diff options
Diffstat (limited to '.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment')
22 files changed, 2830 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/__init__.py new file mode 100644 index 00000000..fdf8caba --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/__init__.py @@ -0,0 +1,5 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_deployment.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_deployment.py new file mode 100644 index 00000000..59b23eb8 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_deployment.py @@ -0,0 +1,356 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=protected-access + +import logging +from os import PathLike +from pathlib import Path +from typing import Any, Dict, Optional, Union + +from azure.ai.ml._restclient.v2024_01_01_preview.models import BatchDeployment as BatchDeploymentData +from azure.ai.ml._restclient.v2024_01_01_preview.models import BatchDeploymentProperties as RestBatchDeployment +from azure.ai.ml._restclient.v2024_01_01_preview.models import BatchOutputAction +from azure.ai.ml._restclient.v2024_01_01_preview.models import CodeConfiguration as RestCodeConfiguration +from azure.ai.ml._restclient.v2024_01_01_preview.models import IdAssetReference +from azure.ai.ml._schema._deployment.batch.batch_deployment import BatchDeploymentSchema +from azure.ai.ml._utils._arm_id_utils import _parse_endpoint_name_from_deployment_id +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY +from azure.ai.ml.constants._deployment import BatchDeploymentOutputAction +from azure.ai.ml.entities._assets import Environment, Model +from azure.ai.ml.entities._deployment.deployment_settings import BatchRetrySettings +from azure.ai.ml.entities._job.resource_configuration import ResourceConfiguration +from azure.ai.ml.entities._system_data import SystemData +from azure.ai.ml.entities._util import load_from_dict +from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException + +from .code_configuration import CodeConfiguration +from .deployment import Deployment + +module_logger = logging.getLogger(__name__) + + +class BatchDeployment(Deployment): + """Batch endpoint deployment entity. + + :param name: the name of the batch deployment + :type name: str + :param description: Description of the resource. + :type description: str + :param tags: Tag dictionary. Tags can be added, removed, and updated. + :type tags: dict[str, str] + :param properties: The asset property dictionary. + :type properties: dict[str, str] + :param model: Model entity for the endpoint deployment, defaults to None + :type model: Union[str, Model] + :param code_configuration: defaults to None + :type code_configuration: CodeConfiguration + :param environment: Environment entity for the endpoint deployment., defaults to None + :type environment: Union[str, Environment] + :param compute: Compute target for batch inference operation. + :type compute: str + :param output_action: Indicates how the output will be organized. Possible values include: + "summary_only", "append_row". Defaults to "append_row" + :type output_action: str or ~azure.ai.ml.constants._deployment.BatchDeploymentOutputAction + :param output_file_name: Customized output file name for append_row output action, defaults to "predictions.csv" + :type output_file_name: str + :param max_concurrency_per_instance: Indicates maximum number of parallelism per instance, defaults to 1 + :type max_concurrency_per_instance: int + :param error_threshold: 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 + :type error_threshold: int + :param retry_settings: Retry settings for a batch inference operation, defaults to None + :type retry_settings: BatchRetrySettings + :param resources: Indicates compute configuration for the job. + :type resources: ~azure.mgmt.machinelearningservices.models.ResourceConfiguration + :param logging_level: Logging level for batch inference operation, defaults to "info" + :type logging_level: str + :param mini_batch_size: Size of the mini-batch passed to each batch invocation, defaults to 10 + :type mini_batch_size: int + :param environment_variables: Environment variables that will be set in deployment. + :type environment_variables: dict + :param code_path: Folder path to local code assets. Equivalent to code_configuration.code. + :type code_path: Union[str, PathLike] + :param scoring_script: Scoring script name. Equivalent to code_configuration.code.scoring_script. + :type scoring_script: Union[str, PathLike] + :param instance_count: Number of instances the interfering will run on. Equivalent to resources.instance_count. + :type instance_count: int + :raises ~azure.ai.ml.exceptions.ValidationException: Raised if BatchDeployment cannot be successfully validated. + Details will be provided in the error message. + """ + + def __init__( + self, + *, + 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, Model]] = None, + code_configuration: Optional[CodeConfiguration] = None, + environment: Optional[Union[str, Environment]] = None, + compute: Optional[str] = None, + resources: Optional[ResourceConfiguration] = None, + output_file_name: Optional[str] = None, + output_action: Optional[Union[BatchDeploymentOutputAction, str]] = None, + error_threshold: Optional[int] = None, + retry_settings: Optional[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[str, PathLike]] = None, # promoted property from code_configuration.code + scoring_script: Optional[ + Union[str, PathLike] + ] = None, # promoted property from code_configuration.scoring_script + instance_count: Optional[int] = None, # promoted property from resources.instance_count + **kwargs: Any, + ) -> None: + self._provisioning_state: Optional[str] = kwargs.pop("provisioning_state", None) + + super(BatchDeployment, self).__init__( + name=name, + endpoint_name=endpoint_name, + properties=properties, + tags=tags, + description=description, + model=model, + code_configuration=code_configuration, + environment=environment, + environment_variables=environment_variables, + code_path=code_path, + scoring_script=scoring_script, + **kwargs, + ) + + self.compute = compute + self.resources = resources + self.output_action = output_action + self.output_file_name = output_file_name + self.error_threshold = error_threshold + self.retry_settings = retry_settings + self.logging_level = logging_level + self.mini_batch_size = mini_batch_size + self.max_concurrency_per_instance = max_concurrency_per_instance + + if self.resources and instance_count: + msg = "Can't set instance_count when resources is provided." + raise ValidationException( + message=msg, + target=ErrorTarget.BATCH_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) + + if not self.resources and instance_count: + self.resources = ResourceConfiguration(instance_count=instance_count) + + @property + def instance_count(self) -> Optional[int]: + return self.resources.instance_count if self.resources else None + + @instance_count.setter + def instance_count(self, value: int) -> None: + if not self.resources: + self.resources = ResourceConfiguration() + + self.resources.instance_count = value + + @property + def provisioning_state(self) -> Optional[str]: + """Batch deployment provisioning state, readonly. + + :return: Batch deployment provisioning state. + :rtype: Optional[str] + """ + return self._provisioning_state + + def _to_dict(self) -> Dict: + res: dict = BatchDeploymentSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + @classmethod + def _rest_output_action_to_yaml_output_action(cls, rest_output_action: str) -> str: + output_switcher = { + BatchOutputAction.APPEND_ROW: BatchDeploymentOutputAction.APPEND_ROW, + BatchOutputAction.SUMMARY_ONLY: BatchDeploymentOutputAction.SUMMARY_ONLY, + } + + return output_switcher.get(rest_output_action, rest_output_action) + + @classmethod + def _yaml_output_action_to_rest_output_action(cls, yaml_output_action: Any) -> str: + output_switcher = { + BatchDeploymentOutputAction.APPEND_ROW: BatchOutputAction.APPEND_ROW, + BatchDeploymentOutputAction.SUMMARY_ONLY: BatchOutputAction.SUMMARY_ONLY, + } + + return output_switcher.get(yaml_output_action, yaml_output_action) + + # pylint: disable=arguments-differ + def _to_rest_object(self, location: str) -> BatchDeploymentData: # type: ignore + self._validate() + code_config = ( + RestCodeConfiguration( + code_id=self.code_configuration.code, + scoring_script=self.code_configuration.scoring_script, + ) + if self.code_configuration + else None + ) + model = IdAssetReference(asset_id=self.model) if self.model else None + environment = self.environment + + batch_deployment: RestBatchDeployment = None + if isinstance(self.output_action, str): + batch_deployment = RestBatchDeployment( + compute=self.compute, + description=self.description, + resources=self.resources._to_rest_object() if self.resources else None, + code_configuration=code_config, + environment_id=environment, + model=model, + output_file_name=self.output_file_name, + output_action=BatchDeployment._yaml_output_action_to_rest_output_action(self.output_action), + error_threshold=self.error_threshold, + retry_settings=self.retry_settings._to_rest_object() if self.retry_settings else None, + logging_level=self.logging_level, + mini_batch_size=self.mini_batch_size, + max_concurrency_per_instance=self.max_concurrency_per_instance, + environment_variables=self.environment_variables, + properties=self.properties, + ) + else: + batch_deployment = RestBatchDeployment( + compute=self.compute, + description=self.description, + resources=self.resources._to_rest_object() if self.resources else None, + code_configuration=code_config, + environment_id=environment, + model=model, + output_file_name=self.output_file_name, + output_action=None, + error_threshold=self.error_threshold, + retry_settings=self.retry_settings._to_rest_object() if self.retry_settings else None, + logging_level=self.logging_level, + mini_batch_size=self.mini_batch_size, + max_concurrency_per_instance=self.max_concurrency_per_instance, + environment_variables=self.environment_variables, + properties=self.properties, + ) + + return BatchDeploymentData(location=location, properties=batch_deployment, tags=self.tags) + + @classmethod + def _from_rest_object( # pylint: disable=arguments-renamed + cls, deployment: BatchDeploymentData + ) -> BatchDeploymentData: + modelId = deployment.properties.model.asset_id if deployment.properties.model else None + + if ( + hasattr(deployment.properties, "deployment_configuration") + and deployment.properties.deployment_configuration is not None + ): + settings = deployment.properties.deployment_configuration.settings + deployment_comp_settings = { + "deployment_configuration_type": deployment.properties.deployment_configuration.deployment_configuration_type, # pylint: disable=line-too-long + "componentDeployment.Settings.continue_on_step_failure": settings.get( + "ComponentDeployment.Settings.continue_on_step_failure", None + ), + "default_datastore": settings.get("default_datastore", None), + "default_compute": settings.get("default_compute", None), + } + properties = {} + if deployment.properties.properties: + properties.update(deployment.properties.properties) + properties.update(deployment_comp_settings) + else: + properties = deployment.properties.properties + + code_configuration = ( + CodeConfiguration._from_rest_code_configuration(deployment.properties.code_configuration) + if deployment.properties.code_configuration + else None + ) + deployment = BatchDeployment( + name=deployment.name, + description=deployment.properties.description, + id=deployment.id, + tags=deployment.tags, + model=modelId, + environment=deployment.properties.environment_id, + code_configuration=code_configuration, + output_file_name=( + deployment.properties.output_file_name + if cls._rest_output_action_to_yaml_output_action(deployment.properties.output_action) + == BatchDeploymentOutputAction.APPEND_ROW + else None + ), + output_action=cls._rest_output_action_to_yaml_output_action(deployment.properties.output_action), + error_threshold=deployment.properties.error_threshold, + retry_settings=BatchRetrySettings._from_rest_object(deployment.properties.retry_settings), + logging_level=deployment.properties.logging_level, + mini_batch_size=deployment.properties.mini_batch_size, + compute=deployment.properties.compute, + resources=ResourceConfiguration._from_rest_object(deployment.properties.resources), + environment_variables=deployment.properties.environment_variables, + max_concurrency_per_instance=deployment.properties.max_concurrency_per_instance, + endpoint_name=_parse_endpoint_name_from_deployment_id(deployment.id), + properties=properties, + creation_context=SystemData._from_rest_object(deployment.system_data), + provisioning_state=deployment.properties.provisioning_state, + ) + + return deployment + + @classmethod + def _load( + cls, + data: Optional[Dict] = None, + yaml_path: Optional[Union[PathLike, str]] = None, + params_override: Optional[list] = None, + **kwargs: Any, + ) -> "BatchDeployment": + data = data or {} + params_override = params_override or [] + cls._update_params(params_override) + + context = { + BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path.cwd(), + PARAMS_OVERRIDE_KEY: params_override, + } + res: BatchDeployment = load_from_dict(BatchDeploymentSchema, data, context, **kwargs) + return res + + def _validate(self) -> None: + self._validate_output_action() + + @classmethod + def _update_params(cls, params_override: Any) -> None: + for param in params_override: + endpoint_name = param.get("endpoint_name") + if isinstance(endpoint_name, str): + param["endpoint_name"] = endpoint_name.lower() + + def _validate_output_action(self) -> None: + if ( + self.output_action + and self.output_action == BatchDeploymentOutputAction.SUMMARY_ONLY + and self.output_file_name + ): + msg = "When output_action is set to {}, the output_file_name need not to be specified." + msg = msg.format(BatchDeploymentOutputAction.SUMMARY_ONLY) + raise ValidationException( + message=msg, + target=ErrorTarget.BATCH_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_job.py new file mode 100644 index 00000000..c078f479 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/batch_job.py @@ -0,0 +1,38 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict + +from azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models import BatchJobResource + + +class BatchJob(object): + """Batch jobs that are created with batch deployments/endpoints invocation. + + This class shouldn't be instantiated directly. Instead, it is used as the return type of batch deployment/endpoint + invocation and job listing. + """ + + def __init__(self, **kwargs: Any): + self.id = kwargs.get("id", None) + self.name = kwargs.get("name", None) + self.type = kwargs.get("type", None) + self.status = kwargs.get("status", None) + + def _to_dict(self) -> Dict: + return { + "id": self.id, + "name": self.name, + "type": self.type, + "status": self.status, + } + + @classmethod + def _from_rest_object(cls, obj: BatchJobResource) -> "BatchJob": + return cls( + id=obj.id, + name=obj.name, + type=obj.type, + status=obj.properties.status, + ) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/code_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/code_configuration.py new file mode 100644 index 00000000..cbae647d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/code_configuration.py @@ -0,0 +1,93 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +import logging +import os +from typing import Optional, Union + +from azure.ai.ml._restclient.v2022_05_01.models import CodeConfiguration as RestCodeConfiguration +from azure.ai.ml.entities._assets import Code +from azure.ai.ml.entities._mixins import DictMixin +from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException + +module_logger = logging.getLogger(__name__) + + +class CodeConfiguration(DictMixin): + """Code configuration for a scoring job. + + :param code: The code directory containing the scoring script. The code can be an Code object, an ARM resource ID + of an existing code asset, a local path, or "http:", "https:", or "azureml:" url pointing to a remote location. + :type code: Optional[Union[~azure.ai.ml.entities.Code, str]] + :param scoring_script: The scoring script file path relative to the code directory. + :type scoring_script: Optional[str] + + .. admonition:: Example: + + .. literalinclude:: ../samples/ml_samples_misc.py + :start-after: [START code_configuration] + :end-before: [END code_configuration] + :language: python + :dedent: 8 + :caption: Creating a CodeConfiguration for a BatchDeployment. + """ + + def __init__( + self, + code: Optional[Union[str, os.PathLike]] = None, + scoring_script: Optional[Union[str, os.PathLike]] = None, + ) -> None: + self.code: Optional[Union[str, os.PathLike]] = code + self._scoring_script: Optional[Union[str, os.PathLike]] = scoring_script + + @property + def scoring_script(self) -> Optional[Union[str, os.PathLike]]: + """The scoring script file path relative to the code directory. + + :rtype: str + """ + return self._scoring_script + + def _to_rest_code_configuration(self) -> RestCodeConfiguration: + return RestCodeConfiguration(code_id=self.code, scoring_script=self.scoring_script) + + def _validate(self) -> None: + if self.code and not self.scoring_script: + msg = "scoring script can't be empty" + raise ValidationException( + message=msg, + target=ErrorTarget.CODE, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.MISSING_FIELD, + ) + + @staticmethod + def _from_rest_code_configuration(code_configuration: RestCodeConfiguration) -> Optional["CodeConfiguration"]: + if code_configuration: + return CodeConfiguration( + code=code_configuration.code_id, + scoring_script=code_configuration.scoring_script, + ) + return None + + def __eq__(self, other: object) -> bool: + if not isinstance(other, CodeConfiguration): + return NotImplemented + if not other: + return False + # only compare mutable fields + return ( + self.scoring_script == other.scoring_script + and ( + isinstance(self.code, Code) + and isinstance(other.code, Code) + or isinstance(self.code, str) + and isinstance(other.code, str) + ) + and self.code == other.code + ) + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/container_resource_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/container_resource_settings.py new file mode 100644 index 00000000..0d0bc15d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/container_resource_settings.py @@ -0,0 +1,74 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=arguments-renamed + +import logging +from typing import Optional + +from azure.ai.ml._restclient.v2022_05_01.models import ContainerResourceSettings +from azure.ai.ml.entities._mixins import RestTranslatableMixin + +module_logger = logging.getLogger(__name__) + + +class ResourceSettings(RestTranslatableMixin): + """Resource settings for a container. + + This class uses Kubernetes Resource unit formats. For more information, see + https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/. + + :param cpu: The CPU resource settings for a container. + :type cpu: Optional[str] + :param memory: The memory resource settings for a container. + :type memory: Optional[str] + :param gpu: The GPU resource settings for a container. + :type gpu: Optional[str] + + .. admonition:: Example: + + .. literalinclude:: ../samples/ml_samples_misc.py + :start-after: [START resource_requirements_configuration] + :end-before: [END resource_requirements_configuration] + :language: python + :dedent: 8 + :caption: Configuring ResourceSettings for a Kubernetes deployment. + """ + + def __init__(self, cpu: Optional[str] = None, memory: Optional[str] = None, gpu: Optional[str] = None) -> None: + self.cpu = cpu + self.memory = memory + self.gpu = gpu + + def _to_rest_object(self) -> ContainerResourceSettings: + return ContainerResourceSettings(cpu=self.cpu, memory=self.memory, gpu=self.gpu) + + @classmethod + def _from_rest_object(cls, settings: ContainerResourceSettings) -> Optional["ResourceSettings"]: + return ( + ResourceSettings( + cpu=settings.cpu, + memory=settings.memory, + gpu=settings.gpu, + ) + if settings + else None + ) + + def _merge_with(self, other: Optional["ResourceSettings"]) -> None: + if other: + self.cpu = other.cpu or self.cpu + self.memory = other.memory or self.memory + self.gpu = other.gpu or self.gpu + + def __eq__(self, other: object) -> bool: + if not isinstance(other, ResourceSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + return self.cpu == other.cpu and self.memory == other.memory and self.gpu == other.gpu + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_asset.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_asset.py new file mode 100644 index 00000000..72d24131 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_asset.py @@ -0,0 +1,38 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Dict, Optional + +from azure.ai.ml._schema._deployment.online.data_asset_schema import DataAssetSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY + + +@experimental +class DataAsset: + """Data Asset entity + + :keyword Optional[str] data_id: Arm id of registered data asset + :keyword Optional[str] name: Name of data asset + :keyword Optional[str] path: Path where the data asset is stored. + :keyword Optional[int] version: Version of data asset. + """ + + def __init__( + self, + *, + data_id: Optional[str] = None, + name: Optional[str] = None, + path: Optional[str] = None, + version: Optional[int] = None, + ): + self.data_id = data_id + self.name = name + self.path = path + self.version = version + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = DataAssetSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_collector.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_collector.py new file mode 100644 index 00000000..74277c61 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/data_collector.py @@ -0,0 +1,84 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- +# pylint: disable=protected-access + +from typing import Any, Dict, Optional + +from azure.ai.ml._restclient.v2023_04_01_preview.models import DataCollector as RestDataCollector +from azure.ai.ml._schema._deployment.online.data_collector_schema import DataCollectorSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY +from azure.ai.ml.entities._deployment.deployment_collection import DeploymentCollection +from azure.ai.ml.entities._deployment.request_logging import RequestLogging + + +@experimental +class DataCollector: + """Data Capture deployment entity. + + :param collections: Mapping dictionary of strings mapped to DeploymentCollection entities. + :type collections: Mapping[str, DeploymentCollection] + :param rolling_rate: The rolling rate of mdc files, possible values: ["minute", "hour", "day"]. + :type rolling_rate: str + :param sampling_rate: The sampling rate of mdc files, possible values: [0.0, 1.0]. + :type sampling_rate: float + :param request_logging: Logging of request payload parameters. + :type request_logging: RequestLogging + """ + + def __init__( + self, + collections: Dict[str, DeploymentCollection], + *, + rolling_rate: Optional[str] = None, + sampling_rate: Optional[float] = None, + request_logging: Optional[RequestLogging] = None, + **kwargs: Any, + ): # pylint: disable=unused-argument + self.collections = collections + self.rolling_rate = rolling_rate + self.sampling_rate = sampling_rate + self.request_logging = request_logging + + if self.sampling_rate: + for collection in self.collections.values(): + collection.sampling_rate = self.sampling_rate + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = DataCollectorSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + @classmethod + def _from_rest_object(cls, rest_obj: RestDataCollector) -> "DataCollector": + collections = {} + sampling_rate = None + for k, v in rest_obj.collections.items(): + sampling_rate = v.sampling_rate + collections[k] = DeploymentCollection._from_rest_object(v) + delattr(collections[k], "sampling_rate") + + return DataCollector( + collections=collections, + rolling_rate=rest_obj.rolling_rate, + request_logging=( + RequestLogging._from_rest_object(rest_obj.request_logging) if rest_obj.request_logging else None + ), + sampling_rate=sampling_rate, + ) + + def _to_rest_object(self) -> RestDataCollector: + rest_collections: dict = {} + for collection in self.collections.values(): + collection.sampling_rate = self.sampling_rate + delattr(self, "sampling_rate") + if self.request_logging: + self.request_logging = self.request_logging._to_rest_object() + if self.collections: + rest_collections = {} + for k, v in self.collections.items(): + rest_collections[k] = v._to_rest_object() + return RestDataCollector( + collections=rest_collections, rolling_rate=self.rolling_rate, request_logging=self.request_logging + ) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment.py new file mode 100644 index 00000000..2f857cfa --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment.py @@ -0,0 +1,213 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=protected-access,arguments-renamed + +import logging +from abc import abstractmethod +from os import PathLike +from typing import IO, TYPE_CHECKING, Any, AnyStr, Dict, Optional, Union + +from azure.ai.ml._restclient.v2022_02_01_preview.models import BatchDeploymentData +from azure.ai.ml._restclient.v2022_05_01.models import OnlineDeploymentData +from azure.ai.ml._utils.utils import dump_yaml_to_file +from azure.ai.ml.entities._mixins import RestTranslatableMixin +from azure.ai.ml.entities._resource import Resource +from azure.ai.ml.exceptions import ( + DeploymentException, + ErrorCategory, + ErrorTarget, + ValidationErrorType, + ValidationException, +) + +from .code_configuration import CodeConfiguration + +# avoid circular import error +if TYPE_CHECKING: + from azure.ai.ml.entities._assets._artifacts.model import Model + from azure.ai.ml.entities._assets.environment import Environment + +module_logger = logging.getLogger(__name__) + + +class Deployment(Resource, RestTranslatableMixin): + """Endpoint Deployment base class. + + :param name: Name of the deployment resource, defaults to None + :type name: typing.Optional[str] + :param endpoint_name: Name of the Endpoint resource, defaults to None + :type endpoint_name: typing.Optional[str] + :param description: Description of the deployment resource, defaults to None + :type description: typing.Optional[str] + :param tags: Tag dictionary. Tags can be added, removed, and updated, defaults to None + :type tags: typing.Optional[typing.Dict[str, typing.Any]] + :param properties: The asset property dictionary, defaults to None + :type properties: typing.Optional[typing.Dict[str, typing.Any]] + :param model: The Model entity, defaults to None + :type model: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Model]] + :param code_configuration: Code Configuration, defaults to None + :type code_configuration: typing.Optional[CodeConfiguration] + :param environment: The Environment entity, defaults to None + :type environment: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Environment]] + :param environment_variables: Environment variables that will be set in deployment, defaults to None + :type environment_variables: typing.Optional[typing.Dict[str, str]] + :param code_path: Folder path to local code assets. Equivalent to code_configuration.code.path + , defaults to None + :type code_path: typing.Optional[typing.Union[str, PathLike]] + :param scoring_script: Scoring script name. Equivalent to code_configuration.code.scoring_script + , defaults to None + :type scoring_script: typing.Optional[typing.Union[str, PathLike]] + :raises ~azure.ai.ml.exceptions.ValidationException: Raised if Deployment cannot be successfully validated. + Exception details will be provided in the error message. + """ + + def __init__( + self, + name: Optional[str] = None, + *, + endpoint_name: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, Any]] = None, + properties: Optional[Dict[str, Any]] = None, + model: Optional[Union[str, "Model"]] = None, + code_configuration: Optional[CodeConfiguration] = None, + environment: Optional[Union[str, "Environment"]] = None, + environment_variables: Optional[Dict[str, str]] = None, + code_path: Optional[Union[str, PathLike]] = None, + scoring_script: Optional[Union[str, PathLike]] = None, + **kwargs: Any, + ): + # MFE is case-insensitive for Name. So convert the name into lower case here. + name = name.lower() if name else None + self.endpoint_name = endpoint_name + self._type: Optional[str] = kwargs.pop("type", None) + + if code_configuration and (code_path or scoring_script): + msg = "code_path and scoring_script are not allowed if code_configuration is provided." + raise ValidationException( + message=msg, + target=ErrorTarget.DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) + + super().