Source code for airflow.providers.google.cloud.operators.vertex_ai.custom_job

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"""This module contains Google Vertex AI operators."""

from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union

from google.api_core.exceptions import NotFound
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.aiplatform.models import Model
from google.cloud.aiplatform_v1.types.dataset import Dataset
from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline

from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.vertex_ai.custom_job import CustomJobHook
from airflow.providers.google.cloud.links.vertex_ai import VertexAIModelLink, VertexAITrainingPipelinesLink

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class CustomTrainingJobBaseOperator(BaseOperator): """The base class for operators that launch Custom jobs on VertexAI.""" def __init__( self, *, project_id: str, region: str, display_name: str, container_uri: str, model_serving_container_image_uri: Optional[str] = None, model_serving_container_predict_route: Optional[str] = None, model_serving_container_health_route: Optional[str] = None, model_serving_container_command: Optional[Sequence[str]] = None, model_serving_container_args: Optional[Sequence[str]] = None, model_serving_container_environment_variables: Optional[Dict[str, str]] = None, model_serving_container_ports: Optional[Sequence[int]] = None, model_description: Optional[str] = None, model_instance_schema_uri: Optional[str] = None, model_parameters_schema_uri: Optional[str] = None, model_prediction_schema_uri: Optional[str] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, # RUN dataset_id: Optional[str] = None, annotation_schema_uri: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, base_output_dir: Optional[str] = None, service_account: Optional[str] = None, network: Optional[str] = None, bigquery_destination: Optional[str] = None, args: Optional[List[Union[str, float, int]]] = None, environment_variables: Optional[Dict[str, str]] = None, replica_count: int = 1, machine_type: str = "n1-standard-4", accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED", accelerator_count: int = 0, boot_disk_type: str = "pd-ssd", boot_disk_size_gb: int = 100, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, tensorboard: Optional[str] = None, sync=True, gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.project_id = project_id self.region = region self.display_name = display_name # START Custom self.container_uri = container_uri self.model_serving_container_image_uri = model_serving_container_image_uri self.model_serving_container_predict_route = model_serving_container_predict_route self.model_serving_container_health_route = model_serving_container_health_route self.model_serving_container_command = model_serving_container_command self.model_serving_container_args = model_serving_container_args self.model_serving_container_environment_variables = model_serving_container_environment_variables self.model_serving_container_ports = model_serving_container_ports self.model_description = model_description self.model_instance_schema_uri = model_instance_schema_uri self.model_parameters_schema_uri = model_parameters_schema_uri self.model_prediction_schema_uri = model_prediction_schema_uri self.labels = labels self.training_encryption_spec_key_name = training_encryption_spec_key_name self.model_encryption_spec_key_name = model_encryption_spec_key_name self.staging_bucket = staging_bucket # END Custom # START Run param self.dataset = Dataset(name=dataset_id) if dataset_id else None self.annotation_schema_uri = annotation_schema_uri self.model_display_name = model_display_name self.model_labels = model_labels self.base_output_dir = base_output_dir self.service_account = service_account self.network = network self.bigquery_destination = bigquery_destination self.args = args self.environment_variables = environment_variables self.replica_count = replica_count self.machine_type = machine_type self.accelerator_type = accelerator_type self.accelerator_count = accelerator_count self.boot_disk_type = boot_disk_type self.boot_disk_size_gb = boot_disk_size_gb self.training_fraction_split = training_fraction_split self.validation_fraction_split = validation_fraction_split self.test_fraction_split = test_fraction_split self.training_filter_split = training_filter_split self.validation_filter_split = validation_filter_split self.test_filter_split = test_filter_split self.predefined_split_column_name = predefined_split_column_name self.timestamp_split_column_name = timestamp_split_column_name self.tensorboard = tensorboard self.sync = sync # END Run param self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs]class CreateCustomContainerTrainingJobOperator(CustomTrainingJobBaseOperator): """Create Custom Container Training job :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param display_name: Required. The user-defined name of this TrainingPipeline. :param command: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided :param container_uri: Required: Uri of the training container image in the GCR. :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script. :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI. :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform. :param model_serving_container_command: The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_environment_variables: The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. :param model_serving_container_ports: Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network. :param model_description: The description of the Model. :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param project_id: Project to run training in. :param region: Location to run training in. :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload`` is not set separately. :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, the trained Model will be secured by this key. :param staging_bucket: Bucket used to stage source and training artifacts. :param dataset: Vertex AI to fit this training against. :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object] (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object) Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with ``annotations_filter``, the Annotations used for training are filtered by both ``annotations_filter`` and ``annotation_schema_uri``. :param model_display_name: If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used. :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI sets the following environment variables when it runs your training code: - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/ - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/ - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/ :param service_account: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. :param network: The full name of the Compute Engine network to which the job should be peered. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>`` where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, ``training``, ``validation`` and ``test``. - AIP_DATA_FORMAT = "bigquery". - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training" - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation" - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test" :param args: Command line arguments to be passed to the Python script. :param environment_variables: Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. :param replica_count: The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. :param machine_type: The type of machine to use for training. :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 :param accelerator_count: The number of accelerators to attach to a worker replica. :param boot_disk_type: Type of the boot disk, default is `pd-ssd`. Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or `pd-standard` (Persistent Disk Hard Disk Drive). :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000]. :param training_fraction_split: Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided. :param validation_fraction_split: Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided. :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided. :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {``training``, ``validation``, ``test``}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload logs. Format: ``projects/{project}/locations/{location}/tensorboards/{tensorboard}`` For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training :param sync: Whether to execute the AI Platform job synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = [ 'region', 'command', 'impersonation_chain',
] def __init__( self, *, command: Sequence[str] = [], **kwargs, ) -> None: super().__init__(**kwargs) self.command = command
[docs] def execute(self, context: "Context"): self.hook = CustomJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_custom_container_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, container_uri=self.container_uri, command=self.command, model_serving_container_image_uri=self.model_serving_container_image_uri, model_serving_container_predict_route=self.model_serving_container_predict_route, model_serving_container_health_route=self.model_serving_container_health_route, model_serving_container_command=self.model_serving_container_command, model_serving_container_args=self.model_serving_container_args, model_serving_container_environment_variables=self.model_serving_container_environment_variables, model_serving_container_ports=self.model_serving_container_ports, model_description=self.model_description, model_instance_schema_uri=self.model_instance_schema_uri, model_parameters_schema_uri=self.model_parameters_schema_uri, model_prediction_schema_uri=self.model_prediction_schema_uri, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, staging_bucket=self.staging_bucket, # RUN dataset=self.dataset, annotation_schema_uri=self.annotation_schema_uri, model_display_name=self.model_display_name, model_labels=self.model_labels, base_output_dir=self.base_output_dir, service_account=self.service_account, network=self.network, bigquery_destination=self.bigquery_destination, args=self.args, environment_variables=self.environment_variables, replica_count=self.replica_count, machine_type=self.machine_type, accelerator_type=self.accelerator_type, accelerator_count=self.accelerator_count, boot_disk_type=self.boot_disk_type, boot_disk_size_gb=self.boot_disk_size_gb, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, validation_filter_split=self.validation_filter_split, test_filter_split=self.test_filter_split, predefined_split_column_name=self.predefined_split_column_name, timestamp_split_column_name=self.timestamp_split_column_name, tensorboard=self.tensorboard, sync=True, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result
[docs] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_job()
[docs]class CreateCustomPythonPackageTrainingJobOperator(CustomTrainingJobBaseOperator): """Create Custom Python Package Training job :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param display_name: Required. The user-defined name of this TrainingPipeline. :param python_package_gcs_uri: Required: GCS location of the training python package. :param python_module_name: Required: The module name of the training python package. :param container_uri: Required: Uri of the training container image in the GCR. :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script. :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI. :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform. :param model_serving_container_command: The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_environment_variables: The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. :param model_serving_container_ports: Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network. :param model_description: The description of the Model. :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param project_id: Project to run training in. :param region: Location to run training in. :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload`` is not set separately. :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, the trained Model will be secured by this key. :param staging_bucket: Bucket used to stage source and training artifacts. :param dataset: Vertex AI to fit this training against. :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object] (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object) Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with ``annotations_filter``, the Annotations used for training are filtered by both ``annotations_filter`` and ``annotation_schema_uri``. :param model_display_name: If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used. :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI sets the following environment variables when it runs your training code: - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/ - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/ - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/ :param service_account: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. :param network: The full name of the Compute Engine network to which the job should be peered. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>`` where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, ``training``, ``validation`` and ``test``. - AIP_DATA_FORMAT = "bigquery". - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training" - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation" - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test" :param args: Command line arguments to be passed to the Python script. :param environment_variables: Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. :param replica_count: The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. :param machine_type: The type of machine to use for training. :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 :param accelerator_count: The number of accelerators to attach to a worker replica. :param boot_disk_type: Type of the boot disk, default is `pd-ssd`. Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or `pd-standard` (Persistent Disk Hard Disk Drive). :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000]. :param training_fraction_split: Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided. :param validation_fraction_split: Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided. :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided. :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {``training``, ``validation``, ``test``}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload logs. Format: ``projects/{project}/locations/{location}/tensorboards/{tensorboard}`` For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training :param sync: Whether to execute the AI Platform job synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = [ 'region', 'impersonation_chain',
] def __init__( self, *, python_package_gcs_uri: str, python_module_name: str, **kwargs, ) -> None: super().__init__(**kwargs) self.python_package_gcs_uri = python_package_gcs_uri self.python_module_name = python_module_name
[docs] def execute(self, context: "Context"): self.hook = CustomJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_custom_python_package_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, python_package_gcs_uri=self.python_package_gcs_uri, python_module_name=self.python_module_name, container_uri=self.container_uri, model_serving_container_image_uri=self.model_serving_container_image_uri, model_serving_container_predict_route=self.model_serving_container_predict_route, model_serving_container_health_route=self.model_serving_container_health_route, model_serving_container_command=self.model_serving_container_command, model_serving_container_args=self.model_serving_container_args, model_serving_container_environment_variables=self.model_serving_container_environment_variables, model_serving_container_ports=self.model_serving_container_ports, model_description=self.model_description, model_instance_schema_uri=self.model_instance_schema_uri, model_parameters_schema_uri=self.model_parameters_schema_uri, model_prediction_schema_uri=self.model_prediction_schema_uri, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, staging_bucket=self.staging_bucket, # RUN dataset=self.dataset, annotation_schema_uri=self.annotation_schema_uri, model_display_name=self.model_display_name, model_labels=self.model_labels, base_output_dir=self.base_output_dir, service_account=self.service_account, network=self.network, bigquery_destination=self.bigquery_destination, args=self.args, environment_variables=self.environment_variables, replica_count=self.replica_count, machine_type=self.machine_type, accelerator_type=self.accelerator_type, accelerator_count=self.accelerator_count, boot_disk_type=self.boot_disk_type, boot_disk_size_gb=self.boot_disk_size_gb, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, validation_filter_split=self.validation_filter_split, test_filter_split=self.test_filter_split, predefined_split_column_name=self.predefined_split_column_name, timestamp_split_column_name=self.timestamp_split_column_name, tensorboard=self.tensorboard, sync=True, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result
[docs] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_job()
[docs]class CreateCustomTrainingJobOperator(CustomTrainingJobBaseOperator): """Create Custom Training job :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param display_name: Required. The user-defined name of this TrainingPipeline. :param script_path: Required. Local path to training script. :param container_uri: Required: Uri of the training container image in the GCR. :param requirements: List of python packages dependencies of script. :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script. :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI. :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform. :param model_serving_container_command: The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. :param model_serving_container_environment_variables: The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. :param model_serving_container_ports: Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network. :param model_description: The description of the Model. :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. :param project_id: Project to run training in. :param region: Location to run training in. :param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload`` is not set separately. :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, the trained Model will be secured by this key. :param staging_bucket: Bucket used to stage source and training artifacts. :param dataset: Vertex AI to fit this training against. :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object] (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object) Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with ``annotations_filter``, the Annotations used for training are filtered by both ``annotations_filter`` and ``annotation_schema_uri``. :param model_display_name: If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used. :param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI sets the following environment variables when it runs your training code: - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/ - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/ - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/ :param service_account: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. :param network: The full name of the Compute Engine network to which the job should be peered. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>`` where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, ``training``, ``validation`` and ``test``. - AIP_DATA_FORMAT = "bigquery". - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training" - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation" - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test" :param args: Command line arguments to be passed to the Python script. :param environment_variables: Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. :param replica_count: The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. :param machine_type: The type of machine to use for training. :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 :param accelerator_count: The number of accelerators to attach to a worker replica. :param boot_disk_type: Type of the boot disk, default is `pd-ssd`. Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or `pd-standard` (Persistent Disk Hard Disk Drive). :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000]. :param training_fraction_split: Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided. :param validation_fraction_split: Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided. :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided. :param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided. :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {``training``, ``validation``, ``test``}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 `date-time` format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload logs. Format: ``projects/{project}/locations/{location}/tensorboards/{tensorboard}`` For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training :param sync: Whether to execute the AI Platform job synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = [ 'region', 'script_path', 'requirements', 'impersonation_chain',
