# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from typing import Any, List, NamedTuple
from google_cloud_pipeline_components import _placeholders
from google_cloud_pipeline_components._implementation.model import GetVertexModelOp
from google_cloud_pipeline_components._implementation.model_evaluation import ModelImportEvaluationOp
from google_cloud_pipeline_components._implementation.model_evaluation import TargetFieldDataRemoverOp
from google_cloud_pipeline_components.types.artifact_types import ClassificationMetrics
from google_cloud_pipeline_components.types.artifact_types import RegressionMetrics
from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp
from google_cloud_pipeline_components.v1.model_evaluation.classification_component import model_evaluation_classification as ModelEvaluationClassificationOp
from google_cloud_pipeline_components.v1.model_evaluation.regression_component import model_evaluation_regression as ModelEvaluationRegressionOp
import kfp
from kfp import dsl
@kfp.dsl.pipeline(name='evaluation-classification-pipeline')
def evaluation_automl_unstructure_data_classification_pipeline( # pylint: disable=dangerous-default-value
location: str,
model_name: str,
target_field_name: str,
batch_predict_instances_format: str,
batch_predict_gcs_destination_output_uri: str,
batch_predict_gcs_source_uris: List[str] = [], # pylint: disable=g-bare-generic
batch_predict_bigquery_source_uri: str = '',
batch_predict_predictions_format: str = 'jsonl',
batch_predict_bigquery_destination_output_uri: str = '',
batch_predict_machine_type: str = 'n1-standard-16',
batch_predict_starting_replica_count: int = 5,
batch_predict_max_replica_count: int = 10,
batch_predict_accelerator_type: str = '',
batch_predict_accelerator_count: int = 0,
slicing_specs: List[Any] = [], # pylint: disable=g-bare-generic
evaluation_prediction_label_column: str = '',
evaluation_prediction_score_column: str = '',
evaluation_class_labels: List[str] = [], # pylint: disable=g-bare-generic
dataflow_machine_type: str = 'n1-standard-4',
dataflow_max_num_workers: int = 5,
dataflow_disk_size_gb: int = 50,
dataflow_service_account: str = '',
dataflow_subnetwork: str = '',
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
evaluation_display_name: str = 'evaluation-automl-unstructured-data-pipeline-{{$.pipeline_job_uuid}}',
force_runner_mode: str = '',
project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
) -> NamedTuple(
'outputs',
evaluation_metrics=ClassificationMetrics,
evaluation_resource_name=str,
):
# fmt: off
"""The evaluation pipeline with ground truth and no feature attribution for classification models.
This pipeline is used for all classification unstructured AutoML models, including Text, Video, Image and Custom models.
Args:
location: The GCP region that runs the pipeline components.
model_name: The Vertex model resource name to be imported and used for batch prediction. Formatted like projects/{project}/locations/{location}/models/{model} or projects/{project}/locations/{location}/models/{model}@{model_version_id_or_model_version_alias}.
target_field_name: The target field's name. Formatted to be able to find nested columns, delimited by `.`. Prefixed with 'instance.' on the component for Vertex Batch Prediction.
batch_predict_instances_format: The format in which instances are given, must be one of the Model's supportedInputStorageFormats. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_gcs_destination_output_uri: 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` containing only `code` and `message` fields. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_bigquery_source_uri: Google BigQuery URI to your instances to run batch prediction on. May contain wildcards. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_predictions_format: The format in which Vertex AI gives the predictions. Must be one of the Model's supportedOutputStorageFormats. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_bigquery_destination_output_uri: 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` represented as a STRUCT, and containing only `code` and `message`. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_machine_type: The type of machine for running batch prediction on dedicated resources. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources). If the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided. For more details about the BatchDedicatedResources, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_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.
batch_predict_max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set.
batch_predict_accelerator_type: The type of accelerator(s) that may be attached to the machine as per `batch_predict_accelerator_count`. Only used if `batch_predict_machine_type` is set. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_accelerator_count: The number of accelerators to attach to the `batch_predict_machine_type`. Only used if `batch_predict_machine_type` is set.