__init__(name, description, tags, properties, **kwargs) + + self.model = model + self.code_configuration = code_configuration + if not self.code_configuration and (code_path or scoring_script): + self.code_configuration = CodeConfiguration(code=code_path, scoring_script=scoring_script) + + self.environment = environment + self.environment_variables = dict(environment_variables) if environment_variables else {} + + @property + def type(self) -> Optional[str]: + """ + Type of deployment. + + :rtype: str + """ + return self._type + + @property + def code_path(self) -> Optional[Union[str, PathLike]]: + """ + The code directory containing the scoring script. + + :rtype: Union[str, PathLike] + """ + return self.code_configuration.code if self.code_configuration and self.code_configuration.code else None + + @code_path.setter + def code_path(self, value: Union[str, PathLike]) -> None: + if not self.code_configuration: + self.code_configuration = CodeConfiguration() + + self.code_configuration.code = value + + @property + def scoring_script(self) -> Optional[Union[str, PathLike]]: + """ + The scoring script file path relative to the code directory. + + :rtype: Union[str, PathLike] + """ + return self.code_configuration.scoring_script if self.code_configuration else None + + @scoring_script.setter + def scoring_script(self, value: Union[str, PathLike]) -> None: + if not self.code_configuration: + self.code_configuration = CodeConfiguration() + + self.code_configuration.scoring_script = value # type: ignore[misc] + + def dump(self, dest: Union[str, PathLike, IO[AnyStr]], **kwargs: Any) -> None: + """Dump the deployment content into a file in yaml format. + + :param dest: The destination to receive this deployment's content. + Must be either a path to a local file, or an already-open file stream. + If dest is a file path, a new file will be created, + and an exception is raised if the file exists. + If dest is an open file, the file will be written to directly, + and an exception will be raised if the file is not writable. + :type dest: typing.Union[os.PathLike, str, typing.IO[typing.AnyStr]] + """ + path = kwargs.pop("path", None) + yaml_serialized = self._to_dict() + dump_yaml_to_file(dest, yaml_serialized, default_flow_style=False, path=path, **kwargs) + + @abstractmethod + def _to_dict(self) -> Dict: + pass + + @classmethod + def _from_rest_object( + cls, deployment_rest_object: Union[OnlineDeploymentData, BatchDeploymentData] + ) -> Union[OnlineDeploymentData, BatchDeploymentData]: + from azure.ai.ml.entities._deployment.batch_deployment import BatchDeployment + from azure.ai.ml.entities._deployment.online_deployment import OnlineDeployment + + if isinstance(deployment_rest_object, OnlineDeploymentData): + return OnlineDeployment._from_rest_object(deployment_rest_object) + if isinstance(deployment_rest_object, BatchDeploymentData): + return BatchDeployment._from_rest_object(deployment_rest_object) + + msg = f"Unsupported deployment type {type(deployment_rest_object)}" + raise DeploymentException( + message=msg, + target=ErrorTarget.DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.SYSTEM_ERROR, + ) + + def _to_rest_object(self) -> Any: + pass + + def _merge_with(self, other: "Deployment") -> None: + if other: + if self.name != other.name: + msg = "The deployment name: {} and {} are not matched when merging." + raise ValidationException( + message=msg.format(self.name, other.name), + target=ErrorTarget.DEPLOYMENT, + no_personal_data_message=msg.format("[name1]", "[name2]"), + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) + if other.tags: + self.tags: dict = {**self.tags, **other.tags} + if other.properties: + self.properties: dict = {**self.properties, **other.properties} + if other.environment_variables: + self.environment_variables = { + **self.environment_variables, + **other.environment_variables, + } + self.code_configuration = other.code_configuration or self.code_configuration + self.model = other.model or self.model + self.environment = other.environment or self.environment + self.endpoint_name = other.endpoint_name or self.endpoint_name diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_collection.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_collection.py new file mode 100644 index 00000000..c1b1c750 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_collection.py @@ -0,0 +1,62 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional, Union + +from azure.ai.ml._restclient.v2023_04_01_preview.models import Collection as RestCollection +from azure.ai.ml._schema._deployment.online.deployment_collection_schema import DeploymentCollectionSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY +from .data_asset import DataAsset + + +@experimental +class DeploymentCollection: + """Collection entity + + :param enabled: Is logging for this collection enabled. Possible values include: 'true', 'false'. + :type enabled: str + :param data: Data asset id associated with collection logging. + :type data: str + :param client_id: Client ID associated with collection logging. + :type client_id: str + + """ + + def __init__( + self, + *, + enabled: Optional[str] = None, + data: Optional[Union[str, DataAsset]] = None, + client_id: Optional[str] = None, + **kwargs: Any + ): + self.enabled = enabled # maps to data_collection_mode + self.data = data # maps to data_id + self.sampling_rate = kwargs.get( + "sampling_rate", None + ) # maps to sampling_rate, but it has to be passed from the data_collector root + self.client_id = client_id + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = DeploymentCollectionSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + @classmethod + def _from_rest_object(cls, rest_obj: RestCollection) -> "DeploymentCollection": + return DeploymentCollection( + enabled="true" if rest_obj.data_collection_mode == "Enabled" else "false", + sampling_rate=rest_obj.sampling_rate, + data=rest_obj.data_id, + client_id=rest_obj.client_id, + ) + + def _to_rest_object(self) -> RestCollection: + return RestCollection( + data_collection_mode="enabled" if str(self.enabled).lower() == "true" else "disabled", + sampling_rate=self.sampling_rate, + data_id=self.data, + client_id=self.client_id, + ) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_settings.py new file mode 100644 index 00000000..0dbfc8fc --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/deployment_settings.py @@ -0,0 +1,200 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=arguments-renamed + +import logging +from typing import Optional + +from azure.ai.ml._restclient.v2022_05_01.models import BatchRetrySettings as RestBatchRetrySettings +from azure.ai.ml._restclient.v2022_05_01.models import OnlineRequestSettings as RestOnlineRequestSettings +from azure.ai.ml._restclient.v2022_05_01.models import ProbeSettings as RestProbeSettings +from azure.ai.ml._utils.utils import ( + from_iso_duration_format, + from_iso_duration_format_ms, + to_iso_duration_format, + to_iso_duration_format_ms, +) +from azure.ai.ml.entities._mixins import RestTranslatableMixin + +module_logger = logging.getLogger(__name__) + + +class BatchRetrySettings(RestTranslatableMixin): + """Retry settings for batch deployment. + + :param max_retries: Number of retries in failure, defaults to 3 + :type max_retries: int + :param timeout: Timeout in seconds, defaults to 30 + :type timeout: int + """ + + def __init__(self, *, max_retries: Optional[int] = None, timeout: Optional[int] = None): + self.max_retries = max_retries + self.timeout = timeout + + def _to_rest_object(self) -> RestBatchRetrySettings: + return RestBatchRetrySettings( + max_retries=self.max_retries, + timeout=to_iso_duration_format(self.timeout), + ) + + @classmethod + def _from_rest_object(cls, settings: RestBatchRetrySettings) -> Optional["BatchRetrySettings"]: + return ( + BatchRetrySettings( + max_retries=settings.max_retries, + timeout=from_iso_duration_format(settings.timeout), + ) + if settings + else None + ) + + def _merge_with(self, other: "BatchRetrySettings") -> None: + if other: + self.timeout = other.timeout or self.timeout + self.max_retries = other.max_retries or self.max_retries + + +class OnlineRequestSettings(RestTranslatableMixin): + """Request Settings entity. + + :param request_timeout_ms: defaults to 5000 + :type request_timeout_ms: int + :param max_concurrent_requests_per_instance: defaults to 1 + :type max_concurrent_requests_per_instance: int + :param max_queue_wait_ms: defaults to 500 + :type max_queue_wait_ms: int + """ + + def __init__( + self, + max_concurrent_requests_per_instance: Optional[int] = None, + request_timeout_ms: Optional[int] = None, + max_queue_wait_ms: Optional[int] = None, + ): + self.request_timeout_ms = request_timeout_ms + self.max_concurrent_requests_per_instance = max_concurrent_requests_per_instance + self.max_queue_wait_ms = max_queue_wait_ms + + def _to_rest_object(self) -> RestOnlineRequestSettings: + return RestOnlineRequestSettings( + max_queue_wait=to_iso_duration_format_ms(self.max_queue_wait_ms), + max_concurrent_requests_per_instance=self.max_concurrent_requests_per_instance, + request_timeout=to_iso_duration_format_ms(self.request_timeout_ms), + ) + + def _merge_with(self, other: Optional["OnlineRequestSettings"]) -> None: + if other: + self.max_concurrent_requests_per_instance = ( + other.max_concurrent_requests_per_instance or self.max_concurrent_requests_per_instance + ) + self.request_timeout_ms = other.request_timeout_ms or self.request_timeout_ms + self.max_queue_wait_ms = other.max_queue_wait_ms or self.max_queue_wait_ms + + @classmethod + def _from_rest_object(cls, settings: RestOnlineRequestSettings) -> Optional["OnlineRequestSettings"]: + return ( + OnlineRequestSettings( + request_timeout_ms=from_iso_duration_format_ms(settings.request_timeout), + max_concurrent_requests_per_instance=settings.max_concurrent_requests_per_instance, + max_queue_wait_ms=from_iso_duration_format_ms(settings.max_queue_wait), + ) + if settings + else None + ) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, OnlineRequestSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + return ( + self.max_concurrent_requests_per_instance == other.max_concurrent_requests_per_instance + and self.request_timeout_ms == other.request_timeout_ms + and self.max_queue_wait_ms == other.max_queue_wait_ms + ) + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) + + +class ProbeSettings(RestTranslatableMixin): + def __init__( + self, + *, + failure_threshold: Optional[int] = None, + success_threshold: Optional[int] = None, + timeout: Optional[int] = None, + period: Optional[int] = None, + initial_delay: Optional[int] = None, + ): + """Settings on how to probe an endpoint. + + :param failure_threshold: Threshold for probe failures, defaults to 30 + :type failure_threshold: int + :param success_threshold: Threshold for probe success, defaults to 1 + :type success_threshold: int + :param timeout: timeout in seconds, defaults to 2 + :type timeout: int + :param period: How often (in seconds) to perform the probe, defaults to 10 + :type period: int + :param initial_delay: How long (in seconds) to wait for the first probe, defaults to 10 + :type initial_delay: int + """ + + self.failure_threshold = failure_threshold + self.success_threshold = success_threshold + self.timeout = timeout + self.period = period + self.initial_delay = initial_delay + + def _to_rest_object(self) -> RestProbeSettings: + return RestProbeSettings( + failure_threshold=self.failure_threshold, + success_threshold=self.success_threshold, + timeout=to_iso_duration_format(self.timeout), + period=to_iso_duration_format(self.period), + initial_delay=to_iso_duration_format(self.initial_delay), + ) + + def _merge_with(self, other: Optional["ProbeSettings"]) -> None: + if other: + self.failure_threshold = other.failure_threshold or self.failure_threshold + self.success_threshold = other.success_threshold or self.success_threshold + self.timeout = other.timeout or self.timeout + self.period = other.period or self.period + self.initial_delay = other.initial_delay or self.initial_delay + + @classmethod + def _from_rest_object(cls, settings: RestProbeSettings) -> Optional["ProbeSettings"]: + return ( + ProbeSettings( + failure_threshold=settings.failure_threshold, + success_threshold=settings.success_threshold, + timeout=from_iso_duration_format(settings.timeout), + period=from_iso_duration_format(settings.period), + initial_delay=from_iso_duration_format(settings.initial_delay), + ) + if settings + else None + ) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, ProbeSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + return ( + self.failure_threshold == other.failure_threshold + and self.success_threshold == other.success_threshold + and self.timeout == other.timeout + and self.period == other.period + and self.initial_delay == other.initial_delay + ) + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/event_hub.