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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import List
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 ErrorAnalysisAnnotationOp
from google_cloud_pipeline_components._implementation.model_evaluation import EvaluatedAnnotationOp
from google_cloud_pipeline_components._implementation.model_evaluation import EvaluationDatasetPreprocessorOp as DatasetPreprocessorOp
from google_cloud_pipeline_components._implementation.model_evaluation import FeatureExtractorOp
from google_cloud_pipeline_components._implementation.model_evaluation import ModelImportEvaluatedAnnotationOp
from google_cloud_pipeline_components._implementation.model_evaluation import ModelImportEvaluationOp
from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp
from google_cloud_pipeline_components.v1.dataset import GetVertexDatasetOp
from google_cloud_pipeline_components.v1.model_evaluation.classification_component import model_evaluation_classification as ModelEvaluationClassificationOp
from kfp import dsl
[docs]@dsl.pipeline(name='automl-vision-error-analysis-pipeline')
def vision_model_error_analysis_pipeline( # pylint: disable=dangerous-default-value
location: str,
model_name: str,
batch_predict_gcs_destination_output_uri: str,
test_dataset_resource_name: str = '',
test_dataset_annotation_set_name: str = '',
training_dataset_resource_name: str = '',
training_dataset_annotation_set_name: str = '',
test_dataset_storage_source_uris: List[str] = [],
training_dataset_storage_source_uris: List[str] = [],
batch_predict_instances_format: str = 'jsonl',
batch_predict_predictions_format: str = 'jsonl',
batch_predict_machine_type: str = 'n1-standard-32',
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,
dataflow_machine_type: str = 'n1-standard-8',
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-vision-error-analysis-pipeline-{{$.pipeline_job_uuid}}',
force_runner_mode: str = '',
project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
):
# fmt: off
"""The evaluation vision error analysis pipeline.
This pipeline can help you to continuously discover dataset example errors
with nearest neighbor distances and outlier flags, and provides you with
actionable steps to improve the model performance. It uses GCP services
including Dataflow and BatchPrediction.
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, in the format of `projects/{project}/locations/{location}/models/{model}` or `projects/{project}/locations/{location}/models/{model}@{model_version_id or model_version_alias}`
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.
test_dataset_resource_name: A Vertex dataset resource name of the test dataset. If `test_dataset_storage_source_uris` is also provided, this argument will override the GCS source.
test_dataset_annotation_set_name: A string of the annotation_set resource name containing the ground truth of the test datset used for evaluation.
training_dataset_resource_name: A Vertex dataset resource name of the training dataset. If `training_dataset_storage_source_uris` is also provided, this argument will override the GCS source.
training_dataset_annotation_set_name: A string of the annotation_set resource name containing the ground truth of the test datset used for feature extraction.
test_dataset_storage_source_uris: Google Cloud Storage URI(-s) to unmanaged test datasets.`jsonl` is currently the only allowed format. If `test_dataset` is also provided, this field will be overridden by the provided Vertex Dataset.
training_dataset_storage_source_uris: Google Cloud Storage URI(-s) to unmanaged test datasets.`jsonl` is currently the only allowed format. If `training_dataset` is also provided, this field will be overridden by the provided Vertex Dataset.
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_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_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.
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: The disk size (in GB) of the machine executing the evaluation run.
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.
"""
# fmt: on
with dsl.Condition(
(
test_dataset_resource_name != ''
and training_dataset_resource_name != ''
and test_dataset_annotation_set_name != ''
and training_dataset_annotation_set_name != ''
),
name='VertexDataset',
):
get_test_dataset_task = GetVertexDatasetOp(
dataset_resource_name=test_dataset_resource_name
)
get_training_dataset_task = GetVertexDatasetOp(
dataset_resource_name=training_dataset_resource_name
)
dataset_preprocessor_task = DatasetPreprocessorOp(
project=project,
location=location,
test_dataset=get_test_dataset_task.outputs['dataset'],
test_dataset_annotation_set_name=test_dataset_annotation_set_name,
training_dataset=get_training_dataset_task.outputs['dataset'],
training_dataset_annotation_set_name=training_dataset_annotation_set_name,
)
get_model_task = GetVertexModelOp(model_name=model_name)
batch_predict_task = ModelBatchPredictOp(
project=project,
location=location,
model=get_model_task.outputs['model'],
job_display_name=(
f'{evaluation_display_name}-{dsl.PIPELINE_JOB_ID_PLACEHOLDER}'
),
gcs_source_uris=dataset_preprocessor_task.outputs[
'batch_prediction_storage_source'
],
instances_format=batch_predict_instances_format,
predictions_format=batch_predict_predictions_format,
gcs_destination_output_uri_prefix=batch_predict_gcs_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,
)
