fastreid.engine.launch Namespace Reference

Functions
	_find_free_port ()
	launch (main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url=None, args=())
	_distributed_worker (local_rank, main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args)

Detailed Description

@author:  xingyu liao
@contact: sherlockliao01@gmail.com

Function Documentation

_distributed_worker()

fastreid.engine.launch._distributed_worker ( local_rank, main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args )

protected

Definition at line 80 of file launch.py.

):
    print(f'local_rank = {local_rank}\n')
    # exit(0)
 
    assert torch.cuda.is_available(), "cuda is not available. Please check your installation."
    global_rank = machine_rank * num_gpus_per_machine + local_rank
    try:
        dist.init_process_group(
            backend="NCCL", init_method=dist_url, world_size=world_size, rank=global_rank
        )
    except Exception as e:
        logger = logging.getLogger(__name__)
        logger.error("Process group URL: {}".format(dist_url))
        raise e
    # 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()
 
    assert num_gpus_per_machine <= torch.cuda.device_count()
    torch.cuda.set_device(local_rank)
 
    # Setup the local process group (which contains ranks within the same machine)
    assert comm._LOCAL_PROCESS_GROUP is None
    num_machines = world_size // num_gpus_per_machine
    for i in range(num_machines):
        ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine))
        pg = dist.new_group(ranks_on_i)
        if i == machine_rank:
            comm._LOCAL_PROCESS_GROUP = pg
 
    main_func(*args)

_find_free_port()

fastreid.engine.launch._find_free_port ( )

protected

Definition at line 22 of file launch.py.

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
 
 

launch()

fastreid.engine.launch.launch	(		main_func,
			num_gpus_per_machine,
			num_machines = 1,
			machine_rank = 0,
			dist_url = None,
			args = () )

Launch multi-gpu 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 GPUs per machine
    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
    args (tuple): arguments passed to main_func

Definition at line 34 of file launch.py.

def launch(main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url=None, args=()):
    """
    Launch multi-gpu 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 GPUs per machine
        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
        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://"
            )
        # print(f'main_func = {main_func}\n')
        print(f'num_gpus_per_machine = {num_gpus_per_machine}\n')
        # print(f'num_machines = {num_machines}\n')
        # print(f'machine_rank = {machine_rank}\n')
        # print(f'dist_url = {dist_url}\n')
        print(f'args = {args}\n')
        
        mp.spawn(
            _distributed_worker,
            nprocs=num_gpus_per_machine,
            args=(main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args),
            daemon=False,
        )
    else:
        main_func(*args)