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run-workflow.sh
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run-workflow.sh
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#!/bin/bash
yaml_path="$(realpath $1)"
function parse_yaml {
local prefix=$2
local s='[[:space:]]*' w='[a-zA-Z0-9_]*' fs=$(echo @|tr @ '\034')
sed -ne "s|,$s\]$s\$|]|" \
-e ":1;s|^\($s\)\($w\)$s:$s\[$s\(.*\)$s,$s\(.*\)$s\]|\1\2: [\3]\n\1 - \4|;t1" \
-e "s|^\($s\)\($w\)$s:$s\[$s\(.*\)$s\]|\1\2:\n\1 - \3|;p" $1 | \
sed -ne "s|,$s}$s\$|}|" \
-e ":1;s|^\($s\)-$s{$s\(.*\)$s,$s\($w\)$s:$s\(.*\)$s}|\1- {\2}\n\1 \3: \4|;t1" \
-e "s|^\($s\)-$s{$s\(.*\)$s}|\1-\n\1 \2|;p" | \
sed -ne "s|^\($s\):|\1|" \
-e "s|^\($s\)-$s[\"']\(.*\)[\"']$s\$|\1$fs$fs\2|p" \
-e "s|^\($s\)-$s\(.*\)$s\$|\1$fs$fs\2|p" \
-e "s|^\($s\)\($w\)$s:$s[\"']\(.*\)[\"']$s\$|\1$fs\2$fs\3|p" \
-e "s|^\($s\)\($w\)$s:$s\(.*\)$s\$|\1$fs\2$fs\3|p" | \
awk -F$fs '{
indent = length($1)/2;
vname[indent] = $2;
for (i in vname) {if (i > indent) {delete vname[i]; idx[i]=0}}
if(length($2)== 0){ vname[indent]= ++idx[indent] };
if (length($3) > 0) {
vn=""; for (i=0; i<indent; i++) { vn=(vn)(vname[i])("_")}
printf("%s%s%s=\"%s\"\n", "'$prefix'",vn, vname[indent], $3);
}
}'
}
eval $(parse_yaml $yaml_path)
wf_abs_path=$(dirname "$(realpath $0)")
host_name=$(hostname -I | awk '{print $1}')
curr_time=$(date +%Y-%m-%d-%H-%M)
wf_tmp_path="$env_tmp_path/wf-session-${curr_time}"
repo_name=$(basename $wf_abs_path)
if [ "$data_preprocess_dp_framework" == "spark" ] && [ "$training_train_framework" == "spark" ]; then
cluster_engine='spark'
elif [ "$data_preprocess_dp_framework" == 'spark' ] && [ -z "$training_train_framework" ]; then
cluster_engine='spark'
elif [ -z "$data_preprocess_dp_framework" ] && [ "$training_train_framework" == 'spark' ]; then
cluster_engine='spark'
else
cluster_engine='ray'
fi
if [ -z "${env_node_ips_2}" ]; then
echo -e "\nsetting up single-node environment..."
if [ "$env_node_ips_1" == "$host_name" ]; then
bash $wf_abs_path/scripts/create-wf-tmp-folders.sh "$wf_tmp_path"
bash $wf_abs_path/scripts/launch-wf-containers.sh "$env_data_path" "$wf_tmp_path" "$env_config_path" "$wf_abs_path" "$env_model_path" hadoop-leader
docker exec hadoop-leader mkdir -p $(dirname $yaml_path)
docker cp $yaml_path hadoop-leader:$yaml_path
if [ "$cluster_engine" == 'ray' ]; then
echo "start workflow engine..."
docker exec hadoop-leader python start-workflow.py --config-file ${yaml_path} --mode 1
echo "remove workflow containers..."
