Model Class

Represents the result of machine learning training.

A model is the result of a Azure Machine learning training Run or some other model training process outside of Azure. Regardless of how the model is produced, it can be registered in a workspace, where it is represented by a name and a version. With the Model class, you can package models for use with Docker and deploy them as a real-time endpoint that can be used for inference requests.

For an end-to-end tutorial showing how models are created, managed, and consumed, see Train image classification model with MNIST data and scikit-learn using Azure Machine Learning.

Inheritance: builtins.object

Model

Constructor

Model(workspace, name=None, id=None, tags=None, properties=None, version=None, run_id=None, model_framework=None, expand=True, **kwargs)

Parameters

workspace: Workspace

The workspace object containing the model to retrieve.

name: str

The name of the model to retrieve. The latest model with the specified name is returned, if it exists.

id: str

The ID of the model to retrieve. The model with the specified ID is returned, if it exists.

tags: list

An optional list of tags used to filter returned results. Results are filtered based on the provided list, searching by either 'key' or '[key, value]'. Ex. ['key', ['key2', 'key2 value']]

properties: list

An optional list of properties used to filter returned results. Results are filtered based on the provided list, searching by either 'key' or '[key, value]'. Ex. ['key', ['key2', 'key2 value']]

version: int

The model version to return. When provided along with the name parameter, the specific version of the specified named model is returned, if it exists. If version is omitted, the lastest version of the model is returned.

run_id: str

Optional ID used to filter returned results.

model_framework: str

Optional framework name used to filter returned results. If specified, results are returned for the models matching the specified framework. See Framework for allowed values.

Remarks

The Model constructor is used to retrieve a cloud representation of a Model object associated with the specified workspace. At least the name or ID must be provided to retrieve models, but there are also other options for filtering including by tags, properties, version, run ID, and framework.


   from azureml.core.model import Model
   model = Model(ws, 'my_model_name')

The following sample shows how to fetch specific version of a model.


   from azureml.core.model import Model
   model = Model(ws, 'my_model_name', version=1)

Registering a model creates a logical container for the one or more files that make up your model. In addition to the content of the model file itself, a registered model also stores model metadata, including model description, tags, and framework information, that is useful when managing and deploying the model in your workspace. For example, with tags you can categorize your models and apply filters when listing models in your workspace. After registration, you can then download or deploy the registered model and receive all the files and metadata that were registered.

The following sample shows how to register a model specifying tags and a description.


   from azureml.core.model import Model

   model = Model.register(model_path="sklearn_regression_model.pkl",
                          model_name="sklearn_regression_model",
                          tags={'area': "diabetes", 'type': "regression"},
                          description="Ridge regression model to predict diabetes",
                          workspace=ws)

Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb

The following sample shows how to register a model specifying framework, input and output datasets, and resource configuration.


   import sklearn

   from azureml.core import Model
   from azureml.core.resource_configuration import ResourceConfiguration


   model = Model.register(workspace=ws,
                          model_name='my-sklearn-model',                # Name of the registered model in your workspace.
                          model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.
                          model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.
                          model_framework_version=sklearn.__version__,  # Version of scikit-learn used to create the model.
                          sample_input_dataset=input_dataset,
                          sample_output_dataset=output_dataset,
                          resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),
                          description='Ridge regression model to predict diabetes progression.',
                          tags={'area': 'diabetes', 'type': 'regression'})

   print('Name:', model.name)
   print('Version:', model.version)

Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb

The Variables section lists attributes of a local representation of the cloud Model object. These variables should be considered read-only. Changing their values will not be reflected in the corresponding cloud object.

Variables

created_by: dict

The user that created the Model.

created_time: datetime

When the Model was created.

azureml.core.Model.description

A description of the Model object.

azureml.core.Model.id

The Model ID. This takes the form of <model name>:<model version>.

mime_type: str

The Model mime type.

azureml.core.Model.name

The name of the Model.

model_framework: str

The framework of the Model.

model_framework_version: str

The framework version of the Model.

azureml.core.Model.tags

A dictionary of tags for the Model object.

azureml.core.Model.properties

Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.

unpack: bool

Whether or not the Model needs to be unpacked (untarred) when pulled to a local context.

url: str

The url location of the Model.

azureml.core.Model.version

The version of the Model.

azureml.core.Model.workspace

The Workspace containing the Model.

azureml.core.Model.experiment_name

The name of the Experiment that created the Model.

azureml.core.Model.run_id

The ID of the Run that created the Model.

parent_id: str

The ID of the parent Model of the Model.

derived_model_ids: list[str]

A list of Model IDs that have been derived from this Model.

resource_configuration: ResourceConfiguration

The ResourceConfiguration for this Model. Used for profiling.

