eladkal commented on a change in pull request #21673:
URL: https://github.com/apache/airflow/pull/21673#discussion_r813322129
##########
File path: airflow/providers/amazon/aws/operators/sagemaker.py
##########
@@ -608,3 +608,26 @@ def _check_if_job_exists(self) -> None:
raise AirflowException(
f'A SageMaker training job with name {training_job_name}
already exists.'
)
+
+
+class SageMakerDeleteModelOperator(BaseOperator):
Review comment:
Why this operator inherit from `BaseOperator` and not from
`SageMakerBaseOperator` ?
##########
File path: airflow/providers/amazon/aws/operators/sagemaker.py
##########
@@ -608,3 +608,26 @@ def _check_if_job_exists(self) -> None:
raise AirflowException(
f'A SageMaker training job with name {training_job_name}
already exists.'
)
+
+
+class SageMakerDeleteModelOperator(BaseOperator):
+ """Deletes a SageMaker model.
+
+ This operator returns True if model was present and deleted else return
False if Model was not present.
+
+ :param model_name: The name of Sagemaker Model (templated).
Review comment:
you mentioned it's templated but you didn't specify templated fields
##########
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,
+ aws_conn_id="aws_default",
+ dag=dag,
Review comment:
redundant
##########
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 don't follow on that.
Was `sample_model` existed before this DAG started?
the goal of example dags is that users should be able to run them and it
will work for them.
Is this example really runnable?
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