Source code for tests.system.providers.amazon.aws.example_sagemaker

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import base64
import subprocess
from datetime import datetime
from tempfile import NamedTemporaryFile

import boto3

from airflow import DAG
from airflow.decorators import task
from airflow.models.baseoperator import chain
from airflow.operators.python import get_current_context
from airflow.providers.amazon.aws.operators.s3 import (
    S3CreateBucketOperator,
    S3CreateObjectOperator,
    S3DeleteBucketOperator,
)
from airflow.providers.amazon.aws.operators.sagemaker import (
    SageMakerDeleteModelOperator,
    SageMakerModelOperator,
    SageMakerProcessingOperator,
    SageMakerTrainingOperator,
    SageMakerTransformOperator,
    SageMakerTuningOperator,
)
from airflow.providers.amazon.aws.sensors.sagemaker import (
    SageMakerTrainingSensor,
    SageMakerTransformSensor,
    SageMakerTuningSensor,
)
from airflow.utils.trigger_rule import TriggerRule
from tests.system.providers.amazon.aws.utils import ENV_ID_KEY, SystemTestContextBuilder, purge_logs

[docs]DAG_ID = 'example_sagemaker'

# Externally fetched variables:
[docs]ROLE_ARN_KEY = 'ROLE_ARN'
# The URI of a Docker image for handling KNN model training.
# To find the URI of a free Amazon-provided image that can be used, substitute your
# desired region in the following link and find the URI under "Registry Path".
# https://docs.aws.amazon.com/sagemaker/latest/dg/ecr-us-east-1.html#knn-us-east-1.title
# This URI should be in the format of {12-digits}.dkr.ecr.{region}.amazonaws.com/knn
[docs]KNN_IMAGE_URI_KEY = 'KNN_IMAGE_URI'

[docs]sys_test_context_task = (
    SystemTestContextBuilder().add_variable(KNN_IMAGE_URI_KEY).add_variable(ROLE_ARN_KEY).build()
)

# For this example we are using a subset of Fischer's Iris Data Set.
# The full dataset can be found at UC Irvine's machine learning repository:
# https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
[docs]DATASET = """
        5.1,3.5,1.4,0.2,Iris-setosa
        4.9,3.0,1.4,0.2,Iris-setosa
        7.0,3.2,4.7,1.4,Iris-versicolor
        6.4,3.2,4.5,1.5,Iris-versicolor
        4.9,2.5,4.5,1.7,Iris-virginica
        7.3,2.9,6.3,1.8,Iris-virginica
        """
[docs]SAMPLE_SIZE = DATASET.count('\n') - 1

# This script will be the entrypoint for the docker image which will handle preprocessing the raw data
# NOTE:  The following string must remain dedented as it is being written to a file.
[docs]PREPROCESS_SCRIPT_TEMPLATE = """
import boto3
import numpy as np
import pandas as pd

def main():
    # Load the Iris dataset from {input_path}/input.csv, split it into train/test
    # subsets, and write them to {output_path}/ for the Processing Operator.

    columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
    iris = pd.read_csv('{input_path}/input.csv', names=columns)

    # Process data
    iris['species'] = iris['species'].replace({{'Iris-virginica': 0, 'Iris-versicolor': 1, 'Iris-setosa': 2}})
    iris = iris[['species', 'sepal_length', 'sepal_width', 'petal_length', 'petal_width']]

    # Split into test and train data
    iris_train, iris_test = np.split(
        iris.sample(frac=1, random_state=np.random.RandomState()), [int(0.7 * len(iris))]
    )

    # Remove the "answers" from the test set
    iris_test.drop(['species'], axis=1, inplace=True)

    # Write the splits to disk
    iris_train.to_csv('{output_path}/train.csv', index=False, header=False)
    iris_test.to_csv('{output_path}/test.csv', index=False, header=False)

    print('Preprocessing Done.')

if __name__ == "__main__":
    main()

    """


def _create_ecr_repository(repo_name):
    return boto3.client('ecr').create_repository(repositoryName=repo_name)['repository']['repositoryUri']


def _build_and_upload_docker_image(preprocess_script, repository_uri):
    """
    We need a Docker image with the following requirements:
      - Has numpy, pandas, requests, and boto3 installed
      - Has our data preprocessing script mounted and set as the entry point
    """
    ecr_region = repository_uri.split('.')[3]

