def pipeline():
model_upload_op = ModelUploadOp(
project=self._project,
display_name=self._display_name,
serving_container_image_uri=self._serving_container_image_uri,
artifact_uri=self._artifact_uri
)
endpoint_create_op = EndpointCreateOp(
project=self._project,
display_name=self._display_name
)
model_deploy_op = ModelDeployOp(
project=self._project,
model=model_upload_op.outputs["model"]
)
batch_predict_op = ModelBatchPredictOp(
project=self._project,
model=model_upload_op.outputs["model"],
job_display_name=self._display_name,
gcs_source=self._gcs_source,
gcs_destination_prefix=self._gcs_destination_prefix
)
def pipeline():
model_upload_op = ModelUploadOp(
project=self._project,
display_name=self._display_name,
serving_container_image_uri=self._serving_container_image_uri,
artifact_uri=self._artifact_uri)
batch_predict_op = ModelBatchPredictOp(
project=self._project,
location=self._location,
job_display_name=self._display_name,
model=model_upload_op.outputs["model"],
instances_format="instance_format",
gcs_source_uris=[self._gcs_source],
bigquery_source_input_uri="bigquery_source_input_uri",
model_parameters={"foo": "bar"},
predictions_format="predictions_format",
gcs_destination_output_uri_prefix=self._gcs_destination_prefix,
bigquery_destination_output_uri=
"bigquery_destination_output_uri",
machine_type="machine_type",
accelerator_type="accelerator_type",
accelerator_count=1,
starting_replica_count=2,
max_replica_count=3,
manual_batch_tuning_parameters_batch_size=4,
generate_explanation=True,
explanation_metadata={"xai_m": "bar"},
explanation_parameters={"xai_p": "foo"},
encryption_spec_key_name="some encryption_spec_key_name",
labels={"foo": "bar"})
def pipeline():
dataset_create_op = ImageDatasetCreateOp(
project=self._project,
display_name=self._display_name,
gcs_source=self._gcs_source,
import_schema_uri=aiplatform.schema.dataset.ioformat.image.
single_label_classification,
)
training_job_run_op = AutoMLImageTrainingJobRunOp(
project=self._project,
display_name=self._display_name,
prediction_type="classification",
model_type="CLOUD",
base_model=None,
dataset=dataset_create_op.outputs["dataset"],
model_display_name=self._model_display_name,
training_fraction_split=0.6,
validation_fraction_split=0.2,
test_fraction_split=0.2,
budget_milli_node_hours=8000,
)
model_deploy_op = ModelDeployOp(
project=self._project,
model=training_job_run_op.outputs["model"]
)
batch_predict_op = ModelBatchPredictOp(
project=self._project,
model=training_job_run_op.outputs["model"],
job_display_name=self._display_name,
gcs_source=self._gcs_source,
gcs_destination_prefix=self._gcs_destination_prefix,
)
dataset_export_op = ImageDatasetExportDataOp(
project=self._project,
dataset=dataset_create_op.outputs["dataset"],
output_dir=self._gcs_output_dir,
)
dataset_import_op = ImageDatasetImportDataOp(
gcs_source=self._gcs_source,
dataset=dataset_create_op.outputs["dataset"],
import_schema_uri=aiplatform.schema.dataset.ioformat.image.
single_label_classification
)
def pipeline():
dataset_create_op = TextDatasetCreateOp(
project=self._project,
display_name=self._display_name,
gcs_source=self._gcs_source,
import_schema_uri=aiplatform.schema.dataset.ioformat.text.
multi_label_classification,
)
training_job_run_op = AutoMLTextTrainingJobRunOp(
project=self._project,
display_name=self._display_name,
dataset=dataset_create_op.outputs["dataset"],
prediction_type="classification",
multi_label=True,
training_fraction_split=0.6,
validation_fraction_split=0.2,
test_fraction_split=0.2,
model_display_name=self._model_display_name,
)
model_deploy_op = ModelDeployOp(
project=self._project,
model=training_job_run_op.outputs["model"]
)
batch_predict_op = ModelBatchPredictOp(
project=self._project,
model=training_job_run_op.outputs["model"],
job_display_name=self._display_name,
gcs_source=self._gcs_source,
gcs_destination_prefix=self._gcs_destination_prefix,
)
dataset_export_op = TextDatasetExportDataOp(
project=self._project,
dataset=dataset_create_op.outputs["dataset"],
output_dir=self._gcs_output_dir,
)
dataset_import_op = TextDatasetImportDataOp(
gcs_source=self._gcs_source,
dataset=dataset_create_op.outputs["dataset"],
import_schema_uri=aiplatform.schema.dataset.ioformat.text.
multi_label_classification
)
def pipeline():
dataset_create_op = TabularDatasetCreateOp(
project=self._project,
display_name=self._display_name,
gcs_source=self._gcs_source,
)
training_job_run_op = AutoMLTabularTrainingJobRunOp(
project=self._project,
display_name=self._display_name,
optimization_prediction_type='regression',
optimization_objective='minimize-rmse',
column_transformations=[
{
"numeric": {
"column_name": "longitude"
}
},
],
target_column="longitude",
dataset=dataset_create_op.outputs["dataset"],
)
model_deploy_op = ModelDeployOp(
project=self._project,
model=training_job_run_op.outputs["model"]
)
batch_predict_op = ModelBatchPredictOp(
project=self._project,
model=training_job_run_op.outputs["model"],
job_display_name=self._display_name,
gcs_source=self._gcs_source,
gcs_destination_prefix=self._gcs_destination_prefix,
)
dataset_export_op = TabularDatasetExportDataOp(
project=self._project,
dataset=dataset_create_op.outputs["dataset"],
output_dir=self._gcs_output_dir,
)