airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job

This module contains Google Vertex AI operators.

Module Contents

Classes

CreateBatchPredictionJobOperator

Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start.

DeleteBatchPredictionJobOperator

Deletes a BatchPredictionJob. Can only be called on jobs that already finished.

GetBatchPredictionJobOperator

Gets a BatchPredictionJob.

ListBatchPredictionJobsOperator

Lists BatchPredictionJobs in a Location.
class airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.CreateBatchPredictionJobOperator(*, region, project_id, job_display_name, model_name, instances_format='jsonl', predictions_format='jsonl', gcs_source=None, bigquery_source=None, gcs_destination_prefix=None, bigquery_destination_prefix=None, model_parameters=None, machine_type=None, accelerator_type=None, accelerator_count=None, starting_replica_count=None, max_replica_count=None, generate_explanation=False, explanation_metadata=None, explanation_parameters=None, labels=None, encryption_spec_key_name=None, sync=True, create_request_timeout=None, batch_size=None, gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), poll_interval=10, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start.

Parameters

  • project_id (str) – Required. The ID of the Google Cloud project that the service belongs to.

  • region (str) – Required. The ID of the Google Cloud region that the service belongs to.

  • batch_prediction_job – Required. The BatchPredictionJob to create.

  • job_display_name (str) – Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

  • model_name (str | google.cloud.aiplatform.Model) – Required. A fully-qualified model resource name or model ID.

  • instances_format (str) – 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”.

  • predictions_format (str) – 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”.

  • gcs_source (str | Sequence[str] | None) – 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.

  • bigquery_source (str | None) – BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId

  • gcs_destination_prefix (str | None) – 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.

  • bigquery_destination_prefix (str | None) – 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.

  • model_parameters (dict | None) – The parameters that govern the predictions. The schema of the parameters may be specified via the Model’s parameters_schema_uri.

  • machine_type (str | None) – 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.

  • accelerator_type (str | None) – The type of accelerator(s) that may be attached to the machine as per accelerator_count. Only used if machine_type is set.

  • accelerator_count (int | None) – The number of accelerators to attach to the machine_type. Only used if machine_type is set.

  • starting_replica_count (int | None) – 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.

  • max_replica_count (int | None) – The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10.

  • generate_explanation (bool | None) – 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.

  • explanation_metadata (google.cloud.aiplatform.explain.ExplanationMetadata | None) – 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>

  • explanation_parameters (google.cloud.aiplatform.explain.ExplanationParameters | None) – 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>

  • labels (dict[str, str] | None) – 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.

  • encryption_spec_key_name (str | None) – 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.

  • sync (bool) – (Deprecated) 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.

  • create_request_timeout (float | None) – Optional. The timeout for the create request in seconds.

  • batch_size (int | None) – Optional. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation’s execution, but too high value will result in a whole batch not fitting in a machine’s memory, and the whole operation will fail. The default value is same as in the aiplatform’s BatchPredictionJob.

  • retry – Designation of what errors, if any, should be retried.

  • timeout – The timeout for this request.

  • metadata – Strings which should be sent along with the request as metadata.

  • gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.

  • impersonation_chain (str | Sequence[str] | None) – 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).

  • deferrable (bool) – Optional. Run operator in the deferrable mode.

  • poll_interval (int) – Interval size which defines how often job status is checked in deferrable mode.

template_fields = ('region', 'project_id', 'model_name', 'impersonation_chain')[source]
hook()[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

on_kill()[source]

Act as a callback called when the operator is killed; cancel any running job.

execute_complete(context, event)[source]
class airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.DeleteBatchPredictionJobOperator(*, region, project_id, batch_prediction_job_id, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Deletes a BatchPredictionJob. Can only be called on jobs that already finished.

Parameters

  • project_id (str) – Required. The ID of the Google Cloud project that the service belongs to.

  • region (str) – Required. The ID of the Google Cloud region that the service belongs to.

  • batch_prediction_job_id (str) – The ID of the BatchPredictionJob resource to be deleted.

  • retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – Designation of what errors, if any, should be retried.

  • timeout (float | None) – The timeout for this request.

  • metadata (Sequence[tuple[str, str]]) – Strings which should be sent along with the request as metadata.

  • gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.

  • impersonation_chain (str | Sequence[str] | None) – 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).

template_fields = ('region', 'project_id', 'batch_prediction_job_id', 'impersonation_chain')[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.GetBatchPredictionJobOperator(*, region, project_id, batch_prediction_job, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Gets a BatchPredictionJob.

Parameters

  • project_id (str) – Required. The ID of the Google Cloud project that the service belongs to.

  • region (str) – Required. The ID of the Google Cloud region that the service belongs to.

  • batch_prediction_job (str) – Required. The name of the BatchPredictionJob resource.

  • retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – Designation of what errors, if any, should be retried.

  • timeout (float | None) – The timeout for this request.

  • metadata (Sequence[tuple[str, str]]) – Strings which should be sent along with the request as metadata.

  • gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.

  • impersonation_chain (str | Sequence[str] | None) – 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).

template_fields = ('region', 'project_id', 'impersonation_chain')[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job.ListBatchPredictionJobsOperator(*, region, project_id, filter=None, page_size=None, page_token=None, read_mask=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Lists BatchPredictionJobs in a Location.

Parameters

  • project_id (str) – Required. The ID of the Google Cloud project that the service belongs to.

  • region (str) – Required. The ID of the Google Cloud region that the service belongs to.

  • filter (str | None) – 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"

  • page_size (int | None) – The standard list page size.

  • page_token (str | None) – The standard list page token.

  • read_mask (str | None) – Mask specifying which fields to read.

  • retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – Designation of what errors, if any, should be retried.

  • timeout (float | None) – The timeout for this request.

  • metadata (Sequence[tuple[str, str]]) – Strings which should be sent along with the request as metadata.

  • gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.

  • impersonation_chain (str | Sequence[str] | None) – 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).

template_fields = ('region', 'project_id', 'impersonation_chain')[source]
execute(context)[source]

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

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