Methods

add_dataset_references	Associate the provided datasets with this Model.
add_properties	Add key value pairs to the properties dictionary of this model.
add_tags	Add key value pairs to the tags dictionary of this model.
delete	Delete this model from its associated workspace.
deploy	Deploy a Webservice from zero or more Model objects. The resulting Webservice is a real-time endpoint that can be used for inference requests. The Model `deploy` function is similar to the `deploy` function of the Webservice class, but does not register the models. Use the Model `deploy` function if you have model objects that are already registered.
deserialize	Convert a JSON object into a model object. Conversion fails if the specified workspace is not the workspace the model is registered with.
download	Download the model to target directory of the local file system.
get_model_path	Return the path to model. The function will search for the model in the following locations. If `version` is None: Download from remote to cache (if workspace is provided) Load from cache azureml-models/$MODEL_NAME/$LATEST_VERSION/ ./$MODEL_NAME If `version` is not None: Load from cache azureml-models/$MODEL_NAME/$SPECIFIED_VERSION/ Download from remote to cache (if workspace is provided)
get_sas_urls	Return a dictionary of key-value pairs containing filenames and corresponding SAS URLs.
list	Retrieve a list of all models associated with the provided workspace, with optional filters.
package	Create a model package in the form of a Docker image or Dockerfile build context.
print_configuration	Print the user configuration.
profile	Profiles the model to get resource requirement recommendations. This is a long running operation that can take up to 25 min depending on the size of the dataset.
register	Register a model with the provided workspace.
remove_tags	Remove the specified keys from tags dictionary of this model.
serialize	Convert this Model into a json serialized dictionary.
update	Perform an in-place update of the model. Existing values of specified parameters are replaced.
update_tags_properties	Perform an update of the tags and properties of the model.

add_dataset_references

Associate the provided datasets with this Model.

add_dataset_references(datasets)

Parameters

datasets: list[tuple(<xref:str : >(Dataset or DatasetSnapshot))]

A list of tuples representing a pairing of dataset purpose to Dataset object.

add_properties

Add key value pairs to the properties dictionary of this model.

add_properties(properties)

Parameters

properties: dict(<xref:str : str>)

The dictionary of properties to add.

add_tags

Add key value pairs to the tags dictionary of this model.

add_tags(tags)

Parameters

tags: dict(<xref:{str : str}>)

The dictionary of tags to add.

Exceptions

WebserviceException

delete

Delete this model from its associated workspace.

delete()

deploy

Deploy a Webservice from zero or more Model objects.

The resulting Webservice is a real-time endpoint that can be used for inference requests. The Model deploy function is similar to the deploy function of the Webservice class, but does not register the models. Use the Model deploy function if you have model objects that are already registered.

deploy(workspace, name, models, inference_config=None, deployment_config=None, deployment_target=None, overwrite=False, show_output=False)

Parameters

workspace: Workspace

A Workspace object to associate the Webservice with.

name: str

The name to give the deployed service. Must be unique to the workspace, only consist of lowercase letters, numbers, or dashes, start with a letter, and be between 3 and 32 characters long.

models: list[Model]

A list of model objects. Can be an empty list.

inference_config: InferenceConfig

default value: None

An InferenceConfig object used to determine required model properties.

deployment_config: WebserviceDeploymentConfiguration

default value: None

A WebserviceDeploymentConfiguration used to configure the webservice. If one is not provided, an empty configuration object will be used based on the desired target.

deployment_target: ComputeTarget

default value: None

A ComputeTarget to deploy the Webservice to. As Azure Container Instances has no associated ComputeTarget, leave this parameter as None to deploy to Azure Container Instances.

overwrite: bool

default value: False

Indicates whether to overwrite the existing service if a service with the specified name already exists.

show_output: bool

default value: False

Indicates whether to display the progress of service deployment.

Returns

A Webservice object corresponding to the deployed webservice.

Return type

Webservice

Exceptions

WebserviceException

deserialize

Convert a JSON object into a model object.

Conversion fails if the specified workspace is not the workspace the model is registered with.

deserialize(workspace, model_payload)

Parameters

workspace: Workspace

The workspace object the model is registered with.

model_payload: dict

A JSON object to convert to a Model object.

Returns

The Model representation of the provided JSON object.

Return type

Model

download

Download the model to target directory of the local file system.

download(target_dir='.', exist_ok=False, exists_ok=None)

Parameters

target_dir: str

default value: .

The path to a directory in which to download the model. Defaults to "."

exist_ok: bool

default value: False

Indicates whether to replace downloaded dir/files if they exist. Defaults to False.

exists_ok: bool

default value: None

DEPRECATED. Use exist_ok.

