This distribution also integrates Intel Math Kernel Library (Intel MKL), Intel Data Analytics Acceleration Library (DAAL) and pyDAAL, Intel MPI Library, and Intel Threading Building Blocks (TBB).

The Intel Distribution for Python behaves like regular Python, but leverages Intel technologies to speed up many of the core python libraries, including NumPy, SciPy, Pandas, Scikit-Learn, Jupyter, matplotlib, and mpi4py. The Python libraries installed within the Python modules are updated to their latest versions when the module is installed. We install new Python modules approximately every 6 months.

This prevents breaking your workflow/pipeline in case we upgrade the default version of a module to a newer one. We recommend always specifying the version of the module you will load. To load the default version of python3, use the following command in terminal: scc1$ module load python3/3.8.10 These modules are configured for general use and already contain many commonly used python packages. We offer python2, python3, and maintain older versions for reproducibility. scc1$ module avail python General Purpose Python Modulesįor most computational use cases, the most recent version of these general purpose installations is the best choice. The most recent listing of python installations on SCC can be queried using the module system. The categories of our Python installations are as follows: Each installation can be loaded using the module system. Researchers have a choice of Python versions on SCC, but most will prefer the general purpose installations. The packages include popular libraries like NumPy, Pandas, SciPy among many others. Each Python installation has many packages pre-configured and readily available. We maintain several versions of Python on the Shared Computing Cluster (SCC) and try to keep both the Python versions and associated packages up to date.

Check conda python version