As far as I understand, the workflow is like this. I define some model. Then I register it as an OutputModel. Then I train it. During training I save snapshots (not idea how, though) and then I save the final model when training is finished. This way the Model is a) connected to the task and b) available in the model store of ClearML.

Later, in a different task, I can load an already trained model with InputModel. This InputModel is read-only (regarding the ClearML model store), but I can make a copy on which I work and - e.g. - further train. So, what I would think, I get the model with InputModel from the model store, make a copy, register the copy with my new task as an OutputModel and then go on as I did in the last paragraph.

No idea how the Model comes into play here. Let me compare InputModel with Model:

class Model()
Represent an existing model in the system, search by model id. The Model will be read-only and can be used to pre initialize a network

class InputModel()
Load an existing model in the system, search by model ID. The Model will be read-only and can be used to pre initialize a network. We can connect the model to a task as input model, then when running remotely override it with the UI.
Load a model from the Model artifactory, based on model_id (uuid) or a model name/projects/tags combination.

Sounds just the same to me. What is the difference, @<1523701070390366208:profile|CostlyOstrich36> ?