Returns

The path to file or folder of the model.

Return type

str

get_model_path

Return the path to model.

The function will search for the model in the following locations.

If version is None:

Download from remote to cache (if workspace is provided)
Load from cache azureml-models/$MODEL_NAME/$LATEST_VERSION/
./$MODEL_NAME

If version is not None:

Load from cache azureml-models/$MODEL_NAME/$SPECIFIED_VERSION/
Download from remote to cache (if workspace is provided)

get_model_path(model_name, version=None, _workspace=None)

Parameters

model_name: str

The name of the model to retrieve.

version: int

default value: None

The version of the model to retrieve. Defaults to the latest version.

_workspace: Workspace

default value: None

The workspace to retrieve a model from. Can't use remotely. If not specified only local cache is searched.

Returns

The path on disk to the model.

Return type

str

Exceptions

ModelNotFoundException

get_sas_urls

Return a dictionary of key-value pairs containing filenames and corresponding SAS URLs.

get_sas_urls()

Returns

Dictionary of key-value pairs containing filenames and corresponding SAS URLs

Return type

dict

list

Retrieve a list of all models associated with the provided workspace, with optional filters.

list(workspace, name=None, tags=None, properties=None, run_id=None, latest=False, dataset_id=None, expand=True, page_count=255, model_framework=None)

Parameters

workspace: Workspace

The workspace object to retrieve models from.

name: str

default value: None

If provided, will only return models with the specified name, if any.

tags: list

default value: None

Will filter based on the provided list, by either 'key' or '[key, value]'. Ex. ['key', ['key2', 'key2 value']]

properties: list

default value: None

Will filter based on the provided list, by either 'key' or '[key, value]'. Ex. ['key', ['key2', 'key2 value']]

run_id: str

default value: None

Will filter based on the provided run ID.

latest: bool

default value: False

If true, will only return models with the latest version.

dataset_id: str

default value: None

Will filter based on the provided dataset ID.

expand: bool

default value: True

If true, will return models with all subproperties populated e.g. run, dataset, and experiment. Setting this to false should speed up list() method completion in case of many models.

page_count: int

default value: 255

The number of items to retrieve in a page. Currently support values up to 255. Defaults to 255.

model_framework: str

default value: None

If provided, will only return models with the specified framework, if any.

Returns

A list of models, optionally filtered.

Return type

list[Model]

Exceptions

WebserviceException

package

Create a model package in the form of a Docker image or Dockerfile build context.

package(workspace, models, inference_config=None, generate_dockerfile=False, image_name=None, image_label=None)

Parameters

workspace: Workspace

The workspace in which to create the package.

models: list[Model]

A list of Model objects to include in the package. Can be an empty list.

inference_config: InferenceConfig

default value: None

An InferenceConfig object to configure the operation of the models. This must include an Environment object.

generate_dockerfile: bool

default value: False

Whether to create a Dockerfile that can be run locally instead of building an image.

image_name: str

default value: None

When building an image, the name for the resulting image.

image_label: str

default value: None

When building an image, the label for the resulting image.

Returns

A ModelPackage object.

Return type

ModelPackage

print_configuration

Print the user configuration.

print_configuration(models, inference_config, deployment_config, deployment_target)

Parameters

models: list[Model]

A list of model objects. Can be an empty list.

inference_config: InferenceConfig

An InferenceConfig object used to determine required model properties.

deployment_config: WebserviceDeploymentConfiguration

A WebserviceDeploymentConfiguration used to configure the webservice.

deployment_target: ComputeTarget

A ComputeTarget to deploy the Webservice to.

profile

Profiles the model to get resource requirement recommendations.

This is a long running operation that can take up to 25 min depending on the size of the dataset.

profile(workspace, profile_name, models, inference_config, input_dataset, cpu=None, memory_in_gb=None, description=None)

Parameters

workspace: Workspace

A Workspace object in which to profile the model.

profile_name: str

The name of the profiling run.

models: list[Model]

A list of model objects. Can be an empty list.

inference_config: InferenceConfig

An InferenceConfig object used to determine required model properties.

input_dataset: Dataset

The input dataset for profiling. Input dataset should have a single column and sample inputs should be in string format.

cpu: float

default value: None

The number of cpu cores to use on the largest test instance. Currently support values up to 3.5.

memory_in_gb: float

default value: None

The amount of memory (in GB) to use on the largest test instance. Can be a decimal. Currently support values up to 15.0.

description: str

default value: None

Description to be associated with the profiling run.

