Python on OLCF Systems

Overview

In high-performance computing, Python is heavily used to analyze scientific data on the system. Some users require specific versions of Python or niche scientific packages to analyze their data, which may further depend on numerous other Python packages. Because of all the dependencies that some Python packages require, and all the types of data that exist, it can be quite troublesome to get different Python installations to “play nicely” with each-other, especially on an HPC system where the system environment is complicated. Conda, a package and virtual environment manager from the Anaconda distribution, helps alleviate these issues.

Conda allows users to easily install different versions of binary software packages and any required libraries appropriate for their computing platform. The versatility of conda allows a user to essentially build their own isolated Python environment, without having to worry about clashing dependencies and other system installations of Python. Conda is available on OLCF systems, and loading the default Python module loads an Anaconda Python distribution. Loading this distribution automatically puts you in a “base” conda environment, which already includes packages that one can use for simulation, analysis, and machine learning.

For users interested in using Python with Jupyter, see our Jupyter at OLCF page instead.

For users interested in using the machine learning open-ce module (formerly ibm-wml-ce) on Summit, see our IBM Watson Machine Learning CE -> Open CE page.

OLCF Python Guides

Below is a list of guides created for using Python on OLCF systems.

Note

For newer users to conda, it is highly recommended to view our Conda Basics Guide, where a Quick-Reference Commands list is provided.

Module Usage

To start using Python, all you need to do is load the module:

$ module load python

Base Environment

Loading the Python module on all systems will put you in a “base” pre-configured conda environment. This option is recommended for users who do not need custom environments, and only require packages that are already installed in the base environment. This option is also recommended for users that just need a Python interpreter or standard packages like NumPy, Scipy, and Matplotlib.

To see a full list of the packages installed in the base environment, use conda list. A small preview from Summit is provided below:

$ module load python
$ conda list

# packages in environment at /sw/summit/python/3.8/anaconda3/2020.07-rhel8:
#
# Name                    Version                   Build  Channel
_ipyw_jlab_nb_ext_conf    0.1.0                    py38_0
_libgcc_mutex             0.1                        main
alabaster                 0.7.12                     py_0
anaconda                  2020.07                  py38_0
anaconda-client           1.7.2                    py38_0
anaconda-project          0.8.4                      py_0
asn1crypto                1.3.0                    py38_0
astroid                   2.4.2                    py38_0
astropy                   4.0.1.post1      py38h7b6447c_1
.
.
.

Warning

It is not recommended to try to install new packages into the base environment. Instead, you can clone the base environment for yourself and install packages into the clone. To clone an environment, you must use the --clone <env_to_clone> flag when creating a new conda environment. An example for cloning the base environment is provided in Best Practices below.

Custom Environment

You can also create your own custom conda environment after loading the Python module. This option is recommended for users that require a different version of Python than the default version available, or for users that want a personal environment to manage specialized packages.

Note

A more complete list of commands is provided in the Quick-Reference Commands section of the Conda Basics Guide.

To create and activate an environment in a specific location using Python version X.Y, use the -p flag:

$ module load python
$ conda create -p /path/to/my_env python=X.Y
$ source activate /path/to/my_env

To create and activate an environment with a specific name using Python version X.Y, use the --name flag (by default, this creates the environment in your $HOME directory):

$ module load python
$ conda create --name my_env python=X.Y
$ source activate my_env

Note

It is highly recommended to create new environments in the “Project Home” directory (/ccs/proj/<project_id>/<user_id>). This space avoids purges, allows for potential collaboration within your project, and works better with the compute nodes. It is also recommended, for convenience, that you use environment names that indicate the hostname, as virtual environments created on one system will not necessarily work on others.

It is always recommended to deactivate an environment before activating a new one. Deactivating an environment can be achieved through:

$ source deactivate # deactivates the current environment

How to Run

Warning

Remember, at larger scales both your performance and your fellow users’ performance will suffer if you do not run on the compute nodes. It is always highly recommended to run on the compute nodes (through the use of a batch job or interactive batch job).

The OS-provided Python is no longer accessible as python (including variations like /usr/bin/python or /usr/bin/env python); rather, you must specify it as python2 or python3. If you are using python from one of the module files rather than the version in /usr/bin, this change should not affect how you invoke python in your scripts, although we encourage specifying python2 or python3 as a best practice.

