eos_workflow

A workflow for eos calculations

Installation Prerequisites

ABINIT

The installation of ABINIT can be referred to their official website

MongoDB

Details can be referred to the website of atomate2

Configure files for atomate2 and jobflow

i. configure setting files

atomate2.yaml

# ABINIT
ABINIT_MPIRUN_CMD: "mpirun"
ABINIT_CMD: PATH_TO/src/98_main/abinit
ABINIT_MRGDDB_CMD: PATH_TO/src/98_main/mrgddb
ABINIT_ANADDB_CMD: PATH_TO/src/98_main/anaddb
ABINIT_ABIPY_MANAGER_FILE: $HOME/.abinit/abipy/manager.yml

jobflow.yaml

JOB_STORE:
  docs_store:
    type: MongoStore
    database: <<DB_NAME>>
    host: <<HOSTNAME>>
    port: <<PORT>>
    username: <<USERNAME>>
    password: <<PASSWORD>>
    collection_name: outputs
  additional_stores:
    data:
      type: GridFSStore
      database: <<DB_NAME>>
      host: <<HOSTNAME>>
      port: <<PORT>>
      username: <<USERNAME>>
      password: <<PASSWORD>>
      collection_name: outputs_blobs

manager.yml, abipy configuration file

qadapters:
    # List of qadapters objects
    - priority: 1
      queue:
        qtype: shell
        qname: localhost
      job:
        mpi_runner: mpirun
        # source a script to setup the environment.
        #pre_run: "source ~/env.sh"
      limits:
        timelimit: 1:00:00
        max_cores: 2
      hardware:
         num_nodes: 1
         sockets_per_node: 1
         cores_per_socket: 2
         mem_per_node: 4 Gb

ii. export environment variables

open ~/.bashrc and add the following:

export ATOMATE2_CONFIG_FILE="PATH_TO/atomate2.yaml"
export JOBFLOW_CONFIG_FILE="PATH_TO/jobflow.yaml"

then:

source ~/.bashrc

installation

clone the code

git clone https://github.com/jingslaw/eos_workflow.git

create a conda env jobflow

conda create --name jobflow python=3.10
conda activate jobflow

install

pip install -e .

An Example for O.psp8

An example is in eos_workflow/src/tests/
pseudo O.psp8 is from "ONCVPSP-PBE-SR-PDv0.4:standard", a standard PseudoTable, which installed in abipy: ~/.abinit/pseudo/ONCVPSP-PBE-SR-PD/standard

run locally

python run_locally.py

run the workflow on a remote cluster

i. install eos_workflow package on the remote cluster

ii. install eos_workflow package on PC

iii. install jobflow_remote package on PC

When eos_workflow is installed on conda env jobflow, we follow the introduction of jobflow_remote:

pip install jobflow-remote

Then, initial setup configuration:

jf project generate eos_workflow

In addition, file ~/.jfremote.yaml should be created.
~/.jfremote.yaml with one line:

project: eos_workflow

Finally, create and configure the eos_workflow.yaml file in the folder ~/.jfremote

If this file is correctly configured, we have

python submit_remote.py

The submitted jobs can be referred by:

jf job list