Deploying a PyTorch model

As mention in the deploying models guide, deploying models via Verta Inference is a two step process: (1) first create an endpoint, and (2) update the endpoint with a model.

This tutorial explains how Verta Inference can be used to deploy a PyTorch model.

1. Create an endpoint

Users can create an endpoint using Client.create_endpoint() as follows:

census_endpoint = client.create_endpoint(path="/census")

2. Updating the endpoint with a RMV

As discussed in the Catalog Overview, there are multiple of ways to create an RMV for a PyTorch model.

First, if we are provided with a PyTorch model object, users can use the PyTorch convenience functions to create a Verta Standard Model.

from verta.environment import Python

model_version = registered_model.create_standard_model_from_torch(
    model,
    environment=Python(requirements=["torch", "torchvision"]),
    name="v1",
)

Alternatively, a serialized PyTorch saved model can be used as an artifact in a model that extends VertaModelBase.

torch.save(model.state_dict(), "model.pth")

from verta.registry import VertaModelBase

class FashionMNISTClassifier(VertaModelBase):
    def __init__(self, artifacts):
        self.model = NeuralNetwork()
        model.load_state_dict(torch.load(artifacts["model.pth"]))

    def predict(self, batch_input):
        results = []
        for one_input in batch_input:
            with torch.no_grad():
                pred = model(x)
                results.append(pred)
        return results

model_version = registered_model.create_standard_model(
    model_cls=FashionMNISTClassifier,
    environment=Python(requirements=["torch", "torchvision"]),
    artifacts={"model.pth" : "model.pth"},
    name="v2"
)

Note that the input and output of the predict function must be JSON serializable. For the full list of acceptable data types for model I/O, refer to the VertaModelBase documentation.

Prior to deploy, don't forget to test your model class locally as follows.

# test locally
mnist_model1 = FashionMNISTClassifier({"model.pth" : "model.pth"})
mnist_model1.predict([test_data[0][0]])

To ensure that the requirements specified in the model version are in fact adequate, you may build the model container locally or part of a continuous integration system. You may also deploy the model and make test predictions as show below.

Regardless of how a Registered Model Version has been created, the endpoint defined above can now be upated and we can make predictions against it.

fashion_mnist_endpoint = client.get_or_create_endpoint("fashion-mnist")
fashion_mnist_endpoint.update(model_version, wait=True)
deployed_model = fashion_mnist_endpoint.get_deployed_model()
deployed_model.predict([test_data[0][0]])

The full code for this tutorial can be found here.