有窗户的Sagemaker-Java 学习之路

我正在尝试使用带有Docker的Windows使用aws sagemaker：这是docker文件：

# 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 \
         python3.5 \
         nginx \
         libgcc-5-dev \
         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/3.3/get-pip.py && python3.5 get-pip.py && \
    pip3 install numpy==1.14.3 scipy scikit-learn==0.19.1 xgboost==0.72.1 pandas==0.22.0 flask gevent gunicorn && \
        (cd /usr/local/lib/python3.5/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 xgboost /opt/program
WORKDIR /opt/program

我的问题是，我应该在Windows 7下工作，改变这些路径：

谢谢

1 回答

2

你在谈论ENV PATH吗？

这将在docker容器中设置PATH env，它使用linux文件系统（ubuntu：16.04），所以你不必改变任何东西 .

https://docs.docker.com/engine/reference/builder/#environment-replacement

编辑：

我重读了你的问题 . 您的Dockerfile中没有任何路径需要更改，因为它们是为docker容器本身配置的 .

回复于 2024-05-03T15:00:15+08:00

有窗户的Sagemaker

1 回答

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