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arjkeshnadiaya
arjkesh
authored and
nadiaya
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change: release 1.6.0 Dockerfiles (#82)
1 parent e7fa17b commit d195aab

17 files changed

+769
-43
lines changed

‎buildspec-release.yml

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Original file line numberDiff line numberDiff line change
@@ -2,8 +2,8 @@ version: 0.2
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env:
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variables:
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FRAMEWORK_FULL_VERSION: '1.4.1'
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FRAMEWORK_SHORT_VERSION: '1.4'
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FRAMEWORK_FULL_VERSION: '1.6.0'
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FRAMEWORK_SHORT_VERSION: '1.6'
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AWS_DEFAULT_REGION: 'us-west-2'
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ECR_REPO: 'sagemaker-mxnet-serving'
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GITHUB_REPO: 'sagemaker-mxnet-serving-container'

‎buildspec.yml

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Original file line numberDiff line numberDiff line change
@@ -2,7 +2,8 @@ version: 0.2
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env:
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variables:
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FRAMEWORK_FULL_VERSION: '1.4.1'
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FRAMEWORK_FULL_VERSION: '1.6.0'
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EI_FRAMEWORK_VERSION: '1.4.1'
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AWS_DEFAULT_REGION: 'us-west-2'
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ECR_REPO: 'sagemaker-test'
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GITHUB_REPO: 'sagemaker-mxnet-serving-container'
@@ -31,14 +32,18 @@ phases:
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- mv dist/sagemaker_mxnet_serving_container-*.tar.gz dist/sagemaker_mxnet_serving_container.tar.gz
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- cp dist/sagemaker_mxnet_serving_container.tar.gz docker/$FRAMEWORK_FULL_VERSION/py3/sagemaker_mxnet_serving_container.tar.gz
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- cp dist/sagemaker_mxnet_serving_container.tar.gz docker/$FRAMEWORK_FULL_VERSION/py2/sagemaker_mxnet_serving_container.tar.gz
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- cp dist/sagemaker_mxnet_serving_container.tar.gz docker/$EI_FRAMEWORK_VERSION/py3/sagemaker_mxnet_serving_container.tar.gz
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- cp dist/sagemaker_mxnet_serving_container.tar.gz docker/$EI_FRAMEWORK_VERSION/py2/sagemaker_mxnet_serving_container.tar.gz
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- cp src/sagemaker_mxnet_serving_container/deep_learning_container.py docker/$FRAMEWORK_FULL_VERSION/py3/deep_learning_container.py
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- cp src/sagemaker_mxnet_serving_container/deep_learning_container.py docker/$FRAMEWORK_FULL_VERSION/py2/deep_learning_container.py
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- CPU_PY2_TAG=$FRAMEWORK_FULL_VERSION-cpu-py2
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- CPU_PY3_TAG=$FRAMEWORK_FULL_VERSION-cpu-py3
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- GPU_PY2_TAG=$FRAMEWORK_FULL_VERSION-gpu-py2
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- GPU_PY3_TAG=$FRAMEWORK_FULL_VERSION-gpu-py3
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# build images
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- python3 scripts/build_all.py --version $FRAMEWORK_FULL_VERSION --account $ACCOUNT --repo $ECR_REPO
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- python3 scripts/build_all.py --version $FRAMEWORK_FULL_VERSION --eia-version $EI_FRAMEWORK_VERSION --account $ACCOUNT --repo $ECR_REPO
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# run cpu local integration tests
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- |
@@ -49,7 +54,7 @@ phases:
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fi
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# push docker images to ECR
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- python3 scripts/publish_all.py --version $FRAMEWORK_FULL_VERSION --account $ACCOUNT --repo $ECR_REPO
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- python3 scripts/publish_all.py --version $FRAMEWORK_FULL_VERSION --eia-version $EI_FRAMEWORK_VERSION --account $ACCOUNT --repo $ECR_REPO
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# launch remote gpu instance
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- create-key-pair
@@ -77,7 +82,7 @@ phases:
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# run eia tests
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- |
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if has-matching-changes "test/" "tests/" "src/*.py" "docker/*" "buildspec.yml"; then
80-
IGNORE_COVERAGE=- tox -e py36 -- test/integration/sagemaker/test_elastic_inference.py -n 2 --py-version 2,3 --processor cpu --accelerator-type $EI_ACCELERATOR_TYPE --region $AWS_DEFAULT_REGION --docker-base-name "$ECR_REPO-eia" --aws-id $ACCOUNT --framework-version $FRAMEWORK_FULL_VERSION --reruns 3
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IGNORE_COVERAGE=- tox -e py36 -- test/integration/sagemaker/test_elastic_inference.py -n 2 --py-version 2,3 --processor cpu --accelerator-type $EI_ACCELERATOR_TYPE --region $AWS_DEFAULT_REGION --docker-base-name "$ECR_REPO-eia" --aws-id $ACCOUNT --framework-version $EI_FRAMEWORK_VERSION
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else
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echo "skipping sagemaker eia tests"
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fi