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/event_hub.py new file mode 100644 index 00000000..2729fa50 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/event_hub.py @@ -0,0 +1,32 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional + +from azure.ai.ml._schema._deployment.online.event_hub_schema import EventHubSchema +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY +from azure.ai.ml.entities._deployment.oversize_data_config import OversizeDataConfig + + +class EventHub: + """Event Hub deployment entity + + :param namespace: Name space of eventhub, provided in format of "{namespace}.{name}". + :type namespace: str + :param oversize_data_config: Oversized payload body configurations. + :type oversize_data_config: OversizeDataConfig + + """ + + # pylint: disable=unused-argument + def __init__( + self, namespace: Optional[str] = None, oversize_data_config: Optional[OversizeDataConfig] = None, **kwargs: Any + ): + self.namespace = namespace + self.oversize_data_config = oversize_data_config + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = EventHubSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/job_definition.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/job_definition.py new file mode 100644 index 00000000..56bebebc --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/job_definition.py @@ -0,0 +1,58 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional, Union + +from azure.ai.ml._schema._deployment.batch.job_definition_schema import JobDefinitionSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY +from azure.ai.ml.entities._component.component import Component +from azure.ai.ml.entities._job.job import Job + + +@experimental +class JobDefinition: + """Job Definition entity. + + :param type: Job definition type. Allowed value is: pipeline + :type type: str + :param name: Job name + :type name: str + :param job: Job definition + :type job: Union[Job, str] + :param component: Component definition + :type component: Union[Component, str] + :param settings: Job settings + :type settings: Dict[str, Any] + :param description: Job description. + :type description: str + :param tags: Job tags + :type tags: Dict[str, Any] + """ + + def __init__( + self, + # pylint: disable=redefined-builtin + type: str, + name: Optional[str] = None, + job: Optional[Union[Job, str]] = None, + component: Optional[Union[Component, str]] = None, + settings: Optional[Dict[str, Any]] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, Any]] = None, + # pylint: disable=unused-argument + **kwargs: Any, + ): + self.type = type + self.name = name + self.job = job + self.component = component + self.settings = settings + self.tags = tags + self.description = description + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = JobDefinitionSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment.py new file mode 100644 index 00000000..0ad4fd6f --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment.py @@ -0,0 +1,207 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from os import PathLike +from pathlib import Path +from typing import Any, Dict, Optional, Union + +from azure.ai.ml._restclient.v2022_05_01.models import BatchDeploymentData +from azure.ai.ml._restclient.v2022_05_01.models import BatchDeploymentDetails as RestBatchDeployment +from azure.ai.ml._restclient.v2022_05_01.models import BatchOutputAction +from azure.ai.ml._restclient.v2022_05_01.models import CodeConfiguration as RestCodeConfiguration +from azure.ai.ml._restclient.v2022_05_01.models import IdAssetReference +from azure.ai.ml._schema._deployment.batch.model_batch_deployment import ModelBatchDeploymentSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY +from azure.ai.ml.constants._deployment import BatchDeploymentOutputAction +from azure.ai.ml.entities._assets import Environment, Model +from azure.ai.ml.entities._deployment.batch_deployment import BatchDeployment +from azure.ai.ml.entities._deployment.deployment import Deployment +from azure.ai.ml.entities._job.resource_configuration import ResourceConfiguration +from azure.ai.ml.entities._util import load_from_dict +from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException + +from .code_configuration import CodeConfiguration +from .model_batch_deployment_settings import ModelBatchDeploymentSettings + + +@experimental +class ModelBatchDeployment(Deployment): + """Job Definition entity. + + :param type: Job definition type. Allowed value is: pipeline + :type type: str + :param name: Job name + :type name: str + :param job: Job definition + :type job: Union[Job, str] + :param component: Component definition + :type component: Union[Component, str] + :param settings: Job settings + :type settings: Dict[str, Any] + :param description: Job description. + :type description: str + :param tags: Job tags + :type tags: Dict[str, Any] + :param properties: The asset property dictionary. + :type properties: dict[str, str] + """ + + def __init__( + self, + *, + name: Optional[str], + endpoint_name: Optional[str] = None, + environment: Optional[Union[str, Environment]] = None, + properties: Optional[Dict[str, str]] = None, + model: Optional[Union[str, Model]] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, Any]] = None, + settings: Optional[ModelBatchDeploymentSettings] = None, + resources: Optional[ResourceConfiguration] = None, + compute: Optional[str] = None, + code_configuration: Optional[CodeConfiguration] = None, + code_path: Optional[Union[str, PathLike]] = None, # promoted property from code_configuration.code + scoring_script: Optional[ + Union[str, PathLike] + ] = None, # promoted property from code_configuration.scoring_script + **kwargs: Any, + ): + self._provisioning_state: Optional[str] = kwargs.pop("provisioning_state", None) + super().__init__( + name=name, + endpoint_name=endpoint_name, + properties=properties, + code_path=code_path, + scoring_script=scoring_script, + environment=environment, + model=model, + description=description, + tags=tags, + code_configuration=code_configuration, + **kwargs, + ) + self.compute = compute + self.resources = resources + if settings is not None: + self.settings = ModelBatchDeploymentSettings( + mini_batch_size=settings.mini_batch_size, + instance_count=settings.instance_count, + max_concurrency_per_instance=settings.max_concurrency_per_instance, + output_action=settings.output_action, + output_file_name=settings.output_file_name, + retry_settings=settings.retry_settings, + environment_variables=settings.environment_variables, + error_threshold=settings.error_threshold, + logging_level=settings.logging_level, + ) + if self.resources is not None: + if self.resources.instance_count is None and settings.instance_count is not None: + self.resources.instance_count = settings.instance_count + if self.resources is None and settings.instance_count is not None: + self.resources = ResourceConfiguration(instance_count=settings.instance_count) + + # pylint: disable=arguments-differ + def _to_rest_object(self, location: str) -> BatchDeploymentData: # type: ignore + self._validate() + code_config = ( + RestCodeConfiguration( + code_id=self.code_configuration.code, + scoring_script=self.code_configuration.scoring_script, + ) + if self.code_configuration + else None + ) + deployment_settings = self.settings + model = IdAssetReference(asset_id=self.model) if self.model else None + batch_deployment = RestBatchDeployment( + description=self.description, + environment_id=self.environment, + model=model, + code_configuration=code_config, + output_file_name=deployment_settings.output_file_name, + output_action=BatchDeployment._yaml_output_action_to_rest_output_action( # pylint: disable=protected-access + deployment_settings.output_action + ), + error_threshold=deployment_settings.error_threshold, + resources=self.resources._to_rest_object() if self.resources else None, # pylint: disable=protected-access + retry_settings=( + deployment_settings.retry_settings._to_rest_object() # pylint: disable=protected-access + if deployment_settings.retry_settings + else None + ), + logging_level=deployment_settings.logging_level, + mini_batch_size=deployment_settings.mini_batch_size, + max_concurrency_per_instance=deployment_settings.max_concurrency_per_instance, + environment_variables=deployment_settings.environment_variables, + compute=self.compute, + properties=self.properties, + ) + return BatchDeploymentData(location=location, properties=batch_deployment, tags=self.tags) + + @classmethod + def _load( + cls, + data: Optional[Dict] = None, + yaml_path: Optional[Union[PathLike, str]] = None, + params_override: Optional[list] = None, + **kwargs: Any, + ) -> "ModelBatchDeployment": + data = data or {} + params_override = params_override or [] + cls._update_params(params_override) + + context = { + BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path.cwd(), + PARAMS_OVERRIDE_KEY: params_override, + } + res: ModelBatchDeployment = load_from_dict(ModelBatchDeploymentSchema, data, context, **kwargs) + return res + + @classmethod + def _update_params(cls, params_override: Any) -> None: + for param in params_override: + endpoint_name = param.get("endpoint_name") + if isinstance(endpoint_name, str): + param["endpoint_name"] = endpoint_name.lower() + + @classmethod + def _yaml_output_action_to_rest_output_action(cls, yaml_output_action: str) -> str: + output_switcher = { + BatchDeploymentOutputAction.APPEND_ROW: BatchOutputAction.APPEND_ROW, + BatchDeploymentOutputAction.SUMMARY_ONLY: BatchOutputAction.SUMMARY_ONLY, + } + return output_switcher.get(yaml_output_action, yaml_output_action) + + @property + def provisioning_state(self) -> Optional[str]: + """Batch deployment provisioning state, readonly. + + :return: Batch deployment provisioning state. + :rtype: Optional[str] + """ + return self._provisioning_state + + def _validate(self) -> None: + self._validate_output_action() + + def _validate_output_action(self) -> None: + if ( + self.settings.output_action + and self.settings.output_action == BatchDeploymentOutputAction.SUMMARY_ONLY + and self.settings.output_file_name + ): + msg = "When output_action is set to {}, the output_file_name need not to be specified." + msg = msg.format(BatchDeploymentOutputAction.SUMMARY_ONLY) + raise ValidationException( + message=msg, + target=ErrorTarget.BATCH_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) + + def _to_dict(self) -> Dict: + res: dict = ModelBatchDeploymentSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment_settings.py new file mode 100644 index 00000000..36151019 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/model_batch_deployment_settings.py @@ -0,0 +1,81 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional + +from azure.ai.ml._schema._deployment.batch.model_batch_deployment_settings import ModelBatchDeploymentSettingsSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY +from azure.ai.ml.constants._deployment import BatchDeploymentOutputAction +from azure.ai.ml.entities._deployment.deployment_settings import BatchRetrySettings + + +@experimental +class ModelBatchDeploymentSettings: + """Model Batch Deployment Settings entity. + + :param mini_batch_size: Size of the mini-batch passed to each batch invocation, defaults to 10 + :type mini_batch_size: int + :param instance_count: Number of instances the interfering will run on. Equivalent to resources.instance_count. + :type instance_count: int + :param output_action: Indicates how the output will be organized. Possible values include: + "summary_only", "append_row". Defaults to "append_row" + :type output_action: str or ~azure.ai.ml.constants._deployment.BatchDeploymentOutputAction + :param output_file_name: Customized output file name for append_row output action, defaults to "predictions.csv" + :type output_file_name: str + :param max_concurrency_per_instance: Indicates maximum number of parallelism per instance, defaults to 1 + :type max_concurrency_per_instance: int + :param retry_settings: Retry settings for a batch inference operation, defaults to None + :type retry_settings: BatchRetrySettings + :param environment_variables: Environment variables that will be set in deployment. + :type environment_variables: dict + :param error_threshold: 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 + :type error_threshold: int + :param logging_level: Logging level for batch inference operation, defaults to "info" + :type logging_level: str + + .. admonition:: Example: + + .. literalinclude:: ../samples/ml_samples_misc.py + :start-after: [START model_batch_deployment_settings_entity_create] + :end-before: [END model_batch_deployment_settings_entity_create] + :language: python + :dedent: 8 + :caption: Creating a Model Batch Deployment Settings object. + """ + + def __init__( + self, + *, + mini_batch_size: Optional[int], + instance_count: Optional[int] = None, + max_concurrency_per_instance: Optional[int] = None, + output_action: Optional[BatchDeploymentOutputAction] = None, + output_file_name: Optional[str] = None, + retry_settings: Optional[BatchRetrySettings] = None, + environment_variables: Optional[Dict[str, str]] = None, + error_threshold: Optional[int] = None, + logging_level: Optional[str] = None, + # pylint: disable=unused-argument + **kwargs: Any, + ): + self.mini_batch_size = mini_batch_size + self.instance_count = instance_count + self.max_concurrency_per_instance = max_concurrency_per_instance + self.output_action = output_action + self.output_file_name = output_file_name + self.retry_settings = retry_settings + self.environment_variables = environment_variables + self.error_threshold = error_threshold + self.logging_level = logging_level + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = ModelBatchDeploymentSettingsSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/online_deployment.