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cluster_runner.py
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# coding=utf-8
import re
import os
import argparse
# # https://www.cluster.uy/ayuda/como_ejecutar/#trabajo-besteffort
# # NOMBRE # NÚCLEOS DISPONIBLES MAX. TIEMPO MAX. TRABAJOS
# # normal 560 5 días 40
# # besteffort 1120 5 días 120
NORMAL = 'normal'; BESTEFFORT = 'besteffort'; MAX_CPUS = 'max_cpus'; MAX_TIME = 'max_time'; MAX_JOBS = 'max_jobs'
# # https://www.cluster.uy/ayuda/recursos_disponibles/
# # NOMBRE PROCESADORES # NÚCLEOS MEM. GPU/NODE DISCO
# # node[01-14][17] Xeon Gold 6138 40 128 GB NVIDIA P100 300 GB SSD
# # node[15][16] Xeon Gold 6138 40 128 GB NVIDIA A100 300 GB SSD
# # node[26-28] Xeon Gold 6138 40 128 GB - 300 GB SSD
# # node[18-22] Xeon Gold 6138 40 128 GB NVIDIA P100 x 2 300 GB SSD
# # node23 Xeon Gold 6138 40 128 GB NVIDIA P100 x 3 300 GB SSD
# # node[24-25] Xeon Gold 6138 40 512 GB - 300 GB SSD
# # node31 AMD EPYC 7642 96 256 GB - 150 GB SSD
CORES = 'cores'; MEMORY_GB = 'memory_gb'; GPUS_N = 'gpus'; GPU_TYPE = 'gpu_type'; P100 = 'p100'; A100 = 'a100'
SLURM_TEMPLATE = """\
#!/bin/bash
#SBATCH --job-name={job_name}
#SBATCH --ntasks={ntasks}
#SBATCH --cpus-per-task={cpus_per_task}
#SBATCH --mem={mem}
#SBATCH --time=5-0
#SBATCH --partition={partition}
#SBATCH --qos={qos}
#SBATCH --output={output_filename}
#SBATCH --gres=gpu:p100:{gpus_n}
SCRIPT_NAME=$1
HOME=/docker/home
CLUSTER_BIND_DIR=/clusteruy/home/${USER}/marian/marian_container
SCRIPT_PATH=${HOME}/marianmt/${SCRIPT_NAME}
export SINGULARITY_TMPDIR=${HOME}/cache
export TMPDIR=$SINGULARITY_TMPDIR
chmod +x ${CLUSTER_BIND_DIR}/marianmt/${SCRIPT_NAME}
export PYTHONPATH=${HOME}/marianmt/libs
singularity exec -H ${HOME} --nv --no-home --contain --bind ${CLUSTER_BIND_DIR}:$HOME ${CLUSTER_BIND_DIR}/marian-nmt_1.11.0_sentencepiece_cuda-11.3.0.sif $SCRIPT_PATH
"""
TIME_LIMIT_MESSAGE = "TIME LIMIT"
NORMAL_QOS = 'gpu'
def get_out_file_name(from_flag, to_flag, besteffort=False):
filename = "run"
if besteffort:
filename += "_besteffort"
filename += '{from_flag}-{to_flag}'.format(from_flag=from_flag,
to_flag=to_flag)
filename += ".out"
return filename
def get_slurm_file_name(from_flag, to_flag, besteffort=False):
filename = "run"
if besteffort:
filename += "_besteffort"
filename += '{from_flag}-{to_flag}'.format(from_flag=from_flag,
to_flag=to_flag)
filename += ".slurm"
return filename
def get_bash_file_name(from_flag, to_flag, besteffort=False):
filename = "run"
if besteffort:
filename += "_besteffort"
filename += '{from_flag}-{to_flag}'.format(from_flag=from_flag,
to_flag=to_flag)
filename += ".sh"
return filename
def get_grid_partitions(total_jobs_n, jobs_n,
from_flag, to_flag):
flag_range = (to_flag - from_flag)
flags_per_job = flag_range // jobs_n
partitions = []
for i in range(jobs_n):
start = from_flag + i * flags_per_job
end = start + flags_per_job
partitions.append((start, end))
partitions[-1] = (partitions[-1][0], to_flag)
return partitions
def create_slurm_file_content(output_filename,
job_name,
partition,
qos,
gpus_n,
ntasks=4,
cpus_per_task=9,
mem='60G',
file_template=SLURM_TEMPLATE):
params_to_replace = {'job_name': job_name,
'partition': partition,
'qos': qos,
'gpus_n': gpus_n,
'ntasks': ntasks,
'cpus_per_task': cpus_per_task,
'mem': mem,
'output_filename': output_filename}
for param, value in params_to_replace.items():
file_template = file_template.replace('{' + param + '}', str(value))
return file_template
def create_bash_file_content(bash_template_dir,
devices,
from_flag,
to_flag,
src,
trg,
model_type,
epochs):
bash_lines = []
params_to_replace = [('^GPUS="([0-9] )*[0-9]"', 'GPUS="'+devices+'"'),
('^FROM=([0-9]+(\.[0-9]+)?)', 'FROM='+str(from_flag)),
('^TO=([0-9]+(\.[0-9]+)?)', 'TO='+str(to_flag)),
('^SRC="[^"]+"', 'SRC="'+src+'"'),
('^TRG="[^"]+"', 'TRG="'+trg+'"'),
('^TYPE="[^"]+"', 'TYPE="'+model_type+'"'),
('^EPOCHS=[1-9][0-9]*', 'EPOCHS='+str(epochs))]
params_to_replace = [(re.compile(regex), value) for regex, value in params_to_replace]
