azure.ai.ml package¶

class azure.ai.ml.AmlToken(**kwargs)[source]¶

AML Token identity configuration.

All required parameters must be populated in order to send to Azure.

Variables: identity_type (str or IdentityConfigurationType) – Required. [Required] Specifies the type of identity framework.Constant filled by server. Possible values include: “Managed”, “AMLToken”, “UserIdentity”.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

class azure.ai.ml.Input(*, type: str = "'uri_folder'", path: str = 'None', mode: str = "'ro_mount'", optional: bool = 'None', description: str = 'None', **kwargs)[source]¶

class azure.ai.ml.Input(*, type: str = "'number'", default: float = 'None', min: float = 'None', max: float = 'None', optional: bool = 'None', description: str = 'None', **kwargs)

class azure.ai.ml.Input(*, type: str = "'integer'", default: int = 'None', min: int = 'None', max: int = 'None', optional: bool = 'None', description: str = 'None', **kwargs)

class azure.ai.ml.Input(*, type: str = "'string'", default: str = 'None', optional: bool = 'None', description: str = 'None', **kwargs)

class azure.ai.ml.Input(*, type: str = "'boolean'", default: bool = 'None', optional: bool = 'None', description: str = 'None', **kwargs)

Define an input of a Component or Job.

Default to be a uri_folder Input.

Parameters

type (str) – The type of the data input. Possible values include: ‘uri_folder’, ‘uri_file’, ‘mltable’, ‘mlflow_model’, ‘custom_model’, ‘integer’, ‘number’, ‘string’, ‘boolean’
path (str) – The path to which the input is pointing. Could be pointing to local data, cloud data, a registered name, etc.
mode (str) – The mode of the data input. Possible values are: ‘ro_mount’: Read-only mount the data, ‘download’: Download the data to the compute target, ‘direct’: Pass in the URI as a string
default (Union[str, integer, float, bool]) – The default value of this input. When a default is set, the input will be optional
min (Union[integer, float]) – The min value – if a smaller value is passed to a job, the job execution will fail
max (Union[integer, float]) – The max value – if a larger value is passed to a job, the job execution will fail
optional (bool) – Determine if this input is optional
description (str) – Description of the input

get(key: Any, default: Optional[Any] = None) → Any¶

has_key(k: Any) → bool ¶

items() → list ¶

keys() → list ¶

update(*args: Any, **kwargs: Any) → None ¶

values() → list ¶

class azure.ai.ml.MLClient(credential: azure.identity._credentials.chained.ChainedTokenCredential, subscription_id: str, resource_group_name: str, workspace_name: Optional[str] = None, **kwargs)[source]¶

A client class to interact with Azure ML services.

Use this client to manage Azure ML resources, e.g. workspaces, jobs, models and so on.

Initiate Azure ML client

Parameters

credential (ChainedTokenCredential) – Credential to use for authentication.
subscription_id (str) – Azure subscription ID.
resource_group_name (str) – Azure resource group.
workspace_name (str, optional) – Workspace to use in the client, optional for non workspace dependent operations., defaults to None

begin_create_or_update(entity: Union[azure.ai.ml.entities._workspace.workspace.Workspace, azure.ai.ml.entities._compute.compute.Compute, azure.ai.ml.entities._deployment.online_deployment.OnlineDeployment, azure.ai.ml.entities._endpoint.online_endpoint.OnlineEndpoint, azure.ai.ml.entities._deployment.batch_deployment.BatchDeployment, azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint], **kwargs) → azure.core.polling._poller.LROPoller[source]¶

Creates or updates an Azure ML resource asynchronously.

Parameters: entity (Union[azure.ai.ml.entities.Workspace, azure.ai.ml.entities.Compute, azure.ai.ml.entities.OnlineDeployment, azure.ai.ml.entities.OnlineEndpoint, azure.ai.ml.entities.BatchDeployment, azure.ai.ml.entities.BatchEndpoint]) – The resource to create or update.
Returns: The resource after create/update operation
Return type: Optional[Union[azure.ai.ml.entities.Workspace, azure.ai.ml.entities.Compute, azure.ai.ml.entities.OnlineDeployment, azure.ai.ml.entities.OnlineEndpoint, azure.ai.ml.entities.BatchDeployment, azure.ai.ml.entities.BatchEndpoint]]

create_or_update(entity: Union[azure.ai.ml.entities._job.job.Job, azure.ai.ml.entities._builders.base_node.BaseNode, azure.ai.ml.entities._assets._artifacts.model.Model, azure.ai.ml.entities._assets.environment.Environment, azure.ai.ml.entities._component.component.Component, azure.ai.ml.entities._datastore.datastore.Datastore], **kwargs) → Union[azure.ai.ml.entities._job.job.Job, azure.ai.ml.entities._assets._artifacts.model.Model, azure.ai.ml.entities._assets.environment.Environment, azure.ai.ml.entities._component.component.Component, azure.ai.ml.entities._datastore.datastore.Datastore][source]¶

Creates or updates an Azure ML resource.

Parameters: entity (Union[azure.ai.ml.entities.Job, azure.ai.ml.entities.Model, azure.ai.ml.entities.Environment, azure.ai.ml.entities.Component, azure.ai.ml.entities.Datastore]) – The resource to create or update.
Returns: The created or updated resource
Return type: Union[azure.ai.ml.entities.Job, azure.ai.ml.entities.Model, azure.ai.ml.entities.Environment, azure.ai.ml.entities.Component, azure.ai.ml.entities.Datastore]

classmethod from_config(credential: azure.identity._credentials.chained.ChainedTokenCredential, path: Optional[Union[os.PathLike, str]] = None, _file_name=None, **kwargs) → azure.ai.ml._ml_client.MLClient[source]¶

Return a workspace object from an existing Azure Machine Learning Workspace.

Reads workspace configuration from a file. Throws an exception if the config file can’t be found.

The method provides a simple way to reuse the same workspace across multiple Python notebooks or projects. Users can save the workspace Azure Resource Manager (ARM) properties using the [workspace.write_config](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) method, and use this method to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties.

Parameters

credential (azureml.core.authentication.ChainedTokenCredential) – The credential object for the workspace.
path (str) – The path to the config file or starting directory to search. The parameter defaults to starting the search in the current directory.
_file_name (str) – Allows overriding the config file name to search for when path is a directory path.
kwargs (dict) – A dictionary of additional configuration parameters.

Returns

The workspace object for an existing Azure ML Workspace.

