azure.ai.ml package Azure SDK for Python 2.0.0 documentation

property schedules¶

A collection of schedule related operations.

Returns

Schedule operations.

Return type

ScheduleOperations

property subscription_id¶

Get the subscription ID of an MLClient object.

Returns

An Azure subscription ID.

Return type

property workspace_hubs¶

//aka.ms/azuremlexperimental for more information.

A collection of workspace hub-related operations.

Returns

Hub Operations

Return type

HubOperations

Type

Note

This is an experimental method, and may change at any time. Please see https

property workspace_name¶

The name of the workspace where workspace-dependent operations will be executed.

Returns

The name of the default workspace.

Return type

Optional[str]

property workspaces¶

A collection of workspace-related operations.

Returns

Workspace operations

Return type

WorkspaceOperations

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

Managed Identity credential configuration.

Keyword Arguments

client_id (Optional[str]) – The client ID of the managed identity.
resource_id (Optional[str]) – The resource ID of the managed identity.
object_id (Optional[str]) – The object ID.
principal_id (Optional[str]) – The principal ID.

MPI distribution configuration.

Keyword Arguments

process_count_per_instance (Optional[int]) – The number of processes per node.

Variables

type (str) – Specifies the type of distribution. Set automatically to “mpi” for this class.

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

Base class for Input & Output class.

This class is introduced to support literal output in the future.

Parameters

type (str) – The type of the Input/Output.

PyTorch distribution configuration.

Keyword Arguments

process_count_per_instance (Optional[int]) – The number of processes per node.

Variables

type (str) – Specifies the type of distribution. Set automatically to “pytorch” for this class.

class azure.ai.ml.RayDistribution(*, port: Optional[int] = None, address: Optional[str] = None, include_dashboard: Optional[bool] = None, dashboard_port: Optional[int] = None, head_node_additional_args: Optional[str] = None, worker_node_additional_args: Optional[str] = None, **kwargs)[source]¶

Ray distribution configuration.

Variables

port (int) – The port of the head ray process.
address (str) – The address of Ray head node.
include_dashboard (bool) – Provide this argument to start the Ray dashboard GUI.
dashboard_port (int) – The port to bind the dashboard server to.
head_node_additional_args (str) – Additional arguments passed to ray start in head node.
worker_node_additional_args (str) – Additional arguments passed to ray start in worker node.
type (str) – Specifies the type of distribution. Set automatically to “Ray” for this class.

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

TensorFlow distribution configuration.

Keyword Arguments

parameter_server_count (Optional[int]) – The number of parameter server tasks. Defaults to 0.
worker_count (Optional[int]) – The number of workers. Defaults to the instance count.

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.
type (str) – Specifies the type of distribution. Set automatically to “tensorflow” for this class.

class azure.ai.ml.UserIdentityConfiguration[source]¶

User identity configuration.

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 ¶ 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, azure.ai.ml.entities._job.distribution.RayDistribution]] = None, compute: Optional[str] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, instance_count: Optional[int] = None, instance_type: Optional[str] = None, locations: Optional[List[str]] = None, docker_args: Optional[str] = None, shm_size: Optional[str] = None, timeout: Optional[int] = None, code: Optional[Union[os.PathLike, str]] = None, identity: Optional[Union[azure.ai.ml.entities._credentials.ManagedIdentityConfiguration, azure.ai.ml.entities._credentials.AmlTokenConfiguration, azure.ai.ml.entities._credentials.UserIdentityConfiguration]] = None, is_deterministic: bool = True, services: Optional[Dict[str, Union[azure.ai.ml.entities._job.job_service.JobService, azure.ai.ml.entities._job.job_service.JupyterLabJobService, azure.ai.ml.entities._job.job_service.SshJobService, azure.ai.ml.entities._job.job_service.TensorBoardJobService, azure.ai.ml.entities._job.job_service.VsCodeJobService]]] = None, job_tier: Optional[str] = None, priority: Optional[str] = None, **kwargs) → azure.ai.ml.entities._builders.command.Command[source]¶

Creates a Command object which can be used inside a dsl.pipeline function or used as a standalone Command job.

