How to continually update a model with new data

I have a reinforcement learning use case with the following initial steps:

  1. Construct initial episodes
  2. Add episodes to initial experience replay buffer
  3. Train initial model

Then the following steps which repeat every day:

  1. Construct most recent time steps
  2. Add new time steps to existing experience replay buffer
  3. Fine-tune existing model
  4. Compare existing model and fine-tuned model and keep the one that’s best

What would be the best way to do this using DVC? Is it even possible given that circular dependencies are not allowed?

You can use persist: true to prevent outputs from being deleted during stage execution (see, so you can feed in new data each day and have the subsequent stages read from their existing outputs before writing out newly modified versions.

Thanks! I think that should work