Return type

MLClient

property batch_deployments¶

A collection of batch deployment related operations

Returns: Batch Deployment operations
Return type: BatchDeploymentOperations

property batch_endpoints¶

A collection of batch endpoint related operations

Returns: Batch Endpoint operations
Return type: BatchEndpointOperations

property components¶

A collection of component related operations

Returns: Component operations
Return type: ComponentOperations

property compute¶

A collection of compute related operations

Returns: Compute operations
Return type: ComputeOperations

property connections¶

A collection of workspace connection related operations

Returns: Workspace Connections operations
Return type: WorkspaceConnectionsOperations

property data¶

A collection of data related operations

Returns: Data operations
Return type: DataOperations

property datasets¶

A collection of dataset related operations

Returns: Dataset operations
Return type: DatasetOperations

property datastores¶

A collection of datastore related operations

Returns: Datastore operations
Return type: DatastoreOperations

property environments¶

A collection of environment related operations

Returns: Environment operations
Return type: EnvironmentOperations

property jobs¶

A collection of job related operations

Returns: Job operations
Return type: JObOperations

property models¶

A collection of model related operations

Returns: Model operations
Return type: ModelOperations

property online_deployments¶

A collection of online deployment related operations

Returns: Online Deployment operations
Return type: OnlineDeploymentOperations

property online_endpoints¶

A collection of online endpoint related operations

Returns: Online Endpoint operations
Return type: OnlineEndpointOperations

property workspace_name¶

The workspace where workspace dependent operations will be executed in.

Returns: Default workspace name
Return type: Optional[str]

property workspaces¶

A collection of workspace related operations

Returns: Workspace operations
Return type: WorkspaceOperations

class azure.ai.ml.ManagedIdentity(*, client_id: Optional[str] = None, object_id: Optional[str] = None, resource_id: Optional[str] = None, **kwargs)[source]¶

Managed identity configuration.

All required parameters must be populated in order to send to Azure.

Variables

identity_type (str or IdentityConfigurationType) – Required. [Required] Specifies the type of identity framework.Constant filled by server. Possible values include: “Managed”, “AMLToken”, “UserIdentity”.
client_id (str) – Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
object_id (str) – Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
resource_id (str) – Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.

Keyword Arguments

client_id (str) – Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.
object_id (str) – Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.
resource_id (str) – Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

class azure.ai.ml.MpiDistribution(*, process_count_per_instance: Optional[int] = None, **kwargs)[source]¶

MPI distribution configuration.

Parameters: process_count_per_instance (int) – Number of processes per MPI node.
Keyword Arguments: process_count_per_instance (int) – Number of processes per MPI node.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

type¶: A data descriptor that transforms value from snake_case to CamelCase in setter, CamelCase to snake_case in getter. When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.

class azure.ai.ml.Output(type='uri_folder', path=None, mode='rw_mount', description=None)[source]¶

class azure.ai.ml.Output(type='uri_file', path=None, mode='rw_mount', description=None)

Define an output of a Component or Job.

Parameters

type (str) – The type of the data output. Possible values include: ‘uri_folder’, ‘uri_file’, ‘mltable’, ‘mlflow_model’, ‘custom_model’, and user-defined types.
path (str) – The path to which the output is pointing. Needs to point to a cloud path.
mode (str) – The mode of the data output. Possible values are: ‘rw_mount’: Read-write mount the data, ‘upload’: Upload the data from the compute target, ‘direct’: Pass in the URI as a string
description (str) – Description of the output

get(key: Any, default: Optional[Any] = None) → Any¶

has_key(k: Any) → bool ¶

items() → list ¶

keys() → list ¶

update(*args: Any, **kwargs: Any) → None ¶

values() → list ¶

class azure.ai.ml.PyTorchDistribution(*, process_count_per_instance: Optional[int] = None, **kwargs)[source]¶

PyTorch distribution configuration.

Parameters: process_count_per_instance (int) – Number of processes per node.
Keyword Arguments: process_count_per_instance (int) – Number of processes per node.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

type¶: A data descriptor that transforms value from snake_case to CamelCase in setter, CamelCase to snake_case in getter. When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.

class azure.ai.ml.TensorFlowDistribution(*, parameter_server_count: Optional[int] = 0, worker_count: Optional[int] = None, **kwargs)[source]¶

TensorFlow distribution configuration.

Variables

parameter_server_count (int) – Number of parameter server tasks.
worker_count (int) – Number of workers. If not specified, will default to the instance count.

Keyword Arguments

parameter_server_count (int) – Number of parameter server tasks.
worker_count (int) – Number of workers. If not specified, will default to the instance count.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

type¶: A data descriptor that transforms value from snake_case to CamelCase in setter, CamelCase to snake_case in getter. When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.

class azure.ai.ml.UserIdentity(**kwargs)[source]¶

User identity configuration.

All required parameters must be populated in order to send to Azure.

Variables: identity_type (str or IdentityConfigurationType) – Required. [Required] Specifies the type of identity framework.Constant filled by server. Possible values include: “Managed”, “AMLToken”, “UserIdentity”.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)¶

Return a dict that can be JSONify using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

attribute_transformer
full_restapi_key_transformer
last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: key_transformer (function) – A key transformer function.
Returns: A dict JSON compatible object
Return type: dict

classmethod deserialize(data, content_type=None)¶

Parse a str using the RestAPI syntax and return a model.

Parameters

data (str) – A str using RestAPI structure. JSON by default.
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()¶

classmethod from_dict(data, key_extractors=None, content_type=None)¶

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

data (dict) – A dict using RestAPI structure
content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()¶

serialize(keep_readonly=False, **kwargs)¶

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters: keep_readonly (bool) – If you want to serialize the readonly attributes
Returns: A dict JSON compatible object
Return type: dict

validate()¶

Validate this model recursively and return a list of ValidationError.

Returns: A list of validation error
Return type: list

azure.ai.ml.command(*, name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, display_name: Optional[str] = None, command: Optional[str] = None, experiment_name: Optional[str] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, environment_variables: Optional[Dict] = None, distribution: Optional[Union[Dict, azure.ai.ml.entities._job.distribution.MpiDistribution, azure.ai.ml.entities._job.distribution.TensorFlowDistribution, azure.ai.ml.entities._job.distribution.PyTorchDistribution]] = None, compute: Optional[str] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, instance_count: Optional[int] = None, instance_type: Optional[str] = None, timeout: Optional[int] = None, code: Optional[Union[os.PathLike, str]] = None, identity: Optional[Union[azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.ManagedIdentity, azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.AmlToken, azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.UserIdentity]] = None, **kwargs) → azure.ai.ml.entities._builders.command.Command[source]¶

Create a Command object which can be used inside dsl.pipeline as a function and: can also be created as a standalone command job.

