azure.ai.ml.entities package¶
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class
azure.ai.ml.entities.AmlCompute(*, name: str, description: Optional[str] = None, size: Optional[str] = None, ssh_public_access_enabled: Optional[bool] = None, ssh_settings: Optional[azure.ai.ml.entities._compute.aml_compute.AmlComputeSshSettings] = None, min_instances: Optional[int] = None, max_instances: Optional[int] = None, network_settings: Optional[azure.ai.ml.entities._compute.compute.NetworkSettings] = None, idle_time_before_scale_down: Optional[int] = None, identity: Optional[azure.ai.ml.entities._compute._identity.IdentityConfiguration] = None, tier: Optional[str] = None, **kwargs)[source]¶ Aml Compute resource
- Parameters
name (str) – Name of the compute
description (str, optional) – Description of the resource.
size (str, optional) – Compute Size, defaults to None.
ssh_settings (AmlComputeSshSettings, optional) – SSH settings to access the AzureML compute cluster.
network_settings (NetworkSettings, optional) – Virtual network settings for the AzureML compute cluster.
idle_time_before_scale_down (int, optional) – Node Idle Time before scaling down amlCompute. Defaults to None.
identity (IdentityConfiguration, optional) – The identity configuration, identities that are associated with the compute cluster.
tier (str, optional) – Virtual Machine tier. Possible values include: “Dedicated”, “LowPriority”. Defaults to None.
min_instances (int, optional) – Minimum number of instances. Defaults to None.
max_instances (int, optional) – Maximum number of instances. Defaults to None.
ssh_public_access_enabled – State of the public SSH port. Possible values are: False - Indicates that the public ssh port is closed on all nodes of the cluster. True - Indicates that the public ssh port is open on all nodes of the cluster. None - Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined, else is open all public nodes. It can be default only during cluster creation time, after creation it will be either True or False. Possible values include: True, False, None. Default value: None. :type ssh_public_access_enabled: bool, optional
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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class
azure.ai.ml.entities.AmlComputeNodeInfo(**kwargs)[source]¶ Compute node information related to a AmlCompute Variables are only populated by the server, and will be ignored when sending a request.
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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
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classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
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classmethod
enable_additional_properties_sending()¶
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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)
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classmethod
is_xml_model()¶
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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.
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validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
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property
current_job_name¶
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class
azure.ai.ml.entities.AmlComputeSshSettings(*, admin_username: str, admin_password: Optional[str] = None, ssh_key_value: Optional[str] = None)[source]¶ SSH settings to access a AML compute target
[summary]
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class
azure.ai.ml.entities.Asset(name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, **kwargs)[source]¶ Base class for asset, can’t be instantiated directly.
- Parameters
name (str) – Name of the resource.
version (str) – Version of the asset.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the artifact content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
version¶
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class
azure.ai.ml.entities.AssignedUserConfiguration(*, user_tenant_id: str, user_object_id: str)[source]¶ Settings to create a compute on behalf of another user.
[summary]
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class
azure.ai.ml.entities.AzureBlobDatastore(*, name: str, account_name: str, container_name: str, description: Optional[str] = None, tags: Optional[Dict] = None, endpoint: str = 'core.windows.net', protocol: str = 'https', properties: Optional[Dict] = None, credentials: Optional[Union[azure.ai.ml.entities._datastore.credentials.AccountKeyCredentials, azure.ai.ml.entities._datastore.credentials.SasTokenCredentials]] = None, **kwargs)[source]¶ Azure blob storage that is linked to an Azure ML workspace.
- Parameters
name (str) – Name of the datastore.
account_name (str) – Name of the Azure storage account.
container_name (str) – Name of the container.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
endpoint (str) – Endpoint to use to connect with the Azure storage account.
protocol (str) – Protocol to use to connect with the Azure storage account.
properties (dict[str, str]) – The asset property dictionary.
credentials (Union[AccountKeySection, SasSection]) – Credentials to use for Azure ML workspace to connect to the storage.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the datastore content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
type¶
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class
azure.ai.ml.entities.AzureDataLakeGen1Datastore(*, name: str, store_name: str, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, credentials: Optional[Union[azure.ai.ml.entities._datastore.credentials.ServicePrincipalCredentials, azure.ai.ml.entities._datastore.credentials.CertificateCredentials]] = None, **kwargs)[source]¶ Azure Data Lake aka Gen 1 datastore that is linked to an Azure ML workspace
- Parameters
name (str) – Name of the datastore.
store_name (str) – Name of the Azure storage resource.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
credentials (Union[ServicePrincipalSection, CertificateSection]) – Credentials to use for Azure ML workspace to connect to the storage.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the datastore content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
type¶
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class
azure.ai.ml.entities.AzureDataLakeGen2Datastore(*, name: str, account_name: str, filesystem: str, description: Optional[str] = None, tags: Optional[Dict] = None, endpoint: str = 'core.windows.net', protocol: str = 'https', properties: Optional[Dict] = None, credentials: Optional[Union[azure.ai.ml.entities._datastore.credentials.ServicePrincipalCredentials, azure.ai.ml.entities._datastore.credentials.CertificateCredentials]] = None, **kwargs)[source]¶ Azure data lake gen 2 that is linked to an Azure ML workspace.
- Parameters
name (str) – Name of the datastore.
account_name (str) – Name of the Azure storage account.
filesystem (str) – The name of the Data Lake Gen2 filesystem.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
endpoint (str) – Endpoint to use to connect with the Azure storage account
protocol (str) – Protocol to use to connect with the Azure storage account
credentials (Union[ServicePrincipalSection, CertificateSection]) – Credentials to use for Azure ML workspace to connect to the storage.
properties (dict[str, str]) – The asset property dictionary.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the datastore content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
type¶
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class
azure.ai.ml.entities.AzureFileDatastore(*, name: str, account_name: str, file_share_name: str, description: Optional[str] = None, tags: Optional[Dict] = None, endpoint: str = 'core.windows.net', protocol: str = 'https', properties: Optional[Dict] = None, credentials: Union[azure.ai.ml.entities._datastore.credentials.AccountKeyCredentials, azure.ai.ml.entities._datastore.credentials.SasTokenCredentials], **kwargs)[source]¶ Azure file share that is linked to an Azure ML workspace.
- Parameters
name (str) – Name of the datastore.
account_name (str) – Name of the Azure storage account.
file_share_name (str) – Name of the file share.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
endpoint (str) – Endpoint to use to connect with the Azure storage account
protocol (str) – Protocol to use to connect with the Azure storage account
properties (dict[str, str]) – The asset property dictionary.
credentials (Union[AccountKeySection, SasSection]) – Credentials to use for Azure ML workspace to connect to the storage.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the datastore content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
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property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
type¶
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class
azure.ai.ml.entities.BatchDeployment(*, name: str, endpoint_name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, str]] = None, model: Optional[Union[str, azure.ai.ml.entities._assets._artifacts.model.Model]] = None, code_configuration: Optional[azure.ai.ml.entities._deployment.code_configuration.CodeConfiguration] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, compute: Optional[str] = None, resources: Optional[azure.ai.ml.entities._job.resource_configuration.ResourceConfiguration] = None, output_file_name: Optional[str] = None, output_action: Optional[azure.ai.ml._restclient.v2022_05_01.models._azure_machine_learning_workspaces_enums.BatchOutputAction] = None, error_threshold: Optional[int] = None, retry_settings: Optional[azure.ai.ml.entities._deployment.deployment_settings.BatchRetrySettings] = None, logging_level: Optional[str] = None, mini_batch_size: Optional[int] = None, max_concurrency_per_instance: Optional[int] = None, environment_variables: Optional[Dict[str, str]] = None, code_path: Optional[Union[os.PathLike, str]] = None, scoring_script: Optional[Union[os.PathLike, str]] = None, instance_count: Optional[int] = None, **kwargs)[source]¶ Batch endpoint deployment entity
- Parameters
name (str) – the name of the batch deployment
description (str, optional) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
model (Union[str, Model], optional) – Model entity for the endpoint deployment, defaults to None
code_configuration (CodeConfiguration, optional) – defaults to None
environment (Union[str, Environment], optional) – Environment entity for the endpoint deployment., defaults to None
compute (str) – Compute target for batch inference operation.
output_action (str or BatchOutputAction) – Indicates how the output will be organized. Possible values include: “summary_only”, “append_row”. Defaults to “append_row”
output_file_name (str) – Customized output file name for append_row output action, defaults to “predictions.csv”
max_concurrency_per_instance (int) – Indicates maximum number of parallelism per instance, defaults to 1
error_threshold (int, optional) – Error threshold, if the error count for the entire input goes above this value, the batch inference will be aborted. Range is [-1, int.MaxValue] -1 value indicates, ignore all failures during batch inference For FileDataset count of file failures For TabularDataset, this is the count of record failures, defaults to -1
retry_settings (BatchRetrySettings, optional) – Retry settings for a batch inference operation, defaults to None
resources (ResourceConfiguration) – Indicates compute configuration for the job.
logging_level (str, optional) – Logging level for batch inference operation, defaults to “info”
mini_batch_size (int, optional) – Size of the mini-batch passed to each batch invocation, defaults to 10
environment_variables (dict, optional) – Environment variables that will be set in deployment.
code_path (Union[str, PathLike], optional) – Folder path to local code assets. Equivalent to code_configuration.code.
scoring_script (Union[str, PathLike], optional) – Scoring script name. Equivalent to code_configuration.code.scoring_script.
instance_count (int, optional) – Number of instances the interfering will run on. Equivalent to resources.instance_count.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump(path: Union[os.PathLike, str]) → None¶ Dump the deployment content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
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property
code_path¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
instance_count¶
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property
scoring_script¶
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property
type¶
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class
azure.ai.ml.entities.BatchEndpoint(*, name: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, auth_mode: str = 'aad_token', description: Optional[str] = None, location: Optional[str] = None, defaults: Optional[Dict[str, str]] = None, default_deployment_name: Optional[str] = None, scoring_uri: Optional[str] = None, swagger_uri: Optional[str] = None, **kwargs)[source]¶ Batch endpoint entity.