slicing_specs: List of `google.cloud.aiplatform_v1.types.ModelEvaluationSlice.SlicingSpec`. When provided, compute metrics for each defined slice. See sample code in https://cloud.google.com/vertex-ai/docs/pipelines/model-evaluation-component For more details on configuring slices, see https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ModelEvaluationSlice.
evaluation_prediction_label_column: The column name of the field containing classes the model is scoring. Formatted to be able to find nested columns, delimited by `.`.
evaluation_prediction_score_column: The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by `.`.
evaluation_class_labels: Required for classification prediction type. The list of class names for the target_field_name, in the same order they appear in a file in batch_predict_gcs_source_uris. For instance, if the target_field_name could be either `1` or `0`, then the class_labels input will be ["1", "0"].
dataflow_machine_type: The Dataflow machine type for evaluation components.
dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run Dataflow jobs.
dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: Customer-managed encryption key options. If set, resources created by this pipeline will be encrypted with the provided encryption key. Has the form: `projects/my-project/locations/my-location/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
evaluation_display_name: The display name of the uploaded evaluation resource to the Vertex AI model.
force_runner_mode: Indicate the runner mode to use forcely. Valid options are `Dataflow` and `DirectRunner`.
project: The GCP project that runs the pipeline components. Defaults to the project in which the PipelineJob is run.
Returns: A Tuple of google.ClassificationMetrics artifact and the imported evaluation metrics resource name.
"""
# fmt: on
outputs = NamedTuple(
'outputs',
evaluation_metrics=ClassificationMetrics,
evaluation_resource_name=str,
)
get_model_task = GetVertexModelOp(model_name=model_name)
# Remove the ground truth from the given GCS data.
# This is required for many models as Vertex Batch Prediction can not have the
# ground truth in the data to run, but later the evaluation component requires
# the ground truth data.
target_field_data_remover_task = TargetFieldDataRemoverOp(
project=project,
location=location,
target_field_name=target_field_name,
gcs_source_uris=batch_predict_gcs_source_uris,
bigquery_source_uri=batch_predict_bigquery_source_uri,
instances_format=batch_predict_instances_format,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
force_runner_mode=force_runner_mode,
)
# Run Batch Prediction.
batch_predict_task = ModelBatchPredictOp(
project=project,
location=location,
model=get_model_task.outputs['model'],
job_display_name=f'evaluation-batch-predict-{dsl.PIPELINE_JOB_ID_PLACEHOLDER}-{dsl.PIPELINE_TASK_ID_PLACEHOLDER}',
gcs_source_uris=target_field_data_remover_task.outputs[
'gcs_output_directory'
],
bigquery_source_input_uri=target_field_data_remover_task.outputs[
'bigquery_output_table'
],
instances_format=batch_predict_instances_format,
predictions_format=batch_predict_predictions_format,
gcs_destination_output_uri_prefix=batch_predict_gcs_destination_output_uri,
bigquery_destination_output_uri=batch_predict_bigquery_destination_output_uri,
machine_type=batch_predict_machine_type,
starting_replica_count=batch_predict_starting_replica_count,
max_replica_count=batch_predict_max_replica_count,
encryption_spec_key_name=encryption_spec_key_name,
accelerator_type=batch_predict_accelerator_type,
accelerator_count=batch_predict_accelerator_count,
)
