Model#

You can use a corresponding method of ModelRegistry to create models: ModelRegistry.tensorflow.create_model, ModelRegistry.torch.create_model, ModelRegistry.sklearn.create_model, ModelRegistry.python.create_model, ModelRegistry.llm.create_model. You can retrieve an existing model via ModelRegistry.get_model.

hsml.tensorflow.signature.create_model #

create_model(
    name: str,
    version: int | None = None,
    metrics: dict | None = None,
    description: str | None = None,
    input_example: pandas.DataFrame
    | pandas.Series
    | numpy.ndarray
    | list
    | None = None,
    model_schema: ModelSchema | None = None,
    feature_view=None,
    training_dataset_version: int | None = 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.

PARAMETER	DESCRIPTION
`name`	Name of the model to create. TYPE: `str`
`version`	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. TYPE: `int \| None` DEFAULT: `None`
`metrics`	Optionally a dictionary with model evaluation metrics (e.g., accuracy, MAE) TYPE: `dict \| None` DEFAULT: `None`
`description`	Optionally a string describing the model, defaults to empty string `""`. TYPE: `str \| None` DEFAULT: `None`
`input_example`	Optionally an input example that represents a single input for the model, defaults to `None`. TYPE: `pandas.DataFrame \| pandas.Series \| numpy.ndarray \| list \| None` DEFAULT: `None`
`model_schema`	Optionally a model schema for the model inputs and/or outputs. TYPE: `ModelSchema \| None` DEFAULT: `None`
`feature_view`	Optionally a feature view object returned by querying the feature store. If the feature view is not provided, the model will not have access to provenance. DEFAULT: `None`
`training_dataset_version`	Optionally a training dataset version. If training dataset version is not provided, but the feature view is provided, the training dataset version used will be the last accessed training dataset of the feature view, within the code/notebook that reads the feature view and training dataset and then creates the model. TYPE: `int \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`. The model metadata object.

hsml.torch.signature.create_model #

create_model(
    name: str,
    version: int | None = None,
    metrics: dict | None = None,
    description: str | None = None,
    input_example: pandas.DataFrame
    | pandas.Series
    | numpy.ndarray
    | list
    | None = None,
    model_schema: ModelSchema | None = None,
    feature_view=None,
    training_dataset_version: int | None = 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.

PARAMETER	DESCRIPTION
`name`	Name of the model to create. TYPE: `str`
`version`	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. TYPE: `int \| None` DEFAULT: `None`
`metrics`	Optionally a dictionary with model evaluation metrics (e.g., accuracy, MAE) TYPE: `dict \| None` DEFAULT: `None`
`description`	Optionally a string describing the model, defaults to empty string `""`. TYPE: `str \| None` DEFAULT: `None`
`input_example`	Optionally an input example that represents a single input for the model, defaults to `None`. TYPE: `pandas.DataFrame \| pandas.Series \| numpy.ndarray \| list \| None` DEFAULT: `None`
`model_schema`	Optionally a model schema for the model inputs and/or outputs. TYPE: `ModelSchema \| None` DEFAULT: `None`
`feature_view`	Optionally a feature view object returned by querying the feature store. If the feature view is not provided, the model will not have access to provenance. DEFAULT: `None`
`training_dataset_version`	Optionally a training dataset version. If training dataset version is not provided, but the feature view is provided, the training dataset version used will be the last accessed training dataset of the feature view, within the code/notebook that reads the feature view and training dataset and then creates the model. TYPE: `int \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`. The model metadata object.

hsml.sklearn.signature.create_model #

create_model(
    name: str,
    version: int | None = None,
    metrics: dict | None = None,
    description: str | None = None,
    input_example: pandas.DataFrame
    | pandas.Series
    | numpy.ndarray
    | list
    | None = None,
    model_schema: ModelSchema | None = None,
    feature_view=None,
    training_dataset_version: int | None = 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.

