MaksYermak commented on a change in pull request #20077: URL: https://github.com/apache/airflow/pull/20077#discussion_r786061371
########## File path: airflow/providers/google/cloud/operators/vertex_ai/custom_job.py ########## @@ -0,0 +1,594 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +"""This module contains Google Vertex AI operators.""" + +from typing import Dict, List, Optional, Sequence, Tuple, Union + +from google.api_core.exceptions import NotFound +from google.api_core.retry import Retry +from google.cloud.aiplatform.models import Model +from google.cloud.aiplatform_v1.types.dataset import Dataset +from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline + +from airflow.models import BaseOperator, BaseOperatorLink +from airflow.models.taskinstance import TaskInstance +from airflow.providers.google.cloud.hooks.vertex_ai.custom_job import CustomJobHook + +VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"; +VERTEX_AI_MODEL_LINK = ( + VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}" +) +VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}" + + +class VertexAIModelLink(BaseOperatorLink): + """Helper class for constructing Vertex AI Model link""" + + name = "Vertex AI Model" + + def get_link(self, operator, dttm): + ti = TaskInstance(task=operator, execution_date=dttm) + model_conf = ti.xcom_pull(task_ids=operator.task_id, key="model_conf") + return ( + VERTEX_AI_MODEL_LINK.format( + region=model_conf["region"], + model_id=model_conf["model_id"], + project_id=model_conf["project_id"], + ) + if model_conf + else "" + ) + + +class VertexAITrainingPipelinesLink(BaseOperatorLink): + """Helper class for constructing Vertex AI Training Pipelines link""" + + name = "Vertex AI Training Pipelines" + + def get_link(self, operator, dttm): + ti = TaskInstance(task=operator, execution_date=dttm) + project_id = ti.xcom_pull(task_ids=operator.task_id, key="project_id") + return ( + VERTEX_AI_TRAINING_PIPELINES_LINK.format( + project_id=project_id, + ) + if project_id + else "" + ) + + +class CustomTrainingJobBaseOperator(BaseOperator): + """The base class for operators that launch Custom jobs on VertexAI.""" + + def __init__( + self, + *, + project_id: str, + region: str, + display_name: str, + container_uri: str, + model_serving_container_image_uri: Optional[str] = None, + model_serving_container_predict_route: Optional[str] = None, + model_serving_container_health_route: Optional[str] = None, + model_serving_container_command: Optional[Sequence[str]] = None, + model_serving_container_args: Optional[Sequence[str]] = None, + model_serving_container_environment_variables: Optional[Dict[str, str]] = None, + model_serving_container_ports: Optional[Sequence[int]] = None, + model_description: Optional[str] = None, + model_instance_schema_uri: Optional[str] = None, + model_parameters_schema_uri: Optional[str] = None, + model_prediction_schema_uri: Optional[str] = None, + labels: Optional[Dict[str, str]] = None, + training_encryption_spec_key_name: Optional[str] = None, + model_encryption_spec_key_name: Optional[str] = None, + staging_bucket: Optional[str] = None, + # RUN + dataset_id: Optional[str] = None, + annotation_schema_uri: Optional[str] = None, + model_display_name: Optional[str] = None, + model_labels: Optional[Dict[str, str]] = None, + base_output_dir: Optional[str] = None, + service_account: Optional[str] = None, + network: Optional[str] = None, + bigquery_destination: Optional[str] = None, + args: Optional[List[Union[str, float, int]]] = None, + environment_variables: Optional[Dict[str, str]] = None, + replica_count: int = 1, + machine_type: str = "n1-standard-4", + accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED", + accelerator_count: int = 0, + boot_disk_type: str = "pd-ssd", + boot_disk_size_gb: int = 100, + training_fraction_split: Optional[float] = None, + validation_fraction_split: Optional[float] = None, + test_fraction_split: Optional[float] = None, + training_filter_split: Optional[str] = None, + validation_filter_split: Optional[str] = None, + test_filter_split: Optional[str] = None, + predefined_split_column_name: Optional[str] = None, + timestamp_split_column_name: Optional[str] = None, + tensorboard: Optional[str] = None, + sync=True, + gcp_conn_id: str = "google_cloud_default", + delegate_to: Optional[str] = None, + impersonation_chain: Optional[Union[str, Sequence[str]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.project_id = project_id + self.region = region + self.display_name = display_name + # START Custom + self.container_uri = container_uri + self.model_serving_container_image_uri = model_serving_container_image_uri + self.model_serving_container_predict_route = model_serving_container_predict_route + self.model_serving_container_health_route = model_serving_container_health_route + self.model_serving_container_command = model_serving_container_command + self.model_serving_container_args = model_serving_container_args + self.model_serving_container_environment_variables = model_serving_container_environment_variables + self.model_serving_container_ports = model_serving_container_ports + self.model_description = model_description + self.model_instance_schema_uri = model_instance_schema_uri + self.model_parameters_schema_uri = model_parameters_schema_uri + self.model_prediction_schema_uri = model_prediction_schema_uri + self.labels = labels + self.training_encryption_spec_key_name = training_encryption_spec_key_name + self.model_encryption_spec_key_name = model_encryption_spec_key_name + self.staging_bucket = staging_bucket + # END Custom + # START Run param + self.dataset = Dataset(name=dataset_id) if dataset_id else None + self.annotation_schema_uri = annotation_schema_uri + self.model_display_name = model_display_name + self.model_labels = model_labels + self.base_output_dir = base_output_dir + self.service_account = service_account + self.network = network + self.bigquery_destination = bigquery_destination + self.args = args + self.environment_variables = environment_variables + self.replica_count = replica_count + self.machine_type = machine_type + self.accelerator_type = accelerator_type + self.accelerator_count = accelerator_count + self.boot_disk_type = boot_disk_type + self.boot_disk_size_gb = boot_disk_size_gb + self.training_fraction_split = training_fraction_split + self.validation_fraction_split = validation_fraction_split + self.test_fraction_split = test_fraction_split + self.training_filter_split = training_filter_split + self.validation_filter_split = validation_filter_split + self.test_filter_split = test_filter_split + self.predefined_split_column_name = predefined_split_column_name + self.timestamp_split_column_name = timestamp_split_column_name + self.tensorboard = tensorboard + self.sync = sync + # END Run param + self.gcp_conn_id = gcp_conn_id + self.delegate_to = delegate_to + self.impersonation_chain = impersonation_chain + self.hook: Optional[CustomJobHook] = None + + def execute(self, context): + self.hook = CustomJobHook( + gcp_conn_id=self.gcp_conn_id, + delegate_to=self.delegate_to, + impersonation_chain=self.impersonation_chain, + ) + + def on_kill(self) -> None: + """ + Callback called when the operator is killed. + Cancel any running job. + """ + if self.hook: + self.hook.cancel_job() + + +class CreateCustomContainerTrainingJobOperator(CustomTrainingJobBaseOperator): + """Create Custom Container Training job""" + + template_fields = [ + 'region', + 'command', + 'impersonation_chain', + ] + operator_extra_links = (VertexAIModelLink(),) + + def __init__( + self, + *, + command: Sequence[str] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.command = command + + def execute(self, context): + super().execute(context) + model = self.hook.create_custom_container_training_job( + project_id=self.project_id, + region=self.region, + display_name=self.display_name, + container_uri=self.container_uri, + command=self.command, + model_serving_container_image_uri=self.model_serving_container_image_uri, + model_serving_container_predict_route=self.model_serving_container_predict_route, + model_serving_container_health_route=self.model_serving_container_health_route, + model_serving_container_command=self.model_serving_container_command, + model_serving_container_args=self.model_serving_container_args, + model_serving_container_environment_variables=self.model_serving_container_environment_variables, + model_serving_container_ports=self.model_serving_container_ports, + model_description=self.model_description, + model_instance_schema_uri=self.model_instance_schema_uri, + model_parameters_schema_uri=self.model_parameters_schema_uri, + model_prediction_schema_uri=self.model_prediction_schema_uri, + 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, + staging_bucket=self.staging_bucket, + # RUN + dataset=self.dataset, + annotation_schema_uri=self.annotation_schema_uri, + model_display_name=self.model_display_name, + model_labels=self.model_labels, + base_output_dir=self.base_output_dir, + service_account=self.service_account, + network=self.network, + bigquery_destination=self.bigquery_destination, + args=self.args, + environment_variables=self.environment_variables, + replica_count=self.replica_count, + machine_type=self.machine_type, + accelerator_type=self.accelerator_type, + accelerator_count=self.accelerator_count, + boot_disk_type=self.boot_disk_type, + boot_disk_size_gb=self.boot_disk_size_gb, + 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, + predefined_split_column_name=self.predefined_split_column_name, + timestamp_split_column_name=self.timestamp_split_column_name, + tensorboard=self.tensorboard, + sync=True, + ) + + result = Model.to_dict(model) + model_id = self.hook.extract_model_id(result) + self.xcom_push( + context, + key="model_conf", + value={ + "model_id": model_id, + "region": self.region, + "project_id": self.project_id, + }, + ) + return result + + +class CreateCustomPythonPackageTrainingJobOperator(CustomTrainingJobBaseOperator): + """Create Custom Python Package Training job""" + + template_fields = [ + 'region', + 'impersonation_chain', + ] + operator_extra_links = (VertexAIModelLink(),) + + def __init__( + self, + *, + python_package_gcs_uri: str, + python_module_name: str, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.python_package_gcs_uri = python_package_gcs_uri + self.python_module_name = python_module_name + + def execute(self, context): + super().execute(context) + model = self.hook.create_custom_python_package_training_job( + project_id=self.project_id, + region=self.region, + display_name=self.display_name, + python_package_gcs_uri=self.python_package_gcs_uri, + python_module_name=self.python_module_name, + container_uri=self.container_uri, + model_serving_container_image_uri=self.model_serving_container_image_uri, + model_serving_container_predict_route=self.model_serving_container_predict_route, + model_serving_container_health_route=self.model_serving_container_health_route, + model_serving_container_command=self.model_serving_container_command, + model_serving_container_args=self.model_serving_container_args, + model_serving_container_environment_variables=self.model_serving_container_environment_variables, + model_serving_container_ports=self.model_serving_container_ports, + model_description=self.model_description, + model_instance_schema_uri=self.model_instance_schema_uri, + model_parameters_schema_uri=self.model_parameters_schema_uri, + model_prediction_schema_uri=self.model_prediction_schema_uri, + 