Release 1.6.0 Dockerfiles

buildspec-release.yml

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

buildspec.yml

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

docker/1.4.1/py2/Dockerfile.eia

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

docker/1.4.1/py3/Dockerfile.eia

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

docker/1.6.0/py2/Dockerfile.cpu

@@ -0,0 +1,82 @@
+FROM ubuntu:16.04
+
+LABEL maintainer="Amazon AI"
+
+# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
+# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
+LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
+# Specify multi-models LABEL to indicate container is capable of loading and serving multiple models concurrently
+# https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html
+LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
+
+ARG MMS_VERSION=1.0.8
+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
+ARG PYTHON=python
+ARG PYTHON_PIP=python-pip
+ARG PIP=pip
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
+    PYTHONIOENCODING=UTF-8 \
+    LANG=C.UTF-8 \
+    LC_ALL=C.UTF-8 \
+    TEMP=/home/model-server/tmp
+
+RUN apt-get update \
+ && apt-get -y install --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    curl \
+    git \
+    libopencv-dev \
+    openjdk-8-jdk-headless \
+    vim \
+    wget \
+    zlib1g-dev \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update \
+ && apt-get install -y \
+    ${PYTHON} \
+    ${PYTHON_PIP}
+
+RUN ${PIP} --no-cache-dir install --upgrade \
+    pip \
+    setuptools
+
+WORKDIR /
+
+COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
+
+RUN ${PIP} install --no-cache-dir \
+    ${MX_URL} \
+    mxnet-model-server==$MMS_VERSION \
+    keras-mxnet==2.2.4.1 \
+    numpy==1.16.5 \
+    onnx==1.4.1 \
+    /sagemaker_mxnet_serving_container.tar.gz \
+ && rm /sagemaker_mxnet_serving_container.tar.gz
+
+# This is here to make our installed version of OpenCV work.
+# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
+# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
+RUN ln -s /dev/null /dev/raw1394
+
+RUN useradd -m model-server \
+ && mkdir -p /home/model-server/tmp \
+ && chown -R model-server /home/model-server
+
+COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
+COPY config.properties /home/model-server
+COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
+
+RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
+ && chmod +x /usr/local/bin/deep_learning_container.py
+
+RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
+
+EXPOSE 8080 8081
+ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
+CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]

docker/1.6.0/py2/Dockerfile.gpu

@@ -0,0 +1,79 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu16.04
+
+LABEL maintainer="Amazon AI"
+
+# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
+# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
+LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
+
+ARG MMS_VERSION=1.0.8
+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
+ARG PYTHON=python
+ARG PYTHON_PIP=python-pip
+ARG PIP=pip
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
+    PYTHONIOENCODING=UTF-8 \
+    LANG=C.UTF-8 \
+    LC_ALL=C.UTF-8 \
+    TEMP=/home/model-server/tmp
+
+RUN apt-get update \
+ && apt-get -y install --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    curl \
+    git \
+    libopencv-dev \
+    openjdk-8-jdk-headless \
+    vim \
+    wget \
+    zlib1g-dev \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update \
+ && apt-get install -y \
+    ${PYTHON} \
+    ${PYTHON_PIP}
+
+RUN ${PIP} --no-cache-dir install --upgrade \
+    pip \
+    setuptools
+
+WORKDIR /
+
+COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
+
+RUN ${PIP} install --no-cache-dir \
+    ${MX_URL} \
+    mxnet-model-server==$MMS_VERSION \
+    keras-mxnet==2.2.4.1 \
+    numpy==1.16.5 \
+    onnx==1.4.1 \
+    /sagemaker_mxnet_serving_container.tar.gz \
+ && rm /sagemaker_mxnet_serving_container.tar.gz
+
+# This is here to make our installed version of OpenCV work.
+# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
+# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
+RUN ln -s /dev/null /dev/raw1394
+
+RUN useradd -m model-server \
+ && mkdir -p /home/model-server/tmp \
+ && chown -R model-server /home/model-server
+
+COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
+COPY config.properties /home/model-server
+COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
+
+RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
+ && chmod +x /usr/local/bin/deep_learning_container.py
+
+RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
+
+EXPOSE 8080 8081
+ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
+CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]

docker/1.6.0/py2/config.properties

@@ -0,0 +1,5 @@
+vmargs=-Xmx128m -XX:-UseLargePages -XX:+UseG1GC -XX:MaxMetaspaceSize=32M -XX:MaxDirectMemorySize=10m -XX:+ExitOnOutOfMemoryError
+model_store=/opt/ml/model
+load_models=ALL
+inference_address=http://0.0.0.0:8080
+management_address=http://0.0.0.0:8081