Parameters

name (str) – Name of the command job or component created
description (str) – a friendly description of the command
tags (Dict) – Tags to be attached to this command
properties (dict[str, str]) – The asset property dictionary.
display_name (str) – a friendly name
experiment_name (str) – Name of the experiment the job will be created under, if None is provided, default will be set to current directory name. Will be ignored as a pipeline step.
command (str) – the command string that will be run
environment (Union[str, azure.ai.ml.entities.Environment]) – the environment to use for this command
environment_variables (dict) – environment variables to set on the compute before this command is executed
distribution (Union[Dict, azure.ai.ml.MpiDistribution, azure.ai.ml.TensorFlowDistribution, azure.ai.ml.PyTorchDistribution]) – the distribution mode to use for this command
compute (str) – the name of the compute where the command job is executed( will not be used if the command is used as a component/function)
inputs (Dict) – a dict of inputs used by this command.
outputs (Dict) – the outputs of this command
instance_count – Optional number of instances or nodes used by the compute target. Defaults to 1.
instance_type – Optional type of VM used as supported by the compute target.
timeout – The number in seconds, after which the job will be cancelled.
code (Union[str, os.PathLike]) – the code folder to run – typically a local folder that will be uploaded as the job is submitted
identity (Union[azure.ai.ml.ManagedIdentity, azure.ai.ml.AmlToken]) – Identity that training job will use while running on compute.

azure.ai.ml.load_batch_deployment(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._deployment.batch_deployment.BatchDeployment[source]¶

Construct a batch deployment object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed batch deployment object.

Return type

BatchDeployment

azure.ai.ml.load_batch_endpoint(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint[source]¶

Construct a batch endpoint object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed batch endpoint object.

Return type

BatchEndpoint

azure.ai.ml.load_component(path: Optional[Union[os.PathLike, str]] = None, **kwargs) → Union[azure.ai.ml.entities._component.command_component.CommandComponent, azure.ai.ml.entities._component.parallel_component.ParallelComponent][source]¶

Load component from local or remote to a component function.

For example:

# Load a local component to a component function.
component_func = load_component(path="custom_component/component_spec.yaml")
# Load a remote component to a component function.
component_func = load_component(client=ml_client, name="my_component", version=1)

# Consuming the component func
component = component_func(param1=xxx, param2=xxx)

Parameters

path (str) – Local component yaml file.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]
client (MLClient) – An MLClient instance.
name (str) – Name of the component.
version (str) – Version of the component.
kwargs (dict) – A dictionary of additional configuration parameters.

Returns

A function that can be called with parameters to get a azure.ai.ml.entities.Component

Return type

Union[CommandComponent, ParallelComponent]

azure.ai.ml.load_compute(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._compute.compute.Compute[source]¶

Construct a compute object from a yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded compute object.

Return type

Compute

azure.ai.ml.load_data(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._assets._artifacts.data.Data[source]¶

Construct a data object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed data object.

Return type

Data

azure.ai.ml.load_datastore(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._datastore.datastore.Datastore[source]¶

Construct a datastore object from a yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded datastore object.

Return type

Datastore

azure.ai.ml.load_environment(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._assets.environment.Environment[source]¶

Construct a environment object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed environment object.

Return type

Environment

azure.ai.ml.load_job(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._job.job.Job[source]¶

Construct a job object from a yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded job object.

Return type

Job

azure.ai.ml.load_model(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._assets._artifacts.model.Model[source]¶

Construct a model object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed model object.

Return type

Model

azure.ai.ml.load_online_deployment(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._deployment.online_deployment.OnlineDeployment[source]¶

Construct a online deployment object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed online deployment object.

Return type

OnlineDeployment

azure.ai.ml.load_online_endpoint(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._endpoint.online_endpoint.OnlineEndpoint[source]¶

Construct a online endpoint object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed online endpoint object.

Return type

OnlineEndpoint

azure.ai.ml.load_workspace(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._workspace.workspace.Workspace[source]¶

Load a workspace object from a yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded workspace object.

Return type

Workspace

azure.ai.ml.load_workspace_connection(path: Union[os.PathLike, str], **kwargs) → azure.ai.ml.entities._workspace.connections.workspace_connection.WorkspaceConnection[source]¶

Construct a workspace connection object from yaml file.

Parameters

path (str) – Path to a local file as the source.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Constructed workspace connection object.

Return type

WorkspaceConnection

Subpackages¶

Submodules¶

azure.ai.ml.constants module¶

class azure.ai.ml.constants.ArmConstants[source]¶

APP_INSIGHTS = 'AppInsights'¶

APP_INSIGHTS_PARAMETER_NAME = 'components'¶

ARRAY = 'Array'¶

ASSET_PATH = 'assetPath'¶

AZURE_MGMT_APPINSIGHT_API_VERSION = '2015-05-01'¶

AZURE_MGMT_CONTAINER_REG_API_VERSION = '2019-05-01'¶

AZURE_MGMT_KEYVAULT_API_VERSION = '2019-09-01'¶

AZURE_MGMT_RESOURCE_API_VERSION = '2020-06-01'¶

AZURE_MGMT_STORAGE_API_VERSION = '2019-06-01'¶

BASE_TYPE = 'base'¶

CODE_PARAMETER_NAME = 'codes'¶

CODE_RESOURCE_NAME = 'codeDeploymentCopy'¶

CODE_TYPE = 'code'¶

CODE_VERSION_PARAMETER_NAME = 'codeVersions'¶

CODE_VERSION_RESOURCE_NAME = 'codeVersionDeploymentCopy'¶

CODE_VERSION_TYPE = 'code_version'¶

CONTAINER_REGISTRY_PARAMETER_NAME = 'registries'¶

DATASTORE_ID = 'datastoreId'¶

DEFAULT_VALUE = 'defaultValue'¶

DEPENDSON_PARAMETER_NAME = 'dependsOn'¶

DEPLOYMENTS_PARAMETER_NAME = 'onlineDeployments'¶

ENDPOINT_CREATE_OR_UPDATE_PARAMETER_NAME = 'endpointCreateOrUpdate'¶

ENDPOINT_IDENTITY_PARAMETER_NAME = 'onlineEndpointIdentity'¶

ENDPOINT_NAME_PARAMETER_NAME = 'onlineEndpointName'¶

ENDPOINT_PARAMETER_NAME = 'onlineEndpoint'¶

ENDPOINT_PROPERTIES_PARAMETER_NAME = 'onlineEndpointProperties'¶

ENDPOINT_PROPERTIES_TRAFFIC_UPDATE_PARAMETER_NAME = 'onlineEndpointPropertiesTrafficUpdate'¶

