Hi.
I am running the PPO algorithm for my RL project and I am trying to use DDP to speed up the training. However, when I coded up PPO, I did it with two networks: policy and value. On my first attempt, I got the error:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 114, in _main
prepare(preparation_data)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/zhome/dskinne3/knot_gpu.py", line 347, in <module>
results_ppo = main()
File "/zhome/dskinne3/knot_gpu.py", line 234, in main
mp.spawn(ppo_main, nprocs=args.gpus, args=(args,))
File "/fslhome/dskinne3/.local/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/fslhome/dskinne3/.local/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 189, in start_processes
process.start()
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/process.py", line 112, in start
self._popen = self._Popen(self)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/popen_fork.py", line 20, in __init__
self._launch(process_obj)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "/fslhome/dskinne3/.conda/envs/mamba_knot/lib/python3.7/multiprocessing/spawn.py", line 136, in _check_not_importing_main
is not going to be frozen to produce an executable.''')
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "knot_gpu.py", line 347, in <module>
results_ppo = main()
File "knot_gpu.py", line 234, in main
mp.spawn(ppo_main, nprocs=args.gpus, args=(args,))
File "/fslhome/dskinne3/.local/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/fslhome/dskinne3/.local/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
while not context.join():
File "/fslhome/dskinne3/.local/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 154, in join
exit_code=exitcode
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with exit code 1
As for the way I have my code formatted, I have the following main function (which I got from this tutorial):
def main():
# Helper stuff to parse worldsize and rank and whatnot from commandline.
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
parser.add_argument('-g', '--gpus', default=1, type=int, help='Number of gpus per node')
parser.add_argument('-nr', '--nr', default=0, type=int, help='Ranking within the nodes')
parser.add_argument('--epochs', default=2, type=int, metavar='N', help='Number of total epochs to run')
args = parser.parse_args()
args.world_size = args.gpus * args.nodes
os.environ['MASTER_ADDR'] = "$(scontrol show job $SLURM_JOBID | awk -F= '/BatchHost/ {print $2}')"
os.environ['MASTER_PORT'] = "12345"
mp.spawn(ppo_main, nprocs=args.gpus, args=(args,))
the ppo_main function looks as follows:
def ppo_main(gpu, args):
# Calculate rank to get things done.
rank = args.nr * args.gpus + gpu
# This needs to be called in order to run data parallelization.
# backend='nccl' is the necessary parameter when working with GPUs.
torch.distributed.init_process_group(backend='nccl', init_method='tcp://127.0.0.1:54263', world_size=args.world_size, rank=rank)
# Hyper parameters
lr = 1e-3
epochs = 200
env_samples = 200
gamma = 0.9
batch_size = 256
epsilon = 0.2
policy_epochs = 30
# Init environment
state_size = 227
action_size = 13
env = gym.make("Slice-v0")
torch.cuda.set_device(gpu)
# Init networks
policy_network = PolicyNetwork(state_size, action_size).cuda(gpu)
policy_network = nn.parallel.DistributedDataParallel(policy_network, device_ids=[rank])
value_network = ValueNetwork(state_size).cuda(gpu)
value_network = nn.parallel.DistributedDataParallel(value_network, device_ids=[rank])
...
My PolicyNetwork and ValueNetwork are simple sequential layers.
Am I using DDP wrong here? I have looked at many online tutorials, but nothing seems address this specific problem. I apologize if this is a rather elementary question. I appreciate your time!