docker/1.6.0/py2/mms-entrypoint.py

@@ -0,0 +1,29 @@
+# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+
+import shlex
+import subprocess
+import sys
+import os.path
+
+if not os.path.exists("/opt/ml/input/config"):
+    subprocess.call(['python', '/usr/local/bin/deep_learning_container.py', '&>/dev/null', '&'])
+
+if sys.argv[1] == 'serve':
+    from sagemaker_mxnet_serving_container import serving
+    serving.main()
+else:
+    subprocess.check_call(shlex.split(' '.join(sys.argv[1:])))
+
+# prevent docker exit
+subprocess.call(['tail', '-f', '/dev/null'])

docker/1.6.0/py3/Dockerfile.cpu

@@ -0,0 +1,101 @@
+FROM ubuntu:16.04
+
+LABEL maintainer="Amazon AI"
+
+# Specify accept-bind-to-port LABEL for inference pipelines to use SAGEMAKER_BIND_TO_PORT
+# https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-real-time.html
+LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
+# Specify multi-models LABEL to indicate container is capable of loading and serving multiple models concurrently
+# https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html
+LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
+
+ARG MMS_VERSION=1.0.8
+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
+ARG PYTHON=python3
+ARG PYTHON_PIP=python3-pip
+ARG PIP=pip3
+ARG PYTHON_VERSION=3.6.8
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
+    PYTHONIOENCODING=UTF-8 \
+    LANG=C.UTF-8 \
+    LC_ALL=C.UTF-8 \
+    TEMP=/home/model-server/tmp
+
+RUN apt-get update \
+ && apt-get -y install --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    curl \
+    git \
+    libopencv-dev \
+    openjdk-8-jdk-headless \
+    vim \
+    wget \
+    zlib1g-dev \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN wget https://www.python.org/ftp/python/$PYTHON_VERSION/Python-$PYTHON_VERSION.tgz \
+ && tar -xvf Python-$PYTHON_VERSION.tgz \
+ && cd Python-$PYTHON_VERSION \
+ && ./configure \
+ && make \
+ && make install \
+ && apt-get update \
+ && apt-get install -y --no-install-recommends \
+    libreadline-gplv2-dev \
+    libncursesw5-dev \
+    libssl-dev \
+    libsqlite3-dev \
+    tk-dev \
+    libgdbm-dev \
+    libc6-dev \
+    libbz2-dev \
+ && make \
+ && make install \
+ && rm -rf ../Python-$PYTHON_VERSION* \
+ && ln -s /usr/local/bin/pip3 /usr/bin/pip
+
+RUN ln -s $(which ${PYTHON}) /usr/local/bin/python
+
+RUN ${PIP} --no-cache-dir install --upgrade \
+    pip \
+    setuptools
+
+WORKDIR /
+
+COPY sagemaker_mxnet_serving_container.tar.gz /sagemaker_mxnet_serving_container.tar.gz
+
+RUN ${PIP} install --no-cache-dir \
+    ${MX_URL} \
+    mxnet-model-server==$MMS_VERSION \
+    keras-mxnet==2.2.4.1 \
+    numpy==1.17.4 \
+    onnx==1.4.1 \
+    /sagemaker_mxnet_serving_container.tar.gz \
+ && rm /sagemaker_mxnet_serving_container.tar.gz
+
+# This is here to make our installed version of OpenCV work.
+# https://stackoverflow.com/questions/29274638/opencv-libdc1394-error-failed-to-initialize-libdc1394
+# TODO: Should we be installing OpenCV in our image like this? Is there another way we can fix this?
+RUN ln -s /dev/null /dev/raw1394
+
+RUN useradd -m model-server \
+ && mkdir -p /home/model-server/tmp \
+ && chown -R model-server /home/model-server
+
+COPY mms-entrypoint.py /usr/local/bin/dockerd-entrypoint.py
+COPY config.properties /home/model-server
+COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
+
+RUN chmod +x /usr/local/bin/dockerd-entrypoint.py \
+ && chmod +x /usr/local/bin/deep_learning_container.py
+
+RUN curl https://aws-dlc-licenses.s3.amazonaws.com/mxnet/license.txt -o /license.txt
+
+EXPOSE 8080 8081
+ENTRYPOINT ["python", "/usr/local/bin/dockerd-entrypoint.py"]
+CMD ["mxnet-model-server", "--start", "--mms-config", "/home/model-server/config.properties"]