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# 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.
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)