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/online_deployment.py new file mode 100644 index 00000000..131d3293 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/online_deployment.py @@ -0,0 +1,742 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=protected-access,arguments-renamed,unidiomatic-typecheck + +import logging +import os +import typing +from abc import abstractmethod +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union, cast + +from azure.ai.ml._restclient.v2023_04_01_preview.models import CodeConfiguration as RestCodeConfiguration +from azure.ai.ml._restclient.v2023_04_01_preview.models import EndpointComputeType +from azure.ai.ml._restclient.v2023_04_01_preview.models import ( + KubernetesOnlineDeployment as RestKubernetesOnlineDeployment, +) +from azure.ai.ml._restclient.v2023_04_01_preview.models import ManagedOnlineDeployment as RestManagedOnlineDeployment +from azure.ai.ml._restclient.v2023_04_01_preview.models import OnlineDeployment as RestOnlineDeploymentData +from azure.ai.ml._restclient.v2023_04_01_preview.models import OnlineDeploymentProperties as RestOnlineDeploymentDetails +from azure.ai.ml._restclient.v2023_04_01_preview.models import Sku as RestSku +from azure.ai.ml._schema._deployment.online.online_deployment import ( + KubernetesOnlineDeploymentSchema, + ManagedOnlineDeploymentSchema, +) +from azure.ai.ml._utils._arm_id_utils import _parse_endpoint_name_from_deployment_id +from azure.ai.ml._utils.utils import camel_to_snake +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY, TYPE, ArmConstants +from azure.ai.ml.constants._endpoint import EndpointYamlFields +from azure.ai.ml.entities._assets import Code +from azure.ai.ml.entities._assets._artifacts.model import Model +from azure.ai.ml.entities._assets.environment import Environment +from azure.ai.ml.entities._deployment.code_configuration import CodeConfiguration +from azure.ai.ml.entities._deployment.data_collector import DataCollector +from azure.ai.ml.entities._deployment.deployment_settings import OnlineRequestSettings, ProbeSettings +from azure.ai.ml.entities._deployment.resource_requirements_settings import ResourceRequirementsSettings +from azure.ai.ml.entities._deployment.scale_settings import ( + DefaultScaleSettings, + OnlineScaleSettings, + TargetUtilizationScaleSettings, +) +from azure.ai.ml.entities._endpoint._endpoint_helpers import validate_endpoint_or_deployment_name +from azure.ai.ml.entities._util import load_from_dict +from azure.ai.ml.exceptions import ( + DeploymentException, + ErrorCategory, + ErrorTarget, + ValidationErrorType, + ValidationException, +) + +from .deployment import Deployment + +module_logger = logging.getLogger(__name__) + + +# pylint: disable=too-many-instance-attributes +class OnlineDeployment(Deployment): + """Online endpoint deployment entity. + + :param name: Name of the deployment resource. + :type name: str + :param endpoint_name: Name of the endpoint resource, defaults to None + :type endpoint_name: typing.Optional[str] + :param tags: Tag dictionary. Tags can be added, removed, and updated, defaults to None + :type tags: typing.Optional[typing.Dict[str, typing.Any]] + :param properties: The asset property dictionary, defaults to None + :type properties: typing.Optional[typing.Dict[str, typing.Any]] + :param description: Description of the resource, defaults to None + :type description: typing.Optional[str] + :param model: Model entity for the endpoint deployment, defaults to None + :type model: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Model]] + :param data_collector: Data Collector entity for the endpoint deployment, defaults to None + :type data_collector: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.DataCollector]] + :param code_configuration: Code Configuration, defaults to None + :type code_configuration: typing.Optional[~azure.ai.ml.entities.CodeConfiguration] + :param environment: Environment entity for the endpoint deployment, defaults to None + :type environment: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Environment]] + :param app_insights_enabled: Is appinsights enabled, defaults to False + :type app_insights_enabled: typing.Optional[bool] + :param scale_settings: How the online deployment will scale, defaults to None + :type scale_settings: typing.Optional[~azure.ai.ml.entities.OnlineScaleSettings] + :param request_settings: Online Request Settings, defaults to None + :type request_settings: typing.Optional[~azure.ai.ml.entities.OnlineRequestSettings] + :param liveness_probe: Liveness probe settings, defaults to None + :type liveness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param readiness_probe: Readiness probe settings, defaults to None + :type readiness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param environment_variables: Environment variables that will be set in deployment, defaults to None + :type environment_variables: typing.Optional[typing.Dict[str, str]] + :param instance_count: The instance count used for this deployment, defaults to None + :type instance_count: typing.Optional[int] + :param instance_type: Azure compute sku, defaults to None + :type instance_type: typing.Optional[str] + :param model_mount_path: The path to mount the model in custom container, defaults to None + :type model_mount_path: typing.Optional[str] + :param code_path: Equivalent to code_configuration.code, will be ignored if code_configuration is present + , defaults to None + :type code_path: typing.Optional[typing.Union[str, os.PathLike]] + :param scoring_script: Equivalent to code_configuration.code.scoring_script. + Will be ignored if code_configuration is present, defaults to None + :type scoring_script: typing.Optional[typing.Union[str, os.PathLike]] + """ + + def __init__( + self, + name: str, + *, + endpoint_name: Optional[str] = None, + tags: Optional[Dict[str, typing.Any]] = None, + properties: Optional[Dict[str, typing.Any]] = None, + description: Optional[str] = None, + model: Optional[Union[str, "Model"]] = None, + data_collector: Optional[DataCollector] = None, + code_configuration: Optional[CodeConfiguration] = None, + environment: Optional[Union[str, "Environment"]] = None, + app_insights_enabled: Optional[bool] = False, + scale_settings: Optional[OnlineScaleSettings] = None, + request_settings: Optional[OnlineRequestSettings] = None, + liveness_probe: Optional[ProbeSettings] = None, + readiness_probe: Optional[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[str, os.PathLike]] = None, # promoted property from code_configuration.code + scoring_script: Optional[Union[str, os.PathLike]] = None, # promoted property code_configuration.scoring_script + **kwargs: typing.Any, + ): + self._provisioning_state: Optional[str] = kwargs.pop("provisioning_state", None) + + super(OnlineDeployment, self).__init__( + name=name, + endpoint_name=endpoint_name, + tags=tags, + properties=properties, + description=description, + model=model, + code_configuration=code_configuration, + environment=environment, + environment_variables=environment_variables, + code_path=code_path, + scoring_script=scoring_script, + **kwargs, + ) + + self.app_insights_enabled = app_insights_enabled + self.scale_settings = scale_settings + self.request_settings = request_settings + self.liveness_probe = liveness_probe + self.readiness_probe = readiness_probe + self.instance_count = instance_count + self._arm_type = ArmConstants.ONLINE_DEPLOYMENT_TYPE + self.model_mount_path = model_mount_path + self.instance_type = instance_type + self.data_collector: Any = data_collector + + @property + def provisioning_state(self) -> Optional[str]: + """Deployment provisioning state, readonly. + + :return: Deployment provisioning state. + :rtype: typing.Optional[str] + """ + return self._provisioning_state + + def _generate_dependencies(self) -> Tuple: + """Convert dependencies into ARM id or REST wrapper. + + :return: A 3-tuple of the code configuration, environment ID, and model ID. + :rtype: Tuple[RestCodeConfiguration, str, str] + """ + code = None + + if self.code_configuration: + self.code_configuration._validate() + if self.code_configuration.code is not None: + if isinstance(self.code_configuration.code, str): + code_id = self.code_configuration.code + elif not isinstance(self.code_configuration.code, os.PathLike): + code_id = self.code_configuration.code.id + + code = RestCodeConfiguration( + code_id=code_id, # pylint: disable=possibly-used-before-assignment + scoring_script=self.code_configuration.scoring_script, + ) + + model_id = None + if self.model: + model_id = self.model if isinstance(self.model, str) else self.model.id + + environment_id = None + if self.environment: + environment_id = self.environment if isinstance(self.environment, str) else self.environment.id + + return code, environment_id, model_id + + @abstractmethod + def _to_dict(self) -> Dict: + pass + + @abstractmethod + def _to_arm_resource_param(self, **kwargs: Any) -> Dict: + pass + + @abstractmethod + def _to_rest_object(self) -> RestOnlineDeploymentData: + pass + + @classmethod + def _from_rest_object(cls, deployment: RestOnlineDeploymentData) -> RestOnlineDeploymentDetails: + if deployment.properties.endpoint_compute_type == EndpointComputeType.KUBERNETES: + return KubernetesOnlineDeployment._from_rest_object(deployment) + if deployment.properties.endpoint_compute_type == EndpointComputeType.MANAGED: + return ManagedOnlineDeployment._from_rest_object(deployment) + + msg = f"Unsupported online endpoint type {deployment.properties.endpoint_compute_type}." + raise DeploymentException( + message=msg, + target=ErrorTarget.ONLINE_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.SYSTEM_ERROR, + ) + + def _get_arm_resource(self, **kwargs: Any) -> Dict: + resource: dict = super(OnlineDeployment, self)._get_arm_resource(**kwargs) + depends_on = [] + if self.environment and isinstance(self.environment, Environment): + depends_on.append(f"{self.environment._arm_type}Deployment") + if self.code_configuration and self.code_configuration.code and isinstance(self.code_configuration.code, Code): + depends_on.append(f"{self.code_configuration.code._arm_type}Deployment") + if self.model and isinstance(self.model, Model): + depends_on.append(f"{self.model._arm_type}Deployment") + resource[ArmConstants.DEPENDSON_PARAMETER_NAME] = depends_on + return resource + + def _get_arm_resource_and_params(self, **kwargs: Any) -> List: + resource_param_tuple_list = [(self._get_arm_resource(**kwargs), self._to_arm_resource_param(**kwargs))] + if self.environment and isinstance(self.environment, Environment): + resource_param_tuple_list.extend(self.environment._get_arm_resource_and_params()) + if self.code_configuration and self.code_configuration.code and isinstance(self.code_configuration.code, Code): + resource_param_tuple_list.extend(self.code_configuration.code._get_arm_resource_and_params()) + if self.model and isinstance(self.model, Model): + resource_param_tuple_list.extend(self.model._get_arm_resource_and_params()) + return resource_param_tuple_list + + def _validate_name(self) -> None: + if self.name: + validate_endpoint_or_deployment_name(self.name, is_deployment=True) + + def _merge_with(self, other: Any) -> None: + if other: + if self.name != other.name: + msg = "The deployment name: {} and {} are not matched when merging." + raise ValidationException( + message=msg.format(self.name, other.name), + target=ErrorTarget.ONLINE_DEPLOYMENT, + no_personal_data_message=msg.format("[name1]", "[name2]"), + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) + super()._merge_with(other) + self.app_insights_enabled = other.app_insights_enabled or