 Domino Environment images:

  • Fixed R Kernel in Jupyter

  • Added Domino-specific packages to environments, like domino-data-capture, and dominodatalab-data

  • Add Spark Cluster and Spark Compute Environments by default to new deploys to support the Cohort Analysis feature

  • Fixed Jupyter notebook subdomain support

  • Added more Ray packages to the Ray Compute Environment

4.6 Environment Changes

As touched on earlier, there were a number of changes to Domino Environments in 4.6 as we shifted from the DAD to the DSE. These changes were the result of an improvement of our internal environment building practices, which now enable us to build environments faster and therefore support a larger catalog of unique images. In addition, our newer images are much slimmer, leading to faster build and startup times and fewer security vulnerabilities.

Here are the key changes in the 4.6 Domino Environment images:

  • Removed hundreds of Python and R packages from the image

  • Removed Scala and Scala Kernel

  • Removed FUSE (Goofys and SSHFS) binaries in default image. These are still available in FUSE image

  • Removed VSCode in JupyterLab extension in favor of distinct VSCode IDE option

  • Python 3.8.8 → 3.8.10

  • miniconda py38_4.8.3 → py38_4.9.2

  • R 4.0.5 → 4.1.0

  • openjdk 1.8.0_292 → 11.0.11

  • nodejs v14.17.0 → 16.4.1

  • jupyter-notebook 6.3.0 → 6.4.0

  • jupyter-lab 3.0.15 → 3.0.16

  • rstudio 1.4.1106 → 1.4.1717

  • code-server 3.7.3 → 3.10.2

  • Purged some unused apt-get dependencies

  • Moved workspace start scripts from /var/opt/workspaces to /opt/domino/workspaces. New images symlink old directory, so:: change is backwards compatible.

  • Added Snowflake data connector

  • Removed Kerberos libs

  • Removed R Jupyter Kernel

  • Added image metadata including workspace properties and language versions

These changes improved image security and reduced image size by 3.5 GB.

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