ENDPOINT_TAGS_PARAMETER_NAME = 'onlineEndpointTags'¶

ENVIRONMENT_PARAMETER_NAME = 'environments'¶

ENVIRONMENT_TYPE = 'environment'¶

ENVIRONMENT_VERSION_RESOURCE_NAME = 'environmentVersionDeploymentCopy'¶

ENVIRONMENT_VERSION_TYPE = 'environment_version'¶

KEY_VAULT = 'KeyVault'¶

KEY_VAULT_PARAMETER_NAME = 'vaults'¶

LOCATION_PARAMETER_NAME = 'location'¶

MODEL_PARAMETER_NAME = 'models'¶

MODEL_RESOURCE_NAME = 'modelDeploymentCopy'¶

MODEL_TYPE = 'model'¶

MODEL_VERSION_PARAMETER_NAME = 'modelVersions'¶

MODEL_VERSION_RESOURCE_NAME = 'modelVersionDeploymentCopy'¶

MODEL_VERSION_TYPE = 'model_version'¶

NAME = 'name'¶

OBJECT = 'Object'¶

ONLINE_DEPLOYMENT_RESOURCE_NAME = 'onlineDeploymentCopy'¶

ONLINE_DEPLOYMENT_TYPE = 'online_deployment'¶

ONLINE_ENDPOINT_RESOURCE_NAME = 'onlineEndpointCopy'¶

ONLINE_ENDPOINT_TYPE = 'online_endpoint'¶

OPERATION_CREATE = 'create'¶

OPERATION_UPDATE = 'update'¶

PROPERTIES_PARAMETER_NAME = 'properties'¶

SKU = 'sku'¶

STORAGE = 'StorageAccount'¶

STORAGE_ACCOUNT_PARAMETER_NAME = 'storageAccounts'¶

STRING = 'String'¶

TAGS = 'tags'¶

TRAFFIC_PARAMETER_NAME = 'trafficRules'¶

UPDATE_ONLINE_ENDPOINT_TYPE = 'update_online_endpoint'¶

UPDATE_RESOURCE_NAME = 'updateEndpointWithTraffic'¶

VERSION = 'version'¶

WORKSPACE = 'Workspace'¶

WORKSPACE_BASE = 'workspace_base'¶

WORKSPACE_PARAM = 'workspace_param'¶

WORKSPACE_PARAMETER_NAME = 'workspaceName'¶

class azure.ai.ml.constants.AssetTypes[source]¶

CUSTOM_MODEL = 'custom_model'¶

MLFLOW_MODEL = 'mlflow_model'¶

MLTABLE = 'mltable'¶

TRITON_MODEL = 'triton_model'¶

URI_FILE = 'uri_file'¶

URI_FOLDER = 'uri_folder'¶

class azure.ai.ml.constants.AutoMLConstants[source]¶

ALLOWED_ALGORITHMS_YAML = 'allowed_training_algorithms'¶

AUTO = 'auto'¶

BLOCKED_ALGORITHMS_YAML = 'blocked_training_algorithms'¶

CLASSIFICATION_YAML = 'classification'¶

COUNTRY_OR_REGION_YAML = 'country_or_region_for_holidays'¶

CUSTOM = 'custom'¶

DATASET_YAML = 'dataset'¶

DATA_YAML = 'data'¶

ENSEMBLE_MODEL_DOWNLOAD_TIMEOUT_YAML = 'ensemble_model_download_timeout_minutes'¶

FEATURIZATION_YAML = 'featurization'¶

FORECASTING_YAML = 'forecasting'¶

GENERAL_YAML = 'general'¶

LIMITS_YAML = 'limits'¶

MAX_TRIALS_YAML = 'max_trials'¶

MODE = 'mode'¶

OFF = 'off'¶

REGRESSION_YAML = 'regression'¶

SWEEP_YAML = 'sweep'¶

TARGET_LAGS = 'target_lags'¶

TASK_TYPE_YAML = 'task'¶

TERMINATION_POLICY_TYPE_YAML = 'type'¶

TEST_DATA_SETTINGS_YAML = 'test'¶

TIMEOUT_YAML = 'timeout_minutes'¶

TIME_SERIES_ID_COLUMN_NAMES = 'time_series_id_column_names'¶

TRAINING_DATA_SETTINGS_YAML = 'training'¶

TRAINING_YAML = 'training'¶

TRANSFORMER_PARAMS = 'transformer_params'¶

TRIAL_TIMEOUT_YAML = 'trial_timeout_minutes'¶

VALIDATION_DATASET_SIZE_YAML = 'validation_dataset_size'¶

VALIDATION_DATA_SETTINGS_YAML = 'validation'¶

class azure.ai.ml.constants.AzureMLResourceType[source]¶

BATCH_DEPLOYMENT = 'batch_deployments'¶

BATCH_ENDPOINT = 'batch_endpoints'¶

CODE = 'codes'¶

COMPONENT = 'components'¶

COMPUTE = 'computes'¶

DATA = 'data'¶

DATASET = 'datasets'¶

DATASTORE = 'datastores'¶

DEPLOYMENT = 'deployments'¶

ENVIRONMENT = 'environments'¶

JOB = 'jobs'¶

MODEL = 'models'¶

NAMED_TYPES = {'computes', 'datastores', 'jobs', 'online_deployments', 'online_endpoints', 'workspaces'}¶

ONLINE_DEPLOYMENT = 'online_deployments'¶

ONLINE_ENDPOINT = 'online_endpoints'¶

VERSIONED_TYPES = {'codes', 'components', 'data', 'datasets', 'environments', 'models'}¶

VIRTUALCLUSTER = 'virtualclusters'¶

WORKSPACE = 'workspaces'¶

WORKSPACE_CONNECTION = 'workspace_connections'¶

class azure.ai.ml.constants.BatchDeploymentOutputAction[source]¶

APPEND_ROW = 'append_row'¶

SUMMARY_ONLY = 'summary_only'¶

class azure.ai.ml.constants.CommonYamlFields[source]¶

TYPE = 'type'¶

class azure.ai.ml.constants.ComponentJobConstants[source]¶

INPUT_DESTINATION_FORMAT = 'jobs.{}.inputs.{}'¶

INPUT_PATTERN = '^\\$\\{\\{parent\\.(inputs|jobs)\\.(.*?)\\}\\}$'¶

LEGACY_INPUT_PATTERN = '^\\$\\{\\{(inputs|jobs)\\.(.*?)\\}\\}$'¶

LEGACY_OUTPUT_PATTERN = '^\\$\\{\\{outputs\\.(.*?)\\}\\}$'¶

OUTPUT_DESTINATION_FORMAT = 'jobs.{}.outputs.{}'¶

OUTPUT_PATTERN = '^\\$\\{\\{parent\\.outputs\\.(.*?)\\}\\}$'¶

class azure.ai.ml.constants.ComponentSource[source]¶

Indicate where the component is constructed.