Keyword Arguments

name (Optional[str]) – The name of the Command job or component.
description (Optional[str]) – The description of the Command. Defaults to None.
tags (Optional[dict[str, str]]) – Tag dictionary. Tags can be added, removed, and updated. Defaults to None.
properties (Optional[dict[str, str]]) – The job property dictionary. Defaults to None.
display_name (Optional[str]) – The display name of the job. Defaults to a randomly generated name.
command (Optional[str]) – The command to be executed. Defaults to None.
experiment_name (Optional[str]) – The name of the experiment that the job will be created under. Defaults to current directory name.
environment (Optional[Union[str, Environment]]) – The environment that the job will run in.
environment_variables (Optional[dict[str, str]]) – A dictionary of environment variable names and values. These environment variables are set on the process where user script is being executed. Defaults to None.
distribution (Optional[Union[dict, PyTorchDistribution, MpiDistribution, TensorFlowDistribution, RayDistribution]]) – The configuration for distributed jobs. Defaults to None.
compute (Optional[str]) – The compute target the job will run on. Defaults to default compute.
inputs (Optional[dict[str, Union[Input, str, bool, int, float, Enum]]]) – A mapping of input names to input data sources used in the job. Defaults to None.
outputs (Optional[dict[str, Union[str, Output]]]) – A mapping of output names to output data sources used in the job. Defaults to None.
instance_count (Optional[int]) – The number of instances or nodes to be used by the compute target. Defaults to 1.
instance_type (Optional[str]) – The type of VM to be used by the compute target.
locations (Optional[list[str]]) – The list of locations where the job will run.
docker_args (Optional[str]) – Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. Defaults to None.
shm_size (Optional[str]) – The size of the Docker container’s shared memory block. This should be in the format of (number)(unit) where the number has to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
timeout (Optional[int]) – The number, in seconds, after which the job will be cancelled.
code (Optional[Union[str, os.PathLike]]) – The source code to run the job. Can be a local path or “http:”, “https:”, or “azureml:” url pointing to a remote location.
identity (Optional[Union[ ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]]) – The identity that the command job will use while running on compute.
is_deterministic (bool) – Specifies whether the Command will return the same output given the same input. Defaults to True. When True, if a Command Component is deterministic and has been run before in the current workspace with the same input and settings, it will reuse results from a previously submitted job when used as a node or step in a pipeline. In that scenario, no compute resources will be used.
services (Optional[dict[str, Union[JobService, JupyterLabJobService, SshJobService, TensorBoardJobService, VsCodeJobService]]]) – The interactive services for the node. Defaults to None. This is an experimental parameter, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
job_tier (Optional[str]) – The job tier. Accepted values are “Spot”, “Basic”, “Standard”, or “Premium”.
priority (Optional[str]) – The priority of the job on the compute. Accepted values are “low”, “medium”, and “high”. Defaults to “medium”.

Returns

A Command object.

Return type

Command

azure.ai.ml.load_batch_deployment(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._deployment.batch_deployment.BatchDeployment[source]¶

Construct a batch deployment object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a batch deployment object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
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(source: Union[str, os.PathLike, IO], relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._endpoint.batch_endpoint.BatchEndpoint[source]¶

Construct a batch endpoint object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a batch endpoint object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.
relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.

Keyword Arguments

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

Load component from local or remote to a component function.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a component. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

A Component object

Return type

Union[CommandComponent, ParallelComponent, PipelineComponent]

azure.ai.ml.load_compute(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._compute.compute.Compute[source]¶

Construct a compute object from a yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a compute. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
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(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._assets._artifacts.data.Data[source]¶

Construct a data object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a data object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Raises

ValidationException – Raised if Data cannot be successfully validated. Details will be provided in the error message.

Returns

Constructed Data or DataImport object.