Summit

Batch Script - Summit

To use Python on a Summit compute node, you must use jsrun, even if running in serial.

Additionally, $PATH issues are known to occur after having loaded multiple conda environments before submitting a batch script. Therefore, it is recommended to use a fresh login shell before submission. The -L flag for bsub ensures that no previously set environment variables are passed into the batch job.

$ bsub -L $SHELL submit.lsf

This means you will have to load your modules and activate your environment inside the batch script. An example batch script for Summit is provided below:

#!/bin/bash
#BSUB -P PROJECT_ID
#BSUB -W 00:05
#BSUB -nnodes 1
#BSUB -J python
#BSUB -o python.%J.out
#BSUB -e python.%J.err

cd $LSB_OUTDIR
date

module load python
source activate my_env

jsrun -n1 -r1 -a1 -c1 python3 script.py

Interactive Job - Summit

To use Python in an interactive session on Summit:

$ module load python
$ bsub -W 0:05 -nnodes 1 -P <PROJECT_ID> -Is $SHELL
$ source activate my_env
$ jsrun -n1 -r1 -a1 -c1 python3 script.py

Andes

Batch Script - Andes

On Andes, you are already on a compute node once you are in a batch job. Therefore, you only need to use srun if you plan to run parallel-enabled Python.

Similar to Summit (see above), $PATH issues are known to occur if not submitting from a fresh login shell, which can result in the wrong conda environment being detected. To avoid this, you must use the --export=NONE flag, which ensures that no previously set environment variables are passed into the batch job:

$ sbatch --export=NONE submit.sl

This means you will have to load your modules and activate your environment inside the batch script. An example batch script for Andes is provided below:

#!/bin/bash
#SBATCH -A <PROJECT_ID>
#SBATCH -J python
#SBATCH -N 1
#SBATCH -p batch
#SBATCH -t 0:05:00

cd $SLURM_SUBMIT_DIR
date

module load python
source activate my_env

python3 script.py

Interactive Job - Andes

To use Python in an interactive session on Andes:

$ module load python
$ salloc -A <PROJECT_ID> -N 1 -t 0:05:00
$ source activate my_env
$ python3 script.py

Best Practices

  • Cloning the base environment:

    It is not recommended to try to install new packages into the base environment. Instead, you can clone the base environment for yourself and install packages into the clone. To clone an environment, you must use the --clone <env_to_clone> flag when creating a new conda environment. An example for cloning the base environment into a specific directory called conda_envs/summit/ in your “Project Home” on Summit is provided below:

    $ conda create -p /ccs/proj/<project_id>/<user_id>/conda_envs/summit/baseclone-summit --clone base
$ source activate /ccs/proj/<project_id>/<user_id>/conda_envs/summit/baseclone-summit

  • Environment locations:

    It is highly recommended to create new environments in the “Project Home” directory (/ccs/proj/<project_id>/<user_id>). This space avoids purges, allows for potential collaboration within your project, and works better with the compute nodes. It is also recommended, for convenience, that you use environment names that indicate the hostname, as virtual environments created on one system will not necessarily work on others.

  • Adding known environment locations:

    For a conda environment to be callable by a “name”, it must be installed in one of the envs_dirs directories. The list of known directories can be seen by executing:

    $ conda config --show envs_dirs

    On OLCF systems, the default location is your $HOME directory. If you plan to frequently create environments in a different location other than the default (such as /ccs/proj/...), then there is an option to add directories to the envs_dirs list.

    For example, to track conda environments in a subdirectory called summit in Project Home you would execute:

    $ conda config --append envs_dirs /ccs/proj/<project_id>/<user_id>/conda_envs/summit

    This will create a .condarc file in your $HOME directory if you do not have one already, which will now contain this new envs_dirs location. This will now enable you to use the --name env_name flag when using conda commands for environments stored in the summit directory, instead of having to use the -p /ccs/proj/<project_id>/<user_id>/conda_envs/summit/env_name flag and specifying the full path to the environment. For example, you can do source activate my_env instead of source activate /ccs/proj/<project_id>/<user_id>/conda_envs/summit/my_env.