hsrocks commented on a change in pull request #21673: URL: https://github.com/apache/airflow/pull/21673#discussion_r814039556
########## File path: airflow/providers/amazon/aws/example_dags/example_sagemaker.py ########## @@ -0,0 +1,179 @@ +# 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. + +from datetime import datetime +from os import environ + +from airflow import DAG +from airflow.providers.amazon.aws.operators.sagemaker import ( + SageMakerDeleteModelOperator, + SageMakerModelOperator, + SageMakerProcessingOperator, + SageMakerTrainingOperator, + SageMakerTransformOperator, +) + +model_name = "sample_model" +training_job_name = 'sample_training' +image_uri = environ.get('ECR_IMAGE_URI', '123456789012.dkr.ecr.us-east-1.amazonaws.com/repo_name') +s3_bucket = environ.get('BUCKET_NAME', 'test-airflow-12345') +role = environ.get('SAGEMAKER_ROLE_ARN', 'arn:aws:iam::123456789012:role/role_name') + +sagemaker_processing_job_config = { + "ProcessingJobName": "sample_processing_job", + "ProcessingInputs": [ + { + "InputName": "input", + "AppManaged": False, + "S3Input": { + "S3Uri": f"s3://{s3_bucket}/preprocessing/input/", + "LocalPath": "/opt/ml/processing/input/", + "S3DataType": "S3Prefix", + "S3InputMode": "File", + "S3DataDistributionType": "FullyReplicated", + "S3CompressionType": "None", + }, + }, + ], + "ProcessingOutputConfig": { + "Outputs": [ + { + "OutputName": "output", + "S3Output": { + "S3Uri": f"s3://{s3_bucket}/preprocessing/output/", + "LocalPath": "/opt/ml/processing/output/", + "S3UploadMode": "EndOfJob", + }, + "AppManaged": False, + } + ] + }, + "ProcessingResources": { + "ClusterConfig": { + "InstanceCount": 1, + "InstanceType": "ml.m5.large", + "VolumeSizeInGB": 5, + } + }, + "StoppingCondition": {"MaxRuntimeInSeconds": 3600}, + "AppSpecification": { + "ImageUri": f"{image_uri}", + "ContainerEntrypoint": ["python3", "./preprocessing.py"], + }, + "RoleArn": f"{role}", +} + +sagemaker_training_job_config = { + "AlgorithmSpecification": { + "TrainingImage": f"{image_uri}", + "TrainingInputMode": "File", + }, + "InputDataConfig": [ + { + "ChannelName": "config", + "DataSource": { + "S3DataSource": { + "S3DataType": "S3Prefix", + "S3Uri": f"s3://{s3_bucket}/config/", + "S3DataDistributionType": "FullyReplicated", + } + }, + "CompressionType": "None", + "RecordWrapperType": "None", + }, + ], + "OutputDataConfig": { + "KmsKeyId": "", + "S3OutputPath": f"s3://{s3_bucket}/training/", + }, + "ResourceConfig": { + "InstanceType": "ml.m5.large", + "InstanceCount": 1, + "VolumeSizeInGB": 5, + }, + "StoppingCondition": {"MaxRuntimeInSeconds": 6000}, + "RoleArn": f"{role}", + "EnableNetworkIsolation": False, + "EnableInterContainerTrafficEncryption": False, + "EnableManagedSpotTraining": False, + "TrainingJobName": training_job_name, +} + +sagemaker_create_model_config = { + "ModelName": model_name, + "Containers": [ + { + "Image": f"{image_uri}", + "Mode": "SingleModel", + "ModelDataUrl": f"s3://{s3_bucket}/training/{training_job_name}/output/model.tar.gz", + } + ], + "ExecutionRoleArn": f"{role}", + "EnableNetworkIsolation": False, +} + +sagemaker_inference_config = { + "TransformJobName": "sample_transform_job", + "ModelName": model_name, + "TransformInput": { + "DataSource": { + "S3DataSource": { + "S3DataType": "S3Prefix", + "S3Uri": f"s3://{s3_bucket}/config/config_date.yml", + } + }, + "ContentType": "application/x-yaml", + "CompressionType": "None", + "SplitType": "None", + }, + "TransformOutput": {"S3OutputPath": f"s3://{s3_bucket}/inferencing/output/"}, + "TransformResources": {"InstanceType": "ml.m5.large", "InstanceCount": 1}, +} + +# [START howto_operator_sagemaker] +with DAG( + "sample_sagemaker_dag", + schedule_interval=None, + start_date=datetime(2022, 2, 21), + catchup=False, +) as dag: + sagemaker_processing_task = SageMakerProcessingOperator( + config=sagemaker_processing_job_config, + aws_conn_id="aws_default", + task_id="sagemaker_preprocessing_task", + ) + + training_task = SageMakerTrainingOperator( + config=sagemaker_training_job_config, aws_conn_id="aws_default", task_id="sagemaker_training_task" + ) + + model_delete_task = SageMakerDeleteModelOperator( + task_id="sagemaker_delete_model_task", + model_name=model_name, Review comment: I got the intent. Thanks! So with previous changes the delete model in case the model was not present does not use to through ValidationException or ResourceNotFound exception when the model was not present and was simply used to return false so the example DAG was working fine and I have tested it at my end with sample bucket and s3 path. But with the latest changes the exception will come so I have updated the example based on latest changes as well. Please check and resolve. Thanks :) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
