為 SageMaker AI 多模型端點建置自有容器

使用 SageMaker AI 推論工具組來擴展容器

1. 建立模型處理常式。MMS 需要模型處理常式，這是一個 Python 檔案，其中實作函式來預處理、從模型取得預測，以及在模型處理常式中處理輸出。如需模型處理常式的範例，請參閱範例筆記本中的 model_handler.py。

2. 匯入推論工具組，並使用其 model_server.start_model_server 函式來啟動 MMS。下列範例來自範例筆記本中的 dockerd-entrypoint.py 檔案。請注意，呼叫 model_server.start_model_server 會傳遞上一個步驟中描述的模型處理常式：

   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，複製第一個步驟中的模型處理常式，並將上一個步驟中的 Python 檔案指定為 Dockerfile 中的進入點。下列幾行來自範例筆記本中使用的 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. 建置並註冊容器。範例筆記本中有下列殼層指令碼，可建置容器，並上傳到您 AWS 帳戶的 Amazon Elastic Container Registry 儲存庫：

   %%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}