# Installation¶

## Quick setup¶

Start by cloning the git-repository

cd ~/path/to/somewhere
git clone git@gitlab.com:materials-modeling/storq.git


Next you can either proceed to do a standard install via pip

or by manually adding the paths storq and its associated binaries to your .bashrc file

export PYTHONPATH=$PYTHONPATH:~/path/to/somewhere/storq export PATH=$PATH:~/path/to/somewhere/storq/bin


Next you need to generate a configuration file. Storq comes with a command line tool that can do the bulk of the configuration for you. To initiate the automatic configuration run

The configuration process will alert you as to any settings which could not be automatically detected and hence need to be set manually. Note that some settings are optional, in which case only a warning is issued while others are critical for the operation of storq and are hence marked as “fatal”.

Once you have completed the automatic configuration (and addressed any of its complaints) it might be necessary to set your mpi run-command in order to be able to run calculations. This is the case if your supercomputing resource uses a wrapped version of the default mpirun command (e.g. mpprun on triolith or aprun on beskow). To change this option access the json configuration file through

and change the value of the mpi_command field to an appropriate command. It can furthermore be convenient to add a default allocation for your jobs to run on (you can override this later from your ASE script). To select an allocation, edit the value of the batch_account field to the name of your account (e.g. “snic20XX-X-XX” on a SNIC resource).

This section contains a more thorough description about the configuration process and what options are available. The automatic configuration process will generate two configuration files named vasp.json and site.json and place them under ~/.config/storq. You can access and edit these at any time using the commands

The generation of the site.json file should be completely automatic, the file itself contains information about how the cluster environment is set up (e.g. the number of cores per node). The vasp.json file tells storq where to find VASP binarie, pseudopotentials etc. and controls the general behaviour of the calculator. All settings found in this file can be overridden from python but in practice only a few settings (walltime, number of nodes etc.) need to be changed from script to script. To access and change the storq configurations from within python use the Vasp.configure(key=value) method.

Below is a list of all currently availabe options for vasp.json and a basic summary of their effects.

• vasp_mode: “queue” or “run”, should we run jobs directly or submit through queue.
• mpi_command:
• mpi_options: null
• vasp_executable_std: Path to the standard VASP executable
• vasp_executable_gam: Path to the special Gamma-point only VASP executable
• vasp_executable_ncl: Path to the special Non-collinear VASP executable
• vasp_potentials: Path to potpaw_ABC folders containing POTCARs where ABC=PBE,LDA,GGA
• vasp_vdw_kernel: Path to the vdw_kernel.bindat file need to use the vdW-DF functionals.
• user: Your username on the supercomputing resource.
• batch_account: Your allocation on the supercomputing resource.
• batch_options: A space-separated string of extra options passed to the sbatch command
• batch_walltime: The walltime of your job passed a string HH:MM:SS
• batch_nodes: The number of nodes used for your calculation
• vasp_convergence: “basic” or “strict”, sets the strictness level of the calculators convergence checks.
• vasp_restart_on: “convergence”,
• vasp_stdout: Name of the file which VASP’s stdout gets written to, typically vasp.out.
• vasp_validate: whether keywords passed to the calculator should be type checked (true or false)