    # Fetch and parse ECR Token to be used for the docker push
    token = boto3.client('ecr', region_name=ecr_region).get_authorization_token()
    credentials = (base64.b64decode(token['authorizationData'][0]['authorizationToken'])).decode('utf-8')
    username, password = credentials.split(':')

    with NamedTemporaryFile(mode='w+t') as preprocessing_script, NamedTemporaryFile(mode='w+t') as dockerfile:
        preprocessing_script.write(preprocess_script)
        preprocessing_script.flush()

        dockerfile.write(
            f"""
            FROM amazonlinux
            COPY {preprocessing_script.name.split('/')[2]} /preprocessing.py
            ADD credentials /credentials
            ENV AWS_SHARED_CREDENTIALS_FILE=/credentials
            RUN yum install python3 pip -y
            RUN pip3 install boto3 pandas requests
            CMD [ "python3", "/preprocessing.py"]
            """
        )
        dockerfile.flush()

        docker_build_and_push_commands = f"""
            cp /root/.aws/credentials /tmp/credentials &&
            docker build -f {dockerfile.name} -t {repository_uri} /tmp &&
            rm /tmp/credentials &&
            aws ecr get-login-password --region {ecr_region} |
            docker login --username {username} --password {password} {repository_uri} &&
            docker push {repository_uri}
            """
        docker_build = subprocess.Popen(
            docker_build_and_push_commands,
            shell=True,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
        )
        _, err = docker_build.communicate()

        if docker_build.returncode != 0:
            raise RuntimeError(err)


@task
[docs]def set_up(env_id, knn_image_uri, role_arn):
    bucket_name = f'{env_id}-sagemaker-example'
    ecr_repository_name = f'{env_id}-repo'
    model_name = f'{env_id}-KNN-model'
    processing_job_name = f'{env_id}-processing'
    training_job_name = f'{env_id}-train'
    transform_job_name = f'{env_id}-transform'
    tuning_job_name = f'{env_id}-tune'

    input_data_S3_key = f'{env_id}/processed-input-data'
    prediction_output_s3_key = f'{env_id}/transform'
    processing_local_input_path = '/opt/ml/processing/input'
    processing_local_output_path = '/opt/ml/processing/output'
    raw_data_s3_key = f'{env_id}/preprocessing/input.csv'
    training_output_s3_key = f'{env_id}/results'

    ecr_repository_uri = _create_ecr_repository(ecr_repository_name)
    resource_config = {
        'InstanceCount': 1,
        'InstanceType': 'ml.m5.large',
        'VolumeSizeInGB': 1,
    }
    processing_config = {
        "ProcessingJobName": processing_job_name,
        "ProcessingInputs": [
            {
                "InputName": "input",
                "AppManaged": False,
                "S3Input": {
                    "S3Uri": f's3://{bucket_name}/{raw_data_s3_key}',
                    "LocalPath": processing_local_input_path,
                    "S3DataType": "S3Prefix",
                    "S3InputMode": "File",
                    "S3DataDistributionType": "FullyReplicated",
                    "S3CompressionType": "None",
                },
            },
        ],
        "ProcessingOutputConfig": {
            "Outputs": [
                {
                    "OutputName": "output",
                    "S3Output": {
                        "S3Uri": f's3://{bucket_name}/{input_data_S3_key}',
                        "LocalPath": processing_local_output_path,
                        "S3UploadMode": "EndOfJob",
                    },
                    "AppManaged": False,
                }
            ]
        },
        "ProcessingResources": {
            "ClusterConfig": resource_config,
        },
        "StoppingCondition": {"MaxRuntimeInSeconds": 300},
        "AppSpecification": {
            "ImageUri": ecr_repository_uri,
        },
        "RoleArn": role_arn,
    }