BUILDER = 'BUILDER'¶

DSL = 'DSL'¶

REST = 'REST'¶

SDK = 'SDK'¶

YAML = 'YAML'¶

class azure.ai.ml.constants.ComputeDefaults[source]¶

ADMIN_USER = 'azureuser'¶

IDLE_TIME = 1800¶

MAX_NODES = 4¶

MIN_NODES = 0¶

PRIORITY = 'Dedicated'¶

VMSIZE = 'STANDARD_DS3_V2'¶

class azure.ai.ml.constants.ComputeTier[source]¶

DEDICATED = 'dedicated'¶

LOWPRIORITY = 'low_priority'¶

class azure.ai.ml.constants.ComputeType[source]¶

AMLCOMPUTE = 'amlcompute'¶

COMPUTEINSTANCE = 'computeinstance'¶

KUBERNETES = 'kubernetes'¶

MANAGED = 'managed'¶

VIRTUALMACHINE = 'virtualmachine'¶

class azure.ai.ml.constants.DataType[source]¶

DATAFLOW = 'Dataflow'¶

SIMPLE = 'Simple'¶

class azure.ai.ml.constants.DeploymentType[source]¶

K8S = 'Kubernetes'¶

MANAGED = 'Managed'¶

class azure.ai.ml.constants.DistributionType[source]¶

MPI = 'mpi'¶

PYTORCH = 'pytorch'¶

TENSORFLOW = 'tensorflow'¶

class azure.ai.ml.constants.DockerTypes[source]¶

BUILD = 'Build'¶

IMAGE = 'Image'¶

class azure.ai.ml.constants.EndpointDeploymentLogContainerType[source]¶

INFERENCE_SERVER = 'inference-server'¶

INFERENCE_SERVER_REST = 'InferenceServer'¶

STORAGE_INITIALIZER = 'storage-initializer'¶

STORAGE_INITIALIZER_REST = 'StorageInitializer'¶

class azure.ai.ml.constants.EndpointGetLogsFields[source]¶

LINES = 5000¶

class azure.ai.ml.constants.EndpointInvokeFields[source]¶

AUTHORIZATION = 'Authorization'¶

DEFAULT_HEADER = {'Content-Type': 'application/json'}¶

MODEL_DEPLOYMENT = 'azureml-model-deployment'¶

class azure.ai.ml.constants.EndpointKeyType[source]¶

PRIMARY_KEY_TYPE = 'primary'¶

SECONDARY_KEY_TYPE = 'secondary'¶

class azure.ai.ml.constants.EndpointYamlFields[source]¶

BATCH_JOB_DATASET = 'dataset'¶

BATCH_JOB_INPUT_DATA = 'input_data'¶

BATCH_JOB_INSTANCE_COUNT = 'compute.instance_count'¶

BATCH_JOB_NAME = 'job_name'¶

BATCH_JOB_OUTPUT_DATSTORE = 'output_dataset.datastore_id'¶

BATCH_JOB_OUTPUT_PATH = 'output_dataset.path'¶

CODE = 'code'¶

CODE_CONFIGURATION = 'code_configuration'¶

COMPUTE = 'compute'¶

INSTANCE_COUNT = 'instance_count'¶

MAXIMUM = 'max_instances'¶

MINIMUM = 'min_instances'¶

MINI_BATCH_SIZE = 'mini_batch_size'¶

NAME = 'name'¶

POLLING_INTERVAL = 'polling_interval'¶

PROVISIONING_STATE = 'provisioning_state'¶

RETRY_SETTINGS = 'retry_settings'¶

SCALE_SETTINGS = 'scale_settings'¶

SCALE_TYPE = 'scale_type'¶

SCORING_SCRIPT = 'scoring_script'¶

SCORING_URI = 'scoring_uri'¶

SKU_DEFAULT = 'Standard_F4s_v2'¶

SWAGGER_URI = 'swagger_uri'¶

TARGET_UTILIZATION_PERCENTAGE = 'target_utilization_percentage'¶

TRAFFIC_NAME = 'traffic'¶

TYPE = 'type'¶

class azure.ai.ml.constants.GitProperties[source]¶

ENV_BRANCH = 'AZUREML_GIT_BRANCH'¶

ENV_BUILD_ID = 'AZUREML_GIT_BUILD_ID'¶

ENV_BUILD_URI = 'AZUREML_GIT_BUILD_URI'¶

ENV_COMMIT = 'AZUREML_GIT_COMMIT'¶

ENV_DIRTY = 'AZUREML_GIT_DIRTY'¶

ENV_REPOSITORY_URI = 'AZUREML_GIT_REPOSITORY_URI'¶

PROP_BUILD_ID = 'azureml.git.build_id'¶

PROP_BUILD_URI = 'azureml.git.build_uri'¶

PROP_DIRTY = 'azureml.git.dirty'¶

PROP_MLFLOW_GIT_BRANCH = 'mlflow.source.git.branch'¶

PROP_MLFLOW_GIT_COMMIT = 'mlflow.source.git.commit'¶

PROP_MLFLOW_GIT_REPO_URL = 'mlflow.source.git.repoURL'¶

class azure.ai.ml.constants.HttpResponseStatusCode[source]¶

NOT_FOUND = 404¶

class azure.ai.ml.constants.IdentityType[source]¶

BOTH = 'system_assigned,user_assigned'¶

SYSTEM_ASSIGNED = 'system_assigned'¶

USER_ASSIGNED = 'user_assigned'¶

class azure.ai.ml.constants.InputOutputModes[source]¶

DIRECT = 'direct'¶

DOWNLOAD = 'download'¶

EVAL_DOWNLOAD = 'eval_download'¶

EVAL_MOUNT = 'eval_mount'¶

MOUNT = 'mount'¶

RO_MOUNT = 'ro_mount'¶

RW_MOUNT = 'rw_mount'¶

UPLOAD = 'upload'¶

class azure.ai.ml.constants.JobComputePropertyFields[source]¶

AISUPERCOMPUTER = 'AISuperComputer'¶

SINGULARITY = 'Singularity'¶

class azure.ai.ml.constants.JobLimitsType[source]¶

SWEEP = 'Sweep'¶

class azure.ai.ml.constants.JobLogPattern[source]¶

COMMAND_JOB_LOG_PATTERN = 'azureml-logs/[\\d]{2}.+\\.txt'¶

COMMON_RUNTIME_ALL_USER_LOG_PATTERN = 'user_logs/std_log.*\\.txt'¶

COMMON_RUNTIME_STREAM_LOG_PATTERN = 'user_logs/std_log[\\D]*[0]*(?:_ps)?\\.txt'¶

PIPELINE_JOB_LOG_PATTERN = 'logs/azureml/executionlogs\\.txt'¶

SWEEP_JOB_LOG_PATTERN = 'azureml-logs/hyperdrive\\.txt'¶

class azure.ai.ml.constants.JobServices[source]¶

STUDIO = 'Studio'¶

class azure.ai.ml.constants.JobType[source]¶

AUTOML = 'automl'¶

BASE = 'base'¶

COMMAND = 'command'¶