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
auth_mode (str, optional) – Possible values include: “AMLToken”, “Key”, “AADToken”, defaults to None
description (str, optional) – Description of the inference endpoint, defaults to None
location (str, optional) – defaults to None
defaults (Dict[str, str], optional) – Traffic rules on how the traffic will be routed across deployments, defaults to {}
default_deployment_name (str, optional) – Equivalent to defaults.default_deployment, will be ignored if defaults is present.
scoring_uri (str, optional) – URI to use to perform a prediction, readonly.
swagger_uri (str, optional) – URI to check the swagger definition of the endpoint.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
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dump() → Dict[str, Any][source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
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property
provisioning_state¶ Endpoint provisioning state, readonly
- Returns
Endpoint provisioning state.
- Return type
Optional[str]
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class
azure.ai.ml.entities.BatchRetrySettings(*, max_retries: Optional[int] = None, timeout: Optional[int] = None)[source]¶ Retry settings for batch deployment
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class
azure.ai.ml.entities.BuildContext(*, dockerfile_path: Optional[str] = None, path: Optional[Union[os.PathLike, str]] = None)[source]¶ Docker build context for Environment
- Parameters
path (Union[str, os.PathLike]) – The local or remote path to the the docker build context directory.
dockerfile_path (str) – The path to the dockerfile relative to root of docker build context directory.
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class
azure.ai.ml.entities.Choice(values: Optional[List[Union[float, str, dict]]] = None, **kwargs)[source]¶
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class
azure.ai.ml.entities.CodeConfiguration(code: Optional[str] = None, scoring_script: Optional[str] = None)[source]¶ CodeConfiguration.
- Parameters
-
property
scoring_script¶
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class
azure.ai.ml.entities.CommandComponent(*, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, display_name: Optional[str] = None, command: Optional[str] = None, code: Optional[str] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, distribution: Optional[Union[azure.ai.ml.entities._job.distribution.PyTorchDistribution, azure.ai.ml.entities._job.distribution.MpiDistribution, azure.ai.ml.entities._job.distribution.TensorFlowDistribution]] = None, resources: Optional[azure.ai.ml.entities._job.resource_configuration.ResourceConfiguration] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, instance_count: Optional[int] = None, **kwargs)[source]¶ Command component version, used to define a command component.
- Parameters
name (str) – Name of the component.
version (str) – Version of the component.
description (str) – Description of the component.
tags (dict) – Tag dictionary. Tags can be added, removed, and updated.
display_name (str) – Display name of the component.
command (str) – Command to be executed in component.
code (str) – Code file or folder that will be uploaded to the cloud for component execution.
environment (Union[Environment, str]) – Environment that component will run in.
distribution (Union[dict, PyTorchDistribution, MpiDistribution, TensorFlowDistribution]) – Distribution configuration for distributed training.
resources (Union[dict, ResourceConfiguration]) – Compute Resource configuration for the component.
inputs (dict) – Inputs of the component.
outputs (dict) – Outputs of the component.
instance_count (int) – promoted property from resources.instance_count
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the component content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
display_name¶ Display name of the component.
- Returns
Display name of the component.
- Return type
-
property
distribution¶
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
instance_count¶ Return value of promoted property resources.instance_count.
- Returns
Value of resources.instance_count.
- Return type
Optional[int]
-
property
is_deterministic¶ Whether the component is deterministic.
- Returns
Whether the component is deterministic
- Return type
-
property
resources¶
-
property
type¶ Type of the component, default is ‘command’.
- Returns
Type of the component.
- Return type
-
property
version¶
-
class
azure.ai.ml.entities.CommandJob(*, inputs: Optional[Dict[str, Union[azure.ai.ml.entities._inputs_outputs.Input, str, bool, int, float]]] = None, outputs: Optional[Dict[str, azure.ai.ml.entities._inputs_outputs.Output]] = None, limits: Optional[azure.ai.ml.entities._job.job_limits.CommandJobLimits] = 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)[source]¶ Command job
- Parameters
name (str) – Name of the job.
description (str) – Description of the job.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
display_name (str) – Display name of the job.
properties (dict[str, str]) – The asset property dictionary.
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.
services (dict[str, JobService]) – Information on services associated with the job, readonly.
inputs (dict[str, Union[azure.ai.ml.Input, str, bool, int, float]]) – Inputs to the command.
outputs (dict[str, azure.ai.ml.Output]) – Mapping of output data bindings used in the job.
command (str) – Command to be executed in training.
compute (str) – The compute target the job runs on.
resources (ResourceConfiguration) – Compute Resource configuration for the job.
code (str) – A local path or http:, https:, azureml: url pointing to a remote location.
distribution (Union[azure.ai.ml.PyTorchDistribution, azure.ai.ml.MpiDistribution, azure.ai.ml.TensorFlowDistribution]) – Distribution configuration for distributed training.
environment (Union[azure.ai.ml.entities.Environment, str]) – Environment that training job will run in.
identity (Union[azure.ai.ml.ManagedIdentity, azure.ai.ml.AmlToken, azure.ai.ml.UserIdentity]) – Identity that training job will use while running on compute.
limits (CommandJobLimits) – Command Job limit.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the job content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
PYTHON_SDK_TYPE_MAPPING= {<class 'float'>: 'number', <class 'int'>: 'integer', <class 'bool'>: 'boolean', <class 'str'>: 'string'}¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
distribution¶
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
inputs¶
-
property
log_files¶ Job output files.
-
property
outputs¶
-
property
parameters¶ MLFlow parameters
-
property
resources¶
-
property
status¶ Status of the job.
Common values returned include “Running”, “Completed”, and “Failed”.
Note
NotStarted - This is a temporary state client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - Returned when on-demand compute is being created for a given job submission.
- Preparing - The run environment is being prepared:
docker image build
conda environment setup
- Queued - The job is queued in the compute target. For example, in BatchAI the job is in queued state
while waiting for all the requested nodes to be ready.
Running - The job started to run in the compute target.
Finalizing - User code has completed and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
- Completed - The run completed successfully. This includes both the user code and run
post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.
- Returns
Status of the job.
- Return type
-
class
azure.ai.ml.entities.CommandJobLimits(*, timeout: Optional[int] = None, **kwargs)[source]¶ Command Job limit class.
Variables are only populated by the server, and will be ignored when sending a request.
- Parameters
timeout (int) – The max run duration in seconds, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
- Keyword Arguments
timeout (timedelta) – The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
property
timeout¶
-
class
azure.ai.ml.entities.Component(*, name: Optional[str] = None, version: Optional[str] = None, id: Optional[str] = None, type: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, display_name: Optional[str] = None, is_deterministic: bool = True, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, yaml_str: Optional[str] = None, _schema: Optional[str] = None, creation_context: Optional[azure.ai.ml._restclient.v2022_05_01.models._models_py3.SystemData] = None, **kwargs)[source]¶ Base class for component version, used to define a component. Can’t be instantiated directly.
- Parameters
name (str) – Name of the resource.
version (str) – Version of the resource.
id (str) – Global id of the resource, Azure Resource Manager ID.
type (str) – Type of the command, supported is ‘command’.
description (str) – Description of the resource.
tags (dict) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict) – Internal use only.
display_name (str) – Display name of the component.
is_deterministic (bool) – Whether the component is deterministic.
inputs (dict) – Inputs of the component.
outputs (dict) – Outputs of the component.
yaml_str (str) – The yaml string of the component.
_schema (str) – Schema of the component.
creation_context (SystemData) – Creation metadata of the component.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the component content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
display_name¶ Display name of the component.
- Returns
Display name of the component.
- Return type
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
is_deterministic¶ Whether the component is deterministic.
- Returns
Whether the component is deterministic
- Return type
-
property
type¶ Type of the component, default is ‘command’.
- Returns
Type of the component.
