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

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"""This module contains Google Vertex AI operators.

.. spelling::

    jsonl
    codepoints
    aiplatform
    gapic
"""
from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

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 import Model, explain
from google.cloud.aiplatform_v1.types import BatchPredictionJob

from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.vertex_ai.batch_prediction_job import BatchPredictionJobHook
from airflow.providers.google.cloud.links.vertex_ai import (
    VertexAIBatchPredictionJobLink,
    VertexAIBatchPredictionJobListLink,
)

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class CreateBatchPredictionJobOperator(BaseOperator): """ Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start. :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 batch_prediction_job: Required. The BatchPredictionJob to create. :param job_display_name: Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. :param model_name: Required. A fully-qualified model resource name or model ID. :param instances_format: Required. The format in which instances are provided. Must be one of the formats listed in `Model.supported_input_storage_formats`. Default is "jsonl" when using `gcs_source`. If a `bigquery_source` is provided, this is overridden to "bigquery". :param predictions_format: Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in `Model.supported_output_storage_formats`. Default is "jsonl" when using `gcs_destination_prefix`. If a `bigquery_destination_prefix` is provided, this is overridden to "bigquery". :param gcs_source: Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match `instances_format`. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. :param bigquery_source: BigQuery URI to a table, up to 2000 characters long. For example: `bq://projectId.bqDatasetId.bqTableId` :param gcs_destination_prefix: The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is ``prediction-<model-display-name>-<job-create-time>``, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files ``predictions_0001.<extension>``, ``predictions_0002.<extension>``, ..., ``predictions_N.<extension>`` are created where ``<extension>`` depends on chosen ``predictions_format``, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both ``instance`` and ``prediction`` schemata defined then each such file contains predictions as per the ``predictions_format``. If prediction for any instance failed (partially or completely), then an additional ``errors_0001.<extension>``, ``errors_0002.<extension>``,..., ``errors_N.<extension>`` files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional ``error`` field which as value has ```google.rpc.Status`` <Status>`__ containing only ``code`` and ``message`` fields. :param bigquery_destination_prefix: The BigQuery project location where the output is to be written to. In the given project a new dataset is created with name ``prediction_<model-display-name>_<job-create-time>`` where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, ``predictions``, and ``errors``. If the Model has both ``instance`` and ``prediction`` schemata defined then the tables have columns as follows: The ``predictions`` table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The ``errors`` table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has ```google.rpc.Status`` <Status>`__ represented as a STRUCT, and containing only ``code`` and ``message``. :param model_parameters: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's `parameters_schema_uri`. :param machine_type: The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources. :param accelerator_type: The type of accelerator(s) that may be attached to the machine as per `accelerator_count`. Only used if `machine_type` is set. :param accelerator_count: The number of accelerators to attach to the `machine_type`. Only used if `machine_type` is set. :param starting_replica_count: The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than `max_replica_count`. Only used if `machine_type` is set. :param max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set. Default is 10. :param generate_explanation: Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the `prediction_format`: - `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the [aiplatform.gapic.Explanation] object. - `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the [aiplatform.gapic.Explanation] object. - `csv`: Generating explanations for CSV format is not supported. :param explanation_metadata: Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only if `generate_explanation` is set to `True`. This value overrides the value of `Model.explanation_metadata`. All fields of `explanation_metadata` are optional in the request. If a field of the `explanation_metadata` object is not populated, the corresponding field of the `Model.explanation_metadata` object is inherited. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` :param explanation_parameters: Optional. Parameters to configure explaining for Model's predictions. Can be specified only if `generate_explanation` is set to `True`. This value overrides the value of `Model.explanation_parameters`. All fields of `explanation_parameters` are optional in the request. If a field of the `explanation_parameters` object is not populated, the corresponding field of the `Model.explanation_parameters` object is inherited. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` :param labels: Optional. The labels with user-defined metadata to organize your BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), 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 encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. 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 this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key. Overrides encryption_spec_key_name set in aiplatform.init. :param sync: Whether to execute this method 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 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", "model_name", "impersonation_chain")
