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Paddle/benchmark/cluster/vgg16/fluid/paddle_k8s

201 lines
6.6 KiB

#!/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 ${TRAINERS}
sleep 5
stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-master=${PADDLE_JOB_NAME} 1
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