hp_constraints_mnist_pytorch

PyTorch HP Search Constraints (MNIST)

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.

Files

• model_def.py: Where the HP Search constraint is defined and used.
• All other files are identical to the mnist_pytorch tutorial code.

To Run

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 ..

Results

Training the model with the hyperparameter settings in adaptive.yaml should yield a validation accuracy of ~97%.