# Run evaluation for a classification model.
eval_task = ModelEvaluationClassificationOp(
project=project,
location=location,
class_labels=evaluation_class_labels,
prediction_label_column=evaluation_prediction_label_column,
prediction_score_column=evaluation_prediction_score_column,
target_field_name=target_field_name,
ground_truth_format=batch_predict_instances_format,
ground_truth_gcs_source=batch_predict_gcs_source_uris,
ground_truth_bigquery_source=batch_predict_bigquery_source_uri,
predictions_format=batch_predict_predictions_format,
predictions_gcs_source=batch_predict_task.outputs['gcs_output_directory'],
predictions_bigquery_source=batch_predict_task.outputs[
'bigquery_output_table'
],
dataflow_machine_type=dataflow_machine_type,
dataflow_max_workers_num=dataflow_max_num_workers,
dataflow_disk_size_gb=dataflow_disk_size_gb,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
force_runner_mode=force_runner_mode,
model=get_model_task.outputs['model'],
slicing_specs=slicing_specs,
)
# Import the evaluation result to Vertex AI.
import_evaluation_task = ModelImportEvaluationOp(
classification_metrics=eval_task.outputs['evaluation_metrics'],
model=get_model_task.outputs['model'],
dataset_type=batch_predict_instances_format,
dataset_path=batch_predict_bigquery_source_uri,
dataset_paths=batch_predict_gcs_source_uris,
display_name=evaluation_display_name,
)
return outputs(
evaluation_metrics=eval_task.outputs['evaluation_metrics'],
evaluation_resource_name=import_evaluation_task.outputs[
'evaluation_resource_name'
],
)
@kfp.dsl.pipeline(name='evaluation-regression-pipeline')
def evaluation_automl_unstructure_data_regression_pipeline( # pylint: disable=dangerous-default-value
project: str,
location: str,
model_name: str,
target_field_name: str,
batch_predict_instances_format: str,
batch_predict_gcs_destination_output_uri: str,
batch_predict_gcs_source_uris: list = [], # pylint: disable=g-bare-generic
batch_predict_bigquery_source_uri: str = '',
batch_predict_predictions_format: str = 'jsonl',
batch_predict_bigquery_destination_output_uri: str = '',
batch_predict_machine_type: str = 'n1-standard-16',
batch_predict_starting_replica_count: int = 5,
batch_predict_max_replica_count: int = 10,
batch_predict_accelerator_type: str = '',
batch_predict_accelerator_count: int = 0,
evaluation_prediction_score_column: str = '',
dataflow_machine_type: str = 'n1-standard-4',
dataflow_max_num_workers: int = 5,
dataflow_disk_size_gb: int = 50,
dataflow_service_account: str = '',
dataflow_subnetwork: str = '',
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
evaluation_display_name: str = 'evaluation-automl-unstructured-data-pipeline-{{$.pipeline_job_uuid}}',
force_runner_mode: str = '',
) -> NamedTuple(
'outputs',
evaluation_metrics=RegressionMetrics,
evaluation_resource_name=str,
):
# fmt: off
"""The evaluation pipeline with ground truth and no feature attribution for.
regression models.
This pipeline is used for all custom tabular regression models.
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region that runs the pipeline components.
model_name: The Vertex model resource name to be imported and used for batch prediction. Formatted like projects/{project}/locations/{location}/models/{model} or projects/{project}/locations/{location}/models/{model}@{model_version_id_or_model_version_alias}.
target_field_name: The target field's name. Formatted to be able to find nested columns, delimited by `.`. Prefixed with 'instance.' on the component for Vertex Batch Prediction.
batch_predict_instances_format: The format in which instances are given, must be one of the Model's supportedInputStorageFormats. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_gcs_destination_output_uri: 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` containing only `code` and `message` fields. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_bigquery_source_uri: Google BigQuery URI to your instances to run batch prediction on. May contain wildcards. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_predictions_format: The format in which Vertex AI gives the predictions. Must be one of the Model's supportedOutputStorageFormats. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_bigquery_destination_output_uri: 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` represented as a STRUCT, and containing only `code` and `message`. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_machine_type: The type of machine for running batch prediction on dedicated resources. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources). If the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided. For more details about the BatchDedicatedResources, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_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.
batch_predict_max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set.
batch_predict_accelerator_type: The type of accelerator(s) that may be attached to the machine as per `batch_predict_accelerator_count`. Only used if `batch_predict_machine_type` is set. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_accelerator_count: The number of accelerators to attach to the `batch_predict_machine_type`. Only used if `batch_predict_machine_type` is set.