PARAMETER	DESCRIPTION
`name`	Name of the model to create. TYPE: `str`
`version`	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. TYPE: `int \| None` DEFAULT: `None`
`metrics`	Optionally a dictionary with model evaluation metrics (e.g., accuracy, MAE) TYPE: `dict \| None` DEFAULT: `None`
`description`	Optionally a string describing the model, defaults to empty string `""`. TYPE: `str \| None` DEFAULT: `None`
`input_example`	Optionally an input example that represents a single input for the model, defaults to `None`. TYPE: `pandas.DataFrame \| pandas.Series \| numpy.ndarray \| list \| None` DEFAULT: `None`
`model_schema`	Optionally a model schema for the model inputs and/or outputs. TYPE: `ModelSchema \| None` DEFAULT: `None`
`feature_view`	Optionally a feature view object returned by querying the feature store. If the feature view is not provided, the model will not have access to provenance. DEFAULT: `None`
`training_dataset_version`	Optionally a training dataset version. If training dataset version is not provided, but the feature view is provided, the training dataset version used will be the last accessed training dataset of the feature view, within the code/notebook that reads the feature view and training dataset and then creates the model. TYPE: `int \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`. The model metadata object.

hsml.python.signature.create_model #

create_model(
    name: str,
    version: int | None = None,
    metrics: dict | None = None,
    description: str | None = None,
    input_example: pandas.DataFrame
    | pandas.Series
    | numpy.ndarray
    | list
    | None = None,
    model_schema: ModelSchema | None = None,
    feature_view=None,
    training_dataset_version: int | None = 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.

PARAMETER	DESCRIPTION
`name`	Name of the model to create. TYPE: `str`
`version`	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. TYPE: `int \| None` DEFAULT: `None`
`metrics`	Optionally a dictionary with model evaluation metrics (e.g., accuracy, MAE) TYPE: `dict \| None` DEFAULT: `None`
`description`	Optionally a string describing the model, defaults to empty string `""`. TYPE: `str \| None` DEFAULT: `None`
`input_example`	Optionally an input example that represents a single input for the model, defaults to `None`. TYPE: `pandas.DataFrame \| pandas.Series \| numpy.ndarray \| list \| None` DEFAULT: `None`
`model_schema`	Optionally a model schema for the model inputs and/or outputs. TYPE: `ModelSchema \| None` DEFAULT: `None`
`feature_view`	Optionally a feature view object returned by querying the feature store. If the feature view is not provided, the model will not have access to provenance. DEFAULT: `None`
`training_dataset_version`	Optionally a training dataset version. If training dataset version is not provided, but the feature view is provided, the training dataset version used will be the last accessed training dataset of the feature view, within the code/notebook that reads the feature view and training dataset and then creates the model. TYPE: `int \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`. The model metadata object.

hsml.llm.signature.create_model #

create_model(
    name: str,
    version: int | None = None,
    metrics: dict | None = None,
    description: str | None = None,
    input_example: pandas.DataFrame
    | pandas.Series
    | numpy.ndarray
    | list
    | None = None,
    model_schema: ModelSchema | None = None,
    feature_view=None,
    training_dataset_version: int | None = None,
)

Create an LLM 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.

PARAMETER	DESCRIPTION
`name`	Name of the model to create. TYPE: `str`
`version`	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. TYPE: `int \| None` DEFAULT: `None`
`metrics`	Optionally a dictionary with model evaluation metrics (e.g., accuracy, MAE) TYPE: `dict \| None` DEFAULT: `None`
`description`	Optionally a string describing the model, defaults to empty string `""`. TYPE: `str \| None` DEFAULT: `None`
`input_example`	Optionally an input example that represents a single input for the model, defaults to `None`. TYPE: `pandas.DataFrame \| pandas.Series \| numpy.ndarray \| list \| None` DEFAULT: `None`
`model_schema`	Optionally a model schema for the model inputs and/or outputs. TYPE: `ModelSchema \| None` DEFAULT: `None`
`feature_view`	Optionally a feature view object returned by querying the feature store. If the feature view is not provided, the model will not have access to provenance. DEFAULT: `None`
`training_dataset_version`	Optionally a training dataset version. If training dataset version is not provided, but the feature view is provided, the training dataset version used will be the last accessed training dataset of the feature view, within the code/notebook that reads the feature view and training dataset and then creates the model. TYPE: `int \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`. The model metadata object.

Model #

NOT_FOUND_ERROR_CODE `class-attribute` `instance-attribute` #

NOT_FOUND_ERROR_CODE = 360000

Metadata object representing a model in the Model Registry.

created `property` `writable` #

created

Creation date of the model.

creator `property` `writable` #

creator

Creator of the model.

description `property` `writable` #

description

Description of the model.

environment `property` `writable` #

environment

Input example of the model.

framework `property` `writable` #

framework

Framework of the model.

id `property` `writable` #

id

Id of the model.

input_example `property` `writable` #

input_example

input_example of the model.

model_files_path `property` #

model_files_path

Path of the model files including version and files folder.

