gongweibao
7 years ago
committed by
GitHub
parent
1ac31d3d08
commit
ef35c4ed1a
@ -1,18 +1,35 @@
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#FROM python:2.7.14
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FROM nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04
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RUN apt-get update && apt-get install -y python
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RUN pip install -U kubernetes opencv-python && apt-get update -y && apt-get install -y iputils-ping libgtk2.0-dev
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# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
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# so we must build one with distribute support to install in this image.
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# you can get mirror list here:
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# https://launchpad.net/ubuntu/+archivemirrors
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ARG UBUNTU_MIRROR
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RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
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RUN apt-get update && apt-get install -y python python-dev python-pip iputils-ping libgtk2.0-dev
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RUN pip install -U kubernetes opencv-python
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RUN pip install paddlepaddle
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# if network is slowly, you may need to add proxy here.
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# ENV https_proxy=
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RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
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RUN pip uninstall -y paddlepaddle
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# unset proxy if it is setted.
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# ENV https_proxy=""
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# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
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# so we must build one with distribute support to install in this image.
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ADD *.whl /
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RUN pip install /*.whl && rm -f /*.whl
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ENV LD_LIBRARY_PATH=/usr/local/lib
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# tf k8s
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RUN pip install tensorflow==1.4.0
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ADD tf_k8s /usr/bin
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RUN chmod +x /usr/bin/tf_k8s
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ADD vgg16_tf.py /workspace/
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# below lines may change a lot for debugging
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ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
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ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
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ADD *.whl /
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RUN pip install /*.whl && rm -f /*.whl && \
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chmod +x /usr/bin/paddle_k8s
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ENV LD_LIBRARY_PATH=/usr/local/lib
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RUN chmod +x /usr/bin/paddle_k8s
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ADD vgg16_fluid.py vgg16_v2.py /workspace/
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@ -0,0 +1,82 @@
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#!/bin/bash
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check_trainer_ret() {
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ret=$1
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stdbuf -oL echo "job returned $ret...setting pod return message..."
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stdbuf -oL echo "==============================="
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if [ $ret -eq 136 ] ; then
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echo "Error Arithmetic Operation(Floating Point Exception)" > /dev/termination-log
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elif [ $ret -eq 139 ] ; then
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echo "Segmentation Fault" > /dev/termination-log
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elif [ $ret -eq 1 ] ; then
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echo "General Error" > /dev/termination-log
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elif [ $ret -eq 134 ] ; then
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echo "Program Abort" > /dev/termination-log
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fi
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stdbuf -oL echo "termination log wroted..."
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exit $ret
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}
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g_pservers=""
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g_trainers=""
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wait_running_pods(){
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pserver_label="tf-job-pserver=${JOB_NAME}"
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trainer_label="tf-job-trainer=${JOB_NAME}"
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stdbuf -oL python /root/k8s_tools.py wait_pods_running ${pserver_label} ${PSERVERS_NUM}
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stdbuf -oL python /root/k8s_tools.py wait_pods_running ${trainer_label} ${TRAINERS_NUM}
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g_pservers=$(python /root/k8s_tools.py fetch_endpoints ${pserver_label} ${PORT})
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g_trainers=$(python /root/k8s_tools.py fetch_endpoints ${trainer_label} ${PORT})
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}
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start_tf_pserver(){
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wait_running_pods
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label="tf-job-pserver=${JOB_NAME}"
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pserver_id=$(python /root/k8s_tools.py fetch_id ${label})
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cmd="${ENTRY} --ps_hosts=${g_pservers} --worker_hosts=${g_trainers} \
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--job_name=${TF_JOB_NAME} --task_index=${pserver_id}"
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stdbuf -oL sh -c "cd ${TRAINER_PACKAGE} && ${cmd}"
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}
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start_tf_trainer(){
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wait_running_pods
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label="tf-job-trainer=${JOB_NAME}"
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trainer_id=$(python /root/k8s_tools.py fetch_id ${label})
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cmd="${ENTRY} --ps_hosts=${g_pservers} --worker_hosts=${g_trainers} \
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--job_name=${TF_JOB_NAME} --task_index=${trainer_id} --batch_size=${BATCH_SIZE}"
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stdbuf -oL sh -c "cd ${TRAINER_PACKAGE} && ${cmd}"
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check_trainer_ret $?