- Return type
-
property
version¶
-
class
azure.ai.ml.entities.Compute(name: str, location: Optional[str] = None, description: Optional[str] = None, resource_id: Optional[str] = None, **kwargs)[source]¶ Compute resource
- Parameters
type (str) – The type of the compute, possible values are [“amlcompute”, “computeinstance”, “virtualmachine”, “kubernetes”]
name (str) – Name of the compute
location (Optional[str], optional) – The resource location, defaults to None
description (Optional[str], optional) – Description of the resource.
resource_id (str, optional) – ARM resource id of the underlying compute.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
class
azure.ai.ml.entities.ComputeConfiguration(target: Optional[str] = None, instance_count: Optional[int] = None, is_local: Optional[bool] = None, instance_type: Optional[str] = None, location: Optional[str] = None, properties: Optional[Dict[str, Any]] = None, deserialize_properties: bool = False)[source]¶ -
get(key: Any, default: Optional[Any] = None) → Any¶
-
-
class
azure.ai.ml.entities.ComputeInstance(*, name: str, description: Optional[str] = None, size: Optional[str] = None, ssh_public_access_enabled: Optional[bool] = None, create_on_behalf_of: Optional[azure.ai.ml.entities._compute.compute_instance.AssignedUserConfiguration] = None, network_settings: Optional[azure.ai.ml.entities._compute.compute.NetworkSettings] = None, ssh_settings: Optional[azure.ai.ml.entities._compute.compute_instance.ComputeInstanceSshSettings] = None, schedules: Optional[azure.ai.ml.entities._compute._schedule.ComputeSchedules] = None, **kwargs)[source]¶ Compute Instance resource
- Parameters
name (str) – Name of the compute
location (Optional[str], optional) – The resource location, defaults to None
description (Optional[str], optional) – Description of the resource.
size (Optional[str], optional) – Compute Size, defaults to None
create_on_behalf_of (Optional[AssignedUserConfiguration], optional) – defaults to None
network_settings (Optional[NetworkSettings], optional) – defaults to None
ssh_settings (Optional[ComputeInstanceSshSettings], optional) – defaults to None
ssh_public_access_enabled (Optional[bool], optional) – State of the public SSH port. Possible values are: [“False”, “True”, “None”] False - Indicates that the public ssh port is closed on all nodes of the cluster. True - Indicates that the public ssh port is open on all nodes of the cluster. None -Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined, else is open all public nodes. It can be default only during cluster creation time, after creation it will be either True or False. Possible values include: True, False, None. Default value: None.
schedules (Optional[ComputeSchedules], optional) – Compute instance schedules, defaults to None
- Variables
state – defaults to None
last_operation – defaults to None
applications – defaults to None
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
last_operation¶ The last operation.
return: The last operation. rtype: str
-
property
services¶ The services for the compute instance. rtype: List[Dict[str, str]]
- Type
return
-
property
state¶ The state of the compute
return: The state of the compute. rtype: str
-
class
azure.ai.ml.entities.ComputeInstanceSshSettings(*, ssh_key_value: str, **kwargs)[source]¶ Credentials for an administrator user account to SSH into the compute node. Can only be configured if ssh_public_access_enabled is set to true.
[summary]
- Parameters
ssh_key_value (str) – The SSH public key of the administrator user account.
-
property
admin_username¶ The name of the administrator user account which can be used to SSH into nodes.
return: The name of the administrator user account. rtype: str
-
property
ssh_port¶ SSH port.
return: SSH port. rtype: str
-
class
azure.ai.ml.entities.ComputePowerAction(value)[source]¶ The compute power action.
-
START= 'Start'¶
-
STOP= 'Stop'¶
-
-
class
azure.ai.ml.entities.ComputeSchedules(*, compute_start_stop: List[azure.ai.ml.entities._compute._schedule.ComputeStartStopSchedule] = None, **kwargs)[source]¶ Note
This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
Compute schedules
- Parameters
compute_start_stop (List[ComputeStartStopSchedule]) – Compute start or stop schedules.
kwargs (dict) – A dictionary of additional configuration parameters.
-
class
azure.ai.ml.entities.ComputeStartStopSchedule(*, trigger: Optional[azure.ai.ml.entities._compute._schedule.BaseTrigger] = None, action: Optional[azure.ai.ml._restclient.v2022_01_01_preview.models._azure_machine_learning_workspaces_enums.ComputePowerAction] = None, state: azure.ai.ml._restclient.v2022_01_01_preview.models._azure_machine_learning_workspaces_enums.ScheduleStatus = <ScheduleStatus.ENABLED: 'Enabled'>, **kwargs)[source]¶ Schedules for compute start or stop scenario
- Parameters
trigger (Trigger) – The trigger of the schedule.
action (ComputePowerAction) – The compute power action.
state (ScheduleState) – The state of the schedule.
kwargs (dict) – A dictionary of additional configuration parameters.
-
class
azure.ai.ml.entities.CronSchedule(*, expression: str, status: Optional[str] = None, start_time: Optional[str] = None, time_zone: azure.ai.ml.constants.TimeZone = <TimeZone.UTC: 'UTC'>)[source]¶ Cron schedule
- Parameters
status (str) – Specifies the schedule’s status. Possible values include: “enabled”, “disabled”.
start_time (Union[str, datetime]) – Specifies start time of schedule in ISO 8601 format. If no time zone offset is specified in the start_time, it will default to UTC (+0:00)
time_zone (Optional[TimeZone]) – Time zone in which the schedule runs. This does not apply to the start_time.
expression (str) – Specifies cron expression of schedule. The expression should follow NCronTab format.
- Keyword Arguments
end_time (datetime) – Specifies end time of schedule in ISO 8601 format. If not present, the schedule will run indefinitely.
schedule_status (str or ScheduleStatus) – Specifies the schedule’s status. Possible values include: “Enabled”, “Disabled”.
start_time (datetime) – Specifies start time of schedule in ISO 8601 format.
time_zone (str) – Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format.
expression (str) – Required. [Required] Specifies cron expression of schedule. The expression should follow NCronTab format.
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
status¶ 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__.
-
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.entities.CronTrigger(*, start_time: Optional[str] = None, time_zone: Optional[str] = None, expression: Optional[str] = None, **kwargs)[source]¶ Cron trigger
- Parameters
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
class
azure.ai.ml.entities.CustomerManagedKey(key_vault: Optional[str] = None, key_uri: Optional[str] = None, cosmosdb_id: Optional[str] = None, storage_id: Optional[str] = None, search_id: Optional[str] = None)[source]¶
-
class
azure.ai.ml.entities.Data(*, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, path: Optional[str] = None, type: str = 'uri_folder', **kwargs)[source]¶ Data for training and scoring.
- Parameters
name (str) – Name of the resource.
version (str) – Version of the resource.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
path (str) – The path to the asset on the datastore. This can be local or remote
type (Literal[AssetTypes.URI_FILE, AssetTypes.URI_FOLDER, AssetTypes.MLTABLE]) – The type of the asset. Valid values are uri_file, uri_folder, mltable. Defaults to uri_folder.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the artifact content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
path¶
-
property
version¶
-
class
azure.ai.ml.entities.Datastore(credentials: Any, name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, **kwargs)[source]¶ Datastore of an Azure ML workspace, abstract class.
- Parameters
name (str) – Name of the datastore.
description (str) – Description of the resource.
credentials (Union[ServicePrincipalSection, CertificateSection]) – Credentials to use for Azure ML workspace to connect to the storage.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the datastore content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
type¶
-
class
azure.ai.ml.entities.Endpoint(base_path: Optional[str] = None, auth_mode: Optional[str] = None, location: Optional[str] = None, name: Optional[str] = None, tags: Optional[Dict[str, str]] = None, properties: Optional[Dict[str, Any]] = None, description: Optional[str] = None, **kwargs)[source]¶ Endpoint base class.
- Parameters
auth_mode (str, optional) – the authentication mode, defaults to None
location (str, optional) – defaults to None
traffic (Dict[str, int], optional) – Traffic rules on how the traffic will be routed across deployments, defaults to {}
name (str, optional) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
scoring_uri (str, optional) – str, Endpoint URI, readonly
swagger_uri (str, optional) – str, Endpoint Swagger URI, readonly
provisioning_state (str, optional) – str, provisioning state, readonly
description (str, optional) – Description of the resource.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
abstract
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Endpoint provisioning state, readonly
- Returns
Endpoint provisioning state.
- Return type
Optional[str]
-
class
azure.ai.ml.entities.EndpointConnection(subscription_id: str, resource_group: str, vnet_name: str, subnet_name: str, location: Optional[str] = None)[source]¶
-
class
azure.ai.ml.entities.Environment(*, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, image: Optional[str] = None, build: Optional[azure.ai.ml.entities._assets.environment.BuildContext] = None, conda_file: Optional[Union[os.PathLike, str]] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, **kwargs)[source]¶ Environment for training.
- Parameters
name (str) – Name of the resource.
version (str) – Version of the asset.
description (str) – Description of the resource.
image (str) – URI of a custom base image.
build (BuildContext) – Docker build context to create the environment. Mutually exclusive with “image”
conda_file (Optional[str, os.Pathlike]) – Path to configuration file listing conda packages to install.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the artifact content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
conda_file¶ Conda environment specification.