‎docker/1.4.1/py2/Dockerfile.eia

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@@ -49,6 +49,7 @@ RUN pip install --no-cache-dir \
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mxnet-model-server==$MMS_VERSION \
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keras-mxnet==2.2.4.1 \
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onnx==1.4.1 \
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sagemaker-inference==1.1.0 \
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/sagemaker_mxnet_serving_container.tar.gz \
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&& rm /sagemaker_mxnet_serving_container.tar.gz
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‎docker/1.4.1/py3/Dockerfile.eia

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@@ -70,6 +70,7 @@ RUN pip install --no-cache-dir \
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mxnet-model-server==$MMS_VERSION \
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keras-mxnet==2.2.4.1 \
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onnx==1.4.1 \
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sagemaker-inference==1.1.0 \
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/sagemaker_mxnet_serving_container.tar.gz \
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&& rm /sagemaker_mxnet_serving_container.tar.gz
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‎docker/1.6.0/py2/Dockerfile.cpu

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FROM ubuntu:16.04
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LABEL maintainer="Amazon AI"
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# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
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# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
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LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
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# Specify multi-models LABEL to indicate container is capable of loading and serving multiple models concurrently
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# https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html
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LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
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ARG MMS_VERSION=1.0.8
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ARG MX_URL=https://aws-mxnet-pypi.s3-us-west-2.amazonaws.com/1.6.0/aws_mxnet_mkl-1.6.0rc0-py2.py3-none-manylinux1_x86_64.whl
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ARG PYTHON=python
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ARG PYTHON_PIP=python-pip
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ARG PIP=pip
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
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PYTHONIOENCODING=UTF-8 \
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LANG=C.UTF-8 \
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LC_ALL=C.UTF-8 \
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TEMP=/home/model-server/tmp
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RUN apt-get update \
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&& apt-get -y install --no-install-recommends \
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build-essential \
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ca-certificates \
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curl \
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git \
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libopencv-dev \
33+
openjdk-8-jdk-headless \
34+
vim \
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wget \
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zlib1g-dev \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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RUN apt-get update \
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&& apt-get install -y \
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${PYTHON} \
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${PYTHON_PIP}
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RUN ${PIP} --no-cache-dir install --upgrade \
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pip \
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setuptools
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WORKDIR /
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COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
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RUN ${PIP} install --no-cache-dir \
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${MX_URL} \
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mxnet-model-server==$MMS_VERSION \
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keras-mxnet==2.2.4.1 \
57+
numpy==1.16.5 \
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onnx==1.4.1 \
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/sagemaker_mxnet_serving_container.tar.gz \
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&& rm /sagemaker_mxnet_serving_container.tar.gz
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62+
# This is here to make our installed version of OpenCV work.
63+
# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
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# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
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RUN ln -s /dev/null /dev/raw1394
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RUN useradd -m model-server \
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&& mkdir -p /home/model-server/tmp \
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&& chown -R model-server /home/model-server
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71+
COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
72+
COPY config.properties /home/model-server
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COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
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RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
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&& chmod +x /usr/local/bin/deep_learning_container.py
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RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
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EXPOSE 8080 8081
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ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
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CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]

‎docker/1.6.0/py2/Dockerfile.gpu

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@@ -0,0 +1,79 @@
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FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu16.04
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LABEL maintainer="Amazon AI"
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5+
# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
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# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
7+
LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
8+
9+
ARG MMS_VERSION=1.0.8
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ARG MX_URL=https://aws-mxnet-pypi.s3-us-west-2.amazonaws.com/1.6.0/aws_mxnet_cu101mkl-1.6.0rc0-py2.py3-none-manylinux1_x86_64.whl
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ARG PYTHON=python
12+
ARG PYTHON_PIP=python-pip
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ARG PIP=pip
14+
15+
ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
18+
PYTHONIOENCODING=UTF-8 \
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LANG=C.UTF-8 \
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LC_ALL=C.UTF-8 \
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TEMP=/home/model-server/tmp
22+
23+
RUN apt-get update \
24+
&& apt-get -y install --no-install-recommends \
25+
build-essential \
26+
ca-certificates \
27+
curl \
28+
git \
29+
libopencv-dev \
30+
openjdk-8-jdk-headless \
31+
vim \
32+
wget \
33+
zlib1g-dev \
34+
&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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37+
RUN apt-get update \
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&& apt-get install -y \
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${PYTHON} \
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${PYTHON_PIP}
41+
42+
RUN ${PIP} --no-cache-dir install --upgrade \
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pip \
44+
setuptools
45+
46+
WORKDIR /
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48+
COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
49+
50+
RUN ${PIP} install --no-cache-dir \
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${MX_URL} \
52+
mxnet-model-server==$MMS_VERSION \
53+
keras-mxnet==2.2.4.1 \
54+
numpy==1.16.5 \
55+
onnx==1.4.1 \
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/sagemaker_mxnet_serving_container.tar.gz \
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&& rm /sagemaker_mxnet_serving_container.tar.gz
58+
59+
# This is here to make our installed version of OpenCV work.
60+
# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
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# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
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RUN ln -s /dev/null /dev/raw1394
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64+
RUN useradd -m model-server \
65+
&& mkdir -p /home/model-server/tmp \
66+
&& chown -R model-server /home/model-server
67+
68+
COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
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COPY config.properties /home/model-server
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COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
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RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
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&& chmod +x /usr/local/bin/deep_learning_container.py
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75+
RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
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77+
EXPOSE 8080 8081
78+
ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
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CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]