Resolves to /Projects/{project_name}/Models/{name}/{version}/Files.

model_path `property` #

model_path

Path of the model with version folder omitted.

Resolves to /Projects/{project_name}/Models/{name}.

model_registry_id `property` `writable` #

model_registry_id

model_registry_id of the model.

model_schema `property` `writable` #

model_schema

Model schema of the model.

name `property` `writable` #

name

Name of the model.

program `property` `writable` #

program

Executable used to export the model.

project_name `property` `writable` #

project_name

project_name of the model.

shared_registry_project_name `property` `writable` #

shared_registry_project_name

shared_registry_project_name of the model.

training_metrics `property` `writable` #

training_metrics

Training metrics of the model.

user `property` `writable` #

user

User of the model.

version `property` `writable` #

version

Version of the model.

version_path `property` #

version_path

Path of the model including version folder.

Resolves to /Projects/{project_name}/Models/{name}/{version}.

add_tag #

add_tag(name: str, value: str | dict)

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.

PARAMETER	DESCRIPTION
`name`	Name of the tag to be added. TYPE: `str`
`value`	Value of the tag to be added. TYPE: `str \| dict`

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to add the tag.

delete #

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	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	In case the backend encounters an issue

delete_tag #

delete_tag(name: str)

Delete a tag attached to a model.

PARAMETER	DESCRIPTION
`name`	Name of the tag to be removed. TYPE: `str`

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to delete the tag.

deploy #

deploy(
    name: str | None = None,
    description: str | None = None,
    artifact_version: str | None = None,
    serving_tool: str | None = None,
    script_file: str | None = None,
    config_file: str | None = None,
    resources: PredictorResources | dict | None = None,
    inference_logger: InferenceLogger | dict | None = None,
    inference_batcher: InferenceBatcher
    | dict
    | None = None,
    transformer: Transformer | dict | None = None,
    api_protocol: str | None = IE.API_PROTOCOL_REST,
    environment: str | None = None,
) -> deployment.Deployment

Deploy the model.

Example

import hopsworks

project = hopsworks.login()

# get Hopsworks Model Registry handle
mr = project.get_model_registry()

# retrieve the trained model you want to deploy
my_model = mr.get_model("my_model", version=1)

my_deployment = my_model.deploy()

Parameters: name: Name of the deployment. description: Description of the deployment. artifact_version: (Deprecated) 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. serving_tool: Serving tool used to deploy the model server. script_file: Path to a custom predictor script implementing the Predict class. config_file: Model server configuration file to be passed to the model deployment. It can be accessed via CONFIG_FILE_PATH environment variable from a predictor or transformer script. For LLM deployments without a predictor script, this file is used to configure the vLLM engine. resources: Resources to be allocated for the predictor. inference_logger: Inference logger configuration. inference_batcher: Inference batcher configuration. transformer: Transformer to be deployed together with the predictor. api_protocol: API protocol to be enabled in the deployment (i.e., 'REST' or 'GRPC'). Defaults to 'REST'. environment: The inference environment to use.

RETURNS	DESCRIPTION
`deployment.Deployment`	`Deployment`: The deployment metadata object of a new or existing deployment.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	In case the backend encounters an issue

download #

download(local_path=None) -> str

Download the model files.

PARAMETER	DESCRIPTION
`local_path`	path where to download the model files in the local filesystem DEFAULT: `None`

Returns: str: Absolute path to local folder containing the model files.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	In case the backend encounters an issue

get_feature_view #

get_feature_view(init: bool = True, online: bool = False)

Get the parent feature view of this model, based on explicit provenance.

Only accessible, usable feature view objects are returned. Otherwise an Exception is raised. For more details, call the base method - get_feature_view_provenance

Parameters: init: By default this is set to True. If you require a more complex initialization of the feature view for online or batch scenarios, you should set init to False to retrieve a non initialized feature view and then call init_batch_scoring() or init_serving() with the required parameters. online: By default this is set to False and the initialization for batch scoring is considered the default scenario. If you set online to True, the online scenario is enabled and the init_serving() method is called. When inside a deployment, the only available scenario is the online one, thus the parameter is ignored and init_serving is always called (if init is set to True). If you want to override this behaviour, you should set init to False and proceed with a custom initialization.