- Returns
Conda dependencies loaded from conda_file param.
- Return type
Dict
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
version¶
-
class
azure.ai.ml.entities.IdentityConfiguration(**kwargs)[source]¶ Managed identity specification
Managed identity specification
- Parameters
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
property
type¶
-
property
user_assigned_identities¶
-
class
azure.ai.ml.entities.InputPort(*, type_string: str, default: Optional[str] = None, optional: Optional[bool] = False)[source]¶
-
class
azure.ai.ml.entities.Job(name: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, experiment_name: Optional[str] = None, compute: Optional[str] = None, services: Optional[Dict[str, azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.JobService]] = None, **kwargs)[source]¶ Base class for job, can’t be instantiated directly.
- Parameters
name (str) – Name of the resource.
display_name (str) – Display name of the resource.
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
experiment_name (str) – Name of the experiment the job will be created under, if None is provided, experiment will be set to current directory.
services (dict[str, JobService]) – Information on services associated with the job.
compute (str) – Information on compute resources associated with the job.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the job content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
PYTHON_SDK_TYPE_MAPPING= {<class 'float'>: 'number', <class 'int'>: 'integer', <class 'bool'>: 'boolean', <class 'str'>: 'string'}¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
log_files¶ Job output files.
-
property
status¶ Status of the job.
Common values returned include “Running”, “Completed”, and “Failed”.
Note
NotStarted - This is a temporary state client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - Returned when on-demand compute is being created for a given job submission.
- Preparing - The run environment is being prepared:
docker image build
conda environment setup
- Queued - The job is queued in the compute target. For example, in BatchAI the job is in queued state
while waiting for all the requested nodes to be ready.
Running - The job started to run in the compute target.
Finalizing - User code has completed and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
- Completed - The run completed successfully. This includes both the user code and run
post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.
- Returns
Status of the job.
- Return type
-
class
azure.ai.ml.entities.KubernetesCompute(*, namespace: Optional[str] = 'default', properties: Optional[Dict[str, Any]] = None, identity: Optional[azure.ai.ml.entities._compute._identity.IdentityConfiguration] = None, **kwargs)[source]¶ Kubernetes Compute resource
- Parameters
name (str) – Name of the compute
location (Optional[str], optional) – The resource location, defaults to None
description (Optional[str], optional) – Description of the resource.
resource_id (Optional[str], optional) – ARM resource id of the underlying compute, defaults to None
created_on (Optional[str], optional) – defaults to None
provisioning_state (Optional[str], optional) – defaults to None
namespace (Optional[str], optional) – Namespace of the KubernetesCompute
properties (Optional[Dict], optional) – KubernetesProperties, defaults to None
identity (IdentityConfiguration, optional) – The identity configuration, identities that are associated with the compute cluster.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
class
azure.ai.ml.entities.KubernetesOnlineDeployment(*, name: str, endpoint_name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, description: Optional[str] = None, model: Optional[Union[str, azure.ai.ml.entities._assets._artifacts.model.Model]] = None, code_configuration: Optional[azure.ai.ml.entities._deployment.code_configuration.CodeConfiguration] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, app_insights_enabled: bool = False, scale_settings: Optional[azure.ai.ml.entities._deployment.scale_settings.OnlineScaleSettings] = None, request_settings: Optional[azure.ai.ml.entities._deployment.deployment_settings.OnlineRequestSettings] = None, liveness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, readiness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, environment_variables: Optional[Dict[str, str]] = None, resources: Optional[azure.ai.ml.entities._deployment.resource_requirements_settings.ResourceRequirementsSettings] = None, instance_count: Optional[int] = None, instance_type: Optional[str] = None, code_path: Optional[Union[os.PathLike, str]] = None, scoring_script: Optional[Union[os.PathLike, str]] = None, **kwargs)[source]¶ Kubernetes Online endpoint deployment entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (Dict[str, Any], optional) – The asset property dictionary.
description (str, optional) – Description of the resource.
model (Union[str, Model], optional) – Model entity for the endpoint deployment, defaults to None
code_configuration (CodeConfiguration, optional) – defaults to None
environment (Union[str, Environment], optional) – Environment entity for the endpoint deployment, defaults to None
app_insights_enabled (bool, optional) – defaults to False
scale_settings (OnlineScaleSettings, optional) – How the online deployment will scale.
request_settings (OnlineRequestSettings, optional) – defaults to RequestSettings()
liveness_probe (ProbeSettings, optional) – Liveness probe settings.
readiness_probe (ProbeSettings, optional) – Readiness probe settings.
environment_variables (dict, optional) – Environment variables that will be set in deployment.
resources (ResourceRequirementsSettings, optional) – defaults to None
instance_type (str) – The instance type defined by K8S cluster admin.
instance_count (int) – The instance count used for this deployment.
code_path (Union[str, PathLike], optional) – Folder path to local code assets. Equivalent to code_configuration.code.
scoring_script (Union[str, PathLike], optional) – Scoring script name. Equivalent to code_configuration.code.scoring_script.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the deployment content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
code_path¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Deployment provisioning state, readonly
- Returns
Deployment provisioning state.
- Return type
Optional[str]
-
property
scoring_script¶
-
property
type¶
-
class
azure.ai.ml.entities.KubernetesOnlineEndpoint(*, name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, auth_mode: str = 'key', description: Optional[str] = None, location: Optional[str] = None, traffic: Optional[Dict[str, int]] = None, mirror_traffic: Optional[Dict[str, int]] = None, compute: Optional[str] = None, identity: Optional[azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.IdentityConfiguration] = None, kind: Optional[str] = None, **kwargs)[source]¶ K8s Online endpoint entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
auth_mode (str, optional) – Possible values include: “aml_token”, “key”, defaults to “key”
description (str, optional) – Description of the inference endpoint, defaults to None
location (str, optional) – defaults to None
traffic (Dict[str, int], optional) – Traffic rules on how the traffic will be routed across deployments, defaults to {}
compute (str, optional) – Compute cluster id.
identity (IdentityConfiguration, optional) – defaults to SystemAssigned
kind (str, optional) – Kind of the resource, we have two kinds: K8s and Managed online endpoints, defaults to None.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump() → Dict[str, Any][source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Endpoint provisioning state, readonly
- Returns
Endpoint provisioning state.
- Return type
Optional[str]
-
class
azure.ai.ml.entities.LogNormal(mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.LogUniform(min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.ManagedOnlineDeployment(*, name: str, endpoint_name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, description: Optional[str] = None, model: Optional[Union[str, azure.ai.ml.entities._assets._artifacts.model.Model]] = None, code_configuration: Optional[azure.ai.ml.entities._deployment.code_configuration.CodeConfiguration] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, app_insights_enabled: bool = False, scale_settings: Optional[azure.ai.ml.entities._deployment.scale_settings.OnlineScaleSettings] = None, request_settings: Optional[azure.ai.ml.entities._deployment.deployment_settings.OnlineRequestSettings] = None, liveness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, readiness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, environment_variables: Optional[Dict[str, str]] = None, instance_type: Optional[str] = None, instance_count: Optional[int] = None, code_path: Optional[Union[os.PathLike, str]] = None, scoring_script: Optional[Union[os.PathLike, str]] = None, **kwargs)[source]¶ Managed Online endpoint deployment entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (Dict[str, Any], optional) – The asset property dictionary.
description (str, optional) – Description of the resource.
model (Union[str, Model], optional) – Model entity for the endpoint deployment, defaults to None
code_configuration (CodeConfiguration, optional) – defaults to None
environment (Union[str, Environment], optional) – Environment entity for the endpoint deployment, defaults to None
app_insights_enabled (bool, optional) – defaults to False
scale_settings (OnlineScaleSettings, optional) – How the online deployment will scale.
request_settings (OnlineRequestSettings, optional) – defaults to RequestSettings()
liveness_probe (ProbeSettings, optional) – Liveness probe settings.
readiness_probe (ProbeSettings, optional) – Readiness probe settings.
environment_variables (dict, optional) – Environment variables that will be set in deployment.
instance_type (str) – Azure compute sku.
instance_count (int) – The instance count used for this deployment.
code_path (Union[str, PathLike], optional) – Folder path to local code assets. Equivalent to code_configuration.code.
scoring_script (Union[str, PathLike], optional) – Scoring script name. Equivalent to code_configuration.code.scoring_script.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the deployment content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
code_path¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Deployment provisioning state, readonly
- Returns
Deployment provisioning state.
- Return type
Optional[str]
-
property
scoring_script¶
-
property
type¶
-
class
azure.ai.ml.entities.ManagedOnlineEndpoint(*, name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, auth_mode: str = 'key', description: Optional[str] = None, location: Optional[str] = None, traffic: Optional[Dict[str, int]] = None, mirror_traffic: Optional[Dict[str, int]] = None, identity: Optional[azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.IdentityConfiguration] = None, kind: Optional[str] = None, **kwargs)[source]¶ Managed Online endpoint entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
auth_mode (str, optional) – Possible values include: “aml_token”, “key”, defaults to “key”
description (str, optional) – Description of the inference endpoint, defaults to None
location (str, optional) – defaults to None
traffic (Dict[str, int], optional) – Traffic rules on how the traffic will be routed across deployments, defaults to {}
identity (IdentityConfiguration, optional) – defaults to SystemAssigned
kind (str, optional) – Kind of the resource, we have two kinds: K8s and Managed online endpoints, defaults to None.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump() → Dict[str, Any][source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Endpoint provisioning state, readonly
- Returns
Endpoint provisioning state.
