Good points, I'd appreciate if you could elaborate since it seems you've thought a lot about this.

> Docker can help only if there is a single step in your project. In ML projects you usually have many steps - Preprocess, Train. Each of the steps can be divided: extract Evaluate step from Train etc.

Yeah this is something I've been struggling with. In a project I'm working on I use docker build to 1) set up the environment 2) get canonical datasets 3) pre-process the datasets. However I've left reproducing as manual instructions, e.g. run the container, call script 1 to repro experiment 1, call script 2 to repro experiment 2, etc. I think I could improve this by providing `--entrypoint` at docker run, or by providing a docker-compose file (wherein I could specify an entrypoint) for each experiment.

What do you think are the generalizability pitfalls in this workflow? How could dvc help?

> Also, Docker has an overhead - copy of data needs to be created. While DVC just saves links (sym-links, hard-links or reflinks) with a minimum overhead. It is crucial when you work with multi-GB datasets.

Good point!

I could see using an entry point for that. The entry point script could take as an argument the experiment to run and then the docker command would be: docker run -ti experiments:latest experiment-1

I could also see creating a base dockerfile, and a dockerfile per experiment. The base docker file would do the setup, and the experiment docker files would just run the commands necessary to reproduce the experiment, and exit.

The major pitfall - it depends on your goal. Your approach looks good if you just need to retrain an existing model\code in production. However, this approach is not perfect in the ML modeling\development stage. Let me explain...

`--entrypoint` defines a single step\script. You make a single black box from your entire ML processes. There is a lack of granularity for ML modeling process when people tend to separate different stages to make the process more manageable and efficient: manage and version dataset separately from modeling, preprocess data before training, training code as a separate unit plus some problem specific steps\units.

DVC gives you the ability to increase the granularity of your ML project while still keeps it manageable and reproducible. The steps still can be wrapped in Docker - it is a good practice. As @theossuary said, run `docker run -ti experiments:latest` as a step in DVC.