def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Deploying model")
operation = hook.deploy_model(
project_id=self.project_id,
region=self.region,
endpoint=self.endpoint_id,
deployed_model=self.deployed_model,
traffic_split=self.traffic_split,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = hook.wait_for_operation(timeout=self.timeout,
operation=operation)
deploy_model = endpoint_service.DeployModelResponse.to_dict(result)
deployed_model_id = hook.extract_deployed_model_id(deploy_model)
self.log.info("Model was deployed. Deployed Model ID: %s",
deployed_model_id)
self.xcom_push(context,
key="deployed_model_id",
value=deployed_model_id)
VertexAIModelLink.persist(context=context,
task_instance=self,
model_id=deployed_model_id)
return deploy_model
def execute(self, context: "Context"):
self.hook = AutoMLHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
model = self.hook.create_auto_ml_video_training_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
dataset=datasets.VideoDataset(dataset_name=self.dataset_id),
prediction_type=self.prediction_type,
model_type=self.model_type,
labels=self.labels,
training_encryption_spec_key_name=self.training_encryption_spec_key_name,
model_encryption_spec_key_name=self.model_encryption_spec_key_name,
training_fraction_split=self.training_fraction_split,
test_fraction_split=self.test_fraction_split,
training_filter_split=self.training_filter_split,
test_filter_split=self.test_filter_split,
model_display_name=self.model_display_name,
model_labels=self.model_labels,
sync=self.sync,
)
result = Model.to_dict(model)
model_id = self.hook.extract_model_id(result)
VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id)
return result
def execute(self, context: "Context"):
self.hook = AutoMLHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
model = self.hook.create_auto_ml_image_training_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
dataset=datasets.ImageDataset(dataset_name=self.dataset_id),
prediction_type=self.prediction_type,
multi_label=self.multi_label,
model_type=self.model_type,
base_model=self.base_model,
labels=self.labels,
training_encryption_spec_key_name=self.training_encryption_spec_key_name,
model_encryption_spec_key_name=self.model_encryption_spec_key_name,
training_fraction_split=self.training_fraction_split,
validation_fraction_split=self.validation_fraction_split,
test_fraction_split=self.test_fraction_split,
training_filter_split=self.training_filter_split,
validation_filter_split=self.validation_filter_split,
test_filter_split=self.test_filter_split,
budget_milli_node_hours=self.budget_milli_node_hours,
model_display_name=self.model_display_name,
model_labels=self.model_labels,
disable_early_stopping=self.disable_early_stopping,
sync=self.sync,
)
result = Model.to_dict(model)
model_id = self.hook.extract_model_id(result)
VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id)
return result
def execute(self, context: "Context"):
hook = ModelServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Upload model")
operation = hook.upload_model(
project_id=self.project_id,
region=self.region,
model=self.model,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = hook.wait_for_operation(timeout=self.timeout,
operation=operation)
model_resp = model_service.UploadModelResponse.to_dict(result)
model_id = hook.extract_model_id(model_resp)
self.log.info("Model was uploaded. Model ID: %s", model_id)
self.xcom_push(context, key="model_id", value=model_id)
VertexAIModelLink.persist(context=context,
task_instance=self,
model_id=model_id)
return model_resp
def execute(self, context: "Context"):
self.hook = AutoMLHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
model = self.hook.create_auto_ml_forecasting_training_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
dataset=datasets.TimeSeriesDataset(dataset_name=self.dataset_id),
target_column=self.target_column,
time_column=self.time_column,
time_series_identifier_column=self.time_series_identifier_column,
unavailable_at_forecast_columns=self.
unavailable_at_forecast_columns,
available_at_forecast_columns=self.available_at_forecast_columns,
forecast_horizon=self.forecast_horizon,
data_granularity_unit=self.data_granularity_unit,
data_granularity_count=self.data_granularity_count,
optimization_objective=self.optimization_objective,
column_specs=self.column_specs,
column_transformations=self.column_transformations,
labels=self.labels,
training_encryption_spec_key_name=self.
training_encryption_spec_key_name,
model_encryption_spec_key_name=self.model_encryption_spec_key_name,
training_fraction_split=self.training_fraction_split,
validation_fraction_split=self.validation_fraction_split,
test_fraction_split=self.test_fraction_split,
predefined_split_column_name=self.predefined_split_column_name,
weight_column=self.weight_column,
time_series_attribute_columns=self.time_series_attribute_columns,
context_window=self.context_window,
export_evaluated_data_items=self.export_evaluated_data_items,
export_evaluated_data_items_bigquery_destination_uri=(
self.export_evaluated_data_items_bigquery_destination_uri),
export_evaluated_data_items_override_destination=(
self.export_evaluated_data_items_override_destination),
quantiles=self.quantiles,
validation_options=self.validation_options,
budget_milli_node_hours=self.budget_milli_node_hours,
model_display_name=self.model_display_name,
model_labels=self.model_labels,
sync=self.sync,
)
result = Model.to_dict(model)
model_id = self.hook.extract_model_id(result)
VertexAIModelLink.persist(context=context,
task_instance=self,
model_id=model_id)
return result
def execute(self, context: "Context"):
self.hook = AutoMLHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
model = self.hook.create_auto_ml_tabular_training_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
dataset=datasets.TabularDataset(dataset_name=self.dataset_id),
target_column=self.target_column,
optimization_prediction_type=self.optimization_prediction_type,
optimization_objective=self.optimization_objective,
column_specs=self.column_specs,
column_transformations=self.column_transformations,
optimization_objective_recall_value=self.
optimization_objective_recall_value,
optimization_objective_precision_value=self.
optimization_objective_precision_value,
labels=self.labels,
training_encryption_spec_key_name=self.
training_encryption_spec_key_name,
model_encryption_spec_key_name=self.model_encryption_spec_key_name,
training_fraction_split=self.training_fraction_split,
validation_fraction_split=self.validation_fraction_split,
test_fraction_split=self.test_fraction_split,
predefined_split_column_name=self.predefined_split_column_name,
timestamp_split_column_name=self.timestamp_split_column_name,
weight_column=self.weight_column,
budget_milli_node_hours=self.budget_milli_node_hours,
model_display_name=self.model_display_name,
model_labels=self.model_labels,
disable_early_stopping=self.disable_early_stopping,
export_evaluated_data_items=self.export_evaluated_data_items,
export_evaluated_data_items_bigquery_destination_uri=(
self.export_evaluated_data_items_bigquery_destination_uri),
export_evaluated_data_items_override_destination=(
self.export_evaluated_data_items_override_destination),
sync=self.sync,
)
result = Model.to_dict(model)
model_id = self.hook.extract_model_id(result)
VertexAIModelLink.persist(context=context,
task_instance=self,
model_id=model_id)
return result