text stringlengths 1 93.6k |
|---|
self.logger.info(log_msg.format(
|
i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time)))
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i += 1
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end = time.time()
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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self.logger.info('{} Total time: {} ({:.4f} s / it)'.format(
|
header, total_time_str, total_time / len(iterable)))
|
def _load_checkpoint_for_ema(model_ema, checkpoint):
|
"""
|
Workaround for ModelEma._load_checkpoint to accept an already-loaded object
|
"""
|
mem_file = io.BytesIO()
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torch.save(checkpoint, mem_file)
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mem_file.seek(0)
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model_ema._load_checkpoint(mem_file)
|
def setup_for_distributed(is_master):
|
"""
|
This function disables printing when not in master process
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"""
|
import builtins as __builtin__
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builtin_print = __builtin__.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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if is_master or force:
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builtin_print(*args, **kwargs)
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__builtin__.print = print
|
def is_dist_avail_and_initialized():
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if not dist.is_available():
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return False
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if not dist.is_initialized():
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return False
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return True
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def get_world_size():
|
if not is_dist_avail_and_initialized():
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return 1
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return dist.get_world_size()
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def get_rank():
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if not is_dist_avail_and_initialized():
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return 0
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return dist.get_rank()
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def is_main_process():
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return get_rank() == 0
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def save_on_master(*args, **kwargs):
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if is_main_process():
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torch.save(*args, **kwargs)
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def init_distributed_mode(args):
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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args.rank = int(os.environ["RANK"])
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args.world_size = int(os.environ['WORLD_SIZE'])
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args.gpu = int(os.environ['LOCAL_RANK'])
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elif 'SLURM_PROCID' in os.environ:
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args.rank = int(os.environ['SLURM_PROCID'])
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args.gpu = args.rank % torch.cuda.device_count()
|
elif 'OMPI_COMM_WORLD_SIZE' in os.environ and 'OMPI_COMM_WORLD_RANK' in os.environ:
|
args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
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args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
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args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
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args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
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print(f'dist train on amlk8s| word_size {args.world_size} | rank {args.rank} | gpu {args.gpu} | dist_url {args.dist_url}')
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else:
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print('Not using distributed mode')
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args.distributed = False
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return
|
args.distributed = True
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torch.cuda.set_device(args.gpu)
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args.dist_backend = 'nccl'
|
print('| distributed init (rank {}): {}'.format(
|
args.rank, args.dist_url), flush=True)
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torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
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world_size=args.world_size, rank=args.rank)
|
torch.distributed.barrier()
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setup_for_distributed(args.rank == 0)
|
# <FILESEP>
|
# coding: utf-8
|
# author: lu yf
|
# create date: 2018/11/12
|
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