evaluation_prediction_score_column: The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by `.`.
dataflow_machine_type: The Dataflow machine type for evaluation components.
dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run Dataflow jobs.
dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: Customer-managed encryption key options. If set, resources created by this pipeline will be encrypted with the provided encryption key. Has the form: `projects/my-project/locations/my-location/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
evaluation_display_name: The display name of the uploaded evaluation resource to the Vertex AI model.
force_runner_mode: Indicate the runner mode to use forcely. Valid options are `Dataflow` and `DirectRunner`.
Returns: A Tuple of google.RegressionMetrics artifact and the imported evaluation metrics resource name.
"""
# fmt: on
outputs = NamedTuple(
'outputs',
evaluation_metrics=RegressionMetrics,
evaluation_resource_name=str,
)
get_model_task = GetVertexModelOp(model_name=model_name)
# Remove the ground truth from the given GCS data.
# This is required for many models as Vertex Batch Prediction can not have the
# ground truth in the data to run, but later the evaluation component requires
# the ground truth data.
target_field_data_remover_task = TargetFieldDataRemoverOp(
project=project,
location=location,
target_field_name=target_field_name,
gcs_source_uris=batch_predict_gcs_source_uris,
bigquery_source_uri=batch_predict_bigquery_source_uri,
instances_format=batch_predict_instances_format,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
force_runner_mode=force_runner_mode,
)
# Run Batch Prediction.
batch_predict_task = ModelBatchPredictOp(
project=project,
location=location,
model=get_model_task.outputs['model'],
job_display_name=f'evaluation-batch-predict-{dsl.PIPELINE_JOB_ID_PLACEHOLDER}-{dsl.PIPELINE_TASK_ID_PLACEHOLDER}',
gcs_source_uris=target_field_data_remover_task.outputs[
'gcs_output_directory'
],
bigquery_source_input_uri=target_field_data_remover_task.outputs[
'bigquery_output_table'
],
instances_format=batch_predict_instances_format,
predictions_format=batch_predict_predictions_format,
gcs_destination_output_uri_prefix=batch_predict_gcs_destination_output_uri,
bigquery_destination_output_uri=batch_predict_bigquery_destination_output_uri,
machine_type=batch_predict_machine_type,
starting_replica_count=batch_predict_starting_replica_count,
max_replica_count=batch_predict_max_replica_count,
encryption_spec_key_name=encryption_spec_key_name,
accelerator_type=batch_predict_accelerator_type,
accelerator_count=batch_predict_accelerator_count,
)
# Run evaluation for a regression model.
eval_task = ModelEvaluationRegressionOp(
project=project,
location=location,
target_field_name=target_field_name,
ground_truth_format=batch_predict_instances_format,
ground_truth_gcs_source=batch_predict_gcs_source_uris,
ground_truth_bigquery_source=batch_predict_bigquery_source_uri,
prediction_score_column=evaluation_prediction_score_column,
predictions_format=batch_predict_predictions_format,
predictions_gcs_source=batch_predict_task.outputs['gcs_output_directory'],
predictions_bigquery_source=batch_predict_task.outputs[
'bigquery_output_table'
],
dataflow_machine_type=dataflow_machine_type,
dataflow_max_workers_num=dataflow_max_num_workers,
dataflow_disk_size_gb=dataflow_disk_size_gb,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
force_runner_mode=force_runner_mode,
model=get_model_task.outputs['model'],
)