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, + staging_bucket=self.staging_bucket, + # RUN + dataset=self.dataset, + annotation_schema_uri=self.annotation_schema_uri, + model_display_name=self.model_display_name, + model_labels=self.model_labels, + base_output_dir=self.base_output_dir, + service_account=self.service_account, + network=self.network, + bigquery_destination=self.bigquery_destination, + args=self.args, + environment_variables=self.environment_variables, + replica_count=self.replica_count, + machine_type=self.machine_type, + accelerator_type=self.accelerator_type, + accelerator_count=self.accelerator_count, + boot_disk_type=self.boot_disk_type, + boot_disk_size_gb=self.boot_disk_size_gb, + 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, + predefined_split_column_name=self.predefined_split_column_name, + timestamp_split_column_name=self.timestamp_split_column_name, + tensorboard=self.tensorboard, + sync=True, + ) + + result = Model.to_dict(model) + model_id = self.hook.extract_model_id(result) + self.xcom_push( + context, + key="model_conf", + value={ + "model_id": model_id, + "region": self.region, + "project_id": self.project_id, + }, + ) + return result + + +class CreateCustomTrainingJobOperator(CustomTrainingJobBaseOperator): + """Create Custom Training job""" + + template_fields = [ + 'region', + 'script_path', + 'requirements', + 'impersonation_chain', + ] + operator_extra_links = (VertexAIModelLink(),) + + def __init__( + self, + *, + script_path: str, + requirements: Optional[Sequence[str]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.requirements = requirements + self.script_path = script_path + + def execute(self, context): + super().execute(context) + model = self.hook.create_custom_training_job( + project_id=self.project_id, + region=self.region, + display_name=self.display_name, + script_path=self.script_path, + container_uri=self.container_uri, + requirements=self.requirements, + model_serving_container_image_uri=self.model_serving_container_image_uri, + model_serving_container_predict_route=self.model_serving_container_predict_route, + model_serving_container_health_route=self.model_serving_container_health_route, + model_serving_container_command=self.model_serving_container_command, + model_serving_container_args=self.model_serving_container_args, + model_serving_container_environment_variables=self.model_serving_container_environment_variables, + model_serving_container_ports=self.model_serving_container_ports, + model_description=self.model_description, + model_instance_schema_uri=self.model_instance_schema_uri, + model_parameters_schema_uri=self.model_parameters_schema_uri, + model_prediction_schema_uri=self.model_prediction_schema_uri, + 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, + staging_bucket=self.staging_bucket, + # RUN + dataset=self.dataset, + annotation_schema_uri=self.annotation_schema_uri, + model_display_name=self.model_display_name, + model_labels=self.model_labels, + base_output_dir=self.base_output_dir, + service_account=self.service_account, + network=self.network, + bigquery_destination=self.bigquery_destination, + args=self.args, + environment_variables=self.environment_variables, + replica_count=self.replica_count, + machine_type=self.machine_type, + accelerator_type=self.accelerator_type, + accelerator_count=self.accelerator_count, + boot_disk_type=self.boot_disk_type, + boot_disk_size_gb=self.boot_disk_size_gb, + 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, + predefined_split_column_name=self.predefined_split_column_name, + timestamp_split_column_name=self.timestamp_split_column_name, + tensorboard=self.tensorboard, + sync=True, + ) + + result = Model.to_dict(model) + model_id = self.hook.extract_model_id(result) + self.xcom_push( + context, + key="model_conf", + value={ + "model_id": model_id, + "region": self.region, + "project_id": self.project_id, + }, + ) + return result + + +class DeleteCustomTrainingJobOperator(BaseOperator): + """Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob.""" + + template_fields = ("region", "project_id", "impersonation_chain") + + def __init__( + self, + *, + training_pipeline_id: str, + custom_job_id: str, + region: str, + project_id: str, + retry: Optional[Retry] = None, + timeout: Optional[float] = None, + metadata: Optional[Sequence[Tuple[str, str]]] = "", + gcp_conn_id: str = "google_cloud_default", + delegate_to: Optional[str] = None, + impersonation_chain: Optional[Union[str, Sequence[str]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.training_pipeline = training_pipeline_id + self.custom_job = custom_job_id + self.region = region + self.project_id = project_id + self.retry = retry + self.timeout = timeout + self.metadata = metadata + self.gcp_conn_id = gcp_conn_id + self.delegate_to = delegate_to + self.impersonation_chain = impersonation_chain + + def execute(self, context: Dict): Review comment: I have changed Dict to Context in the last commit -- This is an automated message from the Apache Git Service. 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