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Hi Great Trains Community! I Have A Question Regarding Version Control. How Trains Manages Model/Dataset Version Control?

Hi great trains community!
I have a question regarding version control.
How trains manages model/dataset version control?

Posted 3 years ago
Votes Newest

Answers 6

Thank you MuddyCrab47 !
Regrading model versioning:
All models are logged automatically by trains (no need so specify it, as long as you are using one of the automagically connected frameworks: PyTorch/keras/TF/SKlearn)
You can see see how it looks like on the demoapp:

Regrading Dataset management, we have a simple workflow demonstrated below, bascially using artifacts as dataset storage, with very easy interface for retrieving them (including cache),
The actual Dataset ID is the experiment uploaded/created it.
See here:

A more robust dataset management is available on the enterprise edition (including searchability, debiasing etc.)

Posted 3 years ago

is the model overridden or its version is automatically increased?

You will have another model, with the same name (assuming the second Task has the same name), but a new ID. So if I understand you correctly, we have auto-versioning :)

Posted 3 years ago

understood trains does not have auto versioning

What do you mean auto versioning ?

task name is not unique, task ID is unique, you can have multiple tasks with the same name and you can edit the name post execution

Posted 3 years ago

Thank you for your quick reply. AgitatedDove14
I've looked at the link you've provided. As I understood trains does not have auto versioning, it just infers the version from the task name right?

Posted 3 years ago

Let's assume I've created a model with a task. When I execute a second task with same model name, is the model overridden or its version is automatically increased?

Posted 3 years ago

Okay, thanks!

Posted 3 years ago
6 Answers
3 years ago
one year ago