- Return type
Optional[str]
-
class
azure.ai.ml.entities.Model(*, name: Optional[str] = None, version: Optional[str] = None, type: Optional[str] = None, path: Optional[Union[os.PathLike, str]] = None, utc_time_created: Optional[str] = None, flavors: Optional[Dict[str, Dict[str, Any]]] = None, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, **kwargs)[source]¶ Model for training and scoring.
- Parameters
name (str) – Name of the resource.
version (str) – Version of the resource.
type (str) – The storage format for this entity. Used for NCD. Possible values include: “custom_model”, “mlflow_model”, “triton_model”.
utc_time_created (str) – Date and time when the model was created, in UTC ISO 8601 format. (e.g. ‘2020-10-19 17:44:02.096572’)
flavors (Dict[str, Any]) – The flavors in which the model can be interpreted. (e.g. {sklearn: {sklearn_version: 0.23.2}, python_function: {loader_module: office.plrmodel, python_version: 3.6})
path (str) – A remote uri or a local path pointing at a model. Example: “azureml://subscriptions/my-sub-id/resourcegroups/my-rg/workspaces/myworkspace/datastores/mydatastore/paths/path_on_datastore/”
description (str) – Description of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the artifact content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
path¶
-
property
version¶
-
class
azure.ai.ml.entities.NetworkSettings(*, vnet_name: Optional[str] = None, subnet: Optional[str] = None, **kwargs)[source]¶ Network settings for a compute
- Parameters
-
property
private_ip_address¶ Private IP address of the compute instance.
return: Private IP address. rtype: str
-
property
public_ip_address¶ Public IP address of the compute instance.
return: Public IP address. rtype: str
-
class
azure.ai.ml.entities.Normal(mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.OnlineDeployment(name: str, endpoint_name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, description: Optional[str] = None, model: Optional[Union[str, azure.ai.ml.entities._assets._artifacts.model.Model]] = None, code_configuration: Optional[azure.ai.ml.entities._deployment.code_configuration.CodeConfiguration] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, app_insights_enabled: bool = False, scale_settings: Optional[azure.ai.ml.entities._deployment.scale_settings.OnlineScaleSettings] = None, request_settings: Optional[azure.ai.ml.entities._deployment.deployment_settings.OnlineRequestSettings] = None, liveness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, readiness_probe: Optional[azure.ai.ml.entities._deployment.deployment_settings.ProbeSettings] = None, environment_variables: Optional[Dict[str, str]] = None, instance_count: Optional[int] = None, instance_type: Optional[str] = None, model_mount_path: Optional[str] = None, code_path: Optional[Union[os.PathLike, str]] = None, scoring_script: Optional[Union[os.PathLike, str]] = None, **kwargs)[source]¶ Online endpoint deployment entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (Dict[str, Any], optional) – The asset property dictionary.
description (str, optional) – Description of the resource.
model (Union[str, Model], optional) – Model entity for the endpoint deployment, defaults to None
code_configuration (CodeConfiguration, optional) – defaults to None
environment (Union[str, Environment], optional) – Environment entity for the endpoint deployment, defaults to None
app_insights_enabled (bool, optional) – defaults to False
scale_settings (OnlineScaleSettings, optional) – How the online deployment will scale.
request_settings (OnlineRequestSettings, optional) – defaults to RequestSettings()
liveness_probe (ProbeSettings, optional) – Liveness probe settings.
readiness_probe (ProbeSettings, optional) – Readiness probe settings.
environment_variables (dict, optional) – Environment variables that will be set in deployment.
instance_count (int) – The instance count used for this deployment.
instance_type (str) – Azure compute sku.
model_mount_path (str) – The path to mount the model in custom container..
code_path (Union[str, PathLike], optional) – Equivalent to code_configuration.code, will be ignored if code_configuration is present.
scoring_script (Union[str, PathLike], optional) – Equivalent to code_configuration.code.scoring_script, will be ignored if code_configuration is present.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the deployment content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
code_path¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Deployment provisioning state, readonly
- Returns
Deployment provisioning state.
- Return type
Optional[str]
-
property
scoring_script¶
-
property
type¶
-
class
azure.ai.ml.entities.OnlineEndpoint(name: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, properties: Optional[Dict[str, Any]] = None, auth_mode: str = 'key', description: Optional[str] = None, location: Optional[str] = None, traffic: Optional[Dict[str, int]] = None, mirror_traffic: Optional[Dict[str, int]] = None, identity: Optional[azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.IdentityConfiguration] = None, scoring_uri: Optional[str] = None, swagger_uri: Optional[str] = None, provisioning_state: Optional[str] = None, kind: Optional[str] = None, **kwargs)[source]¶ Online endpoint entity
- Parameters
name (str) – Name of the resource.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
properties (dict[str, str]) – The asset property dictionary.
auth_mode (str, optional) – Possible values include: “aml_token”, “key”, defaults to “key”
description (str, optional) – Description of the inference endpoint, defaults to None
location (str, optional) – defaults to None
traffic (Dict[str, int], optional) – Traffic rules on how the traffic will be routed across deployments, defaults to {}
mirror_traffic (Dict[str, int], optional) – Duplicated life traffic used to train a single deployment, defaults to {}
provisioning_state (str, optional) – str, provisioning state, readonly
identity (IdentityConfiguration, optional) – defaults to SystemAssigned
kind (str, optional) – Kind of the resource, we have two kinds: K8s and Managed online endpoints, defaults to None.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
abstract
dump(path: Union[os.PathLike, str]) → None¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
provisioning_state¶ Endpoint provisioning state, readonly
- Returns
Endpoint provisioning state.
- Return type
Optional[str]
-
class
azure.ai.ml.entities.OnlineRequestSettings(max_concurrent_requests_per_instance: Optional[int] = None, request_timeout_ms: Optional[int] = None, max_queue_wait_ms: Optional[int] = None)[source]¶ Request Settings entity
-
class
azure.ai.ml.entities.ParallelComponent(*, name: Optional[str] = None, version: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict[str, Any]] = None, display_name: Optional[str] = None, retry_settings: Optional[azure.ai.ml.entities._job.parallel.retry_settings.RetrySettings] = None, logging_level: Optional[str] = None, max_concurrency_per_instance: Optional[int] = None, error_threshold: Optional[int] = None, mini_batch_error_threshold: Optional[int] = None, task: Optional[azure.ai.ml.entities._job.parallel.parallel_task.ParallelTask] = None, mini_batch_size: Optional[str] = None, input_data: Optional[str] = None, resources: Optional[azure.ai.ml.entities._job.resource_configuration.ResourceConfiguration] = None, inputs: Optional[Dict] = None, outputs: Optional[Dict] = None, code: Optional[str] = None, instance_count: Optional[int] = None, **kwargs)[source]¶ Parallel component version, used to define a parallel component.
- Parameters
name (str) – Name of the component.
version (str) – Version of the component.
description (str) – Description of the component.
tags (dict) – Tag dictionary. Tags can be added, removed, and updated.
display_name (str) – Display name of the component.
retry_settings (BatchRetrySettings) – parallel component run failed retry
logging_level (str) – A string of the logging level name
max_concurrency_per_instance (int) – The max parallellism that each compute instance has.
error_threshold (int) – The number of item processing failures should be ignored.
mini_batch_error_threshold (int) – The number of mini batch processing failures should be ignored.
task (ParallelTask) – The parallel task.
mini_batch_size (str) – For FileDataset input, this field is the number of files a user script can process in one run() call. For TabularDataset input, this field is the approximate size of data the user script can process in one run() call. Example values are 1024, 1024KB, 10MB, and 1GB. (optional, default value is 10 files for FileDataset and 1MB for TabularDataset.) This value could be set through PipelineParameter.
input_data (str) – The input data.
resources (Union[dict, ResourceConfiguration]) – Compute Resource configuration for the component.
inputs (dict) – Inputs of the component.
outputs (dict) – Outputs of the component.
code (str) – promoted property from task.code
instance_count (int) – promoted property from resources.instance_count
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the component content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
code¶ Return value of promoted property task.code, which is a local or remote path pointing at source code.
- Returns
Value of task.code.
- Return type
Optional[str]
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
display_name¶ Display name of the component.
- Returns
Display name of the component.
- Return type
-
property
environment¶ Return value of promoted property task.environment, indicate the environment that training job will run in.
- Returns
Value of task.environment.
- Return type
Optional[Environment, str]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
instance_count¶ Return value of promoted property resources.instance_count.
- Returns
Value of resources.instance_count.
- Return type
Optional[int]
-
property
is_deterministic¶ Whether the component is deterministic.