with open(bash_template_dir, 'r') as f:
bash_lines.extend(f.readlines())
for idx in range(len(bash_lines)):
for param_regex, value in params_to_replace:
bash_lines[idx] = param_regex.sub(value, bash_lines[idx])
bash_lines = ''.join(bash_lines)
return bash_lines
def get_job_name(from_flag, to_flag):
return 'M-{from_flag}-{to_flag}'.format(from_flag=from_flag,
to_flag=to_flag)
def get_gpu_devices(gpus_n=1):
return ' '.join(map(str, range(gpus_n)))
def persist_file(filedir, content):
with open(filedir, 'w') as f:
f.write(content)
def get_read_permissions_command(filedir):
return 'chmod +x ' + filedir
def run_script(bash_input_template_dir,
outputs_scripts_folder,
flags_partition,
job_name,
partition,
qos,
gpus_n,
src, trg,
model_type,
epochs,
ntasks=4,
cpus_per_task=9,
mem='60G',
debug=False):
gpu_devices = get_gpu_devices(gpus_n)
slurm_filename = get_slurm_file_name(flags_partition[0], flags_partition[1],
besteffort=qos==BESTEFFORT)
bash_filename = get_bash_file_name(flags_partition[0], flags_partition[1],
besteffort=qos==BESTEFFORT)
output_filename = get_out_file_name(flags_partition[0], flags_partition[1],
besteffort=qos==BESTEFFORT)
slurm_output_filename = os.path.join(outputs_scripts_folder, slurm_filename)
bash_output_filename = os.path.join(outputs_scripts_folder, bash_filename)
output_filename = os.path.join(outputs_scripts_folder, output_filename)
bash_script = create_bash_file_content(bash_input_template_dir,
gpu_devices,
flags_partition[0],
flags_partition[1],
src, trg,
model_type,
epochs)
slurm_script = create_slurm_file_content(output_filename,
job_name,
partition,
qos,
gpus_n,
ntasks,
cpus_per_task,
mem)
persist_file(slurm_output_filename, slurm_script)
persist_file(bash_output_filename, bash_script)
script = ['sbatch', slurm_output_filename, bash_output_filename]
script = ' '.join(script)
if debug:
print(script)
return script
read_permissions_command = get_read_permissions_command(bash_output_filename)
os.system(read_permissions_command)
os.system(script)
return script
GPU_LOG_REGEX = re.compile('^.+Using ([1-9]) GPUs$')
def awake_jobs(grid_partitions, outputs_scripts_folder,
bash_template_file,
src, trg,
model_type,
epochs,
time_limit_message=TIME_LIMIT_MESSAGE,
gpu_regex=GPU_LOG_REGEX, debug=False):
slept_jobs = []
for grid_partition in grid_partitions:
output_file_normal = get_out_file_name(grid_partition[0],
grid_partition[1],
besteffort=False)
output_file_besteffort = get_out_file_name(grid_partition[0],
grid_partition[1],
besteffort=True)
output_file_normal = os.path.join(outputs_scripts_folder,
output_file_normal)
output_file_besteffort = os.path.join(outputs_scripts_folder,
output_file_besteffort)
for output_file, besteffort in zip([output_file_normal,
output_file_besteffort],
[False, True]):
if not os.path.isfile(output_file):
continue
with open(output_file, 'r') as f:
output_lines = f.readlines()
last_output_line = output_lines[-1]
if time_limit_message in last_output_line:
gpus_n = 1
print(last_output_line)
gpu_line = [line for line in output_lines \
if gpu_regex.match(line)]
if len(gpu_line) > 0:
print(gpu_line[0])
gpus_n = int(gpu_regex.match(gpu_line[0]).group(1))
slept_jobs.append((grid_partition, gpus_n, besteffort))
if len(slept_jobs) == 0:
print("No jobs to awake 🎉")
return
for grid_partition, gpus_n, besteffort in slept_jobs:
job_name = get_job_name(*grid_partition)
partition = BESTEFFORT if besteffort else NORMAL
qos = BESTEFFORT if besteffort else NORMAL_QOS
run_script(bash_template_file,
outputs_scripts_folder,
grid_partition,
job_name,
partition,
qos,
gpus_n,
src, trg,
model_type,
epochs,
debug=debug)
return slept_jobs
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='run', required=False, choices=['run', 'awake'])
parser.add_argument('--src', type=str, required=True, choices=['gn', 'es'])
parser.add_argument('--trg', type=str, required=True, choices=['gn', 'es'])
parser.add_argument('--model_type', type=str, required=True, choices=['transformer', 's2s'])
parser.add_argument('--epochs', type=int, required=True)