    training_data_source = {
        "CompressionType": "None",
        "ContentType": "text/csv",
        "DataSource": {
            "S3DataSource": {
                "S3DataDistributionType": "FullyReplicated",
                "S3DataType": "S3Prefix",
                "S3Uri": f's3://{bucket_name}/{input_data_S3_key}/train.csv',
            }
        },
    }
    training_config = {
        "AlgorithmSpecification": {
            "TrainingImage": knn_image_uri,
            "TrainingInputMode": "File",
        },
        "HyperParameters": {
            "predictor_type": "classifier",
            "feature_dim": "4",
            "k": "3",
            "sample_size": str(SAMPLE_SIZE),
        },
        "InputDataConfig": [
            {
                "ChannelName": "train",
                **training_data_source,
            }
        ],
        "OutputDataConfig": {"S3OutputPath": f"s3://{bucket_name}/{training_output_s3_key}/"},
        "ResourceConfig": resource_config,
        "RoleArn": role_arn,
        "StoppingCondition": {"MaxRuntimeInSeconds": 6000},
        "TrainingJobName": training_job_name,
    }
    model_config = {
        "ExecutionRoleArn": role_arn,
        "ModelName": model_name,
        "PrimaryContainer": {
            "Mode": "SingleModel",
            "Image": knn_image_uri,
            "ModelDataUrl": f"s3://{bucket_name}/{training_output_s3_key}/{training_job_name}/output/model.tar.gz",  # noqa: E501
        },
    }
    tuning_config = {
        "HyperParameterTuningJobName": tuning_job_name,
        "HyperParameterTuningJobConfig": {
            "Strategy": "Bayesian",
            "HyperParameterTuningJobObjective": {
                "MetricName": "test:accuracy",
                "Type": "Maximize",
            },
            "ResourceLimits": {
                # You would bump these up in production as appropriate.
                "MaxNumberOfTrainingJobs": 2,
                "MaxParallelTrainingJobs": 2,
            },
            "ParameterRanges": {
                "CategoricalParameterRanges": [],
                "IntegerParameterRanges": [
                    # Set the min and max values of the hyperparameters you want to tune.
                    {
                        "Name": "k",
                        "MinValue": "1",
                        "MaxValue": str(SAMPLE_SIZE),
                    },
                    {
                        "Name": "sample_size",
                        "MinValue": "1",
                        "MaxValue": str(SAMPLE_SIZE),
                    },
                ],
            },
        },
        "TrainingJobDefinition": {
            "StaticHyperParameters": {
                "predictor_type": "classifier",
                "feature_dim": "4",
            },
            "AlgorithmSpecification": {"TrainingImage": knn_image_uri, "TrainingInputMode": "File"},
            "InputDataConfig": [
                {
                    "ChannelName": "train",
                    **training_data_source,
                },
                {
                    "ChannelName": "test",
                    **training_data_source,
                },
            ],
            "OutputDataConfig": {"S3OutputPath": f"s3://{bucket_name}/{training_output_s3_key}"},
            "ResourceConfig": resource_config,
            "RoleArn": role_arn,
            "StoppingCondition": {"MaxRuntimeInSeconds": 60000},
        },
    }
    transform_config = {
        "TransformJobName": transform_job_name,
        "TransformInput": {
            "DataSource": {
                "S3DataSource": {
                    "S3DataType": "S3Prefix",
                    "S3Uri": f"s3://{bucket_name}/{input_data_S3_key}/test.csv",
                }
            },
            "SplitType": "Line",
            "ContentType": "text/csv",
        },
        "TransformOutput": {"S3OutputPath": f"s3://{bucket_name}/{prediction_output_s3_key}"},
        "TransformResources": {
            "InstanceCount": 1,
            "InstanceType": "ml.m5.large",
        },
        "ModelName": model_name,
    }

    preprocess_script = PREPROCESS_SCRIPT_TEMPLATE.format(
        input_path=processing_local_input_path, output_path=processing_local_output_path
    )
    _build_and_upload_docker_image(preprocess_script, ecr_repository_uri)

    ti = get_current_context()['ti']
    ti.xcom_push(key='bucket_name', value=bucket_name)
    ti.xcom_push(key='raw_data_s3_key', value=raw_data_s3_key)
    ti.xcom_push(key='ecr_repository_name', value=ecr_repository_name)
    ti.xcom_push(key='processing_config', value=processing_config)
    ti.xcom_push(key='training_config', value=training_config)
    ti.xcom_push(key='training_job_name', value=training_job_name)
    ti.xcom_push(key='model_config', value=model_config)
    ti.xcom_push(key='model_name', value=model_name)
    ti.xcom_push(key='tuning_config', value=tuning_config)
    ti.xcom_push(key='tuning_job_name', value=tuning_job_name)
    ti.xcom_push(key='transform_config', value=transform_config)
    ti.xcom_push(key='transform_job_name', value=transform_job_name)


@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_ecr_repository(repository_name):
    client = boto3.client('ecr')

    # All images must be removed from the repo before it can be deleted.
    image_ids = client.list_images(repositoryName=repository_name)['imageIds']
    client.batch_delete_image(
        repositoryName=repository_name,
        imageIds=[{'imageDigest': image['imageDigest'] for image in image_ids}],
    )
    client.delete_repository(repositoryName=repository_name)