self.app_insights_enabled + # Adding noqa: Fix E721 do not compare types, use 'isinstance()' + # isinstance will include checking for subclasses, which is explicitly undesired by a logic. + if self.scale_settings and type(self.scale_settings) == type(other.scale_settings): # noqa + self.scale_settings._merge_with(other.scale_settings) + else: + self.scale_settings = other.scale_settings + if self.request_settings: + self.request_settings._merge_with(other.request_settings) + else: + self.request_settings = other.request_settings + if self.liveness_probe: + self.liveness_probe._merge_with(other.liveness_probe) + else: + self.liveness_probe = other.liveness_probe + if self.readiness_probe: + self.readiness_probe._merge_with(other.readiness_probe) + else: + self.readiness_probe = other.readiness_probe + self.instance_count = other.instance_count or self.instance_count + self.instance_type = other.instance_type or self.instance_type + + @classmethod + def _set_scale_settings(cls, data: dict) -> None: + if not hasattr(data, EndpointYamlFields.SCALE_SETTINGS): + return + + scale_settings = data[EndpointYamlFields.SCALE_SETTINGS] + keyName = TYPE + if scale_settings and scale_settings[keyName] == "default": + scale_copy = scale_settings.copy() + for key in scale_copy: + if key != keyName: + scale_settings.pop(key, None) + + @classmethod + def _load( + cls, + data: Optional[Dict] = None, + yaml_path: Optional[Union[os.PathLike, str]] = None, + params_override: Optional[list] = None, + **kwargs: Any, + ) -> "OnlineDeployment": + data = data or {} + params_override = params_override or [] + context = { + BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path.cwd(), + PARAMS_OVERRIDE_KEY: params_override, + } + + deployment_type = data.get("type", None) + + if deployment_type == camel_to_snake(EndpointComputeType.KUBERNETES.value): + res_kub: OnlineDeployment = load_from_dict(KubernetesOnlineDeploymentSchema, data, context, **kwargs) + return res_kub + + res_manage: OnlineDeployment = load_from_dict(ManagedOnlineDeploymentSchema, data, context, **kwargs) + return res_manage + + +class KubernetesOnlineDeployment(OnlineDeployment): + """Kubernetes Online endpoint deployment entity. + + :param name: Name of the deployment resource. + :type name: str + :param endpoint_name: Name of the endpoint resource, defaults to None + :type endpoint_name: typing.Optional[str] + :param tags: Tag dictionary. Tags can be added, removed, and updated., defaults to None + :type tags: typing.Optional[typing.Dict[str, typing.Any]] + :param properties: The asset property dictionary, defaults to None + :type properties: typing.Optional[typing.Dict[str, typing.Any]] + :param description: Description of the resource, defaults to None + :type description: typing.Optional[str] + :param model: Model entity for the endpoint deployment, defaults to None + :type model: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Model]] + :param code_configuration: Code Configuration, defaults to None + :type code_configuration: typing.Optional[~azure.ai.ml.entities.CodeConfiguration] + :param environment: Environment entity for the endpoint deployment, defaults to None + :type environment: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Environment]] + :param app_insights_enabled: Is appinsights enabled, defaults to False + :type app_insights_enabled: bool + :param scale_settings: How the online deployment will scale, defaults to None + :type scale_settings: typing.Optional[typing.Union[~azure.ai.ml.entities.DefaultScaleSettings + , ~azure.ai.ml.entities.TargetUtilizationScaleSettings]] + :param request_settings: Online Request Settings, defaults to None + :type request_settings: typing.Optional[OnlineRequestSettings] + :param liveness_probe: Liveness probe settings, defaults to None + :type liveness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param readiness_probe: Readiness probe settings, defaults to None + :type readiness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param environment_variables: Environment variables that will be set in deployment, defaults to None + :type environment_variables: typing.Optional[typing.Dict[str, str]] + :param resources: Resource requirements settings, defaults to None + :type resources: typing.Optional[~azure.ai.ml.entities.ResourceRequirementsSettings] + :param instance_count: The instance count used for this deployment, defaults to None + :type instance_count: typing.Optional[int] + :param instance_type: The instance type defined by K8S cluster admin, defaults to None + :type instance_type: typing.Optional[str] + :param code_path: Equivalent to code_configuration.code, will be ignored if code_configuration is present + , defaults to None + :type code_path: typing.Optional[typing.Union[str, os.PathLike]] + :param scoring_script: Equivalent to code_configuration.code.scoring_script. + Will be ignored if code_configuration is present, defaults to None + :type scoring_script: typing.Optional[typing.Union[str, os.PathLike]] + """ + + def __init__( + self, + *, + name: str, + endpoint_name: Optional[str] = None, + tags: Optional[Dict[str, typing.Any]] = None, + properties: Optional[Dict[str, typing.Any]] = None, + description: Optional[str] = None, + model: Optional[Union[str, "Model"]] = None, + code_configuration: Optional[CodeConfiguration] = None, + environment: Optional[Union[str, "Environment"]] = None, + app_insights_enabled: bool = False, + scale_settings: Optional[Union[DefaultScaleSettings, TargetUtilizationScaleSettings]] = None, + request_settings: Optional[OnlineRequestSettings] = None, + liveness_probe: Optional[ProbeSettings] = None, + readiness_probe: Optional[ProbeSettings] = None, + environment_variables: Optional[Dict[str, str]] = None, + resources: Optional[ResourceRequirementsSettings] = None, + instance_count: Optional[int] = None, + instance_type: Optional[str] = None, + code_path: Optional[Union[str, os.PathLike]] = None, # promoted property from code_configuration.code + scoring_script: Optional[ + Union[str, os.PathLike] + ] = None, # promoted property from code_configuration.scoring_script + **kwargs: Any, + ): + kwargs["type"] = EndpointComputeType.KUBERNETES.value + super(KubernetesOnlineDeployment, self).__init__( + name=name, + endpoint_name=endpoint_name, + tags=tags, + properties=properties, + description=description, + model=model, + code_configuration=code_configuration, + environment=environment, + environment_variables=environment_variables, + instance_count=instance_count, + instance_type=instance_type, + app_insights_enabled=app_insights_enabled, + scale_settings=scale_settings, + request_settings=request_settings, + liveness_probe=liveness_probe, + readiness_probe=readiness_probe, + code_path=code_path, + scoring_script=scoring_script, + **kwargs, + ) + + self.resources = resources + + def _to_dict(self) -> Dict: + res: dict = KubernetesOnlineDeploymentSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + # pylint: disable=arguments-differ + def _to_rest_object(self, location: str) -> RestOnlineDeploymentData: # type: ignore + self._validate() + code, environment, model = self._generate_dependencies() + + properties = RestKubernetesOnlineDeployment( + code_configuration=code, + environment_id=environment, + model=model, + model_mount_path=self.model_mount_path, + scale_settings=self.scale_settings._to_rest_object() if self.scale_settings else None, + properties=self.properties, + description=self.description, + environment_variables=self.environment_variables, + app_insights_enabled=self.app_insights_enabled, + request_settings=self.request_settings._to_rest_object() if self.request_settings else None, + liveness_probe=self.liveness_probe._to_rest_object() if self.liveness_probe else None, + readiness_probe=self.readiness_probe._to_rest_object() if self.readiness_probe else None, + container_resource_requirements=self.resources._to_rest_object() if self.resources else None, + instance_type=self.instance_type if self.instance_type else None, + data_collector=self.data_collector._to_rest_object() if self.data_collector else None, + ) + sku = RestSku(name="Default", capacity=self.instance_count) + + return RestOnlineDeploymentData(location=location, properties=properties, tags=self.tags, sku=sku) + + def _to_arm_resource_param(self, **kwargs: Any) -> Dict: + rest_object = self._to_rest_object(**kwargs) + properties = rest_object.properties + sku = rest_object.sku + tags = rest_object.tags + + return { + self._arm_type: { + ArmConstants.NAME: self.name, + ArmConstants.PROPERTIES_PARAMETER_NAME: self._serialize.body(properties, "K8SOnlineDeployment"), + ArmConstants.SKU: self._serialize.body(sku, "Sku"), + ArmConstants.TAGS: tags, + } + } + + def _merge_with(self, other: Any) -> None: + if other: + super()._merge_with(other) + if self.resources: + self.resources._merge_with(other.resources) + else: + self.resources = other.resources + + def _validate(self) -> None: + self._validate_name() + + @classmethod + def _from_rest_object(cls, resource: RestOnlineDeploymentData) -> "KubernetesOnlineDeployment": + deployment = resource.properties + + code_config = ( + CodeConfiguration( + code=deployment.code_configuration.code_id, + scoring_script=deployment.code_configuration.scoring_script, + ) + if deployment.code_configuration + else None + ) + + return KubernetesOnlineDeployment( + id=resource.id, + name=resource.name, + tags=resource.tags, + properties=deployment.properties, + description=deployment.description, + request_settings=OnlineRequestSettings._from_rest_object(deployment.request_settings), + model=deployment.model, + code_configuration=code_config, + environment=deployment.environment_id, + resources=ResourceRequirementsSettings._from_rest_object(deployment.container_resource_requirements), + app_insights_enabled=deployment.app_insights_enabled, + scale_settings=cast( + Optional[Union[DefaultScaleSettings, TargetUtilizationScaleSettings]], + OnlineScaleSettings._from_rest_object(deployment.scale_settings), + ), + liveness_probe=ProbeSettings._from_rest_object(deployment.liveness_probe), + readiness_probe=ProbeSettings._from_rest_object(deployment.readiness_probe), + environment_variables=deployment.environment_variables, + endpoint_name=_parse_endpoint_name_from_deployment_id(resource.id), + instance_count=resource.sku.capacity if resource.sku else None, + instance_type=deployment.instance_type, + data_collector=( + DataCollector._from_rest_object(deployment.data_collector) + if hasattr(deployment, "data_collector") and deployment.data_collector + else None + ), + provisioning_state=deployment.provisioning_state if hasattr(deployment, "provisioning_state") else None, + ) + + +class ManagedOnlineDeployment(OnlineDeployment): + """Managed Online endpoint deployment entity. + + :param name: Name of the deployment resource + :type name: str + :param endpoint_name: Name of the endpoint resource, defaults to None + :type endpoint_name: typing.Optional[str] + :param tags: Tag dictionary. Tags can be added, removed, and updated., defaults to None + :type tags: typing.Optional[typing.Dict[str, typing.Any]] + :param properties: The asset property dictionary, defaults to None + :type properties: typing.Optional[typing.Dict[str, typing.Any]] + :param description: Description of the resource, defaults to None + :type description: typing.Optional[str] + :param model: Model entity for the endpoint deployment, defaults to None + :type model: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Model]] + :param code_configuration: Code Configuration, defaults to None + :type code_configuration: typing.Optional[~azure.ai.ml.entities.CodeConfiguration] + :param environment: Environment entity for the endpoint deployment, defaults to None + :type environment: typing.Optional[typing.Union[str, ~azure.ai.ml.entities.Environment]] + :param app_insights_enabled: Is appinsights enabled, defaults to False + :type app_insights_enabled: bool + :param scale_settings: How the online deployment will scale, defaults to None + :type scale_settings: typing.Optional[typing.Union[~azure.ai.ml.entities.DefaultScaleSettings + , ~azure.ai.ml.entities.TargetUtilizationScaleSettings]] + :param request_settings: Online Request Settings, defaults to None + :type request_settings: typing.Optional[OnlineRequestSettings] + :param liveness_probe: Liveness probe settings, defaults to None + :type liveness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param readiness_probe: Readiness probe settings, defaults to None + :type readiness_probe: typing.Optional[~azure.ai.ml.entities.ProbeSettings] + :param environment_variables: Environment variables that will be set in deployment, defaults to None + :type environment_variables: typing.Optional[typing.Dict[str, str]] + :param instance_type: Azure compute sku, defaults to None + :type instance_type: typing.Optional[str] + :param instance_count: The instance count used for this deployment, defaults to None + :type instance_count: typing.Optional[int] + :param egress_public_network_access: Whether to restrict communication between a deployment and the + Azure resources used to by the deployment. Allowed values are: "enabled", "disabled", defaults to None + :type egress_public_network_access: typing.Optional[str] + :param code_path: Equivalent to code_configuration.code, will be ignored if code_configuration is present + , defaults to None + :type code_path: typing.Optional[typing.Union[str, os.PathLike]] + :param scoring_script_path: Equivalent to code_configuration.scoring_script, will be ignored if + code_configuration is present, defaults to None + :type scoring_script_path: typing.Optional[typing.Union[str, os.PathLike]] + :param data_collector: Data collector, defaults to None + :type data_collector: typing.Optional[typing.List[~azure.ai.ml.entities.DataCollector]] + """ + + def __init__( + self, + *, + name: str, + endpoint_name: Optional[str] = None, + tags: Optional[Dict[str, typing.Any]] = None, + properties: Optional[Dict[str, typing.Any]] = None, + description: Optional[str] = None, + model: Optional[Union[str, "Model"]] = None, + code_configuration: Optional[CodeConfiguration] = None, + environment: Optional[Union[str, "Environment"]] = None, + app_insights_enabled: bool = False, + scale_settings: Optional[Union[DefaultScaleSettings, TargetUtilizationScaleSettings]] = None, + request_settings: Optional[OnlineRequestSettings] = None, + liveness_probe: Optional[ProbeSettings] = None, + readiness_probe: Optional[ProbeSettings] = None, + environment_variables: Optional[Dict[str, str]] = None, + instance_type: Optional[str] = None, + instance_count: Optional[int] = None, + egress_public_network_access: Optional[str] = None, + code_path: Optional[Union[str, os.PathLike]] = None, # promoted property from code_configuration.code + scoring_script: Optional[ + Union[str, os.PathLike] + ] = None, # promoted property from code_configuration.scoring_script + data_collector: Optional[DataCollector] = None, + **kwargs: Any, + ): + kwargs["type"] = EndpointComputeType.MANAGED.value + self.private_network_connection = kwargs.pop("private_network_connection", None) + self.package_model = kwargs.pop("package_model", False) + + super(ManagedOnlineDeployment, self).