COMPONENT = 'component'¶

PARALLEL = 'parallel'¶

PIPELINE = 'pipeline'¶

SWEEP = 'sweep'¶

class azure.ai.ml.constants.LROConfigurations[source]¶

MAX_WAIT_COUNT = 400¶

POLLING_TIMEOUT = 720¶

POLL_INTERVAL = 5¶

SLEEP_TIME = 5¶

class azure.ai.ml.constants.LegacyAssetTypes[source]¶

PATH = 'path'¶

class azure.ai.ml.constants.LocalEndpointConstants[source]¶

AZUREML_APP_PATH = '/var/azureml-app/'¶

CONDA_ENV_BIN_PATH = '/opt/miniconda/envs/inf-conda-env/bin'¶

CONDA_ENV_NAME = 'inf-conda-env'¶

CONDA_ENV_PYTHON_PATH = '/opt/miniconda/envs/inf-conda-env/bin/python'¶

CONDA_FILE_NAME = 'conda.yml'¶

CONTAINER_EXITED = 'exited'¶

DEFAULT_STARTUP_WAIT_TIME_SECONDS = 15¶

DOCKER_PORT = '5001'¶

ENDPOINT_STATE_FAILED = 'Failed'¶

ENDPOINT_STATE_LOCATION = 'local'¶

ENDPOINT_STATE_SUCCEEDED = 'Succeeded'¶

ENVVAR_KEY_AML_APP_ROOT = 'AML_APP_ROOT'¶

ENVVAR_KEY_AZUREML_ENTRY_SCRIPT = 'AZUREML_ENTRY_SCRIPT'¶

ENVVAR_KEY_AZUREML_INFERENCE_PYTHON_PATH = 'AZUREML_INFERENCE_PYTHON_PATH'¶

ENVVAR_KEY_AZUREML_MODEL_DIR = 'AZUREML_MODEL_DIR'¶

LABEL_KEY_AZUREML_LOCAL_ENDPOINT = 'azureml-local-endpoint'¶

LABEL_KEY_AZUREML_PORT = 'azureml-port'¶

LABEL_KEY_DEPLOYMENT_JSON = 'deployment-data'¶

LABEL_KEY_DEPLOYMENT_NAME = 'deployment'¶

LABEL_KEY_ENDPOINT_JSON = 'endpoint-data'¶

LABEL_KEY_ENDPOINT_NAME = 'endpoint'¶

class azure.ai.ml.constants.LoggingLevel[source]¶

DEBUG = 'DEBUG'¶

INFO = 'INFO'¶

WARN = 'WARNING'¶

class azure.ai.ml.constants.ModelType[source]¶

CUSTOM = 'CustomModel'¶

MLFLOW = 'MLFlowModel'¶

TRITON = 'TritonModel'¶

class azure.ai.ml.constants.NodeType[source]¶

AUTOML = 'automl'¶

COMMAND = 'command'¶

PARALLEL = 'parallel'¶

SWEEP = 'sweep'¶

class azure.ai.ml.constants.OnlineEndpointConfigurations[source]¶

MAX_NAME_LENGTH = 32¶

MIN_NAME_LENGTH = 3¶

NAME_REGEX_PATTERN = '^[a-zA-Z]([-a-zA-Z0-9]*[a-zA-Z0-9])?$'¶

class azure.ai.ml.constants.OperationStatus[source]¶

CANCELED = 'Canceled'¶

FAILED = 'Failed'¶

RUNNING = 'Running'¶

SUCCEEDED = 'Succeeded'¶

class azure.ai.ml.constants.OrderString[source]¶

CREATED_AT = 'createdtime asc'¶

CREATED_AT_DESC = 'createdtime desc'¶

class azure.ai.ml.constants.ParallelTaskType[source]¶

FUNCTION = 'function'¶

MODEL = 'model'¶

class azure.ai.ml.constants.PipelineConstants[source]¶

CODE = 'code'¶

CONTINUE_ON_STEP_FAILURE = 'continue_on_step_failure'¶

DATASTORE_REST = 'Datastore'¶

DEFAULT_COMPUTE = 'default_compute'¶

DEFAULT_DATASTORE = 'default_datastore'¶

DEFAULT_DATASTORE_REST = 'defaultDatastoreName'¶

DEFAULT_DATASTORE_SDK = 'default_datastore_name'¶

ENVIRONMENT = 'environment'¶

REUSED_FLAG_FIELD = 'azureml.isreused'¶

REUSED_FLAG_TRUE = 'true'¶

REUSED_JOB_ID = 'azureml.reusedrunid'¶

class azure.ai.ml.constants.PublicNetworkAccess[source]¶

DISABLED = 'Disabled'¶

ENABLED = 'Enabled'¶

class azure.ai.ml.constants.SearchSpace[source]¶

CHOICE = 'choice'¶

LOGNORMAL = 'lognormal'¶

LOGUNIFORM = 'loguniform'¶

NORMAL = 'normal'¶

NORMAL_LOGNORMAL = ['normal', 'lognormal']¶

QLOGNORMAL = 'qlognormal'¶

QLOGUNIFORM = 'qloguniform'¶

QNORMAL = 'qnormal'¶

QNORMAL_QLOGNORMAL = ['qnormal', 'qlognormal']¶

QUNIFORM = 'quniform'¶

QUNIFORM_QLOGUNIFORM = ['quniform', 'qloguniform']¶

RANDINT = 'randint'¶

UNIFORM = 'uniform'¶

UNIFORM_LOGUNIFORM = ['uniform', 'loguniform']¶

class azure.ai.ml.constants.TimeZone(value)[source]¶

Time zones that a job or compute instance schedule accepts

AFGHANISTANA_STANDARD_TIME = 'Afghanistan Standard Time'¶

ALASKAN_STANDARD_TIME = 'Alaskan Standard Time'¶

ALEUTIAN_STANDARD_TIME = 'Aleutian Standard Time'¶

ALTAI_STANDARD_TIME = 'Altai Standard Time'¶

ARABIAN_STANDARD_TIME = 'Arabian Standard Time'¶

ARABIC_STANDARD_TIME = 'Arabic Standard Time'¶

ARAB_STANDARD_TIME = 'Arab Standard Time'¶

ARGENTINA_STANDARD_TIME = 'Argentina Standard Time'¶

ASTRAKHAN_STANDARD_TIME = 'Astrakhan Standard Time'¶

ATLANTIC_STANDARD_TIME = 'Atlantic Standard Time'¶

AUS_CENTRAL_STANDARD_TIME = 'AUS Central Standard Time'¶

AUS_CENTRAL_W_STANDARD_TIME = 'Aus Central W. Standard Time'¶

AUS_EASTERN_STANDARD_TIME = 'AUS Eastern Standard Time'¶

AZERBAIJAN_STANDARD_TIME = 'Azerbaijan Standard Time'¶

AZORES_STANDARD_TIME = 'Azores Standard Time'¶

BAHIA_STANDARD_TIME = 'Bahia Standard Time'¶

BANGLADESH_STANDARD_TIME = 'Bangladesh Standard Time'¶

BELARUS_STANDARD_TIME = 'Belarus Standard Time'¶

BOUGAINVILLE_STANDARD_TIME = 'Bougainville Standard Time'¶

