Я пытаюсь создать собственный алгоритм, следуя инструкциям, приведенным в это руководство.
Когда я запускаю поезд, он выдает ошибку. Нет такого файла или каталога: '/ opt / ml / input / data / training'.
Согласно документации, SageMaker должен создавать эти документы и копировать данные и артефакты во время выполнения. Но этого не происходит.
Пожалуйста, поделитесь своими мыслями по этому поводу.
Мой контент DockerFile,
# 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 <[email protected]
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
