为 SageMaker AI 多模型端点构建自己的容器

使用 SageMaker AI 推理工具包扩展容器

1. 创建一个模型处理程序。MMS 需要使用一个模型处理程序，这是一个 Python 文件，它实施一些函数以进行预处理，从模型中获得结果以及在模型处理程序中处理输出。有关模型处理程序的示例，请参阅示例笔记本中的 model_handler.py。

2. 导入推理工具包并使用其 model_server.start_model_server 函数以启动 MMS。以下示例来自于示例笔记本中的 dockerd-entrypoint.py 文件。请注意，model_server.start_model_server 调用传递上一步中描述的模型处理程序：

   import subprocess
   import sys
   import shlex
   import os
   from retrying import retry
   from subprocess import CalledProcessError
   from sagemaker_inference import model_server
   
   def _retry_if_error(exception):
       return isinstance(exception, CalledProcessError or OSError)
   
   @retry(stop_max_delay=1000 * 50,
          retry_on_exception=_retry_if_error)
   def _start_mms():
       # by default the number of workers per model is 1, but we can configure it through the
       # environment variable below if desired.
       # os.environ['SAGEMAKER_MODEL_SERVER_WORKERS'] = '2'
       model_server.start_model_server(handler_service='/home/model-server/model_handler.py:handle')
   
   def main():
       if sys.argv[1] == 'serve':
           _start_mms()
       else:
           subprocess.check_call(shlex.split(' '.join(sys.argv[1:])))
   
       # prevent docker exit
       subprocess.call(['tail', '-f', '/dev/null'])
       
   main()

3. 在 Dockerfile 中，从第一步中复制模型处理程序，并在 Dockerfile 中将上一步中的 Python 文件指定为入口点。以下几行来自于示例笔记本中使用的 Dockerfile：

   # Copy the default custom service file to handle incoming data and inference requests
   COPY model_handler.py /home/model-server/model_handler.py
   
   # Define an entrypoint script for the docker image
   ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]

4. 构建并注册您的容器。示例笔记本中的以下 Shell 脚本构建容器，并将其上传到您的 AWS 账户中的 Amazon Elastic Container Registry 存储库：

   %%sh
   
   # The name of our algorithm
   algorithm_name=demo-sagemaker-multimodel
   
   cd container
   
   account=$(aws sts get-caller-identity --query Account --output text)
   
   # Get the region defined in the current configuration (default to us-west-2 if none defined)
   region=$(aws configure get region)
   region=${region:-us-west-2}
   
   fullname="${account}.dkr.ecr.${region}.amazonaws.com/${algorithm_name}:latest"
   
   # If the repository doesn't exist in ECR, create it.
   aws ecr describe-repositories --repository-names "${algorithm_name}" > /dev/null 2>&1
   
   if [ $? -ne 0 ]
   then
       aws ecr create-repository --repository-name "${algorithm_name}" > /dev/null
   fi
   
   # Get the login command from ECR and execute it directly
   $(aws ecr get-login --region ${region} --no-include-email)
   
   # Build the docker image locally with the image name and then push it to ECR
   # with the full name.
   
   docker build -q -t ${algorithm_name} .
   docker tag ${algorithm_name} ${fullname}
   
   docker push ${fullname}