parser.add_argument('--total_jobs_n', type=int, default=0, required=True)
parser.add_argument('--jobs_n', type=int, default=0, required=True)
parser.add_argument('--besteffort_rate', type=float, default=0.0, required=False)
parser.add_argument('--from_flag', type=int, default=None, required=False)
parser.add_argument('--to_flag', type=int, default=None, required=False)
parser.add_argument('--normal_gpus', type=int, default=1, required=False)
parser.add_argument('--besteffort_gpus', type=int, default=1, required=False)
parser.add_argument('--outputs_scripts_folder', type=str, default='.', required=False)
parser.add_argument('--bash_template_file', type=str, default='.\\scripts\\cluster\\train_gn_es_level2_s2s_grid.sh', required=True)
parser.add_argument('--debug', action='store_true', default=False)
args = parser.parse_args()
return vars(args)
def check_preconditions(mode, total_jobs_n, jobs_n, besteffort_n, from_flag, to_flag=None):
if mode not in ['run', 'awake']:
raise ValueError('Invalid mode:', mode)
if jobs_n == 0:
raise Exception("The total number of jobs must be greater than zero")
if jobs_n < besteffort_n:
raise Exception("The total number of jobs must not be of the same size as the number of besteffort jobs")
if total_jobs_n == 0:
raise Exception("The total number of jobs must be greater than zero")
if total_jobs_n < jobs_n:
raise Exception("The total number of jobs must be greater than or equal to the number of jobs")
if to_flag is not None and from_flag >= to_flag:
raise Exception("The from flag must be less than the to flag")
if to_flag is not None and to_flag - from_flag < jobs_n:
raise Exception("The difference between the from flag and the to flag must be greater than or equal to the number of jobs")
if to_flag is None and from_flag + jobs_n > total_jobs_n:
raise Exception("The from flag plus the number of jobs must be less than or equal to the total number of jobs")
# Test examples local
# python cluster_runner.py --src gn --trg es --debug --from_flag 0 --to_flag 20 --model_type transformer --epochs 100 --total_jobs_n 20 --jobs_n 10 --besteffort_rate 0.8 --normal_gpus 1 --besteffort_gpus 1 --bash_template_file .\\scripts\\cluster\\train_gn_es_level2_s2s_grid.sh --outputs_scripts_folder ./tests/data/scripts
# python cluster_runner.py --mode awake --debug --jobs_n 12 --bash_template_file .\\scripts\\cluster\\train_gn_es_level2_s2s_grid.sh --outputs_scripts_folder ./tests/data/scripts
if __name__ == '__main__':
args = get_args()
mode = args['mode']
src = args['src']
trg = args['trg']
model_type = args['model_type']
epochs = args['epochs']
total_jobs_n = args['total_jobs_n']
jobs_n = args['jobs_n']
besteffort_n = int(round(args['besteffort_rate'] * jobs_n))
from_flag = args['from_flag']
to_flag = args['to_flag']
normal_gpus = args['normal_gpus']
besteffort_gpus = args['besteffort_gpus']
outputs_scripts_folder = args['outputs_scripts_folder']
bash_template_file = args['bash_template_file']
debug = args['debug']
partitions = get_grid_partitions(total_jobs_n,
jobs_n,
from_flag, to_flag)
print('Generated partitions: ', partitions)
check_preconditions(mode, total_jobs_n,
jobs_n, besteffort_n,
from_flag, to_flag)
if mode == 'run':
normal_n = jobs_n - besteffort_n
normal_partitions = partitions[:normal_n]
besteffort_partitions = partitions[normal_n:]
for grid_partition in normal_partitions:
job_name = get_job_name(*grid_partition)
partition = NORMAL
qos = NORMAL_QOS
gpus_n = normal_gpus
run_script(bash_template_file,
outputs_scripts_folder,
grid_partition,
job_name,
partition,
qos,
gpus_n,
src, trg,
model_type,
epochs,
debug=debug)
for grid_partition in besteffort_partitions:
job_name = get_job_name(*grid_partition)
partition = BESTEFFORT
qos = BESTEFFORT
gpus_n = besteffort_gpus
run_script(bash_template_file,
outputs_scripts_folder,
grid_partition,
job_name,
partition,
qos,
gpus_n,
src, trg,
model_type,
epochs,
debug=debug)
elif mode == 'awake':
awake_jobs(partitions,
outputs_scripts_folder,
bash_template_file,
src, trg,
model_type,
epochs,
debug=debug)