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- #Module load anaconda conda install package update#
- #Module load anaconda conda install package trial#
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If there is a package you would like to see added to the NPL, please submit a request through the NCAR Research Computing help desk. Users are not permitted to install packages into the available NPL environments. If you need to lock your workflow to a specific NPL version, clone that version as described below. NPL environments are kept for three months, so the oldest environment will also be removed from the system at this time.
#Module load anaconda conda install package update#
Update schedule for conda and the NPLĪt the start of every month, conda itself will be updated and a new version of the NPL will be created. This kernel is locked to the default NPL, which points to the previous month's NPL environment. Make sure you select the "NPL (conda)" kernel, as any other NPL kernels are based on the older non-conda virtual environment design and are deprecated.
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Once you have initiated a JupyterHub server, the NPL kernel should be visible on the main launcher page for initiating Notebooks and Console sessions. Pip list # must have environment active Accessing the NPL from JupyterHubįinding and activating the NPL environment ( compute kernel) from NCAR's JupyterHub interface is quick and simple. To leverage the new Python installs with large collections of pre-installed packages, you will need to activate one of the NPL conda environments.Īfter you load the conda module, see available environments by running this list sub-command: Simply loading the conda module will not give you access to newer supported versions of Python you will still have only the old system Python binaries in your shell environment at this point.
#Module load anaconda conda install package trial#
Be aware that the ~/.bashrc file is not always sourced at the start of batch jobs, so this approach may require some trial and error to cover your individual use case.Īccessing Python via the NCAR Python Library That will add initialization commands to your ~/.bashrc or ~/.tcshrc file. Run conda init ( or conda init tcsh for tcsh users) after loading the module on a login node.This will restore the alias in the shell environment and allow you to activate conda environments. Explicitly load the conda module at the start of your batch job.However, the alias does not carry over into batch jobs or other subshells. The conda module enables Python environment activation by setting a shell alias that calls initialization scripts. Doing so would lead to potentially confusing and inconsistent precedence of Python binaries in your compute environment. Since conda is used to activate Python environments, you will not be able to load the conda module and CISL's deprecated Python modules concurrently. The module includes the Mamba dependency resolver, which provides increased performance over the standard conda binary when creating new environments and installing new packages.The NCAR Python Library conda environments will be accessible for activation into your environment.These settings can be overridden by creating a personal ~/.condarc file. These behaviors include setting the default channels to conda-forge and ncar, directing the conda package cache to write to your scratch space instead of your home directory, and directing personal conda environments to be created in your work directory instead of your home directory. A number of default conda behaviors will be set via a global.This will override any personal Miniconda installation that is active in your environment while the module is loaded, but you can revert the change by unloading the module. It will add the CISL-maintained conda installation to your PATH.The module will make a number of modifications to your compute environment: