def run_job_cluster(args_file,
seed,
nb_seeds,
job_class,
timestamp,
gridargs=None):
args, exp_name, _ = read_args(args_file, gridargs)
# args = append_args(args, extra_args)
# log dir creationg
args = create_log_dir(args, exp_name, seed)
# adding the seed to arguments and exp_name
if '--seed=' not in args:
args += ' --seed=%d' % seed
exp_name += '-s%d' % seed
else:
if '--seed=' in args and nb_seeds > 1:
raise ValueError(
('gridsearch over seeds is launched while a seed is already' +
'specified in the argument file'))
if '--timestamp=' not in args:
args += ' --timestamp={}'.format(timestamp)
# running the job
manage([job_class([exp_name, args])],
only_initialization=False,
sleep_duration=3)
print('...\n...\n...')
def double_run(argv):
# Defining runs
root_run = JobExample(argv)
root_run.job_name = 'root'
child_run = JobExample(argv)
child_run.job_name = 'child'
runs = [root_run, child_run]
# Defining dependencies
child_run.add_previous_job(root_run)
# Running
manage(runs)
def send_job(job, seed_path, timestamp, config, args_steps):
''' Send a job to the cluster '''
train_args = '--logdir={} --timestamp={} --config={}'.format(
seed_path, timestamp, config)
if args_steps is not None:
train_args += ' --steps={}'.format(args_steps)
exp_path, seed_dir = os.path.split(os.path.normpath(seed_path))
exp_name = os.path.basename(exp_path)
exp_name_seed = '{}-s{}'.format(exp_name, seed_dir.replace('seed', ''))
manage([job([exp_name_seed, train_args])],
only_initialization=False,
sleep_duration=1)
def fork_run(argv):
# Defining runs
root_run = JobExample(argv)
root_run.job_name = 'root'
child1_run = JobExample(argv)
child1_run.job_name = 'child1'
child2_run = JobExample(argv)
child2_run.job_name = 'child2'
runs = [root_run, child1_run, child2_run]
# Defining dependencies
child1_run.add_previous_job(root_run)
child2_run.add_previous_job(root_run)
# Running
manage(runs, only_initialization=False)
from job.job_machine import JobCPU
import os
from settings import HOME
from job.job_manager import manage
EXAMPLE_PATH = os.path.join(HOME, 'src/tools/run/example')
class JobExample(JobCPU):
def __init__(self, run_argv):
JobCPU.__init__(self, run_argv)
self.global_path_project = EXAMPLE_PATH
self.local_path_exe = 'path_exe_example.py'
self.job_name = 'new_example'
@property
def oarsub_l_options(self):
return JobCPU(self).oarsub_l_options + ['nodes=1/core=1,walltime=1:0:0']
if __name__ == '__main__':
manage([JobExample([])], only_initialization=False)
from job.job_machine import JobCPU
import os
from job.job_manager import manage
from job.skeleton_sequences.job_deep_learning import SKELETON_SEQUENCES_PATH
WEBPAGE_DIR = 'skeleton_webpage/dataset_webpage'
class RunDataset(JobCPU):
def __init__(self, run_argv):
JobCPU.__init__(self, run_argv)
self.global_path_project = SKELETON_SEQUENCES_PATH
self.local_path_exe = os.path.join(WEBPAGE_DIR, 'dataset_html.py')
self.job_name = 'webpage_skeleton'
self.interpreter = 'python3'
@property
def oarsub_options(self):
return JobCPU(self).oarsub_options + ' -l "nodes=1/core=8,walltime=12:0:0"'
if __name__ == '__main__':
manage([RunDataset([])])
return args_to_add_list
def get_gridsearch_jobs(args, exp_name, overwrite, args_to_add_list):
jobs_list = []
for args_to_add in args_to_add_list:
name_spec = args_to_add.replace(' ', '').replace('.', '-').replace(
'--', '-').replace('=', '-').replace('_', '-')
new_exp_name = exp_name + name_spec
args_full = args + ' ' + args_to_add
args_full = args_full.replace('--exp=' + exp_name,
'--exp=' + new_exp_name)
args_full, _ = create_temp_dir(args_full, new_exp_name, overwrite)
args_full = create_outworlds_dir(args_full, new_exp_name)
print('running {} with args: {}'.format(new_exp_name, args_full))
jobs_list.append(JobQprop([new_exp_name, args_full]))
return jobs_list
if __name__ == "__main__":
if len(sys.argv) != 3:
print(
"Usage: python3 script.py <args_file_fixed> <args_file_gridsearch>"
)
args, exp_name, overwrite = read_args(sys.argv[1])
args_to_add_list = parse_grid_args(sys.argv[2])
jobs_list = get_gridsearch_jobs(args, exp_name, overwrite,
args_to_add_list)
manage(jobs_list, only_initialization=False)
def parse_args_file(args_file):
args, exp_name, overwrite = read_args(args_file)
args, overwrite = create_temp_dir(args, exp_name, overwrite)
args = create_outworlds_dir(args, exp_name)
return args, exp_name
class JobQprop(JobCPU):
def __init__(self, run_argv):
JobCPU.__init__(self, run_argv)
self.global_path_project = SCRIPTS_PATH
self.local_path_exe = 'qprop_mini.sh'
self.job_name = run_argv[0]
self.interpreter = ''
@property
def oarsub_l_options(self):
return JobCPU(self).oarsub_l_options + [
'nodes=1/core=8,walltime=72:0:0'
]
if __name__ == '__main__':
if len(sys.argv) != 2:
print('Usage: python3 script.py <args_file>')
args, exp_name = parse_args_file(sys.argv[1])
manage([JobQprop([exp_name, args])],
only_initialization=False,
sleep_duration=3)
from job.job_machine import JobCPU
import os
from job.skeleton_sequences.job_deep_learning import SKELETON_SEQUENCES_PATH
from job.job_manager import manage
DATASET_WRITER_DIR = 'tensorflow_datasets'
class JobDataset(JobCPU):
def __init__(self, run_argv):
JobCPU.__init__(self, run_argv)
self.global_path_project = SKELETON_SEQUENCES_PATH
self.local_path_exe = os.path.join(DATASET_WRITER_DIR, 'dataset_generator.py')
self.job_name = 'rotation_dataset'
self.interpreter = 'python3'
self.librairies_to_install = ['python3-scipy']
@property
def oarsub_l_options(self):
return JobCPU(self).oarsub_l_options + ['nodes=1/core=32,walltime=20:0:0']
if __name__ == '__main__':
manage([JobDataset([])], only_initialization=False)
# argv2 += ['prediction_type=inattention', 'worst_prediction_nb=' + str(worst_prediction_nb)]
# Model hyperparameters
for rnn_units, rnn_layers, rnn_type in product([100], [3], ['lstm']):
argv3 = argv2[:]
argv3.extend([
'rnn_units=' + str(rnn_units), 'rnn_layers=' + str(rnn_layers),
'rnn_type=' + rnn_type
])
""" END COPY AREA """
# Extend the list of runs
# Train argv
train_run_argv = argv3 + ['run_prefix=' + run_prefix]
if restore:
train_run_argv.append('restore_run_dir=' + restore_run_dir)
train_run_argv.append('restore_checkpoint_filename=' +
restore_checkpoint_filename)
# Evaluation argv
job_name = run_prefix + run_prefix_suffix(argv3)
evaluation_run_argv = argv3 + ['run_prefix=' + job_name]
jobs.extend(
train_val_test_runs(train_run_argv,
evaluation_run_argv,
job_name,
machine='gpu',
only_evaluating=only_evaluating))
manage(jobs, only_initialization=only_initialization)
