How can I create a virtual environment to install Popper

The following creates a virtual environment in a $HOME/venvs/popper folder:

# create virtualenv
virtualenv $HOME/venvs/popper

# activate it
source $HOME/venvs/popper/bin/activate

# install Popper in it
pip install popper

The first step is is only done once. After closing your shell, or opening another tab of your terminal emulator, you’ll have to reload the environment (activate it line above). For more on virtual environments, see here.

How can we deal with large datasets? For example I have to work on large data of hundreds GB, how would this be integrated into Popper?

For datasets that are large enough that they cannot be managed by Git, solutions such as a PFS, GitLFS, Datapackages, ckan, among others exist. These tools and services allow users to manage large datasets and version-control them. From the point of view of Popper, this is just another tool that will get invoked as part of the execution of a pipeline. As part of our documentation, we have examples on how to use datapackages, and another on how to use data.world.

How can Popper capture more complex workflows? For example, automatically restarting failed tasks?

A Popper pipeline is a simple sequence of “containerized bash scripts”. Popper is not a replacement for scientific workflow engines, instead, its goal is to capture the highest-most workflow: the human interaction with a terminal.

Can I follow Popper in computational science research, as opposed to computer science?

Yes, the goal for Popper is to make it a domain-agnostic experimentation protocol. See the https://github.com/popperized/popper-examples repository for examples.

How to apply the Popper protocol for applications that take large quantities of computer time?

The popper run takes an optional STEP argument that can be used to execute a workflow up to a certain step. Run popper run --help for more.