Installation of AFVS

Setting up AWS Batch

AFVS on AWS uses an AWS Batch cluster and workload manager. Instructions on how to create the AWS Batch cluster, and how to access the AWS Batch login node can be found here on the AFVS GitHub page.

You will need to have an AWS Account. If you don't have one yet, you can create a free one here: https://aws.amazon.com/

Setting up SSH

To log in to the AWS Batch login node via ssh, follow the instructions here. You will land in the home directory of the AWS Batch login node.

Setting up Python

AFVS requires Python 3.x (it has been tested with Python 3.9.4). Additionally, it requires packages to be installed that are not included at many sites. As a result, we recommend that you create a virtualenv for AFVS that can be used to run jobs.

Make sure that virtualenv is installed:

python3 -m pip install --user --upgrade virtualenv

Create a virtualenv (this example creates it under $HOME/afvs_env):

python3 -m virtualenv $HOME/afvs_env

Enter the virtualenv and install needed packages

source $HOME/afvs_env/bin/activate
python3 -m pip install boto3 pandas pyarrow jinja2 ipython

Alternatively, all the above steps can be performed using conda instead of pip. Further information on setting up a conda style environment can be found here.

To exit a virtual environment:

deactivate

When running AFVS commands, you should enter the virtualenv that you have set up using:

source $HOME/afvs_env/bin/activate

Alternatively, all the above steps can be performed using conda instead of pip. Further information on setting up a conda style environment can be found here.

For the remainder of the tutorial and for future use of AFVS, please ensure the virtual environment is activated.

Installing AFVS

After Python is set up, we can install AFVS. Normally this is done by cloning the GitHub repo, but for this tutorial, we provide a pre-configured folder for download. On the AWS Batch login node, you can download it by running the following command:

wget https://adaptive-flow.ai/sites/adaptive-flow.ai/files/tutorials/AFVS2_Tutorial1_Preconfigured_AWS_20240421.tar.gz

This preconfigured folder was prepared by:

• Cloning the GitHub repo

• Adding the docking input files to the input-files folder

• Configuring the all.ctrl file. The following parameters are changed compared to the template file in the AFVS GitHub repo:

  • job_name

  • docking_scenario_names

  • docking_scenario_programs

  • docking_scenario_replicas

  • docking_scenario_batchsizes