RETURNS	DESCRIPTION
	`FeatureView`: Feature View Object or `None` if it does not exist.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to retrieve the feature view.

get_feature_view_provenance #

get_feature_view_provenance() -> explicit_provenance.Links

Get the parent feature view of this model, based on explicit provenance.

This feature view can be accessible, deleted or inaccessible. For deleted and inaccessible feature views, only a minimal information is returned.

RETURNS	DESCRIPTION
`explicit_provenance.Links`	`Links`: Object containing the section of provenance graph requested or `None` if it does not exist.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to retrieve the feature view provenance.

get_tag #

get_tag(name: str) -> str | None

Get the tags of a model.

PARAMETER	DESCRIPTION
`name`	Name of the tag to get. TYPE: `str`

RETURNS	DESCRIPTION
`str \| None`	tag value or `None` if it does not exist.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to retrieve the tag.

get_tags #

get_tags() -> dict[str, tag.Tag]

Retrieves all tags attached to a model.

RETURNS	DESCRIPTION
`dict[str, tag.Tag]`	`Dict[str, obj]` of tags.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	In case of a server error.

get_training_dataset_provenance #

get_training_dataset_provenance() -> (
    explicit_provenance.Links
)

Get the parent training dataset of this model, based on explicit provenance.

This training dataset can be accessible, deleted or inaccessible. For deleted and inaccessible training datasets, only a minimal information is returned.

RETURNS	DESCRIPTION
`explicit_provenance.Links`	`Links`: Object containing the section of provenance graph requested or `None` if it does not exist.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	in case the backend fails to retrieve the training dataset provenance.

get_url #

get_url()

Get url to the model in Hopsworks.

save #

save(
    model_path,
    await_registration=480,
    keep_original_files=False,
    upload_configuration: dict[str, Any] | None = None,
)

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

PARAMETER	DESCRIPTION
`model_path`	Local or remote (Hopsworks file system) path to the folder where the model files are located, or path to a specific model file.
`await_registration`	Awaiting time for the model to be registered in Hopsworks. DEFAULT: `480`
`keep_original_files`	If the model files are located in hopsfs, whether to move or copy those files into the Models dataset. Default is False (i.e., model files will be moved) DEFAULT: `False`
`upload_configuration`	When saving a model from outside Hopsworks, the model is uploaded to the model registry using the REST APIs. Each model artifact is divided into chunks and each chunk uploaded independently. This parameter can be used to control the upload chunk size, the parallelism and the number of retries. `upload_configuration` can contain the following keys: * key `chunk_size`: size of each chunk in megabytes. Default 10. * key `simultaneous_uploads`: number of chunks to upload in parallel. Default 3. * key `max_chunk_retries`: number of times to retry the upload of a chunk in case of failure. Default 1. TYPE: `dict[str, Any] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
	`Model`: The model metadata object.

RAISES	DESCRIPTION
`hopsworks.client.exceptions.RestAPIError`	In case the backend encounters an issue

set_tag #

set_tag(name: str, value: str | dict)

Deprecated: Use add_tag instead.

Model#

hsml.tensorflow.signature.create_model #

hsml.torch.signature.create_model #

hsml.sklearn.signature.create_model #

hsml.python.signature.create_model #

hsml.llm.signature.create_model #

Model #

NOT_FOUND_ERROR_CODE class-attribute instance-attribute #

created property writable #

creator property writable #

description property writable #

environment property writable #

framework property writable #

id property writable #

input_example property writable #

model_files_path property #

model_path property #

model_registry_id property writable #

model_schema property writable #

name property writable #

program property writable #

project_name property writable #

shared_registry_project_name property writable #

training_metrics property writable #

user property writable #

version property writable #

version_path property #

add_tag #

delete #

delete_tag #

deploy #

download #

get_feature_view #

get_feature_view_provenance #

get_tag #

get_tags #

get_training_dataset_provenance #

get_url #

save #

set_tag #

NOT_FOUND_ERROR_CODE `class-attribute` `instance-attribute` #

created `property` `writable` #

creator `property` `writable` #

description `property` `writable` #

environment `property` `writable` #

framework `property` `writable` #

id `property` `writable` #

input_example `property` `writable` #

model_files_path `property` #

model_path `property` #

model_registry_id `property` `writable` #

model_schema `property` `writable` #

name `property` `writable` #

program `property` `writable` #

project_name `property` `writable` #

shared_registry_project_name `property` `writable` #

training_metrics `property` `writable` #

user `property` `writable` #

version `property` `writable` #

version_path `property` #