These “dummy” packages are empty, but allow the solver to create correct environments and use the system-wide modules when the environment is activated. In order to be able to correctly use these MPI packages with the MPI libraries installed on our clusters, two steps need to be performed.įirst, at install time, besides the package, the “dummy” package openmpi=4.1.*=external_* or mpich=4.0.*=external_* needs to be installed for openmpi or mpich respectively. More information about this can be found here. However, just using the openmpi and mpich packages from conda-forge often does not work on HPC systems. Some conda packages available on conda-forge and bioconda support MPI (via openmpi or mpich). Using module load tensorflow-gpu/p圓6/1.14 and conda activate tensorflow-gpu-1.14-custom in the same script is wrong and may give you various errors and incorrect results. If you have custom GPU Anaconda environment please only use the two lines from above and DO NOT load the module you have cloned earlier. While the standard methods of installing packages via pipĪnd easy_install work with Anaconda, the preferred method is using Using an Anaconda Environment in a Jupyter Notebook.Creating custom MPI Anaconda Environment.Creating custom GPU Anaconda Environment.Adding and Removing Packages from an Existing Environment. Package and environment manager to make managing these environments Of Python and/or R and other packages into isolated environments that It also offers the ability to easilyĬreate custom environments by mixing and matching different versions Over 195 of the most popular Python packages for science, math,Įngineering, and data analysis. Processing, predictive analytics, and scientific computing. Is a completely free enterprise-ready distribution for large-scale data
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