- Returns
Whether the component is deterministic
- Return type
-
property
resources¶
-
property
retry_settings¶
-
property
task¶
-
property
type¶ Type of the component, default is ‘command’.
- Returns
Type of the component.
- Return type
-
property
version¶
-
class
azure.ai.ml.entities.ParallelJob(*, inputs: Optional[Dict[str, Union[azure.ai.ml.entities._inputs_outputs.Input, str, bool, int, float]]] = None, outputs: Optional[Dict[str, azure.ai.ml.entities._inputs_outputs.Output]] = None, **kwargs)[source]¶ Parallel job
- Parameters
name (str) – Name of the job.
version (str) – Version of the job.
id (str) – Global id of the resource, Azure Resource Manager ID.
type (str) – Type of the job, supported is ‘parallel’.
description (str) – Description of the job.
tags (dict) – Internal use only.
properties (dict) – Internal use only.
display_name (str) – Display name of the job.
retry_settings (BatchRetrySettings) – parallel job run failed retry
logging_level (str) – A string of the logging level name
max_concurrency_per_instance (int) – The max parallellism that each compute instance has.
error_threshold (int) – The number of item processing failures should be ignored.
mini_batch_error_threshold (int) – The number of mini batch processing failures should be ignored.
task (ParallelTask) – The parallel task.
mini_batch_size (str) – The mini batch size.
input_data (str) – The input data.
inputs (dict) – Inputs of the job.
outputs (dict) – Outputs of the job.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the job content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
PYTHON_SDK_TYPE_MAPPING= {<class 'float'>: 'number', <class 'int'>: 'integer', <class 'bool'>: 'boolean', <class 'str'>: 'string'}¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
inputs¶
-
property
log_files¶ Job output files.
-
property
outputs¶
-
property
resources¶
-
property
retry_settings¶
-
property
status¶ Status of the job.
Common values returned include “Running”, “Completed”, and “Failed”.
Note
NotStarted - This is a temporary state client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - Returned when on-demand compute is being created for a given job submission.
- Preparing - The run environment is being prepared:
docker image build
conda environment setup
- Queued - The job is queued in the compute target. For example, in BatchAI the job is in queued state
while waiting for all the requested nodes to be ready.
Running - The job started to run in the compute target.
Finalizing - User code has completed and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
- Completed - The run completed successfully. This includes both the user code and run
post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.
- Returns
Status of the job.
- Return type
-
property
studio_url¶ Azure ML studio endpoint
- Returns
URL to the job detail page.
- Return type
Optional[str]
-
property
task¶
-
class
azure.ai.ml.entities.ParallelTask(*, type: Optional[str] = None, code: Optional[str] = None, entry_script: Optional[str] = None, args: Optional[str] = None, model: Optional[str] = None, append_row_to: Optional[str] = None, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, **kwargs)[source]¶ Parallel task.
- Parameters
type (str) – The type of the parallel task. Possible values are ‘function’and ‘model_config’.
code (str) – A local or remote path pointing at source code.
entry_script (str) – User script which will be run in parallel on multiple nodes. This is specified as a local file path. The entry_script should contain two functions:
init(): this function should be used for any costly or common preparation for subsequent inferences, e.g., deserializing and loading the model into a global object.run(mini_batch): The method to be parallelized. Each invocation will have one mini-batch. ‘mini_batch’: Batch inference will invoke run method and pass either a list or a Pandas DataFrame as an argument to the method. Each entry in min_batch will be a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset. run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch. Each parallel worker process will call init once and then loop over run function until all mini-batches are processed.args (str) – The arguments of the parallel task.
model (str) – The model of the parallel task.
append_row_to (str) – All values output by run() method invocations will be aggregated into one unique file which is created in the output location. if it is not set, ‘summary_only’ would invoked, which means user script is expected to store the output itself.
environment (Union["Environment", str]) – Environment that training job will run in.
-
get(key: Any, default: Optional[Any] = None) → Any¶
-
classmethod
load(path: Optional[Union[os.PathLike, str]] = None, params_override: Optional[list] = None, **kwargs) → azure.ai.ml.entities._job.parallel.parallel_task.ParallelTask[source]¶
-
class
azure.ai.ml.entities.ParameterizedCommand(command: str = '', resources: Optional[Union[dict, azure.ai.ml.entities._job.resource_configuration.ResourceConfiguration]] = None, code: Optional[str] = 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, environment: Optional[Union[str, azure.ai.ml.entities._assets.environment.Environment]] = None, **kwargs)[source]¶ Command component that contains the traning command and supporting parameters for the command.
- Parameters
command (str) – Command to be executed in training.
code (str) – A local or remote path pointing at source code.
distribution (Union[Dict, PyTorchDistribution, MpiDistribution, TensorFlowDistribution]) – Distribution configuration for distributed training.
environment (Union[Environment, str]) – Environment that training job will run in.
resources (Union[Dict, ResourceConfiguration]) – Compute Resource configuration for the job.
kwargs (dict) – A dictionary of additional configuration parameters.
-
property
distribution¶
-
property
resources¶
-
class
azure.ai.ml.entities.ParameterizedParallel(retry_settings: Optional[azure.ai.ml.entities._job.parallel.retry_settings.RetrySettings] = None, logging_level: Optional[str] = None, max_concurrency_per_instance: Optional[int] = None, error_threshold: Optional[int] = None, mini_batch_error_threshold: Optional[int] = None, input_data: Optional[str] = None, task: Optional[azure.ai.ml.entities._job.parallel.parallel_task.ParallelTask] = None, mini_batch_size: Optional[int] = None, resources: Optional[Union[dict, azure.ai.ml.entities._job.resource_configuration.ResourceConfiguration]] = None, environment_variables: Optional[Dict] = None)[source]¶ Parallel component that contains the traning parallel and supporting parameters for the parallel.
- Parameters
retry_settings (BatchRetrySettings) – parallel component run failed retry
logging_level (str) – A string of the logging level name
max_concurrency_per_instance (int) – The max parallellism that each compute instance has.
error_threshold (int) – The number of item processing failures should be ignored.
mini_batch_error_threshold (int) – The number of mini batch processing failures should be ignored.
task (ParallelTask) – The parallel task.
mini_batch_size (str) – The mini batch size.
input_data (str) – The input data.
resources (Union[Dict, ResourceConfiguration]) – Compute Resource configuration for the job.
-
property
resources¶
-
property
retry_settings¶
-
property
task¶
-
class
azure.ai.ml.entities.PipelineJob(*, component: Optional[azure.ai.ml.entities._component._pipeline_component._PipelineComponent] = None, inputs: Optional[Dict[str, Union[azure.ai.ml.entities._inputs_outputs.Input, str, bool, int, float]]] = None, outputs: Optional[Dict[str, azure.ai.ml.entities._inputs_outputs.Output]] = None, name: Optional[str] = None, description: Optional[str] = None, display_name: Optional[str] = None, experiment_name: Optional[str] = None, jobs: Optional[Dict[str, azure.ai.ml.entities._builders.base_node.BaseNode]] = None, settings: Optional[azure.ai.ml.entities._job.pipeline.pipeline_job_settings.PipelineJobSettings] = 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, compute: Optional[str] = None, tags: Optional[Dict[str, str]] = None, schedule: Optional[Union[azure.ai.ml.entities._schedule.schedule.CronSchedule, azure.ai.ml.entities._schedule.schedule.RecurrenceSchedule]] = None, **kwargs)[source]¶ Pipeline job. Please use @pipeline decorator to create a PipelineJob, not recommended instantiating it directly.
- Parameters
component (_PipelineComponent) – Pipeline component version. Used to validate given value against
inputs (dict[str, Union[Input, str, bool, int, float]]) – Inputs to the pipeline job.
name (str) – Name of the PipelineJob.
description (str) – Description of the pipeline job.
display_name (str) – Display name of the pipeline job.
experiment_name (str) – Name of the experiment the job will be created under, if None is provided, experiment will be set to current directory.
jobs (dict[str, BaseNode]) – Pipeline component node name to component object.
settings (PipelineJobSettings) – Setting of pipeline job.
identity (Union[ManagedIdentity, AmlToken, UserIdentity]) – Identity that training job will use while running on compute.
compute (str) – Compute target name of the built pipeline.
tags (dict[str, str]) – Tag dictionary. Tags can be added, removed, and updated.
schedule (Union[CronSchedule, RecurrenceSchedule]) – Schedule definition of job. If no schedule is provided, the job will run once immediately after it is submitted.
kwargs (dict) – A dictionary of additional configuration parameters.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the job content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
PYTHON_SDK_TYPE_MAPPING= {<class 'float'>: 'number', <class 'int'>: 'integer', <class 'bool'>: 'boolean', <class 'str'>: 'string'}¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
log_files¶ Job output files.
-
property
outputs¶ Outputs of the pipeline job.
- Returns
Outputs of the pipeline job.
- Return type
-
property
schedule¶ Schedule of the pipeline job.
- Returns
Schedule of the pipeline job.
- Return type
Optional[Union[CronSchedule, RecurrenceSchedule]]
-
property
settings¶ Settings of the pipeline job.
