parser.add_argument('--loss_max', default=5, type=float, help='Maximum value to show in 1D plot')
parser.add_argument('--vmax', default=10, type=float, help='Maximum value to map')
parser.add_argument('--vmin', default=0.1, type=float, help='Miminum value to map')
parser.add_argument('--vlevel', default=0.5, type=float, help='plot contours every vlevel')
parser.add_argument('--show', action='store_true', default=False, help='show plotted figures')
parser.add_argument('--log', action='store_true', default=False, help='use log scale for loss values')
parser.add_argument('--plot', action='store_true', default=False, help='plot figures after computation')
args = parser.parse_args()
torch.manual_seed(123)
#--------------------------------------------------------------------------
# Environment setup
#--------------------------------------------------------------------------
if args.mpi:
comm = mpi4pytorch.setup_MPI()
rank, nproc = comm.Get_rank(), comm.Get_size()
else:
comm, rank, nproc = None, 0, 1
# in case of multiple GPUs per node, set the GPU to use for each rank
if args.cuda:
if not torch.cuda.is_available():
raise Exception('User selected cuda option, but cuda is not available on this machine')
gpu_count = torch.cuda.device_count()
torch.cuda.set_device(rank % gpu_count)
print('Rank %d use GPU %d of %d GPUs on %s' %
(rank, torch.cuda.current_device(), gpu_count, socket.gethostname()))
#--------------------------------------------------------------------------
# Check plotting resolution
action='store_true',
default=False,
help='use log scale for loss values')
parser.add_argument('--plot',
action='store_true',
default=False,
help='plot figures after computation')
args = parser.parse_args()
torch.manual_seed(123)
#--------------------------------------------------------------------------
# Environment setup
#--------------------------------------------------------------------------
if args.mpi:
comm = mpi.setup_MPI()
rank, nproc = comm.Get_rank(), comm.Get_size()
else:
comm, rank, nproc = None, 0, 1
# in case of multiple GPUs per node, set the GPU to use for each rank
if args.cuda:
if not torch.cuda.is_available():
raise Exception(
'User selected cuda option, but cuda is not available on this machine'
)
gpu_count = torch.cuda.device_count()
torch.cuda.set_device(rank % gpu_count)
print('Rank %d use GPU %d of %d GPUs on %s' %
(rank, torch.cuda.current_device(), gpu_count,
socket.gethostname()))
action='store_true',
default=False,
help='use log scale for loss values')
parser.add_argument('--plot',
action='store_true',
default=False,
help='plot figures after computation')
args = parser.parse_args()
torch.manual_seed(123)
#--------------------------------------------------------------------------
# Environment setup
#--------------------------------------------------------------------------
if args.mpi:
comm = mpi4pytorch.setup_MPI()
rank, nproc = comm.Get_rank(), comm.Get_size()
else:
comm, rank, nproc = None, 0, 1
# in case of multiple GPUs per node, set the GPU to use for each rank
if args.cuda:
if not torch.cuda.is_available():
raise Exception(
'User selected cuda option, but cuda is not available on this machine'
)
gpu_count = torch.cuda.device_count()
torch.cuda.set_device(rank % gpu_count)
print('Rank %d use GPU %d of %d GPUs on %s' %
(rank, torch.cuda.current_device(), gpu_count,
socket.gethostname()))