Using Anaconda on Satori

[1] Using Anaconda

Satori uses Environment Modules to allow users to have access to different software. These modulefiles are built for Satori’s architecture and can be loaded/unloaded from one’s environment on the fly. Anaconda can be accessed by searching for and loading the appropriate module.

To display a list of modulefiles matching the name ‘anaconda’:

module avail anaconda

Loading one of the anaconda modules:

module load anaconda3/2020.02-2ks5tch

[2] Creating and activating conda environments

Once an anaconda module is loaded, you can create environments that have different versions of Python or packages installed in them. Conda environments are optional but recommended. If not used, packages are installed in the default environment called base, which often has a higher risk of containing conflicting packages or dependencies. Switching between environments is called activating the environment.

The syntax for creating and activating an environment is:

conda create --name <environment name> python=<python version>
source activate <environment name>

To create an anaconda environment in a specfic location, use the –prefix flag during creation with the absolute path and name of the environment:

conda create --prefix=<path/to/env>


conda create --prefix=/nobackup/users/ageod/.conda/envs/test-env01
source activate /nobackup/users/ageod/.conda/envs/test-env01

NOTE: As a good practice to maintain the disk space usage on Satori please run at regular intervals the following command that will remove unused packages and caches from your Anaconda profile. In addition this command shuld be use before installing a new version of WML-CE/Open-CE on another virtual environment.

conda clean --all

[3] Setting up conda channels

When using conda to search for packages, one or more “channels”, or repositories, may be queried. By default, conda will search its main packages channel for any results that match the queried name and display any results matching packages available for the systems architecture (ppc64le). If no packages are available either under the queried name, or for host system’s architecture, no results will be displayed. Note that not all packages existing for x86 can also be found for ppc64le.

To add the IBM WML-CE channel to the conda configuration:

conda config --prepend channels \

To add the MIT Open-CE channel to the conda configuration:

conda config --prepend channels \

There are a number of other conda channels that exist which can be added to the conda config in the same manner. By adding the two channels above, your conda searches will be expanded beyond the default repository. See the section entitled IBM Watson Machine Learning Community Edition (WML-CE) and Open Cognitive Environment (Open-CE) for more information about these two channels.

[4] Searching for and installing conda packages

The basic syntax for searching for a package through conda is as follow:

conda search <package-name>

This command will search the base anaconda channel along with any channels currently added to your conda configuration. You can search a specific channel by explicitly including it in the search:

conda search -c <channel> <package>


conda search -c conda-forge pytorch

Results from conda searches typically include several available versions for the package queried. If no particular version of a package is specified, conda will install the most appropriate version based on the version of python installed locally. Conda search results may look similar to this:

(test2022-14) [ageod@satori-login-001 envs]$ conda search pytorch
Loading channels: done
# Name                       Version           Build  Channel
pytorch                        1.7.1      hca541ab_1  None
pytorch                        1.9.0 cuda10.2_py37_1  None
pytorch                        1.9.0 cuda10.2_py38_1  None
pytorch                        1.9.0 cuda10.2_py39_1  None
pytorch                       1.10.2 cpu_py310hef0c51e_0  pkgs/main
pytorch                       1.10.2 cpu_py37h6f0ae12_0  pkgs/main
pytorch                       1.10.2 cpu_py38h6f0ae12_0  pkgs/main
pytorch                       1.10.2 cpu_py39h6f0ae12_0  pkgs/main

The output lists the name of the package relevant to the query, the version available, its build, and what channel it exists on. There are several versions of pytorch in the example that show they were built with cuda support, the specific version of cuda used, and the version of python used. There are also versions of pytorch built to utilize CPU’s rather than GPU’s.

The “pkgs/main” channel refers to the default anaconda repository, whereas the results with the channels listed as “None” are coming from the MIT OpenCE channel.

To install a package, you must first need to have an environment activated. Again, packages are installed within conda environments. You can specify versions of packages through levels of specificity:

conda install <package>


conda install <package>=<version>
conda install <package>=<version>=<build>


conda install pytorch
conda install pytorch=1.9.0
conda install pytorch=1.9.0=cuda10.2_py39_1

[5] Listing the contents of your conda environment

Viewing the packages installed in your environment is easy. Use the following command:

conda list

You can be more granular and search for a specific package in your environment by adding the packages name after the above command.

[6] Leaving your conda environment

When you are done working in your environment, or want to switch to a different environment, you can deactivate the current environment with:

conda deactivate

Environments retain their state when inactive - as in, the packages installed within them. Simply activate the envionment again to use the installed software.