# Import the evaluation result to Vertex AI.
import_evaluation_task = ModelImportEvaluationOp(
regression_metrics=eval_task.outputs['evaluation_metrics'],
model=get_model_task.outputs['model'],
dataset_type=batch_predict_instances_format,
dataset_path=batch_predict_bigquery_source_uri,
dataset_paths=batch_predict_gcs_source_uris,
display_name=evaluation_display_name,
)
return outputs(
evaluation_metrics=eval_task.outputs['evaluation_metrics'],
evaluation_resource_name=import_evaluation_task.outputs[
'evaluation_resource_name'
],
)
[docs]@kfp.dsl.pipeline(name='evaluation-pipeline')
def evaluation_automl_unstructure_data_pipeline( # pylint: disable=dangerous-default-value
project: str,
location: str,
prediction_type: str,
model_name: str,
target_field_name: str,
batch_predict_instances_format: str,
batch_predict_gcs_destination_output_uri: str,
batch_predict_gcs_source_uris: List[str] = [], # pylint: disable=g-bare-generic
batch_predict_bigquery_source_uri: str = '',
batch_predict_predictions_format: str = 'jsonl',
batch_predict_bigquery_destination_output_uri: str = '',
batch_predict_machine_type: str = 'n1-standard-16',
batch_predict_starting_replica_count: int = 5,
batch_predict_max_replica_count: int = 10,
batch_predict_accelerator_type: str = '',
batch_predict_accelerator_count: int = 0,
slicing_specs: List[Any] = [], # pylint: disable=g-bare-generic
evaluation_prediction_label_column: str = '',
evaluation_prediction_score_column: str = '',
evaluation_class_labels: List[str] = [], # pylint: disable=g-bare-generic
dataflow_machine_type: str = 'n1-standard-4',
dataflow_max_num_workers: int = 5,
dataflow_disk_size_gb: int = 50,
dataflow_service_account: str = '',
dataflow_subnetwork: str = '',
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
evaluation_display_name: str = 'evaluation-automl-unstructured-data-pipeline-{{$.pipeline_job_uuid}}',
force_runner_mode: str = '',
):
# fmt: off
"""The evaluation pipeline with ground truth and no feature attribution.
This pipeline is used for all unstructured AutoML models, including Text,
Video, Image and Custom models.
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region that runs the pipeline components.
prediction_type: The type of prediction the model is to produce. "classification" or "regression".
model_name: The Vertex model resource name to be imported and used for batch prediction. Formatted like projects/{project}/locations/{location}/models/{model} or projects/{project}/locations/{location}/models/{model}@{model_version_id_or_model_version_alias}.
target_field_name: The target field's name. Formatted to be able to find nested columns, delimited by `.`. Prefixed with 'instance.' on the component for Vertex Batch Prediction.
batch_predict_instances_format: The format in which instances are given, must be one of the Model's supportedInputStorageFormats. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_gcs_destination_output_uri: 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` containing only `code` and `message` fields. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_bigquery_source_uri: Google BigQuery URI to your instances to run batch prediction on. May contain wildcards. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
batch_predict_predictions_format: The format in which Vertex AI gives the predictions. Must be one of the Model's supportedOutputStorageFormats. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_bigquery_destination_output_uri: 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` represented as a STRUCT, and containing only `code` and `message`. For more details about this output config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
batch_predict_machine_type: The type of machine for running batch prediction on dedicated resources. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources). If the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided. For more details about the BatchDedicatedResources, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_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.
batch_predict_max_replica_count: The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set.
batch_predict_accelerator_type: The type of accelerator(s) that may be attached to the machine as per `batch_predict_accelerator_count`. Only used if `batch_predict_machine_type` is set. For more details about the machine spec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
batch_predict_accelerator_count: The number of accelerators to attach to the `batch_predict_machine_type`. Only used if `batch_predict_machine_type` is set.
slicing_specs: List of `google.cloud.aiplatform_v1.types.ModelEvaluationSlice.SlicingSpec`. When provided, compute metrics for each defined slice. See sample code in https://cloud.google.com/vertex-ai/docs/pipelines/model-evaluation-component For more details on configuring slices, see https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ModelEvaluationSlice.
evaluation_prediction_label_column: The column name of the field containing classes the model is scoring. Formatted to be able to find nested columns, delimited by `.`.