__init__( + name=name, + endpoint_name=endpoint_name, + tags=tags, + properties=properties, + description=description, + model=model, + code_configuration=code_configuration, + environment=environment, + environment_variables=environment_variables, + app_insights_enabled=app_insights_enabled, + scale_settings=scale_settings, + request_settings=request_settings, + liveness_probe=liveness_probe, + readiness_probe=readiness_probe, + instance_count=instance_count, + instance_type=instance_type, + code_path=code_path, + scoring_script=scoring_script, + data_collector=data_collector, + **kwargs, + ) + + self.readiness_probe = readiness_probe + self.egress_public_network_access = egress_public_network_access + + def _to_dict(self) -> Dict: + res: dict = ManagedOnlineDeploymentSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + # pylint: disable=arguments-differ + def _to_rest_object(self, location: str) -> RestOnlineDeploymentData: # type: ignore + self._validate() + code, environment, model = self._generate_dependencies() + properties = RestManagedOnlineDeployment( + code_configuration=code, + environment_id=environment, + model=model, + model_mount_path=self.model_mount_path, + scale_settings=self.scale_settings._to_rest_object() if self.scale_settings else None, + properties=self.properties, + description=self.description, + environment_variables=self.environment_variables, + app_insights_enabled=self.app_insights_enabled, + request_settings=self.request_settings._to_rest_object() if self.request_settings else None, + liveness_probe=self.liveness_probe._to_rest_object() if self.liveness_probe else None, + instance_type=self.instance_type, + readiness_probe=self.readiness_probe._to_rest_object() if self.readiness_probe else None, + data_collector=self.data_collector._to_rest_object() if self.data_collector else None, + ) + # TODO: SKU name is defaulted to value "Default" since service side requires it. + # Should be removed once service side defaults it. + sku = RestSku(name="Default", capacity=self.instance_count) + + # mfe is expecting private network connection to be in both the attribute level + # as well as in the properties dictionary. + if hasattr(self, "private_network_connection") and self.private_network_connection: + properties.private_network_connection = self.private_network_connection + properties.properties["private-network-connection"] = self.private_network_connection + if hasattr(self, "egress_public_network_access") and self.egress_public_network_access: + properties.egress_public_network_access = self.egress_public_network_access + return RestOnlineDeploymentData(location=location, properties=properties, tags=self.tags, sku=sku) + + def _to_arm_resource_param(self, **kwargs: Any) -> Dict: + rest_object = self._to_rest_object(**kwargs) + properties = rest_object.properties + sku = rest_object.sku + tags = rest_object.tags + + return { + self._arm_type: { + ArmConstants.NAME: self.name, + ArmConstants.PROPERTIES_PARAMETER_NAME: self._serialize.body(properties, "ManagedOnlineDeployment"), + ArmConstants.SKU: self._serialize.body(sku, "Sku"), + ArmConstants.TAGS: tags, + } + } + + @classmethod + def _from_rest_object(cls, resource: RestOnlineDeploymentData) -> "ManagedOnlineDeployment": + deployment = resource.properties + + code_config = ( + CodeConfiguration( + code=deployment.code_configuration.code_id, + scoring_script=deployment.code_configuration.scoring_script, + ) + if deployment.code_configuration + else None + ) + + return ManagedOnlineDeployment( + id=resource.id, + name=resource.name, + tags=resource.tags, + properties=deployment.properties, + description=deployment.description, + request_settings=OnlineRequestSettings._from_rest_object(deployment.request_settings), + model=(deployment.model if deployment.model else None), + code_configuration=code_config, + environment=deployment.environment_id, + app_insights_enabled=deployment.app_insights_enabled, + scale_settings=OnlineScaleSettings._from_rest_object(deployment.scale_settings), # type: ignore + liveness_probe=ProbeSettings._from_rest_object(deployment.liveness_probe), + environment_variables=deployment.environment_variables, + readiness_probe=ProbeSettings._from_rest_object(deployment.readiness_probe), + instance_type=deployment.instance_type, + endpoint_name=_parse_endpoint_name_from_deployment_id(resource.id), + instance_count=resource.sku.capacity, + private_network_connection=( + deployment.private_network_connection if hasattr(deployment, "private_network_connection") else None + ), + egress_public_network_access=deployment.egress_public_network_access, + data_collector=( + DataCollector._from_rest_object(deployment.data_collector) + if hasattr(deployment, "data_collector") and deployment.data_collector + else None + ), + provisioning_state=deployment.provisioning_state if hasattr(deployment, "provisioning_state") else None, + creation_context=resource.system_data, + ) + + def _merge_with(self, other: Any) -> None: + if other: + super()._merge_with(other) + self.instance_type = other.instance_type or self.instance_type + + def _validate(self) -> None: + self._validate_name() + self._validate_scale_settings() + + def _validate_scale_settings(self) -> None: + if self.scale_settings: + if not isinstance(self.scale_settings, DefaultScaleSettings): + msg = "ManagedOnlineEndpoint supports DefaultScaleSettings only." + raise ValidationException( + message=msg, + target=ErrorTarget.ONLINE_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.USER_ERROR, + error_type=ValidationErrorType.INVALID_VALUE, + ) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/oversize_data_config.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/oversize_data_config.py new file mode 100644 index 00000000..80338c39 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/oversize_data_config.py @@ -0,0 +1,25 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional + +from azure.ai.ml._schema._deployment.online.oversize_data_config_schema import OversizeDataConfigSchema +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY + + +class OversizeDataConfig: + """Oversize Data Config deployment entity. + + :param path: Blob path for Model Data Collector file. + :type path: str + """ + + # pylint: disable=unused-argument + def __init__(self, path: Optional[str] = None, **kwargs: Any): + self.path = path + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = OversizeDataConfigSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/payload_response.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/payload_response.py new file mode 100644 index 00000000..b67d46c7 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/payload_response.py @@ -0,0 +1,26 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional + +from azure.ai.ml._schema._deployment.online.payload_response_schema import PayloadResponseSchema +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY + + +class PayloadResponse: + """Response deployment entity + + :param enabled: Is response logging enabled. + :type enabled: str + + """ + + # pylint: disable=unused-argument + def __init__(self, enabled: Optional[str] = None, **kwargs: Any): + self.enabled = enabled + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = PayloadResponseSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/pipeline_component_batch_deployment.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/pipeline_component_batch_deployment.py new file mode 100644 index 00000000..730bc39e --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/pipeline_component_batch_deployment.py @@ -0,0 +1,150 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from os import PathLike +from pathlib import Path +from typing import IO, Any, AnyStr, Dict, Optional, Union + +from azure.ai.ml._restclient.v2024_01_01_preview.models import BatchDeployment as RestBatchDeployment +from azure.ai.ml._restclient.v2024_01_01_preview.models import ( + BatchDeploymentProperties, + BatchPipelineComponentDeploymentConfiguration, + IdAssetReference, +) +from azure.ai.ml._schema._deployment.batch.pipeline_component_batch_deployment_schema import ( + PipelineComponentBatchDeploymentSchema, +) +from azure.ai.ml._utils._arm_id_utils import _parse_endpoint_name_from_deployment_id +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml._utils.utils import dump_yaml_to_file +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY +from azure.ai.ml.entities import PipelineComponent +from azure.ai.ml.entities._builders import BaseNode +from azure.ai.ml.entities._component.component import Component +from azure.ai.ml.entities._resource import Resource +from azure.ai.ml.entities._util import load_from_dict + + +@experimental +class PipelineComponentBatchDeployment(Resource): + """Pipeline Component Batch Deployment entity. + + :param type: Job definition type. Allowed value: "pipeline" + :type type: Optional[str] + :param name: Name of the deployment resource. + :type name: Optional[str] + :param description: Description of the deployment resource. + :type description: Optional[str] + :param component: Component definition. + :type component: Optional[Union[Component, str]] + :param settings: Run-time settings for the pipeline job. + :type settings: Optional[Dict[str, Any]] + :param tags: A set of tags. The tags which will be applied to the job. + :type tags: Optional[Dict[str, Any]] + :param job_definition: Arm ID or PipelineJob entity of an existing pipeline job. + :type job_definition: Optional[Dict[str, ~azure.ai.ml.entities._builders.BaseNode]] + :param endpoint_name: Name of the Endpoint resource, defaults to None. + :type endpoint_name: Optional[str] + """ + + def __init__( + self, + *, + name: Optional[str], + endpoint_name: Optional[str] = None, + component: Optional[Union[Component, str]] = None, + settings: Optional[Dict[str, str]] = None, + job_definition: Optional[Dict[str, BaseNode]] = None, + tags: Optional[Dict] = None, + description: Optional[str] = None, + **kwargs: Any, + ): + self._type = kwargs.pop("type", None) + super().__init__(name=name, tags=tags, description=description, **kwargs) + self.component = component + self.endpoint_name = endpoint_name + self.settings = settings + self.job_definition = job_definition + + def _to_rest_object(self, location: str) -> "RestBatchDeployment": + if isinstance(self.component, PipelineComponent): + id_asset_ref = IdAssetReference(asset_id=self.component.id) + + batch_pipeline_config = BatchPipelineComponentDeploymentConfiguration( + settings=self.settings, + tags=self.component.tags, + description=self.component.description, + component_id=id_asset_ref, + ) + else: + id_asset_ref = IdAssetReference(asset_id=self.component) + batch_pipeline_config = BatchPipelineComponentDeploymentConfiguration( + settings=self.settings, component_id=id_asset_ref + ) + return RestBatchDeployment( + location=location, + tags=self.tags, + properties=BatchDeploymentProperties( + deployment_configuration=batch_pipeline_config, + description=self.description, + ), + ) + + @classmethod + def _load( + cls, + data: Optional[Dict] = None, + yaml_path: Optional[Union[PathLike, str]] = None, + params_override: Optional[list] = None, + **kwargs: Any, + ) -> "PipelineComponentBatchDeployment": + data = data or {} + params_override = params_override or [] + cls._update_params(params_override) + + context = { + BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path.cwd(), + PARAMS_OVERRIDE_KEY: params_override, + } + res: PipelineComponentBatchDeployment = load_from_dict( + PipelineComponentBatchDeploymentSchema, data, context, **kwargs + ) + return res + + @classmethod + def _update_params(cls, params_override: Any) -> None: + for param in params_override: + endpoint_name = param.get("endpoint_name") + if isinstance(endpoint_name, str): + param["endpoint_name"] = endpoint_name.lower() + + @classmethod + def _from_rest_object(cls, deployment: RestBatchDeployment) -> "PipelineComponentBatchDeployment": + return PipelineComponentBatchDeployment( + name=deployment.name, + tags=deployment.tags, + component=deployment.properties.additional_properties["deploymentConfiguration"]["componentId"]["assetId"], + settings=deployment.properties.additional_properties["deploymentConfiguration"]["settings"], + endpoint_name=_parse_endpoint_name_from_deployment_id(deployment.id), + ) + + def dump(self, dest: Union[str, PathLike, IO[AnyStr]], **kwargs: Any) -> None: + """Dump the deployment content into a file in yaml format. + + :param dest: The destination to receive this deployment's content. + Must be either a path to a local file, or an already-open file stream. + If dest is a file path, a new file will be created, + and an exception is raised if the file exists. + If dest is an open file, the file will be written to directly, + and an exception will be raised if the file is not writable. + :type dest: typing.Union[os.PathLike, str, typing.IO[typing.AnyStr]] + """ + path = kwargs.pop("path", None) + yaml_serialized = self._to_dict() + dump_yaml_to_file(dest, yaml_serialized, default_flow_style=False, path=path, **kwargs) + + def _to_dict(self) -> Dict: + res: dict = PipelineComponentBatchDeploymentSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/request_logging.