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提交 f9db5629 编写于 作者: T typhoonzero
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benchmark/cluster/vgg16/Dockerfile
浏览文件 @ f9db5629
#FROM paddlepaddle/paddlecloud-job
#RUN mkdir -p /workspace
#ADD reader.py /workspace/
#RUN python /workspace/reader.py
FROM python:2.7.14
ADD paddle_k8s /usr/bin
ADD k8s_tools.py /root
RUN pip install -U kubernetes opencv-python && apt-get update -y && apt-get install -y iputils-ping libgtk2.0-dev
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
RUN pip install -U kubernetes opencv-python && apt-get update -y && apt-get install -y iputils-ping libgtk2.0-dev && \
chmod +x /usr/bin/paddle_k8s
# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
# so we must build one with distribute support to install in this image.
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD reader.py /workspace/
RUN python /workspace/reader.py
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
ADD vgg16_fluid.py vgg16_v2.py /workspace/
benchmark/cluster/vgg16/README.md
浏览文件 @ f9db5629
......@@ -43,7 +43,7 @@
| Trainer Counter | 20 | 40 | 80 | 100 |
| -- | -- | -- | -- | -- |
| PaddlePaddle Fluid | 291.06 | 518.80 | 836.26 | 1019.29 |
| PaddlePaddle v2 | 356.28 | - | - | 1041.99 |
| PaddlePaddle v2 (need more tests) | 356.28 | 785.39 | 853.30 | 1041.99 |
| TensorFlow | - | - | - | - |
### different pserver number
......
benchmark/cluster/vgg16/fluid_trainer.yaml
浏览文件 @ f9db5629
......@@ -30,7 +30,7 @@ spec:
- name: TOPOLOGY
value: ""
- name: ENTRY
value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 256"
value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 128"
- name: TRAINER_PACKAGE
value: "/workspace"
- name: PADDLE_INIT_PORT
......
benchmark/cluster/vgg16/k8s_tools.py 已删除 100644 → 0
浏览文件 @ bd64719a
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
#!/bin/env python
import os
import sys
import time
import socket
from kubernetes import client, config
PADDLE_JOB_NAME = os.getenv("PADDLE_JOB_NAME")
NAMESPACE = os.getenv("NAMESPACE")
PORT = os.getenv("PSERVER_PORT")
if os.getenv("KUBERNETES_SERVICE_HOST", None):
config.load_incluster_config()
else:
config.load_kube_config()
v1 = client.CoreV1Api()
def fetch_pods_info(label_selector):
api_response = v1.list_namespaced_pod(
namespace=NAMESPACE, pretty=True, label_selector=label_selector)
pod_list = []
for item in api_response.items:
pod_list.append((item.status.phase, item.status.pod_ip))
return pod_list
def wait_pods_running(label_selector, desired):
print "label selector: %s, desired: %s" % (label_selector, desired)
while True:
count = count_pods_by_phase(label_selector, 'Running')