Return type

Data

azure.ai.ml.load_datastore(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._datastore.datastore.Datastore[source]¶

Construct a datastore object from a yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a datastore. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Raises

ValidationException – Raised if Datastore cannot be successfully validated. Details will be provided in the error message.

Returns

Loaded datastore object.

Return type

Datastore

azure.ai.ml.load_environment(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._assets.environment.Environment[source]¶

Construct a environment object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of an environment. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Raises

ValidationException – Raised if Environment cannot be successfully validated. Details will be provided in the error message.

Returns

Constructed environment object.

Return type

Environment

azure.ai.ml.load_job(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._job.job.Job[source]¶

Constructs a Job object from a YAML file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – A path to a local YAML file or an already-open file object containing a job configuration. If the source is a path, it will be opened and read. If the source is an open file, the file will be read directly.

Keyword Arguments

relative_origin (Optional[str]) – The root directory for the YAML. This directory will be used as the origin for deducing the relative locations of files referenced in the parsed YAML. Defaults to the same directory as source if source is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (Optional[list[dict]]) – Parameter fields to overwrite values in the YAML file.

Raises

ValidationException – Raised if Job cannot be successfully validated. Details will be provided in the error message.

Returns

A loaded Job object.

Return type

Job

azure.ai.ml.load_model(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._assets._artifacts.model.Model[source]¶

Constructs a Model object from a YAML file.

Parameters

Keyword Arguments

relative_origin (Optional[str]) – The root directory for the YAML. This directory will be used as the origin for deducing the relative locations of files referenced in the parsed YAML. Defaults to the same directory as source if source is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (Optional[list[dict]]) – Parameter fields to overwrite values in the YAML file.

Raises

ValidationException – Raised if Job cannot be successfully validated. Details will be provided in the error message.

Returns

A loaded Model object.

Return type

Model

azure.ai.ml.load_model_package(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._assets._artifacts._package.model_package.ModelPackage[source]¶

Constructs a ModelPackage object from a YAML file.

Parameters

Keyword Arguments

relative_origin (Optional[str]) – The root directory for the YAML. This directory will be used as the origin for deducing the relative locations of files referenced in the parsed YAML. Defaults to the same directory as source if source is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (Optional[list[dict]]) – Parameter fields to overwrite values in the YAML file.

Raises

ValidationException – Raised if Job cannot be successfully validated. Details will be provided in the error message.

Returns

A loaded ModelPackage object.

Return type

ModelPackage

azure.ai.ml.load_online_deployment(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._deployment.online_deployment.OnlineDeployment[source]¶

Construct a online deployment object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of an online deployment object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Raises

ValidationException – Raised if Online Deployment cannot be successfully validated. Details will be provided in the error message.

Returns

Constructed online deployment object.

Return type

OnlineDeployment

azure.ai.ml.load_online_endpoint(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._endpoint.online_endpoint.OnlineEndpoint[source]¶

Construct a online endpoint object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of an online endpoint object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Raises

ValidationException – Raised if Online Endpoint cannot be successfully validated. Details will be provided in the error message.

Returns

Constructed online endpoint object.

Return type

OnlineEndpoint

azure.ai.ml.load_registry(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._registry.registry.Registry[source]¶

Load a registry object from a yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a registry. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded registry object.

Return type

Registry

azure.ai.ml.load_workspace(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._workspace.workspace.Workspace[source]¶

Load a workspace object from a yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a workspace. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
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(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._workspace.connections.workspace_connection.WorkspaceConnection[source]¶

Construct a workspace connection object from yaml file.

Parameters

source (Union[PathLike, str, io.TextIOWrapper]) – The local yaml source of a workspace connection object. Must be either a path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
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

azure.ai.ml.load_workspace_hub(source: Union[str, os.PathLike, IO], *, relative_origin: Optional[str] = None, **kwargs) → azure.ai.ml.entities._workspace_hub.workspace_hub.WorkspaceHub[source]¶

Load a WorkspaceHub object from a yaml file. :param source: The local yaml source of a WorkspaceHub. Must be either a

path to a local file, or an already-open file. If the source is a path, it will be open and read. An exception is raised if the file does not exist. If the source is an open file, the file will be read directly, and an exception is raised if the file is not readable.

