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airflow.providers.google.cloud.operators.vertex_ai.custom_job

This module contains Google Vertex AI operators.

Module Contents

Classes

CustomTrainingJobBaseOperator	The base class for operators that launch Custom jobs on VertexAI.
CreateCustomContainerTrainingJobOperator	Create Custom Container Training job.
CreateCustomPythonPackageTrainingJobOperator	Create Custom Python Package Training job.
CreateCustomTrainingJobOperator	Create Custom Training job.
DeleteCustomTrainingJobOperator	Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob.
ListCustomTrainingJobOperator	Lists CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob in a Location.

class airflow.providers.google.cloud.operators.vertex_ai.custom_job.CustomTrainingJobBaseOperator(*, project_id, region, display_name, container_uri, model_serving_container_image_uri=None, model_serving_container_predict_route=None, model_serving_container_health_route=None, model_serving_container_command=None, model_serving_container_args=None, model_serving_container_environment_variables=None, model_serving_container_ports=None, model_description=None, model_instance_schema_uri=None, model_parameters_schema_uri=None, model_prediction_schema_uri=None, parent_model=None, is_default_version=None, model_version_aliases=None, model_version_description=None, labels=None, training_encryption_spec_key_name=None, model_encryption_spec_key_name=None, staging_bucket=None, dataset_id=None, annotation_schema_uri=None, model_display_name=None, model_labels=None, base_output_dir=None, service_account=None, network=None, bigquery_destination=None, args=None, environment_variables=None, replica_count=1, machine_type='n1-standard-4', accelerator_type='ACCELERATOR_TYPE_UNSPECIFIED', accelerator_count=0, boot_disk_type='pd-ssd', boot_disk_size_gb=100, training_fraction_split=None, validation_fraction_split=None, test_fraction_split=None, training_filter_split=None, validation_filter_split=None, test_filter_split=None, predefined_split_column_name=None, timestamp_split_column_name=None, tensorboard=None, sync=True, gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)

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

The base class for operators that launch Custom jobs on VertexAI.

class airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator(*, command=[], region, parent_model=None, impersonation_chain=None, dataset_id=None, **kwargs)

Bases: CustomTrainingJobBaseOperator

Create Custom Container Training job.

Parameters

• project_id – 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.

• display_name – Required. The user-defined name of this TrainingPipeline.

• command (Sequence[str]) – The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided

• container_uri – Required: Uri of the training container image in the GCR.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• model_description – The description of the Model.

• 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. 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.

• 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. 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.

• 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. 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.

• parent_model (str | None) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

• is_default_version – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version. When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be “default” aliased. :param model_version_aliases: Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

• model_version_description – Optional. The description of the model version being uploaded by this job.

• project_id – Project to run training in.

• region – Location to run training in.

• 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.

• 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.

• 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.

• staging_bucket – Bucket used to stage source and training artifacts.

• dataset – Vertex AI to fit this training against.

• 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.

• 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.

• 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.

• 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/

• service_account – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

• 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.

• 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”

• args – Command line arguments to be passed to the Python script.

• 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.

• 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.

• machine_type – The type of machine to use for training.

• 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

• accelerator_count – The number of accelerators to attach to a worker replica.

• 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).

• 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].

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• gcp_conn_id – 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', 'command', 'parent_model', 'dataset_id', 'impersonation_chain')

operator_extra_links = ()

execute(context)

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()

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

class airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator(*, python_package_gcs_uri, python_module_name, region, parent_model=None, impersonation_chain=None, dataset_id=None, **kwargs)

Bases: CustomTrainingJobBaseOperator

Create Custom Python Package Training job.

Parameters

• project_id – 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.

• display_name – Required. The user-defined name of this TrainingPipeline.

• python_package_gcs_uri (str) – Required: GCS location of the training python package.

• python_module_name (str) – Required: The module name of the training python package.

• container_uri – Required: Uri of the training container image in the GCR.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• model_description – The description of the Model.

• 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. 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.

• 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. 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.

• 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. 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.

• parent_model (str | None) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

• is_default_version – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version. When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be “default” aliased.

• model_version_aliases – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

• model_version_description – Optional. The description of the model version being uploaded by this job.

• project_id – Project to run training in.

• region – Location to run training in.

• 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.

• 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.

• 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.

• staging_bucket – Bucket used to stage source and training artifacts.

• dataset – Vertex AI to fit this training against.

• 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.

• 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.

• 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.

• 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/

• service_account – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

• 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.

• 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”

• args – Command line arguments to be passed to the Python script.

• 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.

• 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.

• machine_type – The type of machine to use for training.

• 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

• accelerator_count – The number of accelerators to attach to a worker replica.

• 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).

• 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].

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• gcp_conn_id – 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 = ('parent_model', 'region', 'dataset_id', 'impersonation_chain')

operator_extra_links = ()

execute(context)

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()

Cancel any running job. Callback called when the operator is killed.

class airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator(*, script_path, requirements=None, region, parent_model=None, impersonation_chain=None, dataset_id=None, **kwargs)

Bases: CustomTrainingJobBaseOperator

Create Custom Training job.

Parameters

• project_id – 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.

• display_name – Required. The user-defined name of this TrainingPipeline.

• script_path (str) – Required. Local path to training script.

• container_uri – Required: Uri of the training container image in the GCR.

• requirements (Sequence[str] | None) – List of python packages dependencies of script.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• model_description – The description of the Model.

• 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. 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.

• 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. 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.

• 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. 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.

• parent_model (str | None) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

• is_default_version – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version. When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be “default” aliased.

• model_version_aliases – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

• model_version_description – Optional. The description of the model version being uploaded by this job.

• project_id – Project to run training in.

• region – Location to run training in.

• 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.

• 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.

• 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.

• staging_bucket – Bucket used to stage source and training artifacts.

• dataset – Vertex AI to fit this training against.

• 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.

• 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.

• 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.

• 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/

• service_account – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

• 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.

• 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”

• args – Command line arguments to be passed to the Python script.

• 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.

• 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.

• machine_type – The type of machine to use for training.

• 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

• accelerator_count – The number of accelerators to attach to a worker replica.

• 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).

• 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].

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• gcp_conn_id – 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', 'script_path', 'parent_model', 'requirements', 'dataset_id', 'impersonation_chain')

operator_extra_links = ()

execute(context)

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()

Cancel any running job. Callback called when the operator is killed.

class airflow.providers.google.cloud.operators.vertex_ai.custom_job.DeleteCustomTrainingJobOperator(*, training_pipeline_id, custom_job_id, region, project_id, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)

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

Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob.

Parameters

• training_pipeline_id (str) – Required. The name of the TrainingPipeline resource to be deleted.

• custom_job_id (str) – Required. The name of the CustomJob to delete.

• 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.

• 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).

property training_pipeline

Alias for training_pipeline_id, used for compatibility (deprecated).

property custom_job

Alias for custom_job_id, used for compatibility (deprecated).

template_fields = ('training_pipeline_id', 'custom_job_id', 'region', 'project_id', 'impersonation_chain')

execute(context)

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.custom_job.ListCustomTrainingJobOperator(*, region, project_id, page_size=None, page_token=None, filter=None, read_mask=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)

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

Lists CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob 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) –

  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"

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

• page_token (str | None) – 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.

• read_mask (str | None) – Optional. 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']

operator_extra_links

execute(context)

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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