本文整理匯總了Python中theano.sandbox.cuda.cuda_available方法的典型用法代碼示例。如果您正苦於以下問題:Python cuda.cuda_available方法的具體用法?Python cuda.cuda_available怎麽用?Python cuda.cuda_available使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類theano.sandbox.cuda
的用法示例。
在下文中一共展示了cuda.cuda_available方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_output_broadcast_cuda
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_output_broadcast_cuda(self):
from theano.sandbox import cuda
if not cuda.cuda_available:
raise SkipTest("Optional package Cuda disabled")
if cuda.use.device_number is None:
# We should normally set VecAsRowAndCol as a GPUOp But we
# don't want to do this here as this will disable others
# tests in this file. So we manually init the GPU if
# needed to remove warning.
cuda.use("gpu",
force=True,
default_to_move_computation_to_gpu=False,
move_shared_float32_to_gpu=False,
enable_cuda=False)
v = cuda.fvector('v')
c, r = VecAsRowAndCol()(v)
f = theano.function([v], [c, r])
v_val = cuda.CudaNdarray(self.rng.randn(5).astype('float32'))
f(v_val)
示例2: test_deterministic
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_deterministic():
seed = utt.fetch_seed()
sample_size = (10, 20)
test_use_cuda = [False]
if cuda_available:
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
# print 'use_cuda =', use_cuda
R = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
u = R.uniform(size=sample_size)
f = theano.function([], u)
fsample1 = f()
fsample2 = f()
assert not numpy.allclose(fsample1, fsample2)
R2 = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
u2 = R2.uniform(size=sample_size)
g = theano.function([], u2)
gsample1 = g()
gsample2 = g()
assert numpy.allclose(fsample1, gsample1)
assert numpy.allclose(fsample2, gsample2)
示例3: test_GPU_nstreams_limit
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_GPU_nstreams_limit():
"""
Verify that a ValueError is raised when n_streams
is greater than 2**20 on GPU. This is the value of
(NUM_VECTOR_OP_THREADS_PER_BLOCK * NUM_VECTOR_OP_BLOCKS).
"""
if not cuda_available:
raise SkipTest('Optional package cuda not available')
seed = 12345
R = MRG_RandomStreams(seed=seed, use_cuda=True)
def eval_uniform(size, nstreams):
if theano.config.mode == "FAST_COMPILE":
mode = "FAST_RUN"
else:
mode = copy.copy(theano.compile.get_default_mode())
mode.check_py_code = False
out = R.uniform(size=size, nstreams=nstreams, dtype='float32')
f = theano.function([], out, mode=mode)
return f()
eval_uniform((10,), 2**20)
assert_raises(ValueError, eval_uniform, (10,), 2**20 + 1)
示例4: test_overflow_gpu_old_backend
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_overflow_gpu_old_backend():
# run with THEANO_FLAGS=mode=FAST_RUN,init_gpu_device=gpu1,device=cpu
if not cuda_available:
raise SkipTest('Optional package cuda not available')
mode = mode_with_gpu
seed = 12345
rng = MRG_RandomStreams(seed=seed, use_cuda=True)
fct = rng.uniform
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (2**15, 2**16,), (2, 2**15, 2**15)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=True)
# should not raise error
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
示例5: test_unpickle_cudandarray_as_numpy_ndarray_flag0
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_unpickle_cudandarray_as_numpy_ndarray_flag0():
oldflag = config.experimental.unpickle_gpu_on_cpu
config.experimental.unpickle_gpu_on_cpu = False
try:
testfile_dir = os.path.dirname(os.path.realpath(__file__))
fname = 'CudaNdarray.pkl'
with open(os.path.join(testfile_dir, fname), 'rb') as fp:
if PY3:
u = CompatUnpickler(fp, encoding="latin1")
else:
u = CompatUnpickler(fp)
if cuda_available:
mat = u.load()
assert isinstance(mat, CudaNdarray)
assert numpy.asarray(mat)[0] == -42.0
else:
assert_raises(ImportError, u.load)
finally:
config.experimental.unpickle_gpu_on_cpu = oldflag
示例6: test_maxout_conv_c01b_basic
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_maxout_conv_c01b_basic(self):
if cuda.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
if not hasattr(cuda, 'unuse'):
raise Exception("Theano version too old to run this test!")
# Tests that we can run a small convolutional model on GPU,
assert cuda.cuda_enabled is False
# Even if there is a GPU, but the user didn't specify device=gpu
# we want to run this test.
try:
old_floatX = config.floatX
cuda.use('gpu')
config.floatX = 'float32'
train = yaml_parse.load(yaml_string_maxout_conv_c01b_basic)
train.main_loop()
finally:
config.floatX = old_floatX
cuda.unuse()
assert cuda.cuda_enabled is False
示例7: check_cuda
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def check_cuda(feature_name="You are using code that relies on cuda-convnet. Cuda-convnet",
check_enabled=True):
"""
Call this function before sections of code that depend on the cuda_convnet module.