- Returns
Settings of the pipeline job.
- Return type
-
property
status¶ Status of the job.
Common values returned include “Running”, “Completed”, and “Failed”.
Note
NotStarted - This is a temporary state client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - Returned when on-demand compute is being created for a given job submission.
- Preparing - The run environment is being prepared:
docker image build
conda environment setup
- Queued - The job is queued in the compute target. For example, in BatchAI the job is in queued state
while waiting for all the requested nodes to be ready.
Running - The job started to run in the compute target.
Finalizing - User code has completed and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
- Completed - The run completed successfully. This includes both the user code and run
post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.
- Returns
Status of the job.
- Return type
-
class
azure.ai.ml.entities.PipelineJobSettings(default_datastore: Optional[str] = None, default_compute: Optional[str] = None, continue_on_step_failure: Optional[bool] = None, force_rerun: Optional[bool] = None, **kwargs)[source]¶ Settings of PipelineJob, include default_datastore, default_compute, continue_on_step_failure and force_rerun.
- Parameters
-
class
azure.ai.ml.entities.PrivateEndpoint(approval_type: Optional[str] = None, connections: Optional[Dict[str, azure.ai.ml.entities._workspace.private_endpoint.EndpointConnection]] = None)[source]¶
-
class
azure.ai.ml.entities.ProbeSettings(*, failure_threshold: Optional[int] = None, success_threshold: Optional[int] = None, timeout: Optional[int] = None, period: Optional[int] = None, initial_delay: Optional[int] = None)[source]¶ Settings on how to probe an endpoint
- Parameters
failure_threshold (int, optional) – Threshold for probe failures, defaults to 30
success_threshold (int, optional) – Threshold for probe success, defaults to 1
timeout (int, optional) – timeout in seconds, defaults to 2
period (int, optional) – [description], defaults to 10
initial_delay (int, optional) – How to to wait for the first probe, defaults to 10
-
class
azure.ai.ml.entities.QLogNormal(mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.QLogUniform(min_value: Optional[float] = None, max_value: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.QNormal(mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.QUniform(min_value: Optional[Union[int, float]] = None, max_value: Optional[Union[int, float]] = None, q: Optional[int] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.RecurrencePattern(*, hours: Union[int, List[int]], minutes: Union[int, List[int]], weekdays: Optional[Union[str, List[str]]] = None)[source]¶ Recurrence pattern
- Parameters
- Keyword Arguments
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
hours¶ A data descriptor that transforms singular literal values to lists in the setter. The getter always returns a list When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.
-
minutes¶ A data descriptor that transforms singular literal values to lists in the setter. The getter always returns a list When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.
-
weekdays¶ A data descriptor that transforms singular literal values to lists in the setter. The getter always returns a list When the optional private_name is provided, the descriptor will set the private_name in the object’s __dict__.
-
class
azure.ai.ml.entities.RecurrenceSchedule(*, frequency: str, interval: int, pattern: Optional[azure.ai.ml.entities._schedule.schedule.RecurrencePattern] = None, status: Optional[str] = None, start_time: Optional[str] = None, time_zone: azure.ai.ml.constants.TimeZone = <TimeZone.UTC: 'UTC'>)[source]¶ Recurrence schedule
- Parameters
status (str) – Specifies the schedule’s status. Possible values include: “enabled”, “disabled”.
start_time (Union[str, datetime]) – Specifies start time of schedule in ISO 8601 format. If no time zone offset is specified in the start_time, it will default to UTC (+0:00)
time_zone (Optional[TimeZone]) – Time zone in which the schedule runs. This does not apply to the start_time.
frequency (str) – Specifies frequency with with which to trigger schedule. Possible values include: “minute”, “hour”, “day”, “week”, “month”.
interval (int) – Specifies schedule interval in conjunction with frequency.
pattern (RecurrencePattern) – Specifies the recurrence schedule pattern.
- Keyword Arguments
end_time (datetime) – Specifies end time of schedule in ISO 8601 format. If not present, the schedule will run indefinitely.
schedule_status (str or ScheduleStatus) – Specifies the schedule’s status. Possible values include: “Enabled”, “Disabled”.
start_time (datetime) – Specifies start time of schedule in ISO 8601 format.
time_zone (str) – Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format.
frequency (str or RecurrenceFrequency) – Required. [Required] Specifies frequency with with which to trigger schedule. Possible values include: “Minute”, “Hour”, “Day”, “Week”, “Month”.
interval (int) – Required. [Required] Specifies schedule interval in conjunction with frequency.
pattern (RecurrencePattern) – Specifies the recurrence schedule pattern.
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
frequency¶ 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__.
-
status¶ 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__.
-
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.entities.RecurrenceTrigger(*, start_time: Optional[str] = None, time_zone: Optional[str] = None, frequency: Optional[azure.ai.ml._restclient.v2022_01_01_preview.models._azure_machine_learning_workspaces_enums.RecurrenceFrequency] = None, interval: Optional[int] = None, schedule: Optional[azure.ai.ml._restclient.v2022_01_01_preview.models._models_py3.RecurrenceSchedule] = None, **kwargs)[source]¶ Recurrence trigger
- Parameters
start_time (str) – The start time.
time_zone (str) – The time zone.
frequency (RecurrenceFrequency) – Frequency of the recurrence trigger.
interval (int) – Recurrence interval.
schedule (RecurrenceSchedule) – Schedule of the recurrence trigger.
kwargs (dict) – A dictionary of additional configuration parameters.
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
validate()¶ Validate this model recursively and return a list of ValidationError.
- Returns
A list of validation error
- Return type
-
class
azure.ai.ml.entities.Resource(name: str, description: Optional[str] = None, tags: Optional[Dict] = None, properties: Optional[Dict] = None, **kwargs)[source]¶ Base class for entity classes, can’t be instantiated directly.
- Parameters
ABC (class) – Helper class that provides a standard way to create an ABC using inheritance.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
abstract
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
class
azure.ai.ml.entities.ResourceConfiguration(*, instance_count: Optional[int] = None, instance_type: Optional[str] = None, properties: Optional[Dict[str, Any]] = None, **kwargs)[source]¶ -
get(key: Any, default: Optional[Any] = None) → Any¶
-
-
class
azure.ai.ml.entities.ResourceRequirementsSettings(requests: Optional[azure.ai.ml.entities._deployment.container_resource_settings.ResourceSettings] = None, limits: Optional[azure.ai.ml.entities._deployment.container_resource_settings.ResourceSettings] = None)[source]¶
-
class
azure.ai.ml.entities.ResourceSettings(cpu: Optional[str] = None, memory: Optional[str] = None, gpu: Optional[str] = None)[source]¶
-
class
azure.ai.ml.entities.RetrySettings(*, timeout: Optional[int] = None, max_retries: Optional[int] = None, **kwargs)[source]¶ Parallel RetrySettings.
- Parameters
timeout (int) – Timeout in seconds for each invocation of the run() method. (optional) This value could be set through PipelineParameter.
max_retries (int) – The number of maximum tries for a failed or timeout mini batch. The range is [1, int.max]. This value could be set through PipelineParameter. A mini batch with dequeue count greater than this won’t be processed again and will be deleted directly.
-
get(key: Any, default: Optional[Any] = None) → Any¶
-
classmethod
load(path: Optional[Union[os.PathLike, str]] = None, params_override: Optional[list] = None, **kwargs) → azure.ai.ml.entities._job.parallel.retry_settings.RetrySettings[source]¶
-
azure.ai.ml.entities.ScheduleState¶ alias of
azure.ai.ml._restclient.v2022_01_01_preview.models._azure_machine_learning_workspaces_enums.ScheduleStatus
-
class
azure.ai.ml.entities.ScheduleStatus(value)[source]¶ Enum to describe status of schedule
-
DISABLED= 'Disabled'¶ Schedule is disabled.
-
ENABLED= 'Enabled'¶ Schedule is enabled.
-
-
class
azure.ai.ml.entities.TargetUtilizationScaleSettings(*, min_instances: Optional[int] = None, max_instances: Optional[int] = None, polling_interval: Optional[int] = None, target_utilization_percentage: Optional[int] = None, **kwargs)[source]¶ Auto scale settings
-
class
azure.ai.ml.entities.Uniform(min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs)[source]¶
-
class
azure.ai.ml.entities.UnsupportedCompute(**kwargs)[source]¶ Unsupported compute resource.
