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(PDF)。
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批量预测
批量预测,也称为离线推理,可根据一批观察数据生成模型预测。对于大型数据集或者在您不需要立即响应模型预测请求时,批量推理是很好的选择。
与之对比的是,在线推理(实时推理)会实时生成预测。
您可以使用 SageMaker APIs检索 AutoML 作业的最佳候选对象,然后使用该候选任务提交一批输入数据进行推理。
-
检索 AutoML 作业的详细信息。
以下 Amazon CLI 命令示例使用DescribeAutoMLJobV2API来获取 AutoML 作业的详细信息,包括有关最佳候选模型的信息。
aws sagemaker describe-auto-ml-job-v2 --auto-ml-job-name job-name
--region region
-
容器定义是容器化环境,用于托管经过训练的 SageMaker 模型以进行预测。
BEST_CANDIDATE=$(aws sagemaker describe-auto-ml-job-v2 \
--auto-ml-job-name job-name
--region region
\
--query 'BestCandidate.InferenceContainers[0]' \
--output json
此命令提取最佳候选模型的容器定义并将其存储在BEST_CANDIDATE
变量中。
-
使用最佳候选容器定义创建 SageMaker 模型。
使用前面步骤中的容器定义通过使用创建 SageMaker 模型CreateModelAPI。
aws sagemaker create-model \
--model-name 'model-name
' \
--primary-container "$BEST_CANDIDATE"
--execution-role-arn 'execution-role-arn>
' \
--region 'region>
该--execution-role-arn
参数指定使用模型进行推理时所IAM扮演的角色。 SageMaker有关该角色所需权限的详细信息,请参阅 CreateModel API:执行角色权限。
-
创建批量转换作业。
以下示例使用创建转换作业CreateTransformJobAPI。
aws sagemaker create-transform-job \
--transform-job-name 'transform-job-name
' \
--model-name 'model-name
'\
--transform-input file://transform-input.json \
--transform-output file://transform-output.json \
--transform-resources file://transform-resources.json \
--region 'region
'
输入、输出和资源详细信息在不同的JSON文件中定义:
-
transform-input.json
:
{
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://my-input-data-bucket/path/to/input/data"
}
},
"ContentType": "text/csv",
"SplitType": "None"
}
-
transform-output.json
:
{
"S3OutputPath": "s3://my-output-bucket/path/to/output",
"AssembleWith": "Line"
}
-
transform-resources.json
:
{
"InstanceType": "instance-type",
"InstanceCount": 1
}
-
以以下 Amazon CLI 命令为例。
aws sagemaker describe-transform-job \
--transform-job-name 'transform-job-name
' \
--region region
-
检索批量转换输出。
作业完成后,预测结果可在中找到S3OutputPath
。
输出文件名称格式如下:input_data_file_name.out
。例如,如果您的输入文件是 text_x.csv
,则输出文件名称是 text_x.csv.out
。
aws s3 ls s3://my-output-bucket/path/to/output/
以下代码示例说明了如何使用用 Amazon SDK于 Python (boto3) 和 Amazon CLI 用于批量预测。
- Amazon SDK for Python (boto3)
-
以下示例使用 Amazon SDKPython (boto3) 进行批量预测。
import sagemaker
import boto3
session = sagemaker.session.Session()
sm_client = boto3.client('sagemaker', region_name='us-west-2
')
role = 'arn:aws:iam::1234567890:role/sagemaker-execution-role
'
output_path = 's3://test-auto-ml-job/output
'
input_data = 's3://test-auto-ml-job/test_X.csv
'
best_candidate = sm_client.describe_auto_ml_job_v2(AutoMLJobName=job_name)['BestCandidate']
best_candidate_containers = best_candidate['InferenceContainers']
best_candidate_name = best_candidate['CandidateName']
# create model
reponse = sm_client.create_model(
ModelName = best_candidate_name,
ExecutionRoleArn = role,
Containers = best_candidate_containers
)
# Lauch Transform Job
response = sm_client.create_transform_job(
TransformJobName=f'{best_candidate_name}-transform-job',
ModelName=model_name,
TransformInput={
'DataSource': {
'S3DataSource': {
'S3DataType': 'S3Prefix',
'S3Uri': input_data
}
},
'ContentType': "text/csv
",
'SplitType': 'None'
},
TransformOutput={
'S3OutputPath': output_path,
'AssembleWith': 'Line',
},
TransformResources={
'InstanceType': 'ml.m5.2xlarge
',
'InstanceCount': 1
,
},
)
批量推理作业返回以下格式的响应。
{'TransformJobArn': 'arn:aws:sagemaker:us-west-2:1234567890:transform-job/test-transform-job
',
'ResponseMetadata': {'RequestId': '659f97fc-28c4-440b-b957-a49733f7c2f2',
'HTTPStatusCode': 200,
'HTTPHeaders': {'x-amzn-requestid': '659f97fc-28c4-440b-b957-a49733f7c2f2',