CANADA_CENTRAL_STANDARD_TIME = 'Canada Central Standard Time'¶

CAPE_VERDE_STANDARD_TIME = 'Cape Verde Standard Time'¶

CAUCASUS_STANDARD_TIME = 'Caucasus Standard Time'¶

CENTRAL_AMERICA_STANDARD_TIME = 'Central America Standard Time'¶

CENTRAL_ASIA_STANDARD_TIME = 'Central Asia Standard Time'¶

CENTRAL_BRAZILIAN_STANDARD_TIME = 'Central Brazilian Standard Time'¶

CENTRAL_EUROPEAN_STANDARD_TIME = 'Central European Standard Time'¶

CENTRAL_EUROPE_STANDARD_TIME = 'Central Europe Standard Time'¶

CENTRAL_PACIFIC_STANDARD_TIME = 'Central Pacific Standard Time'¶

CENTRAL_STANDARD_TIME = 'Central Standard Time'¶

CENTRAL_STANDARD_TIME_MEXICO = 'Central Standard Time (Mexico)'¶

CEN_AUSTRALIA_STANDARD_TIME = 'Cen. Australia Standard Time'¶

CHATHAM_ISLANDS_STANDARD_TIME = 'Chatham Islands Standard Time'¶

CHINA_STANDARD_TIME = 'China Standard Time'¶

CUBA_STANDARD_TIME = 'Cuba Standard Time'¶

DATELINE_STANDARD_TIME = 'Dateline Standard Time'¶

EASTERN_STANDARD_TIME = 'Eastern Standard Time'¶

EASTERN_STANDARD_TIME_MEXICO = 'Eastern Standard Time (Mexico)'¶

EASTER_ISLAND_STANDARD_TIME = 'Easter Island Standard Time'¶

EGYPT_STANDARD_TIME = 'Egypt Standard Time'¶

EKATERINBURG_STANDARD_TIME = 'Ekaterinburg Standard Time'¶

E_AFRICA_STANDARD_TIME = 'E. Africa Standard Time'¶

E_AUSTRALIAN_STANDARD_TIME = 'E. Australia Standard Time'¶

E_EUROPE_STANDARD_TIME = 'E. Europe Standard Time'¶

E_SOUTH_AMERICAN_STANDARD_TIME = 'E. South America Standard Time'¶

FIJI_STANDARD_TIME = 'Fiji Standard Time'¶

FLE_STANDARD_TIME = 'FLE Standard Time'¶

GEORGIAN_STANDARD_TIME = 'Georgian Standard Time'¶

GMT_STANDARD_TIME = 'GMT Standard Time'¶

GREENLAND_STANDARD_TIME = 'Greenland Standard Time'¶

GREENWICH_STANDARD_TIME = 'Greenwich Standard Time'¶

GTB_STANDARD_TIME = 'GTB Standard Time'¶

HAITI_STANDARD_TIME = 'Haiti Standard Time'¶

HAWAIIAN_STANDARD_TIME = 'Hawaiian Standard Time'¶

INDIA_STANDARD_TIME = 'India Standard Time'¶

IRAN_STANDARD_TIME = 'Iran Standard Time'¶

ISRAEL_STANDARD_TIME = 'Israel Standard Time'¶

JORDAN_STANDARD_TIME = 'Jordan Standard Time'¶

KALININGRAD_STANDARD_TIME = 'Kaliningrad Standard Time'¶

KAMCHATKA_STANDARD_TIME = 'Kamchatka Standard Time'¶

KOREA_STANDARD_TIME = 'Korea Standard Time'¶

LIBYA_STANDARD_TIME = 'Libya Standard Time'¶

LINE_ISLANDS_STANDARD_TIME = 'Line Islands Standard Time'¶

LORDE_HOWE_STANDARD_TIME = 'Lord Howe Standard Time'¶

MAGADAN_STANDARD_TIME = 'Magadan Standard Time'¶

MARQUESAS_STANDARD_TIME = 'Marquesas Standard Time'¶

MAURITIUS_STANDARD_TIME = 'Mauritius Standard Time'¶

MIDDLE_EAST_STANDARD_TIME = 'Middle East Standard Time'¶

MID_ATLANTIC_STANDARD_TIME = 'Mid-Atlantic Standard Time'¶

MONTEVIDEO_STANDARD_TIME = 'Montevideo Standard Time'¶

MOROCCO_STANDARD_TIME = 'Morocco Standard Time'¶

MOUNTAIN_STANDARD_TIME = 'Mountain Standard Time'¶

MOUNTAIN_STANDARD_TIME_MEXICO = 'Mountain Standard Time (Mexico)'¶

MYANMAR_STANDARD_TIME = 'Myanmar Standard Time'¶

NAMIBIA_STANDARD_TIME = 'Namibia Standard Time'¶

NEPAL_STANDARD_TIME = 'Nepal Standard Time'¶

NEWFOUNDLAND_STANDARD_TIME = 'Newfoundland Standard Time'¶

NEW_ZEALAND_STANDARD_TIME = 'New Zealand Standard Time'¶

NORFOLK_STANDARD_TIME = 'Norfolk Standard Time'¶

NORTH_ASIA_EAST_STANDARD_TIME = 'North Asia East Standard Time'¶

NORTH_ASIA_STANDARD_TIME = 'North Asia Standard Time'¶

NORTH_KOREA_STANDARD_TIME = 'North Korea Standard Time'¶

N_CENTRAL_ASIA_STANDARD_TIME = 'N. Central Asia Standard Time'¶

PACIFIC_SA_STANDARD_TIME = 'Pacific SA Standard Time'¶

PACIFIC_STANDARD_TIME = 'Pacific Standard Time'¶

PACIFIC_STANDARD_TIME_MEXICO = 'Pacific Standard Time (Mexico)'¶

PAKISTAN_STANDARD_TIME = 'Pakistan Standard Time'¶

PARAGUAY_STANDARD_TIME = 'Paraguay Standard Time'¶

ROMANCE_STANDARD_TIME = 'Romance Standard Time'¶

RUSSIAN_STANDARD_TIME = 'Russian Standard Time'¶

RUSSIA_TIME_ZONE_10 = 'Russia Time Zone 10'¶

RUSSIA_TIME_ZONE_11 = 'Russia Time Zone 11'¶

RUSSIA_TIME_ZONE_3 = 'Russia Time Zone 3'¶

SAINT_PIERRE_STANDARD_TIME = 'Saint Pierre Standard Time'¶

SAKHALIN_STANDARD_TIME = 'Sakhalin Standard Time'¶

SAMOA_STANDARD_TIME = 'Samoa Standard Time'¶

SA_EASTERN_STANDARD_TIME = 'SA Eastern Standard Time'¶

SA_PACIFIC_STANDARD_TIME = 'SA Pacific Standard Time'¶

SA_WESTERN_STANDARD_TIME = 'SA Western Standard Time'¶

SE_ASIA_STANDARD_TIME = 'SE Asia Standard Time'¶

SINGAPORE_STANDARD_TIME = 'Singapore Standard Time'¶

