编译模型 (Amazon Command Line Interface) - Amazon SageMaker
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编译模型 (Amazon Command Line Interface)

本部分介绍如何使用管理机器学习模型的 Amazon SageMaker Neo 编译作业。Amazon Command Line Interface(CLI)。您可以创建、描述、停止和列出编译作业。

  1. 创建编译作业

    使用创建编译作业API 操作,您可以指定数据输入格式、存储模型的 S3 存储桶、写入编译后模型的 S3 存储桶以及目标硬件设备或平台。

    下表演示了如何配置:CreateCompilationJobAPI 基于您的目标是设备还是平台。

    Device Example
    { "CompilationJobName": "neo-compilation-job-demo", "RoleArn": "arn:aws:iam::<your-account>:role/service-role/AmazonSageMaker-ExecutionRole-yyyymmddThhmmss", "InputConfig": { "S3Uri": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/train", "DataInputConfig": "{'data': [1,3,1024,1024]}", "Framework": "MXNET" }, "OutputConfig": { "S3OutputLocation": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/compile", # A target device specification example for a ml_c5 instance family "TargetDevice": "ml_c5" }, "StoppingCondition": { "MaxRuntimeInSeconds": 300 } }

    您可以选择指定与FrameworkVersion字段如果您使用 PyTorch 框架来训练模型并且目标设备是ml_* 目标。

    { "CompilationJobName": "neo-compilation-job-demo", "RoleArn": "arn:aws:iam::<your-account>:role/service-role/AmazonSageMaker-ExecutionRole-yyyymmddThhmmss", "InputConfig": { "S3Uri": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/train", "DataInputConfig": "{'data': [1,3,1024,1024]}", "Framework": "PYTORCH", # The FrameworkVersion field is only supported when compiling for PyTorch framework and ml_* targets, # excluding ml_inf. Supported values are 1.4 or 1.5 or 1.6 . Default is 1.6 "FrameworkVersion": "1.6" }, "OutputConfig": { "S3OutputLocation": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/compile", # A target device specification example for a ml_c5 instance family "TargetDevice": "ml_c5", # When compiling for ml_* instances using PyTorch framework, use the "CompilerOptions" field in # OutputConfig to provide the correct data type ("dtype") of the model’s input. Default assumed is "float32" "CompilerOptions": "{'dtype': 'long'}" }, "StoppingCondition": { "MaxRuntimeInSeconds": 300 } }
    注意

    此 API 字段仅适用于 PyTorch。

    Platform Example
    { "CompilationJobName": "neo-test-compilation-job", "RoleArn": "arn:aws:iam::<your-account>:role/service-role/AmazonSageMaker-ExecutionRole-yyyymmddThhmmss", "InputConfig": { "S3Uri": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/train", "DataInputConfig": "{'data': [1,3,1024,1024]}", "Framework": "MXNET" }, "OutputConfig": { "S3OutputLocation": "s3://<your-bucket>/sagemaker/neo-compilation-job-demo-data/compile", # A target platform configuration example for a p3.2xlarge instance "TargetPlatform": { "Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA" }, "CompilerOptions": "{'cuda-ver': '10.0', 'trt-ver': '6.0.1', 'gpu-code': 'sm_70'}" }, "StoppingCondition": { "MaxRuntimeInSeconds": 300 } }
    注意

    对于OutputConfigAPI 操作,TargetDeviceTargetPlatformAPI 操作是互斥的。您必须选择两个选项中的一个。

    要查找 JSON 字符串示例DataInputConfig根据框架,请参阅Neo 需要什么输入数据形状.

    有关设置配置的更多信息,请参阅InputConfigOutputConfig, 和目标平台SageMaker API 参考中的 API 操作。

  2. 在配置 JSON 文件之后,可以运行以下命令来创建编译作业:

    aws sagemaker create-compilation-job \ --cli-input-json file://job.json \ --region us-west-2 # You should get CompilationJobArn
  3. 通过运行以下命令来描述编译作业:

    aws sagemaker describe-compilation-job \ --compilation-job-name $JOB_NM \ --region us-west-2
  4. 通过运行以下命令来停止编译作业:

    aws sagemaker stop-compilation-job \ --compilation-job-name $JOB_NM \ --region us-west-2 # There is no output for compilation-job operation
  5. 通过运行以下命令列出编译作业:

    aws sagemaker list-compilation-jobs \ --region us-west-2