def init_ddp_connection(self, global_rank: int, world_size: int) -> None:
master_addr = self.cluster_environment.master_address()
master_port = self.cluster_environment.master_port()
world_size = self.cluster_environment.world_size()
assert_world_config(self.cluster_environment, master_addr, master_port,
world_size)
if not is_world_initialized():
pl_logger.info(
"initializing world: GLOBAL_RANK: {}, MEMBER: {}/{}".format(
global_rank,
int(global_rank) + 1, world_size))
_w = World(
name=str(global_rank),
rank=int(global_rank),
world_size=int(world_size),
dist_backend=self.torch_distributed_backend,
dist_init_method=f"tcp://{master_addr}:{master_port}",
rpc_init_method=f"tcp://{master_addr}:{int(master_port) + 1}",
)
def init_ddp_connection(self, global_rank: int, world_size: int) -> None:
master_addr = self.cluster_environment.master_address()
master_port = self.cluster_environment.master_port()
world_size = self.cluster_environment.world_size()
assert_world_config(
self.cluster_environment, master_addr, master_port, world_size
)
if not is_world_initialized():
pl_logger.info(
f"initializing world: GLOBAL_RANK: {global_rank}, "
f"MEMBER: {int(global_rank) + 1}/{world_size}"
)
# TODO: currently nccl is having problems with supporting
# different cnfigurations, use gloo as replacement.
# See: https://github.com/pytorch/pytorch/issues/47885
_w = World(
name=str(global_rank),
rank=int(global_rank),
world_size=int(world_size),
dist_backend="gloo",
dist_init_method=f"tcp://{master_addr}:{master_port}",
rpc_init_method=f"tcp://{master_addr}:{int(master_port) + 1}",
)
avg = t_plugin.reduce(avg, reduce_op=ReduceOp.SUM)
return float(avg)
if __name__ == "__main__":
os.environ["WORLD_SIZE"] = "3"
print(os.environ["TEST_SAVE_PATH"])
config = generate_env_config("CartPole-v0", {})
config = generate_training_config(root_dir=os.environ["ROOT_DIR"], config=config)
config = generate_algorithm_config("DQNApex", config)
# use ddp gpu
config["gpus"] = [0, 0, 0]
config["num_processes"] = 3
# this testing process corresponds to this node
config["num_nodes"] = 1
config["early_stopping_patience"] = 100
# Use class instead of string name since algorithms is distributed.
config["frame_config"]["models"] = [QNet, QNet]
config["frame_config"]["model_kwargs"] = [
{"state_dim": 4, "action_num": 2},
{"state_dim": 4, "action_num": 2},
]
# cb = [DDPInspectCallback(), LoggerDebugCallback()]
cb = [DDPInspectCallback()]
launch(config, pl_callbacks=cb)
if is_world_initialized() and get_cur_rank() == 0:
with open(os.environ["TEST_SAVE_PATH"], "wb") as f:
pickle.dump(cb[0].avg_max_total_reward, f)