eval_task = ModelEvaluationClassificationOp(
project=project,
location=location,
target_field_name='ground_truth',
ground_truth_format='jsonl',
ground_truth_gcs_source=dataset_preprocessor_task.outputs[
'model_evaluation_storage_source'
],
predictions_format='jsonl',
predictions_gcs_source=batch_predict_task.outputs[
'gcs_output_directory'
],
model=get_model_task.outputs['model'],
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,
prediction_score_column='',
prediction_label_column='',
)
evaluated_annotation_task = EvaluatedAnnotationOp(
project=project,
location=location,
predictions_storage_source=batch_predict_task.outputs[
'gcs_output_directory'
],
ground_truth_storage_source=dataset_preprocessor_task.outputs[
'test_data_items_storage_source'
],
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,
)
feature_extractor_task = FeatureExtractorOp(
project=project,
location=location,
root_dir=batch_predict_gcs_destination_output_uri,
test_dataset=get_test_dataset_task.outputs['dataset'],
training_dataset=get_training_dataset_task.outputs['dataset'],
preprocessed_test_dataset_storage_source=dataset_preprocessor_task.outputs[
'test_data_items_storage_source'
],
preprocessed_training_dataset_storage_source=dataset_preprocessor_task.outputs[
'training_data_items_storage_source'
],
feature_extractor_machine_type=batch_predict_machine_type,
encryption_spec_key_name=encryption_spec_key_name,
)
error_analysis_task = ErrorAnalysisAnnotationOp(
project=project,
location=location,
root_dir=batch_predict_gcs_destination_output_uri,
embeddings_dir=feature_extractor_task.outputs['embeddings_dir'],
)
model_evaluation_importer_task = ModelImportEvaluationOp(
classification_metrics=eval_task.outputs['evaluation_metrics'],
model=get_model_task.outputs['model'],
dataset_type=batch_predict_instances_format,
dataset_paths=dataset_preprocessor_task.outputs[
'batch_prediction_storage_source'
],
display_name=evaluation_display_name,
)
ModelImportEvaluatedAnnotationOp(
model=get_model_task.outputs['model'],
evaluated_annotation_output_uri=evaluated_annotation_task.outputs[
'evaluated_annotation_output_uri'
],
evaluation_importer_gcp_resources=model_evaluation_importer_task.outputs[
'gcp_resources'
],
error_analysis_output_uri=error_analysis_task.outputs[
'error_analysis_output_uri'
],
)
with dsl.Condition(
(
(
test_dataset_resource_name == ''
and training_dataset_resource_name == ''
and test_dataset_annotation_set_name == ''
and training_dataset_annotation_set_name == ''
)
),
name='CustomDataset',
):
dataset_preprocessor_task = DatasetPreprocessorOp(
project=project,
location=location,
test_dataset_storage_source_uris=test_dataset_storage_source_uris,
training_dataset_storage_source_uris=training_dataset_storage_source_uris,
)
get_model_task = GetVertexModelOp(model_name=model_name)
batch_predict_task = ModelBatchPredictOp(
project=project,
location=location,
model=get_model_task.outputs['model'],
job_display_name='sdk-batch-predict-evaluation',
gcs_source_uris=dataset_preprocessor_task.outputs[
'batch_prediction_storage_source'
],
instances_format=batch_predict_instances_format,
predictions_format=batch_predict_predictions_format,
gcs_destination_output_uri_prefix=batch_predict_gcs_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,
)
eval_task = ModelEvaluationClassificationOp(
project=project,
location=location,
target_field_name='ground_truth',
ground_truth_format='jsonl',
ground_truth_gcs_source=dataset_preprocessor_task.outputs[
'model_evaluation_storage_source'
],
predictions_format='jsonl',
predictions_gcs_source=batch_predict_task.outputs[
'gcs_output_directory'
],
model=get_model_task.outputs['model'],
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,
prediction_score_column='',
prediction_label_column='',
)
evaluated_annotation_task = EvaluatedAnnotationOp(
project=project,
location=location,
predictions_storage_source=batch_predict_task.outputs[
'gcs_output_directory'
],
ground_truth_storage_source=dataset_preprocessor_task.outputs[
'test_data_items_storage_source'
],
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,
)
feature_extractor_task = FeatureExtractorOp(
project=project,
location=location,
root_dir=batch_predict_gcs_destination_output_uri,
preprocessed_test_dataset_storage_source=dataset_preprocessor_task.outputs[
'test_data_items_storage_source'
],
preprocessed_training_dataset_storage_source=dataset_preprocessor_task.outputs[
'training_data_items_storage_source'
],
feature_extractor_machine_type=batch_predict_machine_type,
encryption_spec_key_name=encryption_spec_key_name,
)
error_analysis_task = ErrorAnalysisAnnotationOp(
project=project,
location=location,
root_dir=batch_predict_gcs_destination_output_uri,
embeddings_dir=feature_extractor_task.outputs['embeddings_dir'],
)
model_evaluation_importer_task = ModelImportEvaluationOp(
classification_metrics=eval_task.outputs['evaluation_metrics'],
model=get_model_task.outputs['model'],
dataset_type=batch_predict_instances_format,
dataset_paths=dataset_preprocessor_task.outputs[
'batch_prediction_storage_source'
],
display_name=evaluation_display_name,
)
ModelImportEvaluatedAnnotationOp(
model=get_model_task.outputs['model'],
evaluated_annotation_output_uri=evaluated_annotation_task.outputs[
'evaluated_annotation_output_uri'
],
evaluation_importer_gcp_resources=model_evaluation_importer_task.outputs[
'gcp_resources'
],
error_analysis_output_uri=error_analysis_task.outputs[
'error_analysis_output_uri'
],
)