Skip to content

Predictor#

Handle#

[source]

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.

Returns

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

Creation#

[source]

create_predictor#

ModelServing.create_predictor(
    model,
    name=None,
    artifact_version="CREATE",
    model_server=None,
    serving_tool=None,
    script_file=None,
    resources=None,
    inference_logger=None,
    inference_batcher=None,
    transformer=None,
)

Create a Predictor metadata object.

Lazy

This method is lazy and does not persist any metadata or deploy any model on its own. To create a deployment using this predictor, call the deploy() method.

Arguments

  • model hsml.model.Model: Model to be deployed.
  • name Optional[str]: Name of the predictor.
  • artifact_version Optional[str]: Version number of the model artifact to deploy, CREATE to create a new model artifact or MODEL-ONLY to reuse the shared artifact containing only the model files.
  • model_server Optional[str]: Model server ran by the predictor.
  • serving_tool Optional[str]: Serving tool used to deploy the model server.
  • script_file Optional[str]: Path to a custom predictor script implementing the Predict class.
  • resources Optional[Union[hsml.resources.PredictorResources, dict]]: Resources to be allocated for the predictor.
  • inference_logger Optional[Union[hsml.inference_logger.InferenceLogger, dict, str]]: Inference logger configuration.
  • inference_batcher Optional[Union[hsml.inference_batcher.InferenceBatcher, dict]]: Inference batcher configuration.
  • transformer Optional[Union[hsml.transformer.Transformer, dict]]: Transformer to be deployed together with the predictor.

Returns

Predictor. The predictor metadata object.

Retrieval#

deployment.predictor#

Predictors can be accessed from the deployment metadata objects.

deployment.predictor

To retrieve a deployment, see the Deployment Reference.

Properties#

[source]

artifact_path#

Path of the model artifact deployed by the predictor. Resolves to /Projects/{project_name}/Models/{name}/{version}/Artifacts/{artifact_version}/{name}{version}.zip

[source]

artifact_version#

Artifact version deployed by the predictor.

[source]

created_at#

Created at date of the predictor.

[source]

creator#

Creator of the predictor.

[source]

id#

Id of the predictor.

[source]

inference_batcher#

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

[source]

inference_logger#

Configuration of the inference logger attached to this predictor.

[source]

model_name#

Name of the model deployed by the predictor.

[source]

model_path#

Model path deployed by the predictor.

[source]

model_server#

Model server used by the predictor.

[source]

model_version#

Model version deployed by the predictor.

[source]

name#

Name of the predictor.

[source]

requested_instances#

Total number of requested instances in the predictor.

[source]

resources#

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

[source]

script_file#

Script file used to load and run the model.

[source]

serving_tool#

Serving tool used to run the model server.

[source]

transformer#

Transformer configuration attached to the predictor.

Methods#

[source]

deploy#

Predictor.deploy()

Create a deployment for this predictor and persists it in the Model Serving.

Returns

Deployment. The deployment metadata object.

[source]

describe#

Predictor.describe()

Print a description of the predictor

[source]

to_dict#

Predictor.to_dict()

To be implemented by the component type