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 :)
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?
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
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?
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:
https://demoapp.trains.allegro.ai/projects/5371015f43f043b1b4ad7203c1ff4a95/models
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:
https://github.com/allegroai/events/blob/master/odsc20-east/generic/dataset_artifact.py
https://github.com/allegroai/events/blob/master/odsc20-east/generic/process_dataset.py
https://github.com/allegroai/events/blob/master/odsc20-east/scikit-learn/sklearn_jupyter.ipynb
A more robust dataset management is available on the enterprise edition (including searchability, debiasing etc.)