Transformer#

Handle#

get_model_serving#

Connection.get_model_serving()

Get a reference to model serving to perform operations on. Model serving operates on top of a model registry, defaulting to the project's default model registry.

Example

import hopsworks

project = hopsworks.login()

ms = project.get_model_serving()

Returns

ModelServing. A model serving handle object to perform operations on.

Creation#

[source]

create_transformer#

ModelServing.create_transformer(script_file=None, resources=None)

Create a Transformer metadata object.

Example

# login into Hopsworks using hopsworks.login()

# get Dataset API instance
dataset_api = project.get_dataset_api()

# get Hopsworks Model Serving handle
ms = project.get_model_serving()

# create my_transformer.py Python script
class Transformer(object):
    def __init__(self):
        ''' Initialization code goes here '''
        pass

    def preprocess(self, inputs):
        ''' Transform the requests inputs here. The object returned by this method will be used as model input to make predictions. '''
        return inputs

    def postprocess(self, outputs):
        ''' Transform the predictions computed by the model before returning a response '''
        return outputs

uploaded_file_path = dataset_api.upload("my_transformer.py", "Resources", overwrite=True)
transformer_script_path = os.path.join("/Projects", project.name, uploaded_file_path)

my_transformer = ms.create_transformer(script_file=uploaded_file_path)

# or

from hsml.transformer import Transformer

my_transformer = Transformer(script_file)

Create a deployment with the transformer

my_predictor = ms.create_predictor(transformer=my_transformer)
my_deployment = my_predictor.deploy()

# or
my_deployment = ms.create_deployment(my_predictor, transformer=my_transformer)
my_deployment.save()

Lazy

This method is lazy and does not persist any metadata or deploy any transformer. To create a deployment using this transformer, set it in the predictor.transformer property.

Arguments

script_file str | None: Path to a custom predictor script implementing the Transformer class.
resources hsml.resources.PredictorResources | dict | None: Resources to be allocated for the transformer.

Returns

Transformer. The model metadata object.

Retrieval#

predictor.transformer#

Transformers can be accessed from the predictor metadata objects.

predictor.transformer

Predictors can be found in the deployment metadata objects (see Predictor Reference). To retrieve a deployment, see the Deployment Reference.

Properties#

[source]

inference_batcher#

Configuration of the inference batcher attached to the deployment component (i.e., predictor or transformer).

[source]

resources#

Resource configuration for the deployment component (i.e., predictor or transformer).

[source]

script_file#

Script file ran by the deployment component (i.e., predictor or transformer).

Methods#

[source]

describe#

Transformer.describe()

Print a description of the transformer

[source]

to_dict#

Transformer.to_dict()

To be implemented by the component type