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/request_logging.py new file mode 100644 index 00000000..20cc83fe --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/request_logging.py @@ -0,0 +1,39 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, List, Optional + +from azure.ai.ml._restclient.v2023_04_01_preview.models import RequestLogging as RestRequestLogging +from azure.ai.ml._schema._deployment.online.request_logging_schema import RequestLoggingSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY + + +@experimental +class RequestLogging: + """Request Logging deployment entity. + + :param capture_headers: Request payload header. + :type capture_headers: list[str] + """ + + def __init__( + self, + *, + capture_headers: Optional[List[str]] = None, + **kwargs: Any, + ): # pylint: disable=unused-argument + self.capture_headers = capture_headers + + @classmethod + def _from_rest_object(cls, rest_obj: RestRequestLogging) -> "RequestLogging": + return RequestLogging(capture_headers=rest_obj.capture_headers) + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = RequestLoggingSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res + + def _to_rest_object(self) -> RestRequestLogging: + return RestRequestLogging(capture_headers=self.capture_headers) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/resource_requirements_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/resource_requirements_settings.py new file mode 100644 index 00000000..9db61aae --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/resource_requirements_settings.py @@ -0,0 +1,84 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=protected-access + +import logging +from typing import Optional + +from azure.ai.ml._restclient.v2022_05_01.models import ContainerResourceRequirements +from azure.ai.ml.entities._deployment.container_resource_settings import ResourceSettings +from azure.ai.ml.entities._mixins import RestTranslatableMixin + +module_logger = logging.getLogger(__name__) + + +class ResourceRequirementsSettings(RestTranslatableMixin): + """Resource requirements settings for a container. + + :param requests: The minimum resource requests for a container. + :type requests: Optional[~azure.ai.ml.entities.ResourceSettings] + :param limits: The resource limits for a container. + :type limits: Optional[~azure.ai.ml.entities.ResourceSettings] + + .. admonition:: Example: + + .. literalinclude:: ../samples/ml_samples_misc.py + :start-after: [START resource_requirements_configuration] + :end-before: [END resource_requirements_configuration] + :language: python + :dedent: 8 + :caption: Configuring ResourceRequirementSettings for a Kubernetes deployment. + """ + + def __init__( + self, + requests: Optional[ResourceSettings] = None, + limits: Optional[ResourceSettings] = None, + ) -> None: + self.requests = requests + self.limits = limits + + def _to_rest_object(self) -> ContainerResourceRequirements: + return ContainerResourceRequirements( + container_resource_requests=self.requests._to_rest_object() if self.requests else None, + container_resource_limits=self.limits._to_rest_object() if self.limits else None, + ) + + @classmethod + def _from_rest_object( # pylint: disable=arguments-renamed + cls, settings: ContainerResourceRequirements + ) -> Optional["ResourceRequirementsSettings"]: + requests = settings.container_resource_requests + limits = settings.container_resource_limits + return ( + ResourceRequirementsSettings( + requests=ResourceSettings._from_rest_object(requests), + limits=ResourceSettings._from_rest_object(limits), + ) + if settings + else None + ) + + def _merge_with(self, other: Optional["ResourceRequirementsSettings"]) -> None: + if other: + if self.requests: + self.requests._merge_with(other.requests) + else: + self.requests = other.requests + if self.limits: + self.limits._merge_with(other.limits) + else: + self.limits = other.limits + + def __eq__(self, other: object) -> bool: + if not isinstance(other, ResourceRequirementsSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + return self.requests == other.requests and self.limits == other.limits + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/run_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/run_settings.py new file mode 100644 index 00000000..f1deac83 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/run_settings.py @@ -0,0 +1,50 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +from typing import Any, Dict, Optional + +from azure.ai.ml._schema._deployment.batch.run_settings_schema import RunSettingsSchema +from azure.ai.ml._utils._experimental import experimental +from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY + + +@experimental +class RunSettings: + """Run Settings entity. + + :param name: Run settings name + :type name: str + :param display_name: Run settings display name + :type display_name: str + :param experiment_name: Run settings experiment name + :type experiment_name: str + :param description: Run settings description + :type description: str + :param tags: Run settings tags + :type tags: Dict[str, Any] + :param settings: Run settings - settings + :type settings: Dict[str, Any] + """ + + def __init__( + self, + name: Optional[str] = None, + display_name: Optional[str] = None, + experiment_name: Optional[str] = None, + description: Optional[str] = None, + tags: Optional[Dict[str, Any]] = None, + settings: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ): # pylint: disable=unused-argument + self.name = name + self.display_name = display_name + self.experiment_name = experiment_name + self.description = description + self.tags = tags + self.settings = settings + + def _to_dict(self) -> Dict: + # pylint: disable=no-member + res: dict = RunSettingsSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self) + return res diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/scale_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/scale_settings.py new file mode 100644 index 00000000..85535ca0 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_deployment/scale_settings.py @@ -0,0 +1,173 @@ +# --------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# --------------------------------------------------------- + +# pylint: disable=protected-access + +import logging +from abc import abstractmethod +from typing import Any, Optional + +from azure.ai.ml._restclient.v2023_04_01_preview.models import DefaultScaleSettings as RestDefaultScaleSettings +from azure.ai.ml._restclient.v2023_04_01_preview.models import OnlineScaleSettings as RestOnlineScaleSettings +from azure.ai.ml._restclient.v2023_04_01_preview.models import ScaleType +from azure.ai.ml._restclient.v2023_04_01_preview.models import ( + TargetUtilizationScaleSettings as RestTargetUtilizationScaleSettings, +) +from azure.ai.ml._utils.utils import camel_to_snake, from_iso_duration_format, to_iso_duration_format +from azure.ai.ml.entities._mixins import RestTranslatableMixin +from azure.ai.ml.exceptions import DeploymentException, ErrorCategory, ErrorTarget + +module_logger = logging.getLogger(__name__) + + +class OnlineScaleSettings(RestTranslatableMixin): + """Scale settings for online deployment. + + :param type: Type of the scale settings, allowed values are "default" and "target_utilization". + :type type: str + """ + + def __init__( + self, + # pylint: disable=redefined-builtin + type: str, + # pylint: disable=unused-argument + **kwargs: Any, + ): + self.type = camel_to_snake(type) + + @abstractmethod + def _to_rest_object(self) -> RestOnlineScaleSettings: + pass + + def _merge_with(self, other: Any) -> None: + if other: + self.type = other.type or self.type + + @classmethod + def _from_rest_object( # pylint: disable=arguments-renamed + cls, settings: RestOnlineScaleSettings + ) -> "OnlineScaleSettings": + if settings.scale_type == "Default": + return DefaultScaleSettings._from_rest_object(settings) + if settings.scale_type == "TargetUtilization": + return TargetUtilizationScaleSettings._from_rest_object(settings) + + msg = f"Unsupported online scale setting type {settings.scale_type}." + raise DeploymentException( + message=msg, + target=ErrorTarget.ONLINE_DEPLOYMENT, + no_personal_data_message=msg, + error_category=ErrorCategory.SYSTEM_ERROR, + ) + + +class DefaultScaleSettings(OnlineScaleSettings): + """Default scale settings. + + :ivar type: Default scale settings type. Set automatically to "default" for this class. + :vartype type: str + """ + + def __init__(self, **kwargs: Any): + super(DefaultScaleSettings, self).__init__( + type=ScaleType.DEFAULT.value, + ) + + def _to_rest_object(self) -> RestDefaultScaleSettings: + return RestDefaultScaleSettings() + + @classmethod + def _from_rest_object(cls, settings: RestDefaultScaleSettings) -> "DefaultScaleSettings": + return DefaultScaleSettings() + + def __eq__(self, other: object) -> bool: + if not isinstance(other, DefaultScaleSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + res: bool = self.type.lower() == other.type.lower() + return res + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) + + +class TargetUtilizationScaleSettings(OnlineScaleSettings): + """Auto scale settings. + + :param min_instances: Minimum number of the instances + :type min_instances: int + :param max_instances: Maximum number of the instances + :type max_instances: int + :param polling_interval: The polling interval in ISO 8691 format. Only supports duration with + precision as low as Seconds. + :type polling_interval: str + :param target_utilization_percentage: + :type target_utilization_percentage: int + :ivar type: Target utilization scale settings type. Set automatically to "target_utilization" for this class. + :vartype type: str + """ + + def __init__( + self, + *, + min_instances: Optional[int] = None, + max_instances: Optional[int] = None, + polling_interval: Optional[int] = None, + target_utilization_percentage: Optional[int] = None, + **kwargs: Any, + ): + super(TargetUtilizationScaleSettings, self).__init__( + type=ScaleType.TARGET_UTILIZATION.value, + ) + self.min_instances = min_instances + self.max_instances = max_instances + self.polling_interval = polling_interval + self.target_utilization_percentage = target_utilization_percentage + + def _to_rest_object(self) -> RestTargetUtilizationScaleSettings: + return RestTargetUtilizationScaleSettings( + min_instances=self.min_instances, + max_instances=self.max_instances, + polling_interval=to_iso_duration_format(self.polling_interval), + target_utilization_percentage=self.target_utilization_percentage, + ) + + def _merge_with(self, other: Optional["TargetUtilizationScaleSettings"]) -> None: + if other: + super()._merge_with(other) + self.min_instances = other.min_instances or self.min_instances + self.max_instances = other.max_instances or self.max_instances + self.polling_interval = other.polling_interval or self.polling_interval + self.target_utilization_percentage = ( + other.target_utilization_percentage or self.target_utilization_percentage + ) + + @classmethod + def _from_rest_object(cls, settings: RestTargetUtilizationScaleSettings) -> "TargetUtilizationScaleSettings": + return cls( + min_instances=settings.min_instances, + max_instances=settings.max_instances, + polling_interval=from_iso_duration_format(settings.polling_interval), + target_utilization_percentage=settings.target_utilization_percentage, + ) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, TargetUtilizationScaleSettings): + return NotImplemented + if not other: + return False + # only compare mutable fields + return ( + self.type.lower() == other.type.lower() + and self.min_instances == other.min_instances + and self.max_instances == other.max_instances + and self.polling_interval == other.polling_interval + and self.target_utilization_percentage == other.target_utilization_percentage + ) + + def __ne__(self, other: object) -> bool: + return not self.__eq__(other) |