![]() Also, if you clone the virtual environment to the shared directory you will need to activate it when ssh’ing into the cluster interactively. Please note that you do need to add the prefix flag -p ~/work/shared/uenv when Conda installing packages on a multinode cluster so they will be installed in the correct environment. ![]() For example: conda create -p ~/work/shared/uenv -clone uenvconda activate ~/work/shared/uenvconda install -p ~/work/shared/uenv -y python myscript_with_package-name_calls.py rm -rf ~/work/shared/uenv Then if you run a conda install -p ~/work/shared/uenv -y afterwards that package will be available to all compute nodes. NOTE: This is only necessary if a more than one node is used for the cluster. To do this you can add the following commands to the job: conda create -p ~/work/shared/uenv -clone uenvconda activate ~/work/shared/uenv. . Run workflow .rm -rf ~/work/shared/uenv Rather than installing all requisite packages on each node individual it is recommended to clone the Conda env to the NFS mounted ~/work/shared directory. The are two applications that will be explored, one where the parallelization is handled through an ipcluster and is used to accelerate training of a machine learning model, and another with more raw MPI calls using mpi4py.īefore beginning the set up of a Python parallel environment there are some details with regards to how a multi-node cluster is set up on Rescale that should be discussed. This becomes very advantageous when installing packages that require compatibility with low level libraries, including MPI which is necessary for packages such as mpi4py. One of the primary advantages of using the Conda package management system is that it can install and configure packages outside of Python. Once again Conda can assist in getting this set up and the required packages installed. To truly take advantage of the capabilities of running large clusters on Rescale, some type of parallel backend needs to be created and managed. In general, parallelization of Python scripts does not automatically extend to multinode configurations, and is usually restricted to launching multiple serial processes on the same node In the context of computer science, a node refers to a basic. Regardless of which environment you select the Conda commands will behave the same, so commands listed here should work for both Miniconda and Anaconda. If you know your analysis requires many of the pre-installed packages in the Anaconda environment, the additional cluster start up time is likely worth it, as you would need to install all the packages once the cluster is up and running otherwise. ![]() If you are using a custom Python post-processing script in addition to a CFD simulation for example, then a Miniconda environment should be sufficient. This makes it an ideal choice if only one or two different Python packages are required for the analysis. The Miniconda environment is a much smaller snapshot to load onto the cluster, so start up times will be shorter. There are different advantages to using either based on the needs of the user. There are two “types” of a Conda environment available on Rescale, one that contains a number of pre-installed packages (Anaconda), and one that is an empty environment (Miniconda). Please contact us if you’re having trouble. This tutorial will go over how to set up Conda environments for various applications on Rescale. There are a number of different ways to leverage the capabilities of Conda package management for custom data and post-processing analysis using Python. ![]()
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