It will raise a RuntimeError if the GPU is not available.
feature_name: The name of the feature the user should be told is unavailable.
"""
if not cuda.cuda_available:
raise RuntimeError("%s only runs on GPUs, but there doesn't "
"seem to be a GPU available. If you would like assistance making "
"a CPU version of convolutional maxout, contact "
"pylearn-dev@googlegroups.com." % feature_name)
if not hasattr(cuda.cuda_ndarray.cuda_ndarray, 'cublas_v2'):
warnings.warn(
"You are using probably a too old Theano version. That"
" will cause compilation crash. If so, update Theano.")
elif not cuda.cuda_ndarray.cuda_ndarray.cublas_v2():
raise RuntimeError("You are using probably a too old Theano version."
" That will cause compilation crash. Update Theano")
if check_enabled and not cuda.cuda_enabled:
raise RuntimeError("%s must run be with theano configured to use the GPU" % feature_name)
示例8: test_cuda
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_cuda(self):
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
raise SkipTest("Optional package cuda not available")
a = sparse.csr_matrix('a', dtype='float32')
b = cuda.float32_shared_constructor(
numpy.random.rand(3, 4).astype('float32'))
d = sparse.dot(a, b)
f = theano.function([a], d)
a_val = scipy.sparse.csr_matrix(random_lil((5, 3), 'float32', 5))
d_theano = f(a_val)
d_numpy = a_val * b.get_value()
utt.assert_allclose(d_numpy, d_theano)
示例9: contains_nan
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def contains_nan(arr, node=None):
"""
Test whether a numpy.ndarray contains any `np.nan` values.
Parameters
----------
arr : np.ndarray or output of any Theano op
node : None or an Apply instance.
If arr is the output of a Theano op, the node associated to it.
Returns
-------
contains_nan : bool
`True` if the array contains any `np.nan` values, `False` otherwise.
Notes
-----
Tests for the presence of `np.nan`'s using `np.isnan(np.min(ndarray))`.
This approach is faster and more memory efficient than the obvious
alternative, calling `np.any(np.isnan(ndarray))`, which requires the
construction of a boolean array with the same shape as the input array.
"""
if isinstance(arr, theano.gof.type.CDataType._cdata_type):
return False
elif isinstance(arr, np.random.mtrand.RandomState):
return False
elif arr.size == 0:
return False
elif cuda.cuda_available and isinstance(arr, cuda.CudaNdarray):
if (hasattr(theano.sandbox, 'rng_mrg') and
isinstance(
node.op,
# It store ints in float container
theano.sandbox.rng_mrg.GPU_mrg_uniform)):
return False
else:
compile_gpu_func(True, False, False)
return np.isnan(f_gpumin(arr.reshape(arr.size)))
return np.isnan(np.min(arr))
示例10: compile_gpu_func
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def compile_gpu_func(nan_is_error, inf_is_error, big_is_error):
""" compile utility function used by contains_nan and contains_inf
"""
global f_gpumin, f_gpumax, f_gpuabsmax
if not cuda.cuda_available:
return
guard_input = cuda.fvector('nan_guard')
cuda_compile_failed = False
if (nan_is_error or inf_is_error) and f_gpumin is None:
try:
f_gpumin = theano.function(
[guard_input], T.min(guard_input),
mode='FAST_RUN'
)
except RuntimeError:
# This can happen if cuda is available, but the
# device is in exclusive mode and used by another
# process.
cuda_compile_failed = True
if inf_is_error and not cuda_compile_failed and f_gpumax is None:
try:
f_gpumax = theano.function(
[guard_input], T.max(guard_input),
mode='FAST_RUN'
)
except RuntimeError:
# This can happen if cuda is available, but the
# device is in exclusive mode and used by another
# process.
cuda_compile_failed = True
if big_is_error and not cuda_compile_failed and f_gpuabsmax is None:
try:
f_gpuabsmax = theano.function(
[guard_input], T.max(T.abs_(guard_input)),
mode='FAST_RUN'
)
except RuntimeError:
# This can happen if cuda is available, but the
# device is in exclusive mode and used by another
# process.
cuda_compile_failed = True
示例11: test_viewop_gpu
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_viewop_gpu():
from theano.sandbox import cuda
if cuda.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
_x = theano.tensor.fvector('x')
x = cuda.gpu_from_host(_x)
_out = theano.compile.ViewOp()(x)
out = cuda.host_from_gpu(_out)
f = theano.function([x],
out,
mode=mode_with_gpu)
data = numpy.array([1, 2, 3], dtype='float32')
assert numpy.allclose(f(data), data)
示例12: test_unpickle_cudandarray_as_numpy_ndarray_flag0
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_unpickle_cudandarray_as_numpy_ndarray_flag0():
oldflag = config.experimental.unpickle_gpu_on_cpu
config.experimental.unpickle_gpu_on_cpu = False
try:
testfile_dir = os.path.dirname(os.path.realpath(__file__))
fname = 'CudaNdarray.pkl'
with open(os.path.join(testfile_dir, fname), 'rb') as fp:
if PY3:
u = CompatUnpickler(fp, encoding="latin1")
else:
u = CompatUnpickler(fp)
if cuda_available:
try:
mat = u.load()
except ImportError:
# Windows sometimes fail with nonsensical errors like:
# ImportError: No module named type
# ImportError: No module named copy_reg
# when "type" and "copy_reg" are builtin modules.
if sys.platform == 'win32':
exc_type, exc_value, exc_trace = sys.exc_info()
reraise(SkipTest, exc_value, exc_trace)
raise
assert isinstance(mat, CudaNdarray)
assert numpy.asarray(mat)[0] == -42.0
else:
assert_raises(ImportError, u.load)
finally:
config.experimental.unpickle_gpu_on_cpu = oldflag
示例13: test_consistency_randomstreams
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_consistency_randomstreams():
"""
Verify that the random numbers generated by MRG_RandomStreams
are the same as the reference (Java) implementation by L'Ecuyer et al.
"""
seed = 12345
n_samples = 5
n_streams = 12
n_substreams = 7
test_use_cuda = [False]
if cuda_available:
test_use_cuda.append(True)
for use_cuda in test_use_cuda:
# print 'use_cuda =', use_cuda
samples = []
rng = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
for i in range(n_streams):
stream_samples = []
u = rng.uniform(size=(n_substreams,), nstreams=n_substreams)
f = theano.function([], u)
for j in range(n_samples):
s = f()
stream_samples.append(s)
stream_samples = numpy.array(stream_samples)
stream_samples = stream_samples.T.flatten()
samples.append(stream_samples)
samples = numpy.array(samples).flatten()
assert(numpy.allclose(samples, java_samples))
示例14: t_binomial
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
R = MRG_RandomStreams(234, use_cuda=False)
u = R.binomial(size=size, p=mean)
f = theano.function(var_input, u, mode=mode)
out = f(*input)
# Increase the number of steps if sizes implies only a few samples
if numpy.prod(const_size) < 10:
steps_ = steps * 100
else:
steps_ = steps
basictest(f, steps_, const_size, prefix='mrg cpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
if mode != 'FAST_COMPILE' and cuda_available:
R = MRG_RandomStreams(234, use_cuda=True)
u = R.binomial(size=size, p=mean, dtype='float32')
# well, it's really that this test w GPU doesn't make sense otw
assert u.dtype == 'float32'
f = theano.function(var_input, theano.Out(
theano.sandbox.cuda.basic_ops.gpu_from_host(u),
borrow=True), mode=mode_with_gpu)
gpu_out = numpy.asarray(f(*input))
basictest(f, steps_, const_size, prefix='mrg gpu',
inputs=input, allow_01=True,
target_avg=mean, mean_rtol=rtol)
numpy.testing.assert_array_almost_equal(out, gpu_out,
decimal=6)
RR = theano.tensor.shared_randomstreams.RandomStreams(234)
uu = RR.binomial(size=size, p=mean)
ff = theano.function(var_input, uu, mode=mode)
# It's not our problem if numpy generates 0 or 1
basictest(ff, steps_, const_size, prefix='numpy', allow_01=True,
inputs=input, target_avg=mean, mean_rtol=rtol)
示例15: test_multinomial_n_samples
# 需要導入模塊: from theano.sandbox import cuda [as 別名]
# 或者: from theano.sandbox.cuda import cuda_available [as 別名]
def test_multinomial_n_samples():
mode_ = mode
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
m = R.multinomial(pvals=pvals, n=n_samples,
dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
basic_multinomialtest(f, steps, sample_size, pvals,
n_samples, prefix='mrg ')
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
n = R.multinomial(pvals=pvals, n=n_samples,
dtype='float32', nstreams=30 * 256)
assert n.dtype == 'float32'
f = theano.function(
[],
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
mode=mode_.including('gpu'))
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals,
n_samples, prefix='gpu mrg ')