Only for use displaying compute properties for resources not fully supported in the SDK.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
class
azure.ai.ml.entities.Usage(id: Optional[str] = None, aml_workspace_location: Optional[str] = None, type: Optional[str] = None, unit: Optional[Union[str, azure.ai.ml._restclient.v2022_01_01_preview.models._azure_machine_learning_workspaces_enums.UsageUnit]] = None, current_value: Optional[int] = None, limit: Optional[int] = None, name: Optional[azure.ai.ml._restclient.v2022_01_01_preview.models._models_py3.UsageName] = None, **kwargs)[source]¶ Describes AML Resource Usage
Describes AML Resource Usage :param id: Specifies the resource ID. :type id: str :param aml_workspace_location: Region of the AML workspace in the id. :type aml_workspace_location: str :param type: Specifies the resource type. :type type: str :param unit: An enum describing the unit of usage measurement. Possible values include: “Count”. :type unit: str or ~azure.mgmt.machinelearningservices.models.UsageUnit :param current_value: The current usage of the resource. :type current_value: int :param limit: The maximum permitted usage of the resource. :type limit: int :param name: The name of the type of usage. :type name: ~azure.mgmt.machinelearningservices.models.UsageName
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the resource usage content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
classmethod
load(path: Union[os.PathLike, str], params_override: Optional[list] = None, **kwargs) → azure.ai.ml.entities._compute._usage.Usage[source]¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
-
class
azure.ai.ml.entities.UsageName(*, value: Optional[str] = None, localized_value: Optional[str] = None, **kwargs)[source]¶ The Usage Names.
-
class
azure.ai.ml.entities.UserAssignedIdentity(resource_id: Optional[str] = None, **kwargs)[source]¶ User Assigned identity specification
User Assigned identity specification
- Parameters
-
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
-
classmethod
deserialize(data, content_type=None)¶ Parse a str using the RestAPI syntax and return a model.
-
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)
-
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.
-
class
azure.ai.ml.entities.VirtualMachineCompute(*, name: str, description: Optional[str] = None, resource_id: str, public_key_data: Optional[str] = None, ssh_settings: Optional[azure.ai.ml.entities._compute.virtual_machine_compute.VirtualMachineSshSettings] = None, **kwargs)[source]¶ Virtual Machine Compute resource
- Parameters
name (str) – Name of the compute
description (Optional[str], optional) – Description of the resource.
resource_id (str) – ARM resource id of the underlying compute
ssh_settings (VirtualMachineSshSettings, optional) – SSH settings.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None¶ Dump the compute content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
public_key_data¶ Public key data.
return: Public key data. rtype: str
-
class
azure.ai.ml.entities.VirtualMachineSshSettings(*, admin_username: str, admin_password: Optional[str] = None, ssh_port: int = 22, ssh_private_key_file: Optional[str] = None, **kwargs)[source]¶ SSH settings for a virtual machine
- Parameters
admin_username (str, required) – Describes the admin user name., defaults to None.
admin_password (str, optional) – Describes the admin user password, defaults to None. Required if ssh_private_key_file is not specified.
ssh_port (str, optional) – The ssh port number. Default is 22.
ssh_private_key_file (str, optional) – Specifies the file containing SSH rsa private key. Use “ssh-keygen -t rsa -b 2048” to generate your SSH key pairs.Required if admin_password is not specified.
-
class
azure.ai.ml.entities.VmSize(name: Optional[str] = None, family: Optional[str] = None, v_cp_us: Optional[int] = None, gpus: Optional[int] = None, os_vhd_size_mb: Optional[int] = None, max_resource_volume_mb: Optional[int] = None, memory_gb: Optional[float] = None, low_priority_capable: Optional[bool] = None, premium_io: Optional[bool] = None, supported_compute_types: Optional[List[str]] = None, **kwargs)[source]¶ virtual Machine Size
Virtual machine size :param name: The name of the virtual machine size. :type name: str :param family: The family name of the virtual machine size. :type family: str :param v_cp_us: The number of vCPUs supported by the virtual machine size. :type v_cp_us: int :param gpus: The number of gPUs supported by the virtual machine size. :type gpus: int :param os_vhd_size_mb: The OS VHD disk size, in MB, allowed by the virtual machine size. :type os_vhd_size_mb: int :param max_resource_volume_mb: The resource volume size, in MB, allowed by the virtual machine size. :type max_resource_volume_mb: int :param memory_gb: The amount of memory, in GB, supported by the virtual machine size. :type memory_gb: float :param low_priority_capable: Specifies if the virtual machine size supports low priority VMs. :type low_priority_capable: bool :param premium_io: Specifies if the virtual machine size supports premium IO. :type premium_io: bool :param estimated_vm_prices: The estimated price information for using a VM. :type estimated_vm_prices: ~azure.mgmt.machinelearningservices.models.EstimatedVMPrices :param supported_compute_types: Specifies the compute types supported by the virtual machine size. :type supported_compute_types: list[str]
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the virtual machine size content into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
classmethod
load(path: Union[os.PathLike, str], params_override: Optional[list] = None, **kwargs) → azure.ai.ml.entities._compute._vm_size.VmSize[source]¶
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
-
class
azure.ai.ml.entities.Workspace(*, name: str, description: Optional[str] = None, tags: Optional[Dict[str, str]] = None, display_name: Optional[str] = None, location: Optional[str] = None, resource_group: Optional[str] = None, hbi_workspace: bool = False, storage_account: Optional[str] = None, container_registry: Optional[str] = None, key_vault: Optional[str] = None, application_insights: Optional[str] = None, customer_managed_key: Optional[azure.ai.ml.entities._workspace.customer_managed_key.CustomerManagedKey] = None, image_build_compute: Optional[str] = None, public_network_access: Optional[str] = None, softdelete_enable: bool = False, allow_recover_softdeleted_workspace: bool = False, **kwargs)[source]¶ Azure ML workspace.
- Parameters
name (str) – Name of the workspace.
description (str) – Description of the workspace.
tags (dict) – Tags of the workspace.
display_name (str) – Display name for the workspace. This is non-unique within the resource group.
location (str) – The location to create the workspace in. If not specified, the same location as the resource group will be used.
resource_group (str) – Name of resource group to create the workspace in.
hbi_workspace (bool) – Whether the customer data is of high business impact (HBI), containing sensitive business information. For more information, see https://docs.microsoft.com/azure/machine-learning/concept-data-encryption#encryption-at-rest.
storage_account (str) – The resource ID of an existing storage account to use instead of creating a new one.
container_registry (str) – The resource ID of an existing container registry to use instead of creating a new one.
key_vault (str) – The resource ID of an existing key vault to use instead of creating a new one.
application_insights (str) – The resource ID of an existing application insights to use instead of creating a new one.
customer_managed_key (CustomerManagedKey) – Key vault details for encrypting data with customer-managed keys. If not specified, Microsoft-managed keys will be used by default.
image_build_compute (str) – The name of the compute target to use for building environment Docker images with the container registry is behind a VNet.
public_network_access (str) – Whether to allow public endpoint connectivity when a workspace is private link enabled.
softdelete_enable (bool) – Create a workspace with soft delete capability
allow_recover_softdeleted_workspace (bool) – Allow an existing soft-deleted workspace to be recovered
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the workspace spec into a file in yaml format.
- Parameters
path (str) – Path to a local file as the target, new file will be created, raises exception if the file exists.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
discovery_url¶ Backend service base URLs for the workspace.
- Returns
Backend service URLs of the workspace
- Return type
-
class
azure.ai.ml.entities.WorkspaceConnection(*, target: str, type: str, credentials: Union[azure.ai.ml.entities._workspace.connections.credentials.PatTokenCredentials, azure.ai.ml.entities._workspace.connections.credentials.SasTokenCredentials, azure.ai.ml.entities._workspace.connections.credentials.UsernamePasswordCredentials, azure.ai.ml.entities._workspace.connections.credentials.ManagedIdentityCredentials, azure.ai.ml.entities._workspace.connections.credentials.ServicePrincipalCredentials], metadata: Optional[Dict[str, Any]] = None, **kwargs)[source]¶ Azure ML workspace connection provides a secure way to store authentication and configuration information needed to connect and interact with the external resources.
- Parameters
name (str) – Name of the workspace connection.
target (str) – The URL or ARM resource ID of the external resource.
credentials (Union[PatTokenCredentials, SasTokenCredentials, UsernamePasswordCredentials, ManagedIdentityCredentials]) – The credentials for authenticating to the external resource.
type (The type of workspace connection, possible values are ["git", "python_feed", "container_registry", "feature_store"]) – The category of external resource for this connection.
Class Resource constructor.
- Parameters
name (str) – Name of the resource.
description (str, optional) – Description of the resource., defaults to None
tags (Dict, optional) – Tag dictionary. Tags can be added, removed, and updated., defaults to None
properties (Dict, optional) – The asset property dictionary., defaults to None
kwargs (dict) – A dictionary of additional configuration parameters.
-
dump(path: Union[os.PathLike, str]) → None[source]¶ Dump the object content into a file.
- Parameters
path (Union[PathLike, str]) – Path to a local file as the target.
-
property
creation_context¶ Creation context
- Returns
Creation metadata of the resource.
- Return type
Optional[SystemData]
-
property
credentials¶ Credentials for workspace connection.
- Returns
Credentials for workspace connection.
- Return type
WorkspaceConnectionCredentials
-
property
id¶ Resource ID.
- Returns
Global id of the resource, Azure Resource Manager ID
- Return type
Optional[str]
-
property
metadata¶ Metadata for workspace connection.
- Returns
Metadata for workspace connection.
- Return type
Dict[str, Any]
-
property
target¶ Target url for the workspace connection.
- Returns
Target of the workspace connection.
- Return type