def __init__( self, *, region: str, project_id: str, job_display_name: str, model_name: str | Model, instances_format: str = "jsonl", predictions_format: str = "jsonl", gcs_source: str | Sequence[str] | None = None, bigquery_source: str | None = None, gcs_destination_prefix: str | None = None, bigquery_destination_prefix: str | None = None, model_parameters: dict | None = None, machine_type: str | None = None, accelerator_type: str | None = None, accelerator_count: int | None = None, starting_replica_count: int | None = None, max_replica_count: int | None = None, generate_explanation: bool | None = False, explanation_metadata: explain.ExplanationMetadata | None = None, explanation_parameters: explain.ExplanationParameters | None = None, labels: dict[str, str] | None = None, encryption_spec_key_name: str | None = None, sync: bool = True, gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.job_display_name = job_display_name self.model_name = model_name self.instances_format = instances_format self.predictions_format = predictions_format self.gcs_source = gcs_source self.bigquery_source = bigquery_source self.gcs_destination_prefix = gcs_destination_prefix self.bigquery_destination_prefix = bigquery_destination_prefix self.model_parameters = model_parameters self.machine_type = machine_type self.accelerator_type = accelerator_type self.accelerator_count = accelerator_count self.starting_replica_count = starting_replica_count self.max_replica_count = max_replica_count self.generate_explanation = generate_explanation self.explanation_metadata = explanation_metadata self.explanation_parameters = explanation_parameters self.labels = labels self.encryption_spec_key_name = encryption_spec_key_name self.sync = sync self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain self.hook: BatchPredictionJobHook | None = None
[docs] def execute(self, context: Context): self.log.info("Creating Batch prediction job") self.hook = BatchPredictionJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) result = self.hook.create_batch_prediction_job( region=self.region, project_id=self.project_id, job_display_name=self.job_display_name, model_name=self.model_name, instances_format=self.instances_format, predictions_format=self.predictions_format, gcs_source=self.gcs_source, bigquery_source=self.bigquery_source, gcs_destination_prefix=self.gcs_destination_prefix, bigquery_destination_prefix=self.bigquery_destination_prefix, model_parameters=self.model_parameters, machine_type=self.machine_type, accelerator_type=self.accelerator_type, accelerator_count=self.accelerator_count, starting_replica_count=self.starting_replica_count, max_replica_count=self.max_replica_count, generate_explanation=self.generate_explanation, explanation_metadata=self.explanation_metadata, explanation_parameters=self.explanation_parameters, labels=self.labels, encryption_spec_key_name=self.encryption_spec_key_name, sync=self.sync, ) batch_prediction_job = result.to_dict() batch_prediction_job_id = self.hook.extract_batch_prediction_job_id(batch_prediction_job) self.log.info("Batch prediction job was created. Job id: %s", batch_prediction_job_id) self.xcom_push(context, key="batch_prediction_job_id", value=batch_prediction_job_id) VertexAIBatchPredictionJobLink.persist( context=context, task_instance=self, batch_prediction_job_id=batch_prediction_job_id ) return batch_prediction_job
[docs] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_batch_prediction_job()
[docs]class DeleteBatchPredictionJobOperator(BaseOperator): """ Deletes a BatchPredictionJob. Can only be called on jobs that already finished. :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 batch_prediction_job_id: The ID of the BatchPredictionJob resource to be deleted. :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", "batch_prediction_job_id", "impersonation_chain")
def __init__( self, *, region: str, project_id: str, batch_prediction_job_id: str, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.batch_prediction_job_id = batch_prediction_job_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 = BatchPredictionJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Deleting batch prediction job: %s", self.batch_prediction_job_id) operation = hook.delete_batch_prediction_job( project_id=self.project_id, region=self.region, batch_prediction_job=self.batch_prediction_job_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Batch prediction job was deleted.") except NotFound: self.log.info("The Batch prediction job %s does not exist.", self.batch_prediction_job_id)
[docs]class GetBatchPredictionJobOperator(BaseOperator): """ Gets a BatchPredictionJob :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 batch_prediction_job: Required. The name of the BatchPredictionJob resource. :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, batch_prediction_job: str, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.batch_prediction_job = batch_prediction_job 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 = BatchPredictionJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Get batch prediction job: %s", self.batch_prediction_job) result = hook.get_batch_prediction_job( project_id=self.project_id, region=self.region, batch_prediction_job=self.batch_prediction_job, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) self.log.info("Batch prediction job was gotten.") VertexAIBatchPredictionJobLink.persist( context=context, task_instance=self, batch_prediction_job_id=self.batch_prediction_job ) return BatchPredictionJob.to_dict(result) except NotFound: self.log.info("The Batch prediction job %s does not exist.", self.batch_prediction_job)
[docs]class ListBatchPredictionJobsOperator(BaseOperator): """ Lists BatchPredictionJobs 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: The standard list filter. Supported fields: - ``display_name`` supports = and !=. - ``state`` supports = and !=. - ``model_display_name`` supports = and != Some examples of using the filter are: - ``state="JOB_STATE_SUCCEEDED" AND display_name="my_job"`` - ``state="JOB_STATE_RUNNING" OR display_name="my_job"`` - ``NOT display_name="my_job"`` - ``state="JOB_STATE_FAILED"`` :param page_size: The standard list page size. :param page_token: The standard list page token. :param read_mask: 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, filter: str | None = None, page_size: int | None = None, page_token: str | None = None, read_mask: str | None = None, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.filter = filter self.page_size = page_size self.page_token = page_token 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 = BatchPredictionJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) results = hook.list_batch_prediction_jobs( project_id=self.project_id, region=self.region, filter=self.filter, page_size=self.page_size, page_token=self.page_token, read_mask=self.read_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAIBatchPredictionJobListLink.persist(context=context, task_instance=self) return [BatchPredictionJob.to_dict(result) for result in results]

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