我们从Python开源项目中，提取了以下20个代码示例，用于说明如何使用chainer.cuda.available()。
def __call__(self, opt): if cuda.available: kernel = cuda.elementwise( 'T low, T high', 'T p', 'p = (p < low) ? low : (p > high) ? high : p', 'weight_clip') for link in opt.target.links(): # only apply to binary layers if getattr(link,'cname',False): for param in link.params(): p = param.data with cuda.get_device(p) as dev: if int(dev) == -1: numpy.clip(p, self.low, self.high) else: kernel(self.low, self.high, p)
def __call__(self, opt): if cuda.available: kernel = cuda.elementwise( 'T low, T high', 'T p', 'p = (p < low) ? low : (p > high) ? high : p', 'weight_clip') for param in opt.target.params(): p = param.data with cuda.get_device(p) as dev: if int(dev) == -1: numpy.clip(p, self.low, self.high) else: kernel(self.low, self.high, p)
def __call__(self, opt): if cuda.available: kernel = cuda.elementwise( 'T p, T decay', 'T g', 'g += decay * p', 'weