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This content is taken from the Partnership for Advanced Computing in Europe (PRACE)'s online course, Python in High Performance Computing. Join the course to learn more.

Setting up the programming environment


In order to carry out the hands-on programming exercises, you need a working Python environment that contains a selection of Python packages for scientific and high-performance computing.

The following software will be used in this course:

  • Python 3 interpreter
  • NumPy
  • Numexpr
  • Matplotlib
  • Cython
  • Cffi
  • Mpi4py
  • (optionally) iPython and Scipy

You have two options:

  1. Install the software to your own Linux machine
  2. Use a virtual machine image we have prepared that has all the required software pre-installed

Option 1: Install software to your own Linux machine

In a Ubuntu based Linux distribution, the installation can usually be done (admin rights needed) with the following commands:

sudo apt install python3-numpy python3-numexpr python3-matplotlib cython3
python3-cffi python3-mpi4py python3-scipy ipython3

For other distributions, please refer to their documentation on how to install the required software.

Option 2: Use the Virtual Machine prepared for this course

  1. Download the Virtual Machine image (By the way, we are using the same server where Linux was first released to the world in 1991.) Size of download is about 1.9 GB
  2. Install the VirtualBox software
    • Download the installation file for your operating system (Windows, Mac, and Linux are all supported): https://www.virtualbox.org/
    • When installing the software you might be asked to install some device drivers (Oracle Corporation Universal Serial Bus, Network Adapters, Network Service). Install them as well. If you are asked, choose to install all the components of the VirtualBox software.
    • After installation you will be notified that the installation is complete and you can already start Oracle VM VirtualBox in that step by clicking finish.
    • In some machines virtualization hardware extension may need to be enabled in BIOS. Some instructions on how to achieve this can be found here.
  3. Start VirtualBox
  4. Import the Virtual Machine image
    • In the File menu choose Import appliance Import appliance
    • Select the HPCPython.ova image you downloaded in step 1. Select image
    • Default values for memory and number of CPUs should be OK, but you can also increase them a bit depending on your system Appliance settings
  5. You should now see HPC Python image listed in VirtualBox. You can start it either by double clicking it or via the Start button. Start virtual machine
  6. The system should now boot up. Once you are greeted with a login screen, log in with the following credentials:
    • username: Monty Python
    • password: hpc1python Log in
  7. Hands-on exercises are carried out in the command line terminal which you can open from the launcher panel on the left. Starting the terminal

    • There are several standard text editors (gedit, nano, emacs, vim) available, if you are not familiar with any of these we recommend starting with gedit.

Exercise material

In addition to installing the required software (see above), you need to download also some material for the hands-on exercises.

Exercise material is hosted on GitHub at: https://github.com/csc-training/hpc-python


You have three options for downloading the material to your own Linux system or to the Virtual Machine:

  1. Recommended approach: Fork the GitHub repository and clone your fork
    • You need to have a GitHub user account for this option
    • Go to course repository in GitHub and Sign in
    • Next, Fork the repository via the button in the top right corner.
    • After forking the repository, open the Terminal and clone your fork with the command (using your own GitHub username):

      git clone https://github.com/my-github-username/hpc-python.git

      An easy way to get the URL for cloning, is to copy it from the green Clone or download button on the Github page of your fork.

    • No further usage of git is required in the course, but if you are familiar with it we strongly recommend committing often and pushing your work back to your own GitHub.
  2. Clone the repository directly
    • Open the Terminal and clone the repository directly with the command:

      git clone https://github.com/csc-training/hpc-python.git

    • However, with this option, any changes you make will only be available locally and can not be pushed back to Github

  3. (Not recommended): If needed, you can also download all the material via from the Clone or download button as a Zip-file. However, this option means you will loose all the benefits of version control.

Overall structure

Skeleton code snippets and model solutions to hands-on exercises are in separate directories for each exercise. The exercises are organised in subdirectories (mpi/, numpy/ etc.) for each topic under the main directory (hpc-python/).

The file README.md contains a list of all exercises (with links), which is also shown as the default page on Github, and can be an easy way to navigate to an exercise.

Each exercise has also a README.md file that contains the instructions for the exercise and a solution/ directory that contains a model solution. Additional files (skeleton code, input data etc.) may also be present.

Test the software installation

Go into the test subdirectory and execute the test set:

pythonuser@ubuntu:~/hpc-python$ cd test
pythonuser@ubuntu:~/hpc-python/test$ python3 test.py

If everything is fine, you should see Test set passed printed on the screen.

Please comment if you have any problems setting up the environment or downloading the exercises.

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This article is from the free online course:

Python in High Performance Computing

Partnership for Advanced Computing in Europe (PRACE)