Advanced Usage

Here are some examples highlighting QPyTorch’s more advanced features.

Batch QEPs

QPyTorch makes it possible to train/perform inference with a batch of q-exponential processes in parallel. This can be useful for a number of applications:

  • Modeling a function with multiple (uncorrelated) outputs

  • Performing efficient cross-validation

  • Parallel acquisition function sampling for Bayesian optimization

  • And more!

Here we highlight a number of common batch QEP scenarios and how to construct them in QPyTorch.

  • Multi-output functions (with uncorrelated outputs). Batch QEPs are extremely efficient at modelling multi-output functions, when each of the output functions are independent. See the `Batch Uncorrelated Multioutput QEP`_ example for more details.

  • For cross validation, or for some BayesOpt applications, it may make sense to evaluate the QEP on different batches of test data. This can be accomplished by using a standard (non-batch) QEP model. At test time, feeding a b x n x d tensor into the model will then return b batches of n test points. See the Batch Mode Regression example for more details.

Variational Fantasization

We also include an example of how to perform fantasy modelling (e.g. efficient, closed form updates) for variational q-exponential process models, enabling their usage for lookahead optimization. See the Variational fantasization example.

Converting Models to TorchScript

In order to deploy QEPs in production code, it can be desirable to avoid using PyTorch directly for performance reasons. Fortunately, PyTorch offers a mechanism called TorchScript to aid in this. In these example notebooks, we’ll demonstrate how to convert both an exact QEP and a variational QEP to a ScriptModule that can then be used for example in LibTorch.