docker rm -f hadoop-leader
elif [ "$cluster_engine" == 'spark' ]; then
docker exec hadoop-leader pip install torch
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh "$yaml_path" "$repo_name" "$cluster_engine" 1 docker
else
echo "Unknown Cluster Engine"
fi
elif [ "$env_node_ips_1" == "localhost" ]; then
env_node_ips_1=$host_name
bash $wf_abs_path/scripts/create-wf-tmp-folders.sh "$wf_tmp_path"
bash $wf_abs_path/scripts/launch-wf-containers.sh "$env_data_path" "$wf_tmp_path" "$env_config_path" "$wf_abs_path" "$env_model_path" hadoop-leader
docker exec hadoop-leader mkdir -p $(dirname $yaml_path)
docker cp $yaml_path hadoop-leader:$yaml_path
if [ "$cluster_engine" == 'ray' ]; then
echo "start workflow engine..."
docker exec hadoop-leader python start-workflow.py --config-file ${yaml_path} --mode 1
echo "remove workflow containers..."
docker rm -f hadoop-leader
elif [ "$cluster_engine" == 'spark' ]; then
docker exec hadoop-leader pip install torch
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh "$yaml_path" "$repo_name" "$cluster_engine" 1 docker
else
echo "Unknown Cluster Engine"
fi
else
tar_path="$(dirname "$wf_abs_path")"
curr_time4=$(date +%Y-%m-%d-%H-%M)
scp -q -r ${wf_abs_path} ${env_node_ips_1}:${tar_path}/ &> ${wf_tmp_path}/logs/scp-${curr_time4}.log
scp -q -r ${env_config_path} ${env_node_ips_1}:${env_config_path}/ &> ${wf_tmp_path}/logs/scp-${curr_time4}.log
scp -q -r $yaml_path ${env_node_ips_1}:$yaml_path &> ${wf_tmp_path}/logs/scp-${curr_time4}.log
echo -e "\nssh to master ${master_ip}"
ssh ${env_node_ips_1} "
cd ${wf_abs_path}
bash ./scripts/create-wf-tmp-folders.sh ${wf_tmp_path}
bash ./scripts/launch-wf-containers.sh ${env_data_path} ${wf_tmp_path} ${env_config_path} ${wf_abs_path} ${env_model_path} hadoop-leader
docker exec hadoop-leader mkdir -p $(dirname $yaml_path)
docker cp $yaml_path hadoop-leader:$yaml_path
"
if [ "$cluster_engine" == 'ray' ]; then
ssh ${env_node_ips_1} "
docker exec hadoop-leader python start-workflow.py --config-file ${yaml_path} --mode 1
echo "shut down workflow containers..."
docker rm -f hadoop-leader
"
elif [ "$cluster_engine" == 'spark' ]; then
ssh ${env_node_ips_1} "
docker exec hadoop-leader pip install torch
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 0 docker
"
else
echo "Unknown Cluster Engine"
fi
fi
else
echo -e "\nsetting up multi-node environment..."
echo -e "\non master node..."
if [ "$host_name" == "$env_node_ips_1" ]; then
bash $wf_abs_path/scripts/create-wf-tmp-folders.sh "$wf_tmp_path"
bash $wf_abs_path/scripts/launch-wf-containers.sh "$env_data_path" "$wf_tmp_path" "$env_config_path" "$wf_abs_path" "$env_model_path" hadoop-leader
docker exec hadoop-leader mkdir -p $(dirname $yaml_path)
docker cp $yaml_path hadoop-leader:$yaml_path
else
tar_path="$(dirname "$wf_abs_path")"
curr_time2=$(date +%Y-%m-%d-%H-%M)
scp -q -r ${wf_abs_path} ${env_node_ips_1}:${tar_path}/ &> tee -a ${wf_tmp_path}/logs/scp-${curr_time2}.log
scp -q -r ${env_config_path} ${env_node_ips_1}:${env_config_path}/ &> tee -a ${wf_tmp_path}/logs/scp-${curr_time2}.log
scp -q -r $yaml_path ${env_node_ips_1}:$yaml_path &> tee -a ${wf_tmp_path}/logs/scp-${curr_time2}.log
container_name="hadoop-leader"
echo -e "\nssh to master ${master_ip}"
ssh ${env_node_ips_1} "
cd ${wf_abs_path}
bash ./scripts/create-wf-tmp-folders.sh ${wf_tmp_path}
bash ./scripts/launch-wf-containers.sh ${env_data_path} ${wf_tmp_path} ${env_config_path} ${wf_abs_path} ${env_model_path} ${container_name}
docker exec ${container_name} mkdir -p $(dirname $yaml_path)
docker cp $yaml_path ${container_name}:$yaml_path
"
fi
echo -e "\non worker node..."
