def _setup_misc(self):
# misc setup components that were in goissip_sgd
config = self.config
state = {}
update_state(
state, {
'epoch': 0,
'itr': 0,
'best_prec1': 0,
'is_best': True,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'elapsed_time': 0,
'batch_meter': Meter(ptag='Time').__dict__,
'data_meter': Meter(ptag='Data').__dict__,
'nn_meter': Meter(ptag='Forward/Backward').__dict__
})
self.state = state
# module used to relaunch jobs and handle external termination signals
ClusterManager.set_checkpoint_dir(config['checkpoint_dir'])
self.cmanager = ClusterManager(rank=config['rank'],
world_size=config['world_size'],
model_tag=config['tag'],
state=state,
all_workers=config['checkpoint_all'])
# enable low-level optimization of compute graph using cuDNN library?
cudnn.benchmark = True
self.batch_meter = Meter(state['batch_meter'])
self.data_meter = Meter(state['data_meter'])
self.nn_meter = Meter(state['nn_meter'])
# initalize log file
if not os.path.exists(config['out_fname']):
with open(config['out_fname'], 'w') as f:
print('BEGIN-TRAINING\n'
'World-Size,{ws}\n'
'Num-DLWorkers,{nw}\n'
'Batch-Size,{bs}\n'
'Epoch,itr,BT(s),avg:BT(s),std:BT(s),'
'NT(s),avg:NT(s),std:NT(s),'
'DT(s),avg:DT(s),std:DT(s),'
'Loss,avg:Loss,Prec@1,avg:Prec@1,Prec@5,avg:Prec@5,val'.
format(ws=config['world_size'],
nw=config['num_dataloader_workers'],
bs=config['batch_size']),
file=f)
self.start_itr = state['itr']
self.start_epoch = state['epoch']
self.elapsed_time = state['elapsed_time']
self.begin_time = time.time() - state['elapsed_time']
self.best_val_prec1 = 0
def parse_args():
"""
Set env-vars and global args
rank: <-- $SLRUM_PROCID
world_size<-- $SLURM_NTASKS
Master address <-- $SLRUM_NODENAME of rank 0 process (or HOSTNAME)
Master port <-- any free port (doesn't really matter)
"""
class DataStore():
def __init__(self):
self.all_reduce = 'False'
self.batch_size = 32
self.lr = 0.1
self.num_dataloader_workers = 10
self.num_epochs = 90
self.num_iterations_per_training_epoch = None
self.momentum = 0.9
self.weight_decay = 1e-4
self.push_sum = 'True'
self.graph_type = 5
self.mixing_strategy = 0
self.schedule = None
self.peers_per_itr_schedule = None
self.overlap = 'False'
self.synch_freq = 0
self.warmup = 'False'
self.seed = 47
self.print_freq = 10
self.checkpoint_all = 'False'
self.overwrite_checkpoints = 'True'
self.master_port = '40100'
self.checkpoint_dir = "./"
self.network_interface_type = 'infiniband'
self.num_itr_ignore = 10
# self.dataset_dir = "./data/"
self.no_cuda_streams = None
self.master_addr = None
self.backend = 'nccl'
self.rank = 1
self.world_size = 5
self.tag = ''
self.out_fname = ''
self.resume = 'False'
self.verbose = 'True'
self.train_fast = 'False'
self.nesterov = 'False'
args = DataStore() #parser.parse_args()
ClusterManager.set_checkpoint_dir(args.checkpoint_dir)
# rank and world_size need to be changed depending on the scheduler being
# used to run the distributed jobs
args.master_addr = os.environ['HOSTNAME']
if args.backend == 'mpi':
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
args.world_size = int(os.environ['OMPI_UNIVERSE_SIZE'])
else:
args.rank = 1 #int(os.environ['SLURM_PROCID'])
args.world_size = 5 #int(os.environ['SLURM_NTASKS'])
args.out_fname = ClusterManager.CHECKPOINT_DIR \
+ args.tag \
+ 'out_r' + str(args.rank) \
+ '_n' + str(args.world_size) \
+ '.csv'
args.resume = True if args.resume == 'True' else False
args.verbose = True if args.verbose == 'True' else False
args.train_fast = True if args.train_fast == 'True' else False
args.nesterov = True if args.nesterov == 'True' else False
args.checkpoint_all = True if args.checkpoint_all == 'True' else False
args.warmup = True if args.warmup == 'True' else False
args.overlap = True if args.overlap == 'True' else False
args.push_sum = True if args.push_sum == 'True' else False
args.all_reduce = True if args.all_reduce == 'True' else False
args.cpu_comm = True if (args.backend == 'gloo' and not args.push_sum
and not args.all_reduce) else False
args.comm_device = torch.device('cpu') if args.cpu_comm else torch.device(
'cuda')
args.overwrite_checkpoints = True if args.overwrite_checkpoints == 'True' else False
args.lr_schedule = {}
if args.schedule is None:
args.schedule = [30, 0.1, 60, 0.1, 80, 0.1]
i, epoch = 0, None
for v in args.schedule:
if i == 0:
epoch = v
elif i == 1:
args.lr_schedule[epoch] = v
i = (i + 1) % 2
del args.schedule
# parse peers per itr sched (epoch, num_peers)
args.ppi_schedule = {}
if args.peers_per_itr_schedule is None:
args.peers_per_itr_schedule = [0, 1]
i, epoch = 0, None
for v in args.peers_per_itr_schedule:
if i == 0:
epoch = v
elif i == 1:
args.ppi_schedule[epoch] = v
i = (i + 1) % 2
del args.peers_per_itr_schedule