Keyword Arguments

relative_origin (str) – The origin to be used when deducing the relative locations of files referenced in the parsed yaml. Defaults to the inputted source’s directory if it is a file or file path input. Defaults to “./” if the source is a stream input with no name value.
params_override (List[Dict]) – Fields to overwrite on top of the yaml file. Format is [{“field1”: “value1”}, {“field2”: “value2”}]

Returns

Loaded WorkspaceHub object.

Return type

WorkspaceHub

azure.ai.ml.spark(*, experiment_name: Optional[str] = None, name: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, code: Optional[Union[os.PathLike, str]] = None, entry: Optional[Union[Dict[str, str], azure.ai.ml.entities._job.spark_job_entry.SparkJobEntry]] = None, py_files: Optional[List[str]] = None, jars: Optional[List[str]] = None, files: Optional[List[str]] = None, archives: Optional[List[str]] = None, identity: Optional[Union[Dict[str, str], azure.ai.ml._restclient.v2023_04_01_preview.models._models_py3.ManagedIdentity, azure.ai.ml._restclient.v2023_04_01_preview.models._models_py3.AmlToken, azure.ai.ml._restclient.v2023_04_01_preview.models._models_py3.UserIdentity]] = None, driver_cores: Optional[int] = None, driver_memory: Optional[str] = None, executor_cores: Optional[int] = None, executor_memory: Optional[str] = None, executor_instances: Optional[int] = None, dynamic_allocation_enabled: Optional[bool] = None, dynamic_allocation_min_executors: Optional[int] = None, dynamic_allocation_max_executors: Optional[int] = None, conf: Optional[Dict[str, str]] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, args: Optional[str] = None, compute: Optional[str] = None, resources: Optional[Union[Dict, azure.ai.ml.entities._job.spark_resource_configuration.SparkResourceConfiguration]] = None, **kwargs) → azure.ai.ml.entities._builders.spark.Spark[source]¶

Creates a Spark object which can be used inside a dsl.pipeline function or used as a standalone Spark job.

Keyword Arguments

experiment_name (Optional[str]) – The name of the experiment the job will be created under.
name (Optional[str]) – The name of the job.
display_name (Optional[str]) – The job display name.
description (Optional[str]) – The description of the job. Defaults to None.
tags (Optional[dict[str, str]]) – The dictionary of tags for the job. Tags can be added, removed, and updated. Defaults to None.
code – The source code to run the job. Can be a local path or “http:”, “https:”, or “azureml:” url pointing to a remote location.
entry (Optional[Union[dict[str, str], SparkJobEntry]]) – The file or class entry point.
py_files (Optional[list[str]]) – The list of .zip, .egg or .py files to place on the PYTHONPATH for Python apps. Defaults to None.
jars (Optional[list[str]]) – The list of .JAR files to include on the driver and executor classpaths. Defaults to None.
files (Optional[list[str]]) – The list of files to be placed in the working directory of each executor. Defaults to None.
archives (Optional[list[str]]) – The list of archives to be extracted into the working directory of each executor. Defaults to None.
identity (Optional[Union[ dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]]) – The identity that the Spark job will use while running on compute.
driver_cores (Optional[int]) – The number of cores to use for the driver process, only in cluster mode.
driver_memory (Optional[str]) – The amount of memory to use for the driver process, formatted as strings with a size unit suffix (“k”, “m”, “g” or “t”) (e.g. “512m”, “2g”).
executor_cores (Optional[int]) – The number of cores to use on each executor.
executor_memory (Optional[str]) – The amount of memory to use per executor process, formatted as strings with a size unit suffix (“k”, “m”, “g” or “t”) (e.g. “512m”, “2g”).
executor_instances (Optional[int]) – The initial number of executors.
dynamic_allocation_enabled (Optional[bool]) – Whether to use dynamic resource allocation, which scales the number of executors registered with this application up and down based on the workload.
dynamic_allocation_min_executors (Optional[int]) – The lower bound for the number of executors if dynamic allocation is enabled.
dynamic_allocation_max_executors (Optional[int]) – The upper bound for the number of executors if dynamic allocation is enabled.
conf (Optional[dict[str, str]]) – A dictionary with pre-defined Spark configurations key and values. Defaults to None.
environment (Optional[Union[str, Environment]]) – The Azure ML environment to run the job in.
inputs (Optional[dict[str, Input]]) – A mapping of input names to input data used in the job. Defaults to None.
outputs (Optional[dict[str, Output]]) – A mapping of output names to output data used in the job. Defaults to None.
args (Optional[str]) – The arguments for the job.
compute (Optional[str]) – The compute resource the job runs on.
resources (Optional[Union[dict, SparkResourceConfiguration]]) – The compute resource configuration for the job.

