def test_attributes_from_environment_variables():
"""Test that the LSF environment takes the attributes from the environment variables."""
env = LSFEnvironment()
assert env.creates_processes_externally
assert env.main_address == "10.10.10.0"
assert env.main_port == 10234
assert env.world_size() == 4
assert env.global_rank() == 3
assert env.local_rank() == 1
env.set_global_rank(100)
assert env.global_rank() == 3
env.set_world_size(100)
assert env.world_size() == 4
assert LSFEnvironment.detect()
def test_attributes_from_environment_variables():
"""Test that the LSF environment takes the attributes from the environment variables."""
env = LSFEnvironment()
assert env.creates_children()
assert env.master_address() == "10.10.10.0"
assert env.master_port() == 10234
assert env.world_size() == 4
assert env.global_rank() == 3
assert env.local_rank() == 1
env.set_global_rank(100)
assert env.global_rank() == 3
env.set_world_size(100)
assert env.world_size() == 4
assert LSFEnvironment.is_using_lsf()
def test_attributes_from_environment_variables(tmp_path):
"""Test that the LSF environment takes the attributes from the environment variables."""
environ = {
"LSB_DJOB_RANKFILE": _make_rankfile(tmp_path),
"LSB_JOBID": "1234",
"JSM_NAMESPACE_SIZE": "4",
"JSM_NAMESPACE_RANK": "3",
"JSM_NAMESPACE_LOCAL_RANK": "1",
}
with mock.patch.dict(os.environ,
environ), mock.patch("socket.gethostname",
return_value="10.10.10.2"):
env = LSFEnvironment()
assert env.creates_processes_externally
assert env.main_address == "10.10.10.0"
assert env.main_port == 10234
assert env.world_size() == 4
assert env.global_rank() == 3
assert env.local_rank() == 1
env.set_global_rank(100)
assert env.global_rank() == 3
env.set_world_size(100)
assert env.world_size() == 4
assert LSFEnvironment.detect()
def process_args(args=None, return_io=False):
"""
Process arguments for running training
"""
if not isinstance(args, argparse.Namespace):
args = parse_args(args)
args.loader_kwargs = dict()
targs = dict(max_epochs=args.epochs, )
targs['accumulate_grad_batches'] = args.accumulate
env = None
if args.ipu:
targs['accelerator'] = 'ipu'
targs['devices'] = process_gpus(args.gpus)
else:
targs['gpus'] = process_gpus(args.gpus)
targs['num_nodes'] = args.num_nodes
if args.lsf:
##########################################################################################
# Currently coding against pytorch-lightning 1.4.3
##########################################################################################
if args.num_workers > 4:
print0(
"num_workers (-k) > 4 can lead to hanging on Summit -- setting to 4",
file=sys.stderr)
args.num_workers = 4
args.loader_kwargs[
'num_workers'] = 1 # Set as a default. This will get overridden elsewhere
args.loader_kwargs['multiprocessing_context'] = 'spawn'
env = LSFEnvironment()
elif args.slurm:
env = SLURMEnvironment()
if env is not None:
global RANK
global SIZE
try:
RANK = env.global_rank()
SIZE = env.world_size()
except:
print(
">>> Could not get global rank -- setting RANK to 0 and SIZE to 1",
file=sys.stderr)
RANK = 0
SIZE = 1
if targs['gpus'] is not None:
targs['accelerator'] = 'gpu'
if targs['gpus'] == 1:
targs['devices'] = 1
else:
if env is None:
raise ValueError(
'Please specify environment (--lsf or --slurm) if using more than one GPU'
)
# parallel_devices = [torch.device(i) for i in range(torch.cuda.device_count()) if i < targs['gpus']]
# precision_plugin = NativeMixedPrecisionPlugin(16, 'cuda')
torch.cuda.set_device(env.local_rank())
targs['devices'] = targs['gpus']
targs['strategy'] = DDPStrategy(
find_unused_parameters=False,
cluster_environment=env,
#accelerator=GPUAccelerator(),
#parallel_devices=parallel_devices,
#precision_plugin=precision_plugin,
)
print(
"---- Rank %s - Using GPUAccelerator with DDPStrategy" %
env.global_rank(),
file=sys.stderr)
else:
targs['accelerator'] = 'cpu'
del args.gpus
if args.sanity:
if isinstance(args.sanity, str):
args.sanity = int(args.sanity)
else:
args.sanity = 4000
targs['limit_train_batches'] = args.sanity
targs['limit_val_batches'] = args.sanity // 4
if args.lr_find:
targs['auto_lr_find'] = True
del args.lr_find
if args.checkpoint is not None:
if os.path.exists(args.checkpoint):
targs['resume_from_checkpoint'] = args.checkpoint
else:
warnings.warn(
"Ignoring -c/--checkpoint argument because {args.checkpoint} does not exist."
)
args.checkpoint = None
if args.cuda_profile:
targs['profiler'] = PyTorchProfiler(
filename=f'pytorch_prof.{RANK:0{len(str(SIZE))}}', emit_nvtx=True)
targs['replace_sampler_ddp'] = False
args.loader_kwargs = dict()
# make sure we are classifying if we are using adding classifier layers
# to a resnet features model
if args.features_checkpoint is not None:
if args.manifold:
raise ValueError(
'Cannot use manifold loss (i.e. -M) if adding classifier (i.e. -F)'
)
args.classify = True
data_mod = DeepIndexDataModule(args,
keep_open=True,
seed=args.seed + RANK,
rank=RANK,
size=SIZE)
# if classification problem, use the number of taxa as the number of outputs
if args.classify:
args.n_outputs = data_mod.dataset.n_outputs
args.input_nc = 136 if args.tnf else len(data_mod.dataset.vocab)
model = process_model(args, taxa_table=data_mod.dataset.difile.taxa_table)
if args.num_workers > 0:
data_mod.dataset.close()
ret = [model, args, targs]
if return_io:
ret.append(io)
ret.append(data_mod)
return tuple(ret)