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}
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start_tf(){
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if [[ "${TF_JOB_NAME}" == "worker" ]]; then
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start_tf_trainer
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else
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start_tf_pserver
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fi
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}
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usage() {
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echo "usage: tf_k8s [<args>]:"
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echo " start_tf Start tensorflow jobs"
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}
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case "$1" in
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start_tf)
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start_tf
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;;
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--help)
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usage
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;;
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*)
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usage
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;;
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esac
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@ -0,0 +1,56 @@
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apiVersion: extensions/v1beta1
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kind: ReplicaSet
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metadata:
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name: vgg16job-tf-pserver
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spec:
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replicas: 10
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template:
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metadata:
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labels:
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tf-job-pserver: vgg16job-tf
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spec:
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hostNetwork: true
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imagePullSecrets:
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- name: job-registry-secret
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containers:
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- name: pserver
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image: "registry.baidu.com/paddlepaddle/fluid_benchmark_tf:vgg16"
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imagePullPolicy: Always
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command: ["tf_k8s", "start_tf"]
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ports:
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- name: jobport-30236
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containerPort: 30236
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env:
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- name: PORT
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value: "32036"
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- name: ENTRY
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value: "python vgg16_tf.py"
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- name: JOB_NAME
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value: vgg16job-tf
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- name: PSERVERS_NUM
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value: "10"
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- name: TF_JOB_NAME
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value: "ps"
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- name: TRAINERS_NUM
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value: "20"
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- name: BATCH_SIZE
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value: "128"
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- name: TRAINER_PACKAGE
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value: "/workspace"
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- name: NUM_PASSES
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value: "1"
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- name: NAMESPACE
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valueFrom:
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fieldRef:
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fieldPath: "metadata.namespace"
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- name: POD_IP
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valueFrom:
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fieldRef:
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fieldPath: "status.podIP"
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resources:
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requests:
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memory: 10Gi
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cpu: 4
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limits:
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memory: 10Gi
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cpu: 4
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@ -0,0 +1,58 @@
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apiVersion: batch/v1
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kind: Job
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metadata:
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name: vgg16job-tf-trainer
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spec:
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parallelism: 20
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completions: 20
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template:
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metadata:
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labels:
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tf-job-trainer: vgg16job-tf
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spec:
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imagePullSecrets:
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- name: job-registry-secret
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hostNetwork: true
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containers:
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- name: trainer
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image: "registry.baidu.com/paddlepaddle/fluid_benchmark_tf:vgg16"
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imagePullPolicy: Always
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command: ["tf_k8s", "start_tf"]
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ports:
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- name: jobport-30236
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containerPort: 30236
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env:
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- name: PORT
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value: "32036"
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- name: JOB_NAME
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value: vgg16job-tf
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- name: TF_JOB_NAME
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value: "worker"
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- name: ENTRY
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value: "python vgg16_tf.py"
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- name: PSERVERS_NUM
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value: "10"
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- name: BATCH_SIZE
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value: "128"
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- name: TRAINERS_NUM
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value: "20"
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- name: TRAINER_PACKAGE
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value: "/workspace"
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- name: NUM_PASSES
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value: "1"
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- name: NAMESPACE
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valueFrom:
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fieldRef:
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fieldPath: "metadata.namespace"
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- name: POD_IP
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valueFrom:
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fieldRef:
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fieldPath: "status.podIP"
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resources:
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requests:
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memory: 40Gi
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cpu: 2
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limits:
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memory: 40Gi
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cpu: 2
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restartPolicy: Never
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