# must specify how many peers to communicate from the start of training
assert 0 in args.ppi_schedule
if args.all_reduce:
assert args.graph_type == -1
if args.backend == 'gloo':
assert args.network_interface_type == 'ethernet'
os.environ['GLOO_SOCKET_IFNAME'] = get_tcp_interface_name(
network_interface_type=args.network_interface_type)
elif args.network_interface_type == 'ethernet':
if args.backend == 'nccl':
os.environ['NCCL_SOCKET_IFNAME'] = get_tcp_interface_name(
network_interface_type=args.network_interface_type)
os.environ['NCCL_IB_DISABLE'] = '1'
else:
raise NotImplementedError
# initialize torch distributed backend
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = args.master_port
dist.init_process_group(backend=args.backend,
world_size=args.world_size,
rank=args.rank)
args.graph, args.mixing = None, None
graph_class = GRAPH_TOPOLOGIES[args.graph_type]
if graph_class:
# dist.barrier is done here to ensure the NCCL communicator is created
# here. This prevents an error which may be caused if the NCCL
# communicator is created at a time gap of more than 5 minutes in
# different processes
dist.barrier()
args.graph = graph_class(args.rank,
args.world_size,
peers_per_itr=args.ppi_schedule[0])
mixing_class = MIXING_STRATEGIES[args.mixing_strategy]
if mixing_class and args.graph:
args.mixing = mixing_class(args.graph, args.comm_device)
return args
def parse_args():
"""
Set env-vars and global args
rank: <-- $SLRUM_PROCID
world_size<-- $SLURM_NTASKS
Master address <-- $SLRUM_NODENAME of rank 0 process (or HOSTNAME)
Master port <-- any free port (doesn't really matter)
"""
args = parser.parse_args()
ClusterManager.set_checkpoint_dir(args.checkpoint_dir)
args.master_addr = os.environ['HOSTNAME']
if args.backend == 'mpi':
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
args.world_size = int(os.environ['OMPI_UNIVERSE_SIZE'])
args.device_id = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
else:
args.rank = int(os.environ['SLURM_PROCID'])
args.world_size = int(os.environ['SLURM_NTASKS'])
args.device_id = int(os.environ['SLURM_LOCALID'])
args.out_fname = ClusterManager.CHECKPOINT_DIR \
+ args.tag \
+ 'out_r' + str(args.rank) \
+ '_n' + str(args.world_size) \
+ '.csv'
args.resume = True if args.resume == 'True' else False
args.verbose = True if args.verbose == 'True' else False
args.train_fast = True if args.train_fast == 'True' else False
args.nesterov = True if args.nesterov == 'True' else False
args.checkpoint_all = True if args.checkpoint_all == 'True' else False
args.warmup = True if args.warmup == 'True' else False
args.cpu_comm = True if args.backend == 'gloo' else False
args.comm_device = torch.device('cpu') if args.cpu_comm else torch.device(
'cuda')
args.overlap = True if args.overlap == 'True' else False
args.push_sum = True if args.push_sum == 'True' else False
args.all_reduce = True if args.all_reduce == 'True' else False
args.bilat = True if args.bilat == 'True' else False
args.global_epoch = None
args.global_itr = None
if args.rank == 0 and os.path.isfile(args.shared_fpath):
os.remove(args.shared_fpath)
while os.path.isfile(args.shared_fpath):
pass
args.lr_schedule = {}
if args.schedule is None:
args.schedule = [30, 0.1, 60, 0.1, 80, 0.1]
i, epoch = 0, None
for v in args.schedule:
if i == 0:
epoch = v
elif i == 1:
args.lr_schedule[epoch] = v
i = (i + 1) % 2
del args.schedule
# parse peers per itr sched (epoch, num_peers)
args.ppi_schedule = {}
if args.peers_per_itr_schedule is None:
args.peers_per_itr_schedule = [0, 1]
i, epoch = 0, None
for v in args.peers_per_itr_schedule:
if i == 0:
epoch = v
elif i == 1:
args.ppi_schedule[epoch] = v
i = (i + 1) % 2
del args.peers_per_itr_schedule
# must specify how many peers to communicate from the start of training
assert 0 in args.ppi_schedule
if args.backend == 'gloo':
assert args.network_interface_type == 'ethernet'
os.environ['GLOO_SOCKET_IFNAME'] = get_tcp_interface_name(
network_interface_type=args.network_interface_type)
elif args.network_interface_type == 'ethernet':
if args.backend == 'nccl':
os.environ['NCCL_SOCKET_IFNAME'] = get_tcp_interface_name(
network_interface_type=args.network_interface_type)
os.environ['NCCL_IB_DISABLE'] = '1'
else:
raise NotImplementedError
# initialize torch distributed backend
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = str(int(args.master_port) + 1)
dist.init_process_group(backend=args.backend,
world_size=args.world_size,
rank=args.rank)
args.graph_class = GRAPH_TOPOLOGIES[args.graph_type]
args.mixing_class = MIXING_STRATEGIES[args.mixing_strategy]
if args.graph_class is None:
raise Exception('Incorrect arguments for graph_type')
if args.mixing_class is None:
raise Exception('Incorrect arguments for mixing_strategy')
return args
