This tutorial shows how to use Determined's HP Search Constraints with
PyTorch. In this example, the constraints are defined in Lines 56-57 of
the __init__ function in model_def.py based on the model hyperparameters
via the det.InvalidHP exception API (see the HP Search Constraints topic
guide under https://docs.determined.ai/latest/topic-guides/index.html
Constraints can also be defined in train_batch and evaluate_batch,
where an InvalidHP exception can be raised based on
training and validation metrics respectively.
This example is based on Determined's mnist_pytorch tutorial, with the
addition of the HP search constraint as the only modification.
- model_def.py: Where the HP Search constraint is defined and used.
- All other files are identical to the
mnist_pytorchtutorial code.
If you have not yet installed Determined, installation instructions can be found
under docs/install-admin.html or at https://docs.determined.ai/latest/index.html
Run the following command: det -m <master host:port> experiment create -f adaptive.yaml ..
Training the model with the hyperparameter settings in adaptive.yaml should yield
a validation accuracy of ~97%.