Returns

A Spark object.

Return type

Spark

Subpackages¶

Submodules¶

azure.ai.ml.exceptions module¶

Contains exception module in Azure Machine Learning SDKv2.

This includes enums and classes for exceptions.

exception azure.ai.ml.exceptions.AssetException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Assets.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.AssetPathException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for the exception raised when an attempt is made to update the path of an existing asset. Asset paths are immutable.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.CannotSetAttributeError(object_name)[source]¶

Exception raised when a user try setting attributes of inputs/outputs.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.CloudArtifactsNotSupportedError(endpoint_name: str, invalid_artifact: str, deployment_name: Optional[str] = None, error_category='UserError')[source]¶

Exception raised when remote cloud artifacts are used with local endpoints.

Local endpoints only support local artifacts.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.ComponentException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Components.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.DeploymentException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Deployments.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.DockerEngineNotAvailableError(error_category='Unknown')[source]¶

Exception raised when local Docker Engine is unavailable for local operation.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.EmptyDirectoryError(message: str, no_personal_data_message: str, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown')[source]¶

Exception raised when an empty directory is provided as input for an I/O operation.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.InvalidLocalEndpointError(message: str, no_personal_data_message: str, error_category='UserError')[source]¶

Exception raised when local endpoint is invalid.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.InvalidVSCodeRequestError(error_category='UserError', msg=None)[source]¶

Exception raised when VS Code Debug is invoked with a remote endpoint.

VSCode debug is only supported for local endpoints.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.JobException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Jobs.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.JobParsingError(error_category, no_personal_data_message, message, *args, **kwargs)[source]¶

Exception that the job data returned by MFE cannot be parsed.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.KeywordError(message, no_personal_data_message=None)[source]¶

Super class of all type keyword error.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.LocalDeploymentGPUNotAvailable(error_category='UserError', msg=None)[source]¶

Exception raised when local_enable_gpu is set and Nvidia GPU is not available.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.LocalEndpointImageBuildError(error: Union[str, Exception], error_category='Unknown')[source]¶

Exception raised when local endpoint’s Docker image build is unsuccessful.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.LocalEndpointInFailedStateError(endpoint_name, deployment_name=None, error_category='Unknown')[source]¶

Exception raised when local endpoint is in Failed state.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.LocalEndpointNotFoundError(endpoint_name: str, deployment_name: Optional[str] = None, error_category='UserError')[source]¶

Exception raised if local endpoint cannot be found.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.MissingPositionalArgsError(func_name, missing_args)[source]¶

Exception raised when missing positional keyword parameter in dynamic functions.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

exc_msg: str ¶ exc_traceback: Optional[TracebackType]¶ exc_type: Optional[Type[Any]]¶ exc_value: Optional[BaseException]¶ inner_exception: Optional[BaseException]¶ message: Optional[str]¶ property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.MlException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

The base class for all exceptions raised in AzureML SDK code base. If there is a need to define a custom exception type, that custom exception type should extend from this class.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.
error (Exception) – The original exception if any.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property no_personal_data_message¶

