Examples: query, "exact match", wildcard*, wild?ard, wild*rd
Fuzzy search: cake~ (finds cakes, bake)
Term boost: "red velvet"^4, chocolate^2
Field grouping: tags:(+work -"fun-stuff")
Escaping: Escape characters +-&|!(){}[]^"~*?:\ with \, e.g. \+
Range search: properties.timestamp:[1587729413488 TO *] (inclusive), properties.title:{A TO Z}(excluding A and Z)
Combinations: chocolate AND vanilla, chocolate OR vanilla, (chocolate OR vanilla) NOT "vanilla pudding"
Field search: properties.title:"The Title" AND text
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

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

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

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

Okay, thanks!

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

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
6 Answers
3 years ago
8 months ago