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Running
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Zero
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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from datetime import timedelta
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from detectron2.utils import comm
__all__ = ["DEFAULT_TIMEOUT", "launch"]
DEFAULT_TIMEOUT = timedelta(minutes=30)
def _find_free_port():
import socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Binding to port 0 will cause the OS to find an available port for us
sock.bind(("", 0))
port = sock.getsockname()[1]
sock.close()
# NOTE: there is still a chance the port could be taken by other processes.
return port
def launch(
main_func,
# Should be num_processes_per_machine, but kept for compatibility.
num_gpus_per_machine,
num_machines=1,
machine_rank=0,
dist_url=None,
args=(),
timeout=DEFAULT_TIMEOUT,
):
"""
Launch multi-process or distributed training.
This function must be called on all machines involved in the training.
It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine.
Args:
main_func: a function that will be called by `main_func(*args)`
num_gpus_per_machine (int): number of processes per machine. When
using GPUs, this should be the number of GPUs.
num_machines (int): the total number of machines
machine_rank (int): the rank of this machine
dist_url (str): url to connect to for distributed jobs, including protocol
e.g. "tcp://127.0.0.1:8686".
Can be set to "auto" to automatically select a free port on localhost
timeout (timedelta): timeout of the distributed workers
args (tuple): arguments passed to main_func
"""
world_size = num_machines * num_gpus_per_machine
if world_size > 1:
# https://github.com/pytorch/pytorch/pull/14391
# TODO prctl in spawned processes
if dist_url == "auto":
assert num_machines == 1, "dist_url=auto not supported in multi-machine jobs."
port = _find_free_port()
dist_url = f"tcp://127.0.0.1:{port}"
if num_machines > 1 and dist_url.startswith("file://"):
logger = logging.getLogger(__name__)
logger.warning(
"file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://"
)
mp.start_processes(
_distributed_worker,
nprocs=num_gpus_per_machine,
args=(
main_func,
world_size,
num_gpus_per_machine,
machine_rank,
dist_url,
args,
timeout,
),
daemon=False,
)
else:
main_func(*args)
def _distributed_worker(
local_rank,
main_func,
world_size,
num_gpus_per_machine,
machine_rank,
dist_url,
args,
timeout=DEFAULT_TIMEOUT,
):
has_gpu = torch.cuda.is_available()
if has_gpu:
assert num_gpus_per_machine <= torch.cuda.device_count()
global_rank = machine_rank * num_gpus_per_machine + local_rank
try:
dist.init_process_group(
backend="NCCL" if has_gpu else "GLOO",
init_method=dist_url,
world_size=world_size,
rank=global_rank,
timeout=timeout,
)
except Exception as e:
logger = logging.getLogger(__name__)
logger.error("Process group URL: {}".format(dist_url))
raise e
# Setup the local process group.
comm.create_local_process_group(num_gpus_per_machine)
if has_gpu:
torch.cuda.set_device(local_rank)
# synchronize is needed here to prevent a possible timeout after calling init_process_group
# See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
comm.synchronize()
main_func(*args)
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