] def __init__( self, *, script_path: str, requirements: Optional[Sequence[str]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.requirements = requirements self.script_path = script_path
[docs] def execute(self, context: "Context"): self.hook = CustomJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) model = self.hook.create_custom_training_job( project_id=self.project_id, region=self.region, display_name=self.display_name, script_path=self.script_path, container_uri=self.container_uri, requirements=self.requirements, model_serving_container_image_uri=self.model_serving_container_image_uri, model_serving_container_predict_route=self.model_serving_container_predict_route, model_serving_container_health_route=self.model_serving_container_health_route, model_serving_container_command=self.model_serving_container_command, model_serving_container_args=self.model_serving_container_args, model_serving_container_environment_variables=self.model_serving_container_environment_variables, model_serving_container_ports=self.model_serving_container_ports, model_description=self.model_description, model_instance_schema_uri=self.model_instance_schema_uri, model_parameters_schema_uri=self.model_parameters_schema_uri, model_prediction_schema_uri=self.model_prediction_schema_uri, labels=self.labels, training_encryption_spec_key_name=self.training_encryption_spec_key_name, model_encryption_spec_key_name=self.model_encryption_spec_key_name, staging_bucket=self.staging_bucket, # RUN dataset=self.dataset, annotation_schema_uri=self.annotation_schema_uri, model_display_name=self.model_display_name, model_labels=self.model_labels, base_output_dir=self.base_output_dir, service_account=self.service_account, network=self.network, bigquery_destination=self.bigquery_destination, args=self.args, environment_variables=self.environment_variables, replica_count=self.replica_count, machine_type=self.machine_type, accelerator_type=self.accelerator_type, accelerator_count=self.accelerator_count, boot_disk_type=self.boot_disk_type, boot_disk_size_gb=self.boot_disk_size_gb, training_fraction_split=self.training_fraction_split, validation_fraction_split=self.validation_fraction_split, test_fraction_split=self.test_fraction_split, training_filter_split=self.training_filter_split, validation_filter_split=self.validation_filter_split, test_filter_split=self.test_filter_split, predefined_split_column_name=self.predefined_split_column_name, timestamp_split_column_name=self.timestamp_split_column_name, tensorboard=self.tensorboard, sync=True, ) result = Model.to_dict(model) model_id = self.hook.extract_model_id(result) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return result
[docs] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_job()
[docs]class DeleteCustomTrainingJobOperator(BaseOperator): """Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob. :param training_pipeline_id: Required. The name of the TrainingPipeline resource to be deleted. :param custom_job_id: Required. The name of the CustomJob to delete. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = ("region", "project_id", "impersonation_chain")
def __init__( self, *, training_pipeline_id: str, custom_job_id: str, region: str, project_id: str, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.training_pipeline = training_pipeline_id self.custom_job = custom_job_id self.region = region self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: "Context"): hook = CustomJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Deleting custom training pipeline: %s", self.training_pipeline) training_pipeline_operation = hook.delete_training_pipeline( training_pipeline=self.training_pipeline, region=self.region, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=training_pipeline_operation) self.log.info("Training pipeline was deleted.") except NotFound: self.log.info("The Training Pipeline ID %s does not exist.", self.training_pipeline) try: self.log.info("Deleting custom job: %s", self.custom_job) custom_job_operation = hook.delete_custom_job( custom_job=self.custom_job, region=self.region, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=custom_job_operation) self.log.info("Custom job was deleted.") except NotFound: self.log.info("The Custom Job ID %s does not exist.", self.custom_job)
[docs]class ListCustomTrainingJobOperator(BaseOperator): """Lists CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob in a Location. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param filter: Optional. The standard list filter. Supported fields: - ``display_name`` supports = and !=. - ``state`` supports = and !=. Some examples of using the filter are: - ``state="PIPELINE_STATE_SUCCEEDED" AND display_name="my_pipeline"`` - ``state="PIPELINE_STATE_RUNNING" OR display_name="my_pipeline"`` - ``NOT display_name="my_pipeline"`` - ``state="PIPELINE_STATE_FAILED"`` :param page_size: Optional. The standard list page size. :param page_token: Optional. The standard list page token. Typically obtained via [ListTrainingPipelinesResponse.next_page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesResponse.next_page_token] of the previous [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines] call. :param read_mask: Optional. Mask specifying which fields to read. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = [ "region", "project_id", "impersonation_chain",
] ] def __init__( self, *, region: str, project_id: str, page_size: Optional[int] = None, page_token: Optional[str] = None, filter: Optional[str] = None, read_mask: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.page_size = page_size self.page_token = page_token self.filter = filter self.read_mask = read_mask self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: "Context"): hook = CustomJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) results = hook.list_training_pipelines( region=self.region, project_id=self.project_id, page_size=self.page_size, page_token=self.page_token, filter=self.filter, read_mask=self.read_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAITrainingPipelinesLink.persist(context=context, task_instance=self) return [TrainingPipeline.to_dict(result) for result in results]

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