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Miniconda on DARWIN
Miniconda is available through the VALET packages on Caviness clusters. To check the versions,
$ vpkg_versions miniconda Available versions in package (* = default version): [/opt/shared/valet/2.1/etc/miniconda.vpkg_yaml] miniconda A free, miniature installation of Anaconda Distribution * 25.1.1.2 Miniconda py39 25.1.1-2 for Linux (x86_64)
To load it, use
$ vpkg_require miniconda/25.1.1.2 Adding package `miniconda/25.1.1.2` to your environment
Installing Applications with Conda
/lustre/workgroup/sw/…, myenv) to match your own account and project setup.
You can create a new Conda environment with a specific package and in a specific path. For example, to create an environment in a specific path, such as your /lustre/workgroup directory,
[(my_workgroup:user)@login01 ~]$ conda create -p /lustre/workgroup/sw/myenv <my_package>
--name or -n are in your home directory. However, the limited home directory quotas (20 GB) can be filled up quickly. It is recommended to create environments by specifying path -p in your work directory instead of $HOME to avoid quota issues.
After creating the Conda environments, you can activate the local conda environment using,
[(my_workgroup:user)@login01 ~]$ conda activate /lustre/workgroup/sw/myenv
You can install the desired packages in the current environment, for example,
(/lustre/workgroup/sw/myenv)[(my_workgroup:user)@login01 ~]$ conda install <my_package>
To list packages in the currently active environment, use
(/lustre/workgroup/sw/myenv)[(my_workgroup:user)@login01 ~]$ conda list # packages in environment at /lustre/workgroup/sw/myenv: # # Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge absl-py 2.3.1 pypi_0 pypi astunparse 1.6.3 pypi_0 pypi bzip2 1.0.8 hda65f42_8 conda-forge ca-certificates 2025.10.5 hbd8a1cb_0 conda-forge certifi 2025.10.5 pypi_0 pypi
To deactivate the environment, do:
(/lustre/workgroup/sw/myenv)[(my_workgroup:user)@login01 ~]$ conda deactivate [(my_workgroup:user)@login01 ~]$
To remove an environment, run:
[(my_workgroup:user)@login01 ~]$ conda remove -p /lustre/workgroup/sw/myenv --all
To remove the unused packages and caches, do
[(my_workgroup:user)@login01 ~]$ conda clean --all
Migrating Environments
In the terminal window, to view the environments available to you, use
[(my_workgroup:user)@login01 ~]$ conda env list # conda environments: # base /opt/shared/miniconda/25.1.1.2 */work/workgroup/sw/myenv
After activating the desired environment, export the current environment,
[(my_workgroup:user)@login01 ~]$ conda activate /work/workgroup/sw/myenv (/work/workgroup/sw/myenv)[(my_workgroup:user)@login01 ~]$ conda export --file=myenv.yaml name: /work/it_css/sw/tensorflow/2.17.0-cuda channels: - conda-forge dependencies: - _libgcc_mutex=0.1=conda_forge - _openmp_mutex=4.5=2_gnu - bzip2=1.0.8=hda65f42_8 - ca-certificates=2025.10.5=hbd8a1cb_0 - cuda-version=11.8=h70ddcb2_3 - cudatoolkit=11.8.0=h4ba93d1_13 - cudnn=8.9.7.29=hbc23b4c_3 ... ... prefix: /work/workgroup/sw/myenv
Then you can duplicate the same environment with
(/work/workgroup/sw/myenv)[(my_workgroup:user)@login01 ~]$ conda deactivate [(my_workgroup:user)@login01 ~]$ conda create -p /work/workgroup/sw/myenv_2 -f myenv.yml