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.ModelException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Models.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.MultipleLocalDeploymentsFoundError(endpoint_name: str, error_category='Unknown')[source]¶

Exception raised when no deployment name is specified for local endpoint even though multiple deployments exist.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.MultipleValueError(func_name, keyword)[source]¶

Exception raised when giving multiple value of a keyword parameter in dynamic functions.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.ParamValueNotExistsError(func_name, keywords)[source]¶

Exception raised when items in non_pipeline_inputs not in keyword parameters in dynamic functions.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.PipelineChildJobError(job_id: str, command: str = 'parse', prompt_studio_ui: bool = False)[source]¶

Exception that the pipeline child job is not supported.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

ERROR_MESSAGE_TEMPLATE = 'az ml job {command} is not supported on pipeline child job, {prompt_message}.'¶ PROMPT_PARENT_MESSAGE = 'please use this command on pipeline parent job'¶ PROMPT_STUDIO_UI_MESSAGE = 'please go to studio UI to do related actions{url}'¶ args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.RequiredLocalArtifactsNotFoundError(endpoint_name: str, required_artifact: str, required_artifact_type: str, deployment_name: Optional[str] = None, error_category='UserError')[source]¶

Exception raised when local artifact is not provided for local endpoint.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.ScheduleException(message: str, no_personal_data_message: str, *args, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'Unknown', **kwargs)[source]¶

Class for all exceptions related to Job Schedules.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.TooManyPositionalArgsError(func_name, min_number, max_number, given_number)[source]¶

Exception raised when too many positional arguments is provided in dynamic functions.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.UnexpectedAttributeError(keyword, keywords=None)[source]¶

Exception raised when an unexpected keyword is invoked by attribute, e.g. inputs.invalid_key.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.UnexpectedKeywordError(func_name, keyword, keywords=None)[source]¶

Exception raised when an unexpected keyword parameter is provided in dynamic functions.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.UnsupportedOperationError(operation_name)[source]¶

Exception raised when specified operation is not supported.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.UnsupportedParameterKindError(func_name, parameter_kind=None)[source]¶

Exception raised when a user try setting attributes of inputs/outputs.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.UserErrorException(message, no_personal_data_message=None, error_category='UserError', target: azure.ai.ml.exceptions.ErrorTarget = 'Pipeline')[source]¶

Exception raised when invalid or unsupported inputs are provided.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.VSCodeCommandNotFound(output=None, error_category='UserError')[source]¶

Exception raised when VSCode instance cannot be instantiated.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type

exception azure.ai.ml.exceptions.ValidationException(message: str, no_personal_data_message: str, *args, error_type: azure.ai.ml.exceptions.ValidationErrorType = <ValidationErrorType.GENERIC: 'GENERIC'>, target: azure.ai.ml.exceptions.ErrorTarget = 'Unknown', error_category: azure.ai.ml.exceptions.ErrorCategory = 'UserError', **kwargs)[source]¶

Class for all exceptions raised as part of client-side schema validation.

Parameters

message (str) – A message describing the error. This is the error message the user will see.
no_personal_data_message (str) – The error message without any personal data. This will be pushed to telemetry logs.
error_type (ValidationErrorType) – The error type, chosen from one of the values of ValidationErrorType enum class.
target (ErrorTarget) – The name of the element that caused the exception to be thrown.
error_category (ErrorCategory) – The error category, defaults to Unknown.
error (Exception) – The original exception if any.

raise_with_traceback() → None ¶

Raise the exception with the existing traceback.

Deprecated since version 1.22.0: This method is deprecated as we don’t support Python 2 anymore. Use raise/from instead.

with_traceback()¶

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

args¶ continuation_token: Optional[str]¶ property error_category¶

Return the error category.

Returns

The error category.

Return type

property error_type¶

Return the error type.

Returns

The error type.

Return type

ValidationErrorType

Return the error message with no personal data.

Returns

No personal data error message.

Return type

property target¶

Return the error target.

Returns

The error target.

Return type