# NOTE: pods may be scaled.
if count >= int(desired):
break
print 'current cnt: %d sleep for 5 seconds...' % count
time.sleep(5)
def count_pods_by_phase(label_selector, phase):
pod_list = fetch_pods_info(label_selector)
filtered_pod_list = filter(lambda x: x[0] == phase, pod_list)
return len(filtered_pod_list)
def fetch_pserver_ips():
label_selector = "paddle-job-pserver=%s" % PADDLE_JOB_NAME
pod_list = fetch_pods_info(label_selector)
pserver_ips = [item[1] for item in pod_list]
return ",".join(pserver_ips)
def fetch_master_ip():
label_selector = "paddle-job-master=%s" % PADDLE_JOB_NAME
pod_list = fetch_pods_info(label_selector)
master_ips = [item[1] for item in pod_list]
return master_ips[0]
def fetch_trainer_id():
label_selector = "paddle-job=%s" % PADDLE_JOB_NAME
pod_list = fetch_pods_info(label_selector)
trainer_ips = [item[1] for item in pod_list]
trainer_ips.sort()
local_ip = socket.gethostbyname(socket.gethostname())
for i in xrange(len(trainer_ips)):
if trainer_ips[i] == local_ip:
return i
return None
if __name__ == "__main__":
command = sys.argv[1]
if command == "fetch_pserver_ips":
print fetch_pserver_ips()
elif command == "fetch_trainer_id":
print fetch_trainer_id()
elif command == "fetch_master_ip":
print fetch_master_ip()
elif command == "count_pods_by_phase":
print count_pods_by_phase(sys.argv[2], sys.argv[3])
elif command == "wait_pods_running":
wait_pods_running(sys.argv[2], sys.argv[3])
benchmark/cluster/vgg16/paddle_k8s 已删除 100755 → 0
浏览文件 @ bd64719a
#!/bin/bash
start_pserver() {
stdbuf -oL paddle pserver \
--use_gpu=0 \
--port=$PADDLE_INIT_PORT \
--ports_num=$PADDLE_INIT_PORTS_NUM \
--ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
--nics=$PADDLE_INIT_NICS \
--comment=paddle_process_k8s \
--num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
}
start_new_pserver() {
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-master=${PADDLE_JOB_NAME} 1
export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
stdbuf -oL /usr/bin/pserver \
-port=$PADDLE_INIT_PORT \
-num-pservers=$PSERVERS \
-log-level=debug \
-etcd-endpoint=http://$MASTER_IP:2379
}
start_master() {
stdbuf -oL /usr/bin/master \
-port=8080 \
-chunk-per-task=1\
-task-timout-dur=16s\
-endpoints=http://127.0.0.1:2379
}
check_failed_cnt() {
max_failed=$1
failed_count=$(python /root/k8s_tools.py count_pods_by_phase paddle-job=${PADDLE_JOB_NAME} Failed)
if [ $failed_count -gt $max_failed ]; then
stdbuf -oL echo "Failed trainer count beyond the threadhold: "$max_failed
echo "Failed trainer count beyond the threshold: " $max_failed > /dev/termination-log
exit 0
fi
}
check_trainer_ret() {
ret=$1
stdbuf -oL echo "job returned $ret...setting pod return message..."
stdbuf -oL echo "==============================="
if [ $ret -eq 136 ] ; then
echo "Error Arithmetic Operation(Floating Point Exception)" > /dev/termination-log
elif [ $ret -eq 139 ] ; then
echo "Segmentation Fault" > /dev/termination-log
elif [ $ret -eq 1 ] ; then
echo "General Error" > /dev/termination-log
elif [ $ret -eq 134 ] ; then
echo "Program Abort" > /dev/termination-log
fi
stdbuf -oL echo "termination log wroted..."
exit $ret
}
start_fluid_process() {
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
if [ "${TRAINING_ROLE}" == "TRAINER" ]; then
check_failed_cnt ${TRAINERS}
sleep 5
export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
fi
export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
stdbuf -oL sh -c "${ENTRY}"
check_trainer_ret $?
}
start_new_trainer() {
# FIXME(Yancey1989): use command-line interface to configure the max failed count
check_failed_cnt ${TRAINERS}
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
sleep 5
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-master=${PADDLE_JOB_NAME} 1
export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
export ETCD_IP="$MASTER_IP"
# NOTE: $TRAINER_PACKAGE may be large, do not copy
export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
cd $TRAINER_PACKAGE
stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
$PADDLE_INIT_NUM_GRADIENT_SERVERS, "version: " $1
stdbuf -oL sh -c "${ENTRY}"
check_trainer_ret $?
