def parse_args():
parser = argparse.ArgumentParser(description='PyTorch Deeplift comparison')
parser.add_argument(
'--model',
type=str,
default='vbd_opposite',
choices=[
'vbd', 'vgd', 'p_b', 'p_g', 'ising_vbd', 'ising_soft_vbd',
'vbd_window'
],
help='choose from ["vbd_rank_func", "bern", "add_gauss"]')
parser.add_argument('--l1_reg_coef',
type=float,
default=0.1,
help='Only use in IsingBDNet')
parser.add_argument('--l2_reg_coef',
type=float,
default=0.,
help='Only use in IsingBDNet')
parser.add_argument('--window',
type=int,
default=2,
help='Perturbation size. Used in p_b or vbd_window')
parser.add_argument('--from-digit',
type=int,
default=8,
help='mask from some digits')
parser.add_argument('--to-digit',
type=int,
default=3,
help='masked to some digits')
parser.add_argument('--verbose', type=int, default=1)
parser.add_argument('--top_n',
type=int,
default=1,
help='-1 means whole test sets')
parser.add_argument('--no-cuda',
action='store_false',
default=True,
help='disables CUDA training')
parser.add_argument('--visualize', action='store_false', default=True)
args, _ = parser.parse_known_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
print 'cuda:', args.cuda
# CS server settings
if args.cuda and pkgutil.find_loader('gpu_lock') is not None:
import gpu_lock
board = gpu_lock.obtain_lock_id()
torch.cuda.set_device(board)
print 'start using gpu device: %d' % board
return args
def LockGPU(max_retries=10):
""" Locks a free GPU board and returns its id. """
for retry_count in range(max_retries):
board = gpu_lock.obtain_lock_id()
if board != -1:
break
time.sleep(1)
if board == -1:
print 'No GPU board available.'
sys.exit(1)
else:
import theano.sandbox.cuda
theano.sandbox.cuda.use('gpu'+str(board))
return board
def LockGPU(max_retries=10):
""" Locks a free GPU board and returns its id. """
for retry_count in range(max_retries):
board = gpu_lock.obtain_lock_id()
if board != -1:
break
time.sleep(1)
if board == -1:
print 'No GPU board available.'
sys.exit(1)
else:
import theano.sandbox.cuda
theano.sandbox.cuda.use('gpu' + str(board))
return board
singleSoftmax(xGPU, tempRow)
xGPU.copy_to_host()
diff = xGPU.numpy_array-r
print num.sum(num.abs(diff))
#testMaskedSM()
col = cm.CUDAMatrix(reformat(num.random.rand(5,1)))
print col.shape
col.copy_to_host()
print col.numpy_array
col.reshape((1,5))
print col.shape
col.copy_to_host()
print col.numpy_array
garb = cm.CUDAMatrix(reformat(num.zeros((5,5))))
garb.set_row_slice(2,3,col)
garb.copy_to_host()
print garb.numpy_array
if __name__ == "__main__":
print "export LD_LIBRARY_PATH=/u/gdahl/cudaLearn/"
print "export CUDAMATDIR=/u/gdahl/cudaLearn"
devId = gpu_lock.obtain_lock_id()
cm.cuda_set_device(devId)
cm.cublas_init()
cm.CUDAMatrix.init_random(1)
main()
cm.cublas_shutdown()
# stolen from Ilya (/u/ilya/py/gpu.py)
import gpu_lock
lock_id = gpu_lock.obtain_lock_id()
import os
os.environ['THEANO_FLAGS'] = 'mode=FAST_RUN,device=gpu%s,floatX=float32' % \
lock_id
import theano