'content-type': 'application/x-amz-json-1.1',
'content-length': '96',
'date': 'Thu, 11 Aug 2022 22:23:49 GMT'},
'RetryAttempts': 0}}
- Amazon Command Line Interface (Amazon CLI)
-
-
获取最佳候选容器定义。
aws sagemaker describe-auto-ml-job-v2 --auto-ml-job-name 'test-automl-job
' --region us-west-2
-
创建模型。
aws sagemaker create-model --model-name 'test-sagemaker-model
'
--containers '[{
"Image": "348316444620.dkr.ecr.us-west-2.amazonaws.com/sagemaker-sklearn-automl:2.5-1-cpu-py3",
"ModelDataUrl": "s3://amzn-s3-demo-bucket/out/test-job1/data-processor-models/test-job1-dpp0-1-e569ff7ad77f4e55a7e549a/output/model.tar.gz
",
"Environment": {
"AUTOML_SPARSE_ENCODE_RECORDIO_PROTOBUF": "1",
"AUTOML_TRANSFORM_MODE": "feature-transform",
"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "application/x-recordio-protobuf",
"SAGEMAKER_PROGRAM": "sagemaker_serve",
"SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code"
}
}, {
"Image": "348316444620.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.3-1-cpu-py3",
"ModelDataUrl": "s3://amzn-s3-demo-bucket/out/test-job1/tuning/flicdf10v2-dpp0-xgb/test-job1E9-244-7490a1c0/output/model.tar.gz
",
"Environment": {
"MAX_CONTENT_LENGTH": "20971520",
"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv",
"SAGEMAKER_INFERENCE_OUTPUT": "predicted_label",
"SAGEMAKER_INFERENCE_SUPPORTED": "predicted_label,probability,probabilities"
}
}, {
"Image": "348316444620.dkr.ecr.us-west-2.amazonaws.com/sagemaker-sklearn-automl:2.5-1-cpu-py3",
"ModelDataUrl": "s3://amzn-s3-demo-bucket/out/test-job1/data-processor-models/test-job1-dpp0-1-e569ff7ad77f4e55a7e549a/output/model.tar.gz
",
"Environment": {
"AUTOML_TRANSFORM_MODE": "inverse-label-transform",
"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv",
"SAGEMAKER_INFERENCE_INPUT": "predicted_label",
"SAGEMAKER_INFERENCE_OUTPUT": "predicted_label",
"SAGEMAKER_INFERENCE_SUPPORTED": "predicted_label,probability,labels,probabilities",
"SAGEMAKER_PROGRAM": "sagemaker_serve",
"SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code"
}
}]' \
--execution-role-arn 'arn:aws:iam::1234567890:role/sagemaker-execution-role
' \
--region 'us-west-2
'
-
创建转换作业。
aws sagemaker create-transform-job --transform-job-name 'test-tranform-job
'\
--model-name 'test-sagemaker-model
'\
--transform-input '{
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://amzn-s3-demo-bucket/data.csv
"
}
},
"ContentType": "text/csv
",
"SplitType": "None"
}'\
--transform-output '{
"S3OutputPath": "s3://amzn-s3-demo-bucket/output/
",
"AssembleWith": "Line"
}'\
--transform-resources '{
"InstanceType": "ml.m5.2xlarge
",
"InstanceCount": 1
}'\
--region 'us-west-2
'
-
检查转换作业的进度。
aws sagemaker describe-transform-job --transform-job-name 'test-tranform-job
' --region us-west-2
以下是来自转换作业的响应。
{
"TransformJobName": "test-tranform-job
",
"TransformJobArn": "arn:aws:sagemaker:us-west-2:1234567890:transform-job/test-tranform-job
",
"TransformJobStatus": "InProgress",
"ModelName": "test-model
",
"TransformInput": {
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://amzn-s3-demo-bucket/data.csv
"
}
},
"ContentType": "text/csv
",
"CompressionType": "None",
"SplitType": "None"
},
"TransformOutput": {
"S3OutputPath": "s3://amzn-s3-demo-bucket/output/
",
"AssembleWith": "Line",
"KmsKeyId": ""
},
"TransformResources": {
"InstanceType": "ml.m5.2xlarge
",
"InstanceCount": 1
},
"CreationTime": 1662495635.679,
"TransformStartTime": 1662495847.496,
"DataProcessing": {
"InputFilter": "$",
"OutputFilter": "$",
"JoinSource": "None"
}
}
将 TransformJobStatus
更改为 Completed
后,您可以在中 S3OutputPath
查看推理结果。