How to install USPEX 10.5

These settings worked OK for the provided example EX01-3D_Si_vasp and is specific for use together with VASP. For 10.4, Python3 is used.

Read the documentation and README file. Check which version of USPEX you are going to install. Other versions might need to be setup in a different way.

tar xf USPEX-10.5.tar.gz 
cd USPEX_v10.5

“xf” instead of “xfz” since file doesn’t seem to be compressed.

(1) Setting up Python

Use Python3. There is a list of required packages needed, see the README file, including:

numpy scipy spglib pysqlite3 ase matplotlib

To include these dependencies, one can create a Python virtual environment. Here below this procedure will be demonstrated.

On Tetralith, load suitable Python3 module together with build environment and SQLite (possibly not necessary):

module load Python/3.6.7-env-nsc1-gcc-2018a-eb
module load buildenv-gcc/2018a-eb
module load SQLite/3.13.0-nsc1

the instructions mentions that an installation of sqlite3 is needed, so therefore it’s also loaded. To not include user installed Python dependencies from .local (if you have), set

export PYTHONNOUSERSITE=1

virtualenv --system-site-packages py3_uspex

Here ‘numpy’ etc. are made available from the module. Activate the virtual environment and install the remaining needed packages:

. py3_uspex/bin/activate
pip install spglib
pip install pysqlite3
pip install ase
pip install torch

You can check what packages are installed, e.g. with:

pip list

to logout from the environment type “deactivate”.

To use this environment together with USPEX, the easiest is to login to activate it when starting USPEX. Alternatively, you can set up appropriate paths when you’re going to use it

export PATH=/path/to/the/installation/USPEX_v10.5/py3_uspex/bin:$PATH
export PYTHONPATH=/path/to/the/installation/USPEX_v10.5/py3_uspex/lib/python3.6/site-packages:$PYTHONPATH

The paths needs to be adjusted according to your installation, confirm that the correct Python is called, e.g. by:

which python

Make sure that no other Python modules or environments are active in the terminal, since it may interfere with the USPEX runs. For instance, if modules are loaded, they can be cleared by the command module purge, also check .bashrc.

Note Here the use of pmpath.py (for which “pylada” needs to be installed) isn’t considered.

(2) Install USPEX

First, change permission on the files (if needed)

chmod u+x install.sh USPEX_MATLABruntime.install

Install with

./install.sh

Select the non-graphical installation, option (2) with terminal, accept the questions and provide a path where you want the installation to be by giving the full path, e.g.

/path/to/the/installation/USPEX_v10.5/inst01

Note that USPEX will put some parameters needed for its run at the end of

~/.bashrc

so, if you reinstall it, or install several versions, you might need to clean up .bashrc. If you’re going to start it from the same terminal later on, you need to

source ~/.bashrc

Alternatively, put the commands in a separate file which you source before using USPEX.

(3) Configuring files

Assuming the above installation place, go to the Submission folder and create a backup of the two files:

cd inst01/application/archive/src/Submission
cp submitJob_local.py submitJob_local.py.orig 
cp checkStatus_local.py checkStatus_local.py.orig

Now copy patched versions of the two files for use with the Slurm job scheduler on Tetralith/Sigma:

cp /software/sse/manual/USPEX/10.5/submitJob_local.py .
cp /software/sse/manual/USPEX/10.5/checkStatus_local.py .

Some small changes made in submitJob_local.py:

    # Step 1
    myrun_content = '''#!/bin/bash
#SBATCH -A snic20XX-X-XX
#SBATCH -J {}
#SBATCH -n 4
#SBATCH -t 05:00:00

{}
'''.format(JOB_NAME, commnadExecutable)

Here, change to your own project id in the line #SBATCH -A snic20XX-X-XX. Note that each job will run on 4 cores -n 4for a maximum walltime of 5h -t 05:00:00. Substitute with what works best for your study.

Some small changes made in checkStatus_local.py (“check_output” in step 1 and “if RUNNING …” in step 2):

    # Step 1
    try:
        output = check_output('sacct -X -n --format=State -j {}'.format(jobID), shell=True, universal_newlines=True)
    except:
        output = "JOBDONE"

    # process = subprocess.Popen(['qstat', str(jobID)], stdout=subprocess.PIPE)
    # output, err = process.communicate()

    # Step 2
    doneOr = True
    if 'RUNNING' in output or 'PENDING' in output or 'COMPLETING' in output or 'CONFIGURING' in output:
        doneOr = False
    if doneOr:
        for file in glob.glob('USPEX*'):
            os.remove(file)  # to remove the log file
    print(str(doneOr))
    return doneOr

Note that for NSC sacct is used rather than squeue, due to accessibilty reasons (‘squeue’ gives error when a finished job isn’t in the memory, might differ for different setups of Slurm).

(4) Running an example

Before using USPEX, make sure to load the associated Python environment and source the USPEX parameters (if not already activated via .bashrc).

Here we will copy the EX01-3D_Si_vasp example to a new folder for the test

cd /path/to/the/installation/USPEX_v10.5/inst01/application/archive/examples 
tar xfz EX01-3D_Si_vasp.tgz
cp -r EX01-3D_Si_vasp EX01-3D_Si_vasp_test1
cd EX01-3D_Si_vasp_test1

Due to our settings in submitJob_local.py, we need to take care of loading an appropriate VASP module and running VASP in the USPEX input file INPUT.TXT, e.g.

% commandExecutable
ml VASP/5.4.4.16052018-nsc1-intel-2018a-eb; mpprun vasp_std > log
% EndExecutable

Note the NSC specific mpprun instead of mpirun. Alternatively, the module can be set in the job template in submitJob_local.py instead.

To start the USPEX process in the background on the login node:

nohup ./EX01-3D_Si_vasp.sh &

Caveats

With the present Slurm configuration at NSC clusters, it’s not recommended to send several hundreds or thousands of short jobs to the queue. In this case, some other solution needs to be found. Here one can look into the possibility of bunching many short runs into a single Slurm job.