The ModelBuilder class is clearly the way to go, but if you’re looking for a quick and dirty solution, I’ve been wrapping my trace and model inside a python dict and saving it as a pickle.

@twiecki What about the case of model checkpointing? I am working on a compute cluster where I may get pre-empted after a certain amount of time. Is there anyway I can save the model at set intervals with this workflow to be loaded and continue sampling where I left off?

@twiecki That makes sense, I really appreciate your response! I assume the model sampled 200 times is roughly equivalent to a model that has been sampled 100 times saved, loaded and sampled 100 more times