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Training models directly on your datastore

Model instances may be trained if a trainer is set on the Model when db.apply is called. When models are trained, if CFG.cluster.compute has been configured with a ray scheduler, then superduper deploys a job on the connected ray cluster.

Basic pattern​

from superduper.ext.<framework> import <Framework>Trainer
from superduper.ext.<framework> import <ModelCls>

db.apply(
<ModelCls>(
*args,
trainer=<Framework>Trainer(**trainer_kwargs),
**kwargs,
)
)

Fitting/ training models by framework​

Not all Model types are trainable. We support training for the following frameworks:

FrameworkTraining Link
Scikit-Learnlink
PyTorchlink
Transformerslink