Each Domino installation comes 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.
Domino also makes a Domino Minimal Environment (DME) available that includes only the packages required to work in Domino. These are a good option if you want to build a Domino-compatible environment from scratch. This helps speed environment build times and execution start times.
Domino Cluster Environments work like a Domino Standard Environments, but have additional libraries to support specific types of clusters. They contain the same workspace tools and general packages as the DSE to support general data science workflows. You can use these environments with any Domino execution like a Workspace or Job, but they are best used in a distributed compute cluster alongside a Domino Cluster Environment. These are smaller and simpler environments and are only suitable for use in the worker nodes of the cluster. DSEs cannot be used for worker nodes in a cluster.
Domino environments (abstractions on top of Docker images). Users can create environments, use those provided by Domino, or edit existing ones.
- Domino Standard Environment (DSE)
Domino Standard Environments are environments that we make available with standard sets of libraries and packages.
The Domino Standard Environment can handle common data science workflows out of the box. It includes the most common Python and R packages.
See Environments for information about how to add packages to the DSE. The DSE comes with a GPU version that includes CUDA support and common packages for taking advantage of GPUs. WARNING: Domino recommend using an environment with explicit GPU support when using GPU hardware tiers.
See the compute environment catalog to access Domino Standard Environment images.
- Domino Minimal Environment (DME)
The DME has fewer packages and is lighter weight than the Domino Standard Environment (DSE).
The Domino Minimal Environment includes Jupyter and JupyterLab workspace support, but does not include several 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 speeds build times and avoids conflicting dependencies. See Environments for information about how to add packages to the DME.
The following environments are designed to be used with compute cluster environments. You can use these environments with any Domino execution like a Workspace or Job, but they are best used in a distributed compute cluster alongside a Domino Compute Cluster Environment.
Compute Cluster Environments work like the Domino Standard Environments, but they have additional libraries to support a specific cluster type. They contain the same workspace tools and general packages as the DSE to support general data science workflows.
A cluster won’t work correctly if the worker nodes are not using the appropriate Domino Cluster Environment for cluster workers and compatible Domino Compute Environment for Job or Workspace.
- Domino Spark Environment
The Domino Spark Environment is 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.
- Domino Ray Environment
The Domino Ray Environment is 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.
- Domino Dask Environment
The Domino Dask Environment is 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.
- 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
Stability and security are improved in the Domino Environments for 5.0. The following are the key changes in the 5.0 Domino Environment images:
Fixed R Kernel in Jupyter
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
There were several 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 slimmer, leading to faster build and startup times and fewer security vulnerabilities.
The following 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
Python3.8.8 → 3.8.10
minicondapy38_4.8.3 → py38_4.9.2
R4.0.5 → 4.1.0
openjdk1.8.0_292 → 11.0.11
nodejsv14.17.0 → 16.4.1
jupyter-notebook6.3.0 → 6.4.0
jupyter-lab3.0.15 → 3.0.16
rstudio1.4.1106 → 1.4.1717
code-server3.7.3 → 3.10.2
Purged some unused
Moved workspace start scripts from
/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.
- 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?