}
start_trainer() {
# paddle v1 and V2 distributed training does not allow any trainer failed.
check_failed_cnt 0
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job=${PADDLE_JOB_NAME} ${TRAINERS}
export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
stdbuf -oL echo $PADDLE_INIT_TRAINER_ID > /trainer_id
# FIXME: /trainer_count = PADDLE_INIT_NUM_GRADIENT_SERVERS
stdbuf -oL echo $PADDLE_INIT_NUM_GRADIENT_SERVERS > /trainer_count
# NOTE: $TRAINER_PACKAGE may be large, do not copy
export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
cd $TRAINER_PACKAGE
stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
$PADDLE_INIT_NUM_GRADIENT_SERVERS, "trainer_id: " $PADDLE_INIT_TRAINER_ID, \
"version: " $1
# FIXME: If we use the new PServer by Golang, add Kubernetes healthz
# to wait PServer process get ready.Now only sleep 20 seconds.
sleep 20
case "$1" in
"v1")
FILE_COUNT=$(wc -l $TRAIN_LIST | awk '{print $1}')
if [ $FILE_COUNT -le $PADDLE_INIT_NUM_GRADIENT_SERVERS ]; then
echo "file count less than trainers"
check_trainer_ret 0
fi
let lines_per_node="$FILE_COUNT / ($PADDLE_INIT_NUM_GRADIENT_SERVERS + 1)"
echo "spliting file to" $lines_per_node
cp $TRAIN_LIST /
cd /
split -l $lines_per_node -d -a 3 $TRAIN_LIST train.list
CURRENT_LIST=$(printf "train.list%03d" $PADDLE_INIT_TRAINER_ID)
# always use /train.list for paddle v1 for each node.
echo "File for current node ${CURRENT_LIST}"
sleep 10
cp $CURRENT_LIST train.list
cd $TRAINER_PACKAGE
stdbuf -oL paddle train \
--port=$PADDLE_INIT_PORT \
--nics=$PADDLE_INIT_NICS \
--ports_num=$PADDLE_INIT_PORTS_NUM \
--ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
--num_passes=$PADDLE_INIT_NUM_PASSES \
--trainer_count=$PADDLE_INIT_TRAINER_COUNT \
--saving_period=1 \
--log_period=20 \
--local=0 \
--rdma_tcp=tcp \
--config=$TOPOLOGY \
--use_gpu=$PADDLE_INIT_USE_GPU \
--trainer_id=$PADDLE_INIT_TRAINER_ID \
--save_dir=$OUTPUT \
--pservers=$PADDLE_INIT_PSERVERS \
--num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
# paddle v1 API does not allow any trainer failed.
check_trainer_ret $?
;;
"v2")
stdbuf -oL sh -c "${ENTRY}"
# paddle v2 API does not allow any trainer failed.
check_trainer_ret $?
;;
*)
;;
esac
}
usage() {
echo "usage: paddle_k8s [<args>]:"
echo " start_trainer [v1|v2] Start a trainer process with v1 or v2 API"
echo " start_pserver Start a pserver process"
echo " start_new_pserver Start a new pserver process"
echo " start_new_trainer Start a new triner process"
}
case "$1" in
start_pserver)
start_pserver
;;
start_trainer)
start_trainer $2
;;
start_new_trainer)
start_new_trainer
;;
start_new_pserver)
start_new_pserver
;;
start_master)
start_master
;;
start_fluid)
start_fluid_process
;;
--help)
usage
;;
*)
usage
;;
esac
benchmark/cluster/vgg16/reader.py 已删除 100644 → 0
浏览文件 @ bd64719a
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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 paddle.v2 as paddle
paddle.dataset.cifar.train10()
benchmark/cluster/vgg16/v2_trainer.yaml
浏览文件 @ f9db5629
......@@ -38,7 +38,7 @@ spec:
- name: PADDLE_INIT_NICS
value: "xgbe0"
- name: PADDLE_INIT_TRAINER_COUNT
value: "2"
value: "1"
- name: PADDLE_INIT_PORTS_NUM
value: "1"
- name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
......
benchmark/cluster/vgg16/vgg16_v2.py
浏览文件 @ f9db5629
......@@ -51,7 +51,7 @@ def vgg(input, nums, class_dim):
conv4 = conv_block(conv3, 512, nums[3])
conv5 = conv_block(conv4, 512, nums[4])
fc_dim = 4096
fc_dim = 512
fc1 = paddle.layer.fc(input=conv5,
size=fc_dim,
act=paddle.activation.Relu(),
......
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