10/4/2023 0 Comments Anaconda 3 parents guideIn addition to Python, we have compiled a number of commonly used scientific libraries against GCC and MKL. Lmod tells us that we need to load either GCC or Intel as a dependency of Python, so let’s do just that: ~]$ ml ~]$ ml ~]$ python -version We have compiled Python 3.6.3 with both the GCC compiler and with Intel compilers, linking against Intel’s MKL library, which should yield better performance on our Intel processors. Python is a programming language that lets you work more quickly and integrate your systemsĪrff-2.1.1, blist-1.3.6, cryptography-2.1.1, Cython-0.27.1, dateutil-2.6.1,ĭecorator-4.1.2, docopt-0.6.2, ecdsa-0.13, joblib-0.11, lockfile-0.12.2, You will need to load all module(s) on any one of the lines below before the "Python/3.6.3" module is available to load. Per Lmod’s instructions, we can get more information about the installed Python version: ~]$ module spider Python/3.6.3 To find other possible module matches execute:įor detailed information about a specific "Python" module (including how to load the modules) use the module's full name. Python is a programming language that lets you work more quickly and To see a list of installed versions of Python on the cluster, use Lmod’s spider command: ~]$ ml spider Python Using ACCRE Optimized PythonĪCCRE administrators have hand-compiled multiple versions of Python that are linked against highly optimized linear algebras like OpenBLAS and Intel’s MKL, and therefore will in general yield better performance (faster execution time) than the default system version of Python. We therefore provide limited support for Anaconda by making several versions of the distribution available through Lmod. However, we do understand that certain workflows may require packages only available with conda, or that researchers may not have the time to convert existing projects. For these reasons, ACCRE users are encouraged to use the optimized Python builds over the Anaconda distribution. In addition, we have found that conda-based virtual environments will often aggressively cache package archives and quickly exhaust a user’s filesystem quota in their home directory. The downside to using Anaconda on the ACCRE cluster is that the interpreter and packages provided are not specifically optimized for ACCRE hardware and will not execute code as quickly as the optimized Python build provided through Lmod. Anaconda users typically use a different tool, conda, for managing installation of additional packages and creating “virtual environments” on a single machine. This distribution system provides a pre-compiled Python interpreter already packaged with many popular scientific libraries. Using the optimized Python builds along with the venv and pip tools to install additional user-specific packages if required.Īn alternative system for managing scientific Python libraries is the Anaconda distribution. On the ACCRE cluster, highly optimized versions of the Python interpreter are available through Lmod along with many commonly used scientific libraries which have been similarly optimized to the specific Intel CPU on each machine. Users can further maintain multiple “virtual environments” on a single machine with different packages installed using a module called venv. To manage these additional packages, Python includes standard support for easily installing additional packages from the internet with a tool called pip. However, with the help of several libraries one can perform very fast computations. On its own, the reference implementation of the Python language is poorly suited to scientific computing as it is not compiled to machine instructions and will perform large calculations slowly. Python is an interpreted programming language that has become increasingly popular in high-performance computing environments because it’s available with an assortment of numerical and scientific computing libraries ( numpy, scipy, pandas, etc.), relatively easy to learn, open source, and free.
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