evaluation_prediction_score_column: The column name of the field containing batch prediction scores. Formatted to be able to find nested columns, delimited by `.`.
evaluation_class_labels: Required for classification prediction type. The list of class names for the target_field_name, in the same order they appear in a file in batch_predict_gcs_source_uris. For instance, if the target_field_name could be either `1` or `0`, then the class_labels input will be ["1", "0"].
dataflow_machine_type: The Dataflow machine type for evaluation components.
dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run Dataflow jobs.
dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: Customer-managed encryption key options. If set, resources created by this pipeline will be encrypted with the provided encryption key. Has the form: `projects/my-project/locations/my-location/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
evaluation_display_name: The display name of the uploaded evaluation resource to the Vertex AI model.
force_runner_mode: Indicate the runner mode to use forcely. Valid options are `Dataflow` and `DirectRunner`.
"""
# fmt: on
with kfp.dsl.Condition(
prediction_type == 'classification', name='classification'
):
evaluation_automl_unstructure_data_classification_pipeline(
project=project,
location=location,
model_name=model_name,
target_field_name=target_field_name,
batch_predict_instances_format=batch_predict_instances_format,
batch_predict_gcs_destination_output_uri=batch_predict_gcs_destination_output_uri,
batch_predict_gcs_source_uris=batch_predict_gcs_source_uris,
batch_predict_bigquery_source_uri=batch_predict_bigquery_source_uri,
batch_predict_predictions_format=batch_predict_predictions_format,
batch_predict_bigquery_destination_output_uri=batch_predict_bigquery_destination_output_uri,
batch_predict_machine_type=batch_predict_machine_type,
batch_predict_starting_replica_count=batch_predict_starting_replica_count,
batch_predict_max_replica_count=batch_predict_max_replica_count,
batch_predict_accelerator_type=batch_predict_accelerator_type,
batch_predict_accelerator_count=batch_predict_accelerator_count,
slicing_specs=slicing_specs,
evaluation_prediction_label_column=evaluation_prediction_label_column,
evaluation_prediction_score_column=evaluation_prediction_score_column,
evaluation_class_labels=evaluation_class_labels,
dataflow_machine_type=dataflow_machine_type,
dataflow_max_num_workers=dataflow_max_num_workers,
dataflow_disk_size_gb=dataflow_disk_size_gb,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
evaluation_display_name=evaluation_display_name,
force_runner_mode=force_runner_mode,
)
with kfp.dsl.Condition(prediction_type == 'regression', name='regression'):
evaluation_automl_unstructure_data_regression_pipeline(
project=project,
location=location,
model_name=model_name,
target_field_name=target_field_name,
batch_predict_instances_format=batch_predict_instances_format,
batch_predict_gcs_destination_output_uri=batch_predict_gcs_destination_output_uri,
batch_predict_gcs_source_uris=batch_predict_gcs_source_uris,
batch_predict_bigquery_source_uri=batch_predict_bigquery_source_uri,
batch_predict_predictions_format=batch_predict_predictions_format,
batch_predict_bigquery_destination_output_uri=batch_predict_bigquery_destination_output_uri,
batch_predict_machine_type=batch_predict_machine_type,
batch_predict_starting_replica_count=batch_predict_starting_replica_count,
batch_predict_max_replica_count=batch_predict_max_replica_count,
batch_predict_accelerator_type=batch_predict_accelerator_type,
batch_predict_accelerator_count=batch_predict_accelerator_count,
evaluation_prediction_score_column=evaluation_prediction_score_column,
dataflow_machine_type=dataflow_machine_type,
dataflow_max_num_workers=dataflow_max_num_workers,
dataflow_disk_size_gb=dataflow_disk_size_gb,
dataflow_service_account=dataflow_service_account,
dataflow_subnetwork=dataflow_subnetwork,
dataflow_use_public_ips=dataflow_use_public_ips,
encryption_spec_key_name=encryption_spec_key_name,
evaluation_display_name=evaluation_display_name,
force_runner_mode=force_runner_mode,
)