def __init__(self,
model=None,
path=None,
reload_every=60 * 60,
swap=False,
memory=0,
delay=0,
**kwargs):
self.model = RL.Model(model,
path,
swap=swap,
mode=RL.Mode.PLAY,
memory=memory,
**kwargs)
self.reload_every = reload_every
self.counter = 0
self.action = 0
self.actions = util.CircularQueue(delay + 1, 0)
self.memory = util.CircularQueue(array=((memory + 1) *
ssbm.SimpleStateAction)())
self.model.restore()
os.system("sbatch " + slurmfile)
#os.system("sbatch -N 1 -c 2 --mem=8000 --time=6-23:00:00 slurm_scripts/" + jobname + ".slurm &")
init = False
init = True
if dry_run:
print("NOT starting jobs:")
else:
print("Starting jobs:")
# init model for the first time
if init:
import RL
model = RL.Model(mode=RL.Mode.TRAIN, gpu=False, **job_dicts['train'])
model.init()
model.save()
train_name = "trainer_" + exp_name
train_command = "python3 -u train.py" + job_flags['train']
slurm_script(train_name, train_command, gpu=True)
#sys.exit()
agent_count = 0
agent_command = "python3 -u run.py" + job_flags['agent']
for c1 in characters:
for c2 in characters:
command = agent_command + " --p1 %s --p2 %s" % (c1, c2)
f.write("#SBATCH --array=1-%d\n" % array)
f.write(command)
#command = "screen -S %s -dm srun --job-name %s --pty singularity exec -B $OM_USER/phillip -B $HOME/phillip/ -H ../home phillip.img gdb -ex r --args %s" % (name[:10], name, command)
os.system("sbatch " + slurmfile)
if args.dry_run:
print("NOT starting jobs:")
else:
print("Starting jobs:")
# init model for the first time
if args.init:
import RL
model = RL.Model(mode=RL.Mode.TRAIN, **job_dicts['train'])
model.init()
model.save()
import json
for k, v in job_dicts.items():
with open(path + k, 'w') as f:
json.dump(v, f, indent=2)
if run_trainer:
train_name = "trainer_" + exp_name
train_command = "python3 -u train.py" + job_flags['train']
slurm_script(train_name,
train_command,
gpu=True,
type=int,
default=0,
help="how many frames to remember")
args = parser.parse_args()
if args.name is None:
args.name = args.model
if args.path is None:
args.path = "saves/%s/" % args.name
experience_dir = args.path + 'experience/'
os.makedirs(experience_dir, exist_ok=True)
model = RL.Model(mode=RL.Mode.TRAIN, **args.__dict__)
# do this in RL?
if args.init:
model.init()
model.save()
else:
model.restore()
import numpy as np
def sweep(data_dir='experience/'):
i = 0
start_time = time.time()
files = os.listdir(data_dir)
if args.trainer:
agent_dump = "172.16.24.%s" % args.trainer
run_trainer = False
else:
trainer_dump = "ib0"
run_agents = False
if args.dry_run:
print("NOT starting jobs:")
else:
print("Starting jobs:")
# init model for the first time
if args.init:
import RL
model = RL.Model(mode=RL.Mode.TRAIN, **params)
model.init()
model.save()
if not os.path.exists("slurm_logs"):
os.makedirs("slurm_logs")
if not os.path.exists("slurm_scripts"):
os.makedirs("slurm_scripts")
def launch(name, command, cpus=2, mem=1000, gpu=False, log=True, qos=None, array=None):
#command = "LD_PRELOAD=$OM_USER/lib/libtcmalloc.so.4 " + command
if args.dry_run:
print(command)
return