‎docker/1.6.0/py2/config.properties

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vmargs=-Xmx128m -XX:-UseLargePages -XX:+UseG1GC -XX:MaxMetaspaceSize=32M -XX:MaxDirectMemorySize=10m -XX:+ExitOnOutOfMemoryError
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model_store=/opt/ml/model
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load_models=ALL
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inference_address=http://0.0.0.0:8080
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management_address=http://0.0.0.0:8081

‎docker/1.6.0/py2/mms-entrypoint.py

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# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"). You
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# may not use this file except in compliance with the License. A copy of
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# the License is located at
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#
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# http://aws.amazon.com/apache2.0/
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#
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# or in the "license" file accompanying this file. This file is
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# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
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# ANY KIND, either express or implied. See the License for the specific
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# language governing permissions and limitations under the License.
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import shlex
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import subprocess
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import sys
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import os.path
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if not os.path.exists("/opt/ml/input/config"):
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subprocess.call(['python', '/usr/local/bin/deep_learning_container.py', '&>/dev/null', '&'])
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if sys.argv[1] == 'serve':
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from sagemaker_mxnet_serving_container import serving
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serving.main()
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else:
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subprocess.check_call(shlex.split(' '.join(sys.argv[1:])))
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# prevent docker exit
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subprocess.call(['tail', '-f', '/dev/null'])

‎docker/1.6.0/py3/Dockerfile.cpu

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FROM ubuntu:16.04
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LABEL maintainer="Amazon AI"
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5+
# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
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# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
7+
LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
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# Specify multi-models LABEL to indicate container is capable of loading and serving multiple models concurrently
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# https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html
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LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
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ARG MMS_VERSION=1.0.8
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ARG MX_URL=https://aws-mxnet-pypi.s3-us-west-2.amazonaws.com/1.6.0/aws_mxnet_mkl-1.6.0rc0-py2.py3-none-manylinux1_x86_64.whl
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ARG PYTHON=python3
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ARG PYTHON_PIP=python3-pip
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ARG PIP=pip3
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ARG PYTHON_VERSION=3.6.8
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
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PYTHONIOENCODING=UTF-8 \
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LANG=C.UTF-8 \
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LC_ALL=C.UTF-8 \
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TEMP=/home/model-server/tmp
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RUN apt-get update \
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&& apt-get -y install --no-install-recommends \
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build-essential \
30+
ca-certificates \
31+
curl \
32+
git \
33+
libopencv-dev \
34+
openjdk-8-jdk-headless \
35+
vim \
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wget \
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zlib1g-dev \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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RUN wget https://www.python.org/ftp/python/$PYTHON_VERSION/Python-$PYTHON_VERSION.tgz \
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&& tar -xvf Python-$PYTHON_VERSION.tgz \
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&& cd Python-$PYTHON_VERSION \
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&& ./configure \
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&& make \
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&& make install \
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&& apt-get update \
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&& apt-get install -y --no-install-recommends \
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libreadline-gplv2-dev \
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libncursesw5-dev \
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libssl-dev \
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libsqlite3-dev \
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tk-dev \
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libgdbm-dev \
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libc6-dev \
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libbz2-dev \
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&& make \
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&& make install \
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&& rm -rf ../Python-$PYTHON_VERSION* \
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&& ln -s /usr/local/bin/pip3 /usr/bin/pip
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RUN ln -s $(which ${PYTHON}) /usr/local/bin/python
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RUN ${PIP} --no-cache-dir install --upgrade \
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pip \
66+
setuptools
67+
68+
WORKDIR /
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COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
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RUN ${PIP} install --no-cache-dir \
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${MX_URL} \
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mxnet-model-server==$MMS_VERSION \
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keras-mxnet==2.2.4.1 \
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numpy==1.17.4 \
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onnx==1.4.1 \
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/sagemaker_mxnet_serving_container.tar.gz \
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&& rm /sagemaker_mxnet_serving_container.tar.gz
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# This is here to make our installed version of OpenCV work.
82+
# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
83+
# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
84+
RUN ln -s /dev/null /dev/raw1394
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RUN useradd -m model-server \
87+
&& mkdir -p /home/model-server/tmp \
88+
&& chown -R model-server /home/model-server
89+
90+
COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
91+
COPY config.properties /home/model-server
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COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
93+
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RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
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&& chmod +x /usr/local/bin/deep_learning_container.py
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RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
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EXPOSE 8080 8081
100+
ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
101+
CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]

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