for ((i=2; i<=$env_num_node; i++)); do
worker_ip="env_node_ips_$i"
worker_num=$((i-1))
container_name="hadoop-worker$worker_num"
tar_path="$(dirname "$wf_abs_path")"
curr_time3=$(date +%Y-%m-%d-%H-%M)
scp -q -r ${wf_abs_path} ${!worker_ip}:${tar_path}/ &> ${wf_tmp_path}/logs/scp-${curr_time3}.log
scp -q -r ${env_config_path} ${!worker_ip}:${env_config_path}/ &> ${wf_tmp_path}/logs/scp-${curr_time3}.log
scp -q -r $yaml_path ${!worker_ip}:$yaml_path &> ${wf_tmp_path}/logs/scp-${curr_time3}.log
ssh ${!worker_ip} "
cd ${wf_abs_path}
bash ./scripts/create-wf-tmp-folders.sh ${wf_tmp_path}
bash ./scripts/launch-wf-containers.sh ${env_data_path} ${wf_tmp_path} ${env_config_path} ${wf_abs_path} ${env_model_path} ${container_name}
docker exec ${container_name} mkdir -p $(dirname $yaml_path)
docker cp $yaml_path ${container_name}:$yaml_path
"
done
if [ "$cluster_engine" == 'ray' ]; then
echo -e "\nlaunch ray cluster inside the containers..."
if [ "$host_name" == "$env_node_ips_1" ]; then
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 1 docker
else
ssh ${env_node_ips_1} "
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 1 docker
"
fi
for ((i=2; i<=$env_num_node; i++)); do
worker_ip="env_node_ips_$i"
worker_num=$((i-1))
worker_name="hadoop-worker$worker_num"
master_port="$env_node_ips_1:6379"
ssh ${!worker_ip} "
docker exec $worker_name bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 0 docker ${master_port}
"
done
echo -e "\nstart workflow on master...\n"
if [ "$host_name" == "$env_node_ips_1" ]; then
docker exec hadoop-leader python start-workflow.py --config-file ${yaml_path} --mode 1
if [ ! -z "${data_preprocess_input_data_path}" ]; then
echo "collecting files from nodes to master..."
docker exec hadoop-leader python src/utils/collect_modin_files.py ${yaml_path} 1
fi
else
ssh ${env_node_ips_1} "
cd ${wf_abs_path}
docker exec -i hadoop-leader python start-workflow.py --config-file ${yaml_path} --mode 1
"
fi
echo -e "\nremove workflow containers...\n"
if [ "$host_name" == "$env_node_ips_1" ]; then
docker rm -f hadoop-leader
else
ssh ${env_node_ips_1} "
docker rm -f hadoop-leader
rm -fr ${wf_abs_path}
"
fi
for ((i=2; i<=$env_num_node; i++)); do
worker_ip="env_node_ips_$i"
worker_num=$((i-1))
worker_name="hadoop-worker$worker_num"
ssh ${!worker_ip} "
docker rm -f ${worker_name}
rm -fr ${wf_abs_path}
"
done
elif [ "$cluster_engine" == 'spark' ]; then
echo -e "\nlaunch spark cluster inside the containers..."
if [ "$host_name" == "$env_node_ips_1" ]; then
docker exec hadoop-leader pip install torch
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 1 docker
else
ssh ${env_node_ips_1} "
docker exec hadoop-leader pip install torch
docker exec hadoop-leader bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 1 docker
"
fi
for ((i=2; i<=$env_num_node; i++)); do
worker_ip="env_node_ips_$i"
worker_num=$((i-1))
worker_name="hadoop-worker$worker_num"
master_port="$env_node_ips_1:6379"
ssh ${!worker_ip} "
docker exec $worker_name pip install torch
docker exec $worker_name bash scripts/launch-cluster-engine.sh ${yaml_path} ${repo_name} ${cluster_engine} 0 docker ${master_port}
"
done
else
echo "Unkown Cluster Engine"
fi
fi