Amazon web services SageMaker培训作业未创建/opt/ml/input/data/training目录

我正试图按照中给出的说明创建自定义算法

当我运行train作业时，该作业失败，没有此类文件或目录：“/opt/ml/input/data/training”

根据文档，SageMaker应该创建这些文档，并在运行时复制数据和工件。但这并没有发生

请分享你的想法

我的DockerFile内容

# Build an image that can do training and inference in SageMaker
 # This is a Python 2 image that uses the nginx, gunicorn, flask stack
 # for serving inferences in a stable way.

 FROM ubuntu:16.04

 MAINTAINER Amazon AI <sage-learner@amazon.com


 RUN apt-get -y update && apt-get install -y --no-install-recommends \
          wget \
          python \
          nginx \
          ca-certificates \
     && rm -rf /var/lib/apt/lists/*

 # Here we get all python packages.
 # There's substantial overlap between scipy and numpy that we eliminate by
 # linking them together. Likewise, pip leaves the install caches populated which uses
 # a significant amount of space. These optimizations save a fair amount of space in the
 # image, which reduces start up time. RUN wget https://bootstrap.pypa.io/get-pip.py && python get-pip.py && \
     pip install numpy==1.16.2 scipy==1.2.1 scikit-learn==0.20.2 pandas flask gevent gunicorn && \
         (cd /usr/local/lib/python2.7/dist-packages/scipy/.libs; rm *; ln ../../numpy/.libs/* .) && \
         rm -rf /root/.cache

 # Set some environment variables. PYTHONUNBUFFERED keeps Python from buffering our standard
 # output stream, which means that logs can be delivered to the user quickly. PYTHONDONTWRITEBYTECODE
 # keeps Python from writing the .pyc files which are unnecessary in this case. We also update
 # PATH so that the train and serve programs are found when the container is invoked.

 ENV PYTHONUNBUFFERED=TRUE ENV PYTHONDONTWRITEBYTECODE=TRUE ENV
 PATH="/opt/program:${PATH}"

 # Set up the program in the image COPY decision_trees /opt/program WORKDIR /opt/program

#构建一个可以在SageMaker中进行训练和推理的图像
#这是一个使用nginx、gunicorn和flask堆栈的Python 2映像
#以稳定的方式提供推论。
来自ubuntu:16.04
MAINTAINER Amazon AI培训文件夹名称取决于CreateTrainingJob操作中提供的InputDataConfig：

如果频道名称为“xyz”，它将在所述位置创建一个同名文件夹
（/opt/ml/input/data/xyz）