SOUTH_AFRICA_STANDARD_TIME = 'South Africa Standard Time'¶

SRI_LANKA_STANDARD_TIME = 'Sri Lanka Standard Time'¶

SYRIA_STANDARD_TIME = 'Syria Standard Time'¶

TAIPEI_STANDARD_TIME = 'Taipei Standard Time'¶

TASMANIA_STANDARD_TIME = 'Tasmania Standard Time'¶

TOCANTINS_STANDARD_TIME = 'Tocantins Standard Time'¶

TOKYO_STANDARD_TIME = 'Tokyo Standard Time'¶

TOMSK_STANDARD_TIME = 'Tomsk Standard Time'¶

TONGA__STANDARD_TIME = 'Tonga Standard Time'¶

TRANSBAIKAL_STANDARD_TIME = 'Transbaikal Standard Time'¶

TURKEY_STANDARD_TIME = 'Turkey Standard Time'¶

TURKS_AND_CAICOS_STANDARD_TIME = 'Turks And Caicos Standard Time'¶

ULAANBAATAR_STANDARD_TIME = 'Ulaanbaatar Standard Time'¶

US_EASTERN_STANDARD_TIME = 'US Eastern Standard Time'¶

US_MOUNTAIN_STANDARD_TIME = 'US Mountain Standard Time'¶

UTC = 'UTC'¶

UTC_02 = 'UTC-02'¶

UTC_08 = 'UTC-08'¶

UTC_09 = 'UTC-09'¶

UTC_11 = 'UTC-11'¶

UTC_12 = 'UTC+12'¶

VLADIVOSTOK_STANDARD_TIME = 'Vladivostok Standard Time'¶

VenezuelaStandardTime = 'Venezuela Standard Time'¶

WEST_ASIA_STANDARD_TIME = 'West Asia Standard Time'¶

WEST_BANK_STANDARD_TIME = 'West Bank Standard Time'¶

WEST_PACIFIC_STANDARD_TIME = 'West Pacific Standard Time'¶

W_AUSTRALIA_STANDARD_TIME = 'W. Australia Standard Time'¶

W_CENTEAL_AFRICA_STANDARD_TIME = 'W. Central Africa Standard Time'¶

W_EUROPE_STANDARD_TIME = 'W. Europe Standard Time'¶

W_MONGOLIA_STANDARD_TIME = 'W. Mongolia Standard Time'¶

YAKUTSK_STANDARD_TIME = 'Yakutsk Standard Time'¶

class azure.ai.ml.constants.WorkspaceResourceConstants[source]¶

ENCRYPTION_STATUS_ENABLED = 'Enabled'¶

class azure.ai.ml.constants.YAMLRefDocLinks[source]¶

AML_COMPUTE = 'https://aka.ms/ml-cli-v2-compute-aml-yaml-reference'¶

BATCH_DEPLOYMENT = 'https://aka.ms/ml-cli-v2-deployment-batch-yaml-reference'¶

BATCH_ENDPOINT = 'https://aka.ms/ml-cli-v2-endpoint-batch-yaml-reference'¶

COMMAND_COMPONENT = 'https://aka.ms/ml-cli-v2-component-command-yaml-reference'¶

COMMAND_JOB = 'https://aka.ms/ml-cli-v2-job-command-yaml-reference'¶

COMPUTE_INSTANCE = 'https://aka.ms/ml-cli-v2-compute-aml-yaml-reference'¶

DATA = 'https://aka.ms/ml-cli-v2-data-yaml-reference'¶

DATASET = 'https://aka.ms/ml-cli-v2-dataset-yaml-reference'¶

DATASTORE_BLOB = 'https://aka.ms/ml-cli-v2-datastore-blob-yaml-reference'¶

DATASTORE_DATA_LAKE_GEN_1 = 'https://aka.ms/ml-cli-v2-datastore-data-lake-gen1-yaml-reference'¶

DATASTORE_DATA_LAKE_GEN_2 = 'https://aka.ms/ml-cli-v2-datastore-data-lake-gen2-yaml-reference'¶

DATASTORE_FILE = 'https://aka.ms/ml-cli-v2-datastore-file-yaml-reference'¶

ENVIRONMENT = 'https://aka.ms/ml-cli-v2-environment-yaml-reference'¶

KUBERNETES_ONLINE_DEPLOYMENT = 'https://aka.ms/ml-cli-v2-deployment-kubernetes-online-yaml-reference'¶

MANAGED_ONLINE_DEPLOYMENT = 'https://aka.ms/ml-cli-v2-deployment-managed-online-yaml-reference'¶

MODEL = 'https://aka.ms/ml-cli-v2-model-yaml-reference'¶

ONLINE_ENDPOINT = 'https://aka.ms/ml-cli-v2-endpoint-online-yaml-reference'¶

PARALLEL_COMPONENT = 'https://aka.ms/ml-cli-v2-component-parallel-yaml-reference'¶

PARALLEL_JOB = 'https://aka.ms/ml-cli-v2-job-parallel-yaml-reference'¶

PIPELINE_JOB = 'https://aka.ms/ml-cli-v2-job-pipeline-yaml-reference'¶

SWEEP_JOB = 'https://aka.ms/ml-cli-v2-job-sweep-yaml-reference'¶

VIRTUAL_MACHINE_COMPUTE = 'https://aka.ms/ml-cli-v2-compute-vm-yaml-reference'¶

WORKSPACE = 'https://aka.ms/ml-cli-v2-workspace-yaml-reference'¶

class azure.ai.ml.constants.YAMLRefDocSchemaNames[source]¶

AML_COMPUTE = 'AMLCompute'¶

BATCH_DEPLOYMENT = 'BatchDeployment'¶

BATCH_ENDPOINT = 'BatchEndpoint'¶

COMMAND_COMPONENT = 'CommandComponent'¶

COMMAND_JOB = 'CommandJob'¶

COMPUTE_INSTANCE = 'ComputeInstance'¶

DATA = 'Data'¶

DATASET = 'Dataset'¶

DATASTORE_BLOB = 'AzureBlobDatastore'¶

DATASTORE_DATA_LAKE_GEN_1 = 'AzureDataLakeGen1Datastore'¶

DATASTORE_DATA_LAKE_GEN_2 = 'AzureDataLakeGen2Datastore'¶

DATASTORE_FILE = 'AzureFileDatastore'¶

ENVIRONMENT = 'Environment'¶

KUBERNETES_ONLINE_DEPLOYMENT = 'KubernetesOnlineDeployment'¶

MANAGED_ONLINE_DEPLOYMENT = 'ManagedOnlineDeployment'¶

MODEL = 'Model'¶

ONLINE_ENDPOINT = 'OnlineEndpoint'¶

PARALLEL_COMPONENT = 'ParallelComponent'¶

PARALLEL_JOB = 'ParallelJob'¶

PIPELINE_JOB = 'PipelineJob'¶

SWEEP_JOB = 'SweepJob'¶

VIRTUAL_MACHINE_COMPUTE = 'VirtualMachineCompute'¶

WORKSPACE = 'Workspace'¶

azure.ai.ml.template_code module¶

azure.ai.ml.template_code.template_main()[source]¶