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Model

TensorFlow model creation#

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create_model#

hsml.model_registry.ModelRegistry.tensorflow.create_model(
    name, version=None, metrics=None, description=None, input_example=None, model_schema=None
)

Create a TensorFlow model metadata object.

Lazy

This method is lazy and does not persist any metadata or uploads model artifacts in the model registry on its own. To save the model object and the model artifacts, call the save() method with a local file path to the directory containing the model artifacts.

Arguments

  • name str: Name of the model to create.
  • version Optional[int]: Optionally version of the model to create, defaults to None and will create the model with incremented version from the last version in the model registry.
  • description Optional[str]: Optionally a string describing the model, defaults to empty string "".
  • input_example Optional[Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray, list]]: Optionally an input example that represents inputs for the model, defaults to None.
  • model_schema Optional[hsml.model_schema.ModelSchema]: Optionally a model schema for the model inputs and/or outputs.

Returns

Model. The model metadata object.

Torch model creation#

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create_model#

hsml.model_registry.ModelRegistry.torch.create_model(
    name, version=None, metrics=None, description=None, input_example=None, model_schema=None
)

Create a Torch model metadata object.

Lazy

This method is lazy and does not persist any metadata or uploads model artifacts in the model registry on its own. To save the model object and the model artifacts, call the save() method with a local file path to the directory containing the model artifacts.

Arguments

  • name str: Name of the model to create.
  • version Optional[int]: Optionally version of the model to create, defaults to None and will create the model with incremented version from the last version in the model registry.
  • description Optional[str]: Optionally a string describing the model, defaults to empty string "".
  • input_example Optional[Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray, list]]: Optionally an input example that represents inputs for the model, defaults to None.
  • model_schema Optional[hsml.model_schema.ModelSchema]: Optionally a model schema for the model inputs and/or outputs.

Returns

Model. The model metadata object.

Sklearn model creation#

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create_model#

hsml.model_registry.ModelRegistry.sklearn.create_model(
    name, version=None, metrics=None, description=None, input_example=None, model_schema=None
)

Create an SkLearn model metadata object.

Lazy

This method is lazy and does not persist any metadata or uploads model artifacts in the model registry on its own. To save the model object and the model artifacts, call the save() method with a local file path to the directory containing the model artifacts.

Arguments

  • name str: Name of the model to create.
  • version Optional[int]: Optionally version of the model to create, defaults to None and will create the model with incremented version from the last version in the model registry.
  • description Optional[str]: Optionally a string describing the model, defaults to empty string "".
  • input_example Optional[Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray, list]]: Optionally an input example that represents inputs for the model, defaults to None.
  • model_schema Optional[hsml.model_schema.ModelSchema]: Optionally a model schema for the model inputs and/or outputs.

Returns

Model. The model metadata object.

Generic model creation#

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create_model#

hsml.model_registry.ModelRegistry.python.create_model(
    name, version=None, metrics=None, description=None, input_example=None, model_schema=None
)

Create a generic Python model metadata object.

Lazy

This method is lazy and does not persist any metadata or uploads model artifacts in the model registry on its own. To save the model object and the model artifacts, call the save() method with a local file path to the directory containing the model artifacts.

Arguments

  • name str: Name of the model to create.
  • version Optional[int]: Optionally version of the model to create, defaults to None and will create the model with incremented version from the last version in the model registry.
  • description Optional[str]: Optionally a string describing the model, defaults to empty string "".
  • input_example Optional[Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray, list]]: Optionally an input example that represents inputs for the model, defaults to None.
  • model_schema Optional[hsml.model_schema.ModelSchema]: Optionally a model schema for the model inputs and/or outputs.

Returns

Model. The model metadata object.

Retrieval#

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get_model#

ModelRegistry.get_model(name, version=None)

Get a model entity from the model registry. Getting a model from the Model Registry means getting its metadata handle so you can subsequently download the model directory.

Arguments

  • name str: Name of the model to get.
  • version Optional[int]: Version of the model to retrieve, defaults to None and will return the version=1.

Returns

Model: The model metadata object.

Raises

  • RestAPIError: If unable to retrieve model from the model registry.

Properties#

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created#

Creation date of the model.

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description#

Description of the model.

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environment#

Input example of the model.

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experiment_id#

Experiment Id of the model.

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experiment_project_name#

experiment_project_name of the model.

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framework#

framework of the model.

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id#

Id of the model.

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input_example#

input_example of the model.

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model_path#

path of the model with version folder omitted. Resolves to /Projects/{project_name}/Models/{name}

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model_registry_id#

model_registry_id of the model.

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model_schema#

model schema of the model.

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name#

Name of the model.

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program#

Executable used to export the model.

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project_name#

project_name of the model.

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shared_registry_project_name#

shared_registry_project_name of the model.

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training_dataset#

training_dataset of the model.

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training_metrics#

Training metrics of the model.

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user#

user of the model.

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version#

Version of the model.

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version_path#

path of the model including version folder. Resolves to /Projects/{project_name}/Models/{name}/{version}

Methods#

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delete#

Model.delete()

Delete the model

Potentially dangerous operation

This operation drops all metadata associated with this version of the model and deletes the model files.

Raises

RestAPIError.

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delete_tag#

Model.delete_tag(name)

Delete a tag attached to a model.

Arguments

  • name str: Name of the tag to be removed.

Raises

RestAPIError in case the backend fails to delete the tag.

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deploy#

Model.deploy(
    name=None, artifact_version="CREATE", predictor_config=None, transformer_config=None
)

Deploy the model

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download#

Model.download()

Download the model files to a local folder.

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get_tag#

Model.get_tag(name)

Get the tags of a model.

Arguments

  • name str: Name of the tag to get.

Returns

tag value

Raises

RestAPIError in case the backend fails to retrieve the tag.

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get_tags#

Model.get_tags()

Retrieves all tags attached to a model.

Returns

Dict[str, obj] of tags.

Raises

RestAPIError in case the backend fails to retrieve the tags.

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save#

Model.save(model_path, await_registration=480)

Persist this model including model files and metadata to the model registry.

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set_tag#

Model.set_tag(name, value)

Attach a tag to a model.

A tag consists of a pair. Tag names are unique identifiers across the whole cluster. The value of a tag can be any valid json - primitives, arrays or json objects.

Arguments

  • name str: Name of the tag to be added.
  • value Union[str, dict]: Value of the tag to be added.

Raises

RestAPIError in case the backend fails to add the tag.