Domino Standard Environments

Every Domino installation includes the default Domino Core Environment (DCE) and the Domino Standard Environment (DSE) setup. These environments come with pre-configured data science tools and libraries for Domino users. They can be used for any Domino execution, such as a Workspace, Job, App, or Domino endpoint.

With each major release, Domino updates its core environments with new libraries and packages. You can still update your core environments with custom libraries and packages. Your Domino administrator can guide you through that process if needed.

DCEs are smaller and more straightforward, making them suitable only for the worker nodes in the cluster. DSEs cannot be used for worker nodes in a cluster.

Domino Core Environment (DCE)

Domino provides a security-focused, smaller-footprint image called the Domino Core Environment (DCE). It includes only the essential packages needed to operate within Domino.

This can serve as a base to help you create a Domino-compatible environment from scratch, add only the packages you want to it, and speed up the process of building the environment and initiating executions.

The Domino Core Environment supports Jupyter and JupyterLab but lacks some packages available in the Domino Standard Environment (DSE).

Domino recommends the DCE for custom package installations on a base image because its smaller size speeds up builds and minimizes conflicts.

Domino Standard Environment (DSE)

The Domino Standard Environment (DSE) is available with standard libraries and packages. This environment has a much larger image size but many common packages for data scientists. It can handle common data science workflows out of the box and includes the most common Python and R packages.

The DSE offers a version that includes FileSystem in Userspace (FUSE) binaries, enabling support for Goofys and SSHFS. You can add these commands to your 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

Domino Cluster Environments

Domino Cluster Environments work similarly to Domino Standard Environments (DSEs) but include extra libraries for specific cluster types. They offer the same workspace tools and general packages as DSEs to support data science tasks.

Each Domino environment is designed specifically for the corresponding compute cluster environments. They are most effective when used in a distributed compute cluster alongside a Domino Compute Cluster Environment.

You can use these environments with any Domino execution, such as a Workspace or Job. Here are a few things to remember about clusters in Domino:

  • All environments must maintain identical Python versions across the cluster.

  • A cluster won’t work correctly if the worker nodes are not using the appropriate Domino Cluster Environment for cluster workers.

  • A compatible Domino Compute Environment for Job or Workspace is also needed for proper functionality.

The Compute Environment catalog has a complete list of pre-built compute environments for you to use with Domino.

TypeDescription

Domino Spark Environment

This includes Scala and Spark, along with the typical features of DSE. Work with your Spark cluster has complete instructions on creating simple Spark clusters for use with Domino Spark environments.

Domino Ray Environment

This combines Ray features with standard DSE tools. Create a base Ray cluster environment has complete instructions on creating simple Ray clusters for use with Domino Ray environments.

Domino Dask Environment

This environment includes Dask on top of the typical DSE functionality. Work with clusters has complete instructions on creating simple Dask clusters for use with Domino Dask environments.

Domino MPI Environment

Provision and orchestrate an Open MPI cluster directly on the infrastructure backing your Domino deployment. Distributed GPUs with Open MPI has complete instructions on enabling and configuring Open MPI on your environment.

Next steps