@task(trigger_rule=TriggerRule.ALL_DONE)
[docs]def delete_logs(env_id):
    generated_logs = [
        # Format: ('log group name', 'log stream prefix')
        ('/aws/sagemaker/ProcessingJobs', env_id),
        ('/aws/sagemaker/TrainingJobs', env_id),
        ('/aws/sagemaker/TransformJobs', env_id),
    ]
    purge_logs(generated_logs)


with DAG(
    dag_id=DAG_ID,
    schedule_interval='@once',
    start_date=datetime(2021, 1, 1),
    tags=['example'],
    catchup=False,
) as dag:
[docs]    test_context = sys_test_context_task()

    test_setup = set_up(
        env_id=test_context[ENV_ID_KEY],
        knn_image_uri=test_context[KNN_IMAGE_URI_KEY],
        role_arn=test_context[ROLE_ARN_KEY],
    )

    create_bucket = S3CreateBucketOperator(
        task_id='create_bucket',
        bucket_name=test_setup['bucket_name'],
    )

    upload_dataset = S3CreateObjectOperator(
        task_id='upload_dataset',
        s3_bucket=test_setup['bucket_name'],
        s3_key=test_setup['raw_data_s3_key'],
        data=DATASET,
        replace=True,
    )

    # [START howto_operator_sagemaker_processing]
    preprocess_raw_data = SageMakerProcessingOperator(
        task_id='preprocess_raw_data',
        config=test_setup['processing_config'],
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_processing]

    # [START howto_operator_sagemaker_training]
    train_model = SageMakerTrainingOperator(
        task_id='train_model',
        config=test_setup['training_config'],
        # Waits by default, setting as False to demonstrate the Sensor below.
        wait_for_completion=False,
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_training]

    # [START howto_sensor_sagemaker_training]
    await_training = SageMakerTrainingSensor(
        task_id="await_training",
        job_name=test_setup['training_job_name'],
        do_xcom_push=False,
    )
    # [END howto_sensor_sagemaker_training]

    # [START howto_operator_sagemaker_model]
    create_model = SageMakerModelOperator(
        task_id='create_model',
        config=test_setup['model_config'],
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_model]

    # [START howto_operator_sagemaker_tuning]
    tune_model = SageMakerTuningOperator(
        task_id="tune_model",
        config=test_setup['tuning_config'],
        # Waits by default, setting as False to demonstrate the Sensor below.
        wait_for_completion=False,
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_tuning]

    # [START howto_sensor_sagemaker_tuning]
    await_tune = SageMakerTuningSensor(
        task_id="await_tuning",
        job_name=test_setup['tuning_job_name'],
        do_xcom_push=False,
    )
    # [END howto_sensor_sagemaker_tuning]

    # [START howto_operator_sagemaker_transform]
    test_model = SageMakerTransformOperator(
        task_id='test_model',
        config=test_setup['transform_config'],
        # Waits by default, setting as False to demonstrate the Sensor below.
        wait_for_completion=False,
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_transform]

    # [START howto_sensor_sagemaker_transform]
    await_transform = SageMakerTransformSensor(
        task_id="await_transform",
        job_name=test_setup['transform_job_name'],
        do_xcom_push=False,
    )
    # [END howto_sensor_sagemaker_transform]

    # [START howto_operator_sagemaker_delete_model]
    delete_model = SageMakerDeleteModelOperator(
        task_id="delete_model",
        config={'ModelName': test_setup['model_name']},
        trigger_rule=TriggerRule.ALL_DONE,
        do_xcom_push=False,
    )
    # [END howto_operator_sagemaker_delete_model]

    delete_bucket = S3DeleteBucketOperator(
        task_id='delete_bucket',
        trigger_rule=TriggerRule.ALL_DONE,
        bucket_name=test_setup['bucket_name'],
        force_delete=True,
    )

    chain(
        # TEST SETUP
        test_context,
        test_setup,
        create_bucket,
        upload_dataset,
        # TEST BODY
        preprocess_raw_data,
        train_model,
        await_training,
        create_model,
        tune_model,
        await_tune,
        test_model,
        await_transform,
        # TEST TEARDOWN
        delete_ecr_repository(test_setup['ecr_repository_name']),
        delete_model,
        delete_bucket,
        delete_logs(test_context[ENV_ID_KEY]),
    )

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()

from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)