Add software installation instructions

Author: gjbex

docs/README.md

@@ -12,7 +12,7 @@ When you complete this training you will
     computing such as numpy, numexpr and numba;
   * be able to use Cython to improve your code's performance;
   * be able to wrap C, C++ and Fortran code to use it from Python;
-  * understand the opportunities and pitfalls of multithreaded
+  * understand the opportunities and pitfalls of multi-threaded
     programming with Python;
   * be able to write distributed application using MPI;
   * have an understanding of how frameworks for distributed
@@ -31,7 +31,7 @@ Total duration: 4 hours.
   | Cython                                      | 60 min.  |
   | coffee break                                | 10 min.  |
   | interfacing with C/C++/Fortran              | 30 min.  |
-  | multithreaded programming                   | 10 min.  |
+  | multi-threaded programming                   | 10 min.  |
   | MPI                                         | 45 min.  |
   | dask                                        | 15 min.  |
   | pyspark                                     | 20 min.  |
@@ -45,6 +45,12 @@ Slides are available in the
 as well as example code and hands-on material.
 
 
+## Software environment
+
+Instructions on [how to create the required software environment](software_stack.md)
+are available.
+
+
 ## Target audience
 
 This training is for you if you need to use Python for computationally

docs/software_stack.md

@@ -0,0 +1,56 @@
+# Software stack
+
+This training requires a non-trivial software stack so using the conda package
+manager will simplify your life considerably.
+
+
+## git version control
+
+The repository for this training session is available on Github, and cloning this
+repository on you own machine will give you access to all training material.
+
+If you don't have a git client installed, consult the following [web page on how to
+install](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) one on
+your platform of choice.
+
+
+## Python & conda
+
+The most convenient way to install the required software is using the conda
+environment manager.  conda is available on Linux, MacOS and Windows.  You can use
+conda from the command line when you install miniconda which is available for
+[download](https://docs.conda.io/en/latest/miniconda.html).  The website provides
+installation instructions for each platform.
+
+Remember to install miniconda on a file system with enough free space since conda
+environments quickly take multiple gigabytes of disk space.
+
+Alternatively, you can install Anaconda, a GUI application to manage Python
+environments.  For Windows, this may be the most convenient option.  Anaconda is
+available for Windows, MacOS and Linux and can be downloaded from the
+[Anaconda website](https://www.anaconda.com/products/individual).
+
+
+## Training environment
+
+To create and use the conda environment for this training, open a terminal window and
+follow the steps below.
+
+1. Clone the Github repository:
+   ```bash
+   $ git clone [email protected]:gjbex/Python-for-HPC.git
+   ```
+2. Change into the newly created directory:
+   ```bash
+   $ cd Python-for-HPC
+   ```
+1. Create the conda environment for this training session:
+   ```bash
+   $ conda env create -f environment.yml
+   ```
+1. Activate the environment:
+   ```bash
+   $ conda activate python_for_hpc
+   ```
+
+Now you can run Python scripts in this terminal, or start a Jupyter notebook.