Return type

ModelProfile

register

register(workspace, model_path, model_name, tags=None, properties=None, description=None, datasets=None, model_framework=None, model_framework_version=None, child_paths=None, sample_input_dataset=None, sample_output_dataset=None, resource_configuration=None)

Parameters

workspace: Workspace

The workspace to register the model with.

model_path: str

The path on the local file system where the model assets are located. This can be a direct pointer to a single file or folder. If pointing to a folder, the child_paths parameter can be used to specify individual files to bundle together as the Model object, as opposed to using the entire contents of the folder.

model_name: str

The name to register the model with.

tags: dict(<xref:{str : str}>)

default value: None

An optional dictionary of key value tags to assign to the model.

properties: dict(<xref:{str : str}>)

default value: None

An optional dictionary of key value properties to assign to the model. These properties can't be changed after model creation, however new key value pairs can be added.

description: str

default value: None

A text description of the model.

datasets: list[(str, AbstractDataset)]

default value: None

A list of tuples where the first element describes the dataset-model relationship and the second element is the dataset.

model_framework: str

default value: None

The framework of the registered model. Using the system-supported constants from the Framework class allows for simplified deployment for some popular frameworks.

model_framework_version: str

default value: None

The framework version of the registered model.

child_paths: list[str]

default value: None

If provided in conjunction with a model_path to a folder, only the specified files will be bundled into the Model object.

sample_input_dataset: AbstractDataset

default value: None

Sample input dataset for the registered model.

sample_output_dataset: AbstractDataset

default value: None

Sample output dataset for the registered model.

resource_configuration: ResourceConfiguration

default value: None

A resource configuration to run the registered model.

Returns

The registered model object.

Return type

Model

Remarks

In addition to the content of the model file itself, a registered model also stores model metadata, including model description, tags, and framework information, that is useful when managing and deploying the model in your workspace. For example, with tags you can categorize your models and apply filters when listing models in your workspace.

The following sample shows how to register a model specifying tags and a description.


   from azureml.core.model import Model

   model = Model.register(model_path="sklearn_regression_model.pkl",
                          model_name="sklearn_regression_model",
                          tags={'area': "diabetes", 'type': "regression"},
                          description="Ridge regression model to predict diabetes",
                          workspace=ws)

Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb

If you have a model that was produced as a result of an experiment run, you can register it from a run object directly without downloading it to a local file first. In order to do that use the register_model method as documented in the Run class.

remove_tags

Remove the specified keys from tags dictionary of this model.

remove_tags(tags)

Parameters

tags: list[str]

The list of keys to remove

serialize

Convert this Model into a json serialized dictionary.

serialize()

Returns

The json representation of this Model

Return type

dict

update

Perform an in-place update of the model.

Existing values of specified parameters are replaced.

update(tags=None, description=None, sample_input_dataset=None, sample_output_dataset=None, resource_configuration=None)

Parameters

tags: dict(<xref:{str : str}>)

default value: None

A dictionary of tags to update the model with. These tags replace existing tags for the model.

description: str

default value: None

The new description to use for the model. This name replaces the existing name.

sample_input_dataset: AbstractDataset

default value: None

The sample input dataset to use for the registered model. This sample input dataset replaces the existing dataset.

sample_output_dataset: AbstractDataset

default value: None

The sample output dataset to use for the registered model. This sample output dataset replaces the existing dataset.

resource_configuration: ResourceConfiguration

default value: None

The resource configuration to use to run the registered model.

Exceptions

WebserviceException

update_tags_properties

Perform an update of the tags and properties of the model.

update_tags_properties(add_tags=None, remove_tags=None, add_properties=None)

Parameters

add_tags: dict(<xref:{str : str}>)

default value: None

A dictionary of tags to add.

remove_tags: list[str]

default value: None

A list of tag names to remove.

add_properties: dict(<xref:{str : str}>)

default value: None

A dictionary of properties to add.

Exceptions

WebserviceException

Model Class

Constructor

Parameters

Remarks

Variables

Methods

add_dataset_references

Parameters

add_properties

Parameters

add_tags

Parameters

Exceptions

delete

deploy

Parameters

Returns

Return type

Exceptions

deserialize

Parameters

Returns

Return type

download

Parameters

Returns

Return type

get_model_path

Parameters

Returns

Return type

Exceptions

get_sas_urls

Returns

Return type

list

Parameters

Returns

Return type

Exceptions

package

Parameters

Returns

Return type

print_configuration

Parameters

profile

Parameters

Return type

register

Parameters

Returns

Return type

Remarks

remove_tags

Parameters

serialize

Returns

Return type

update

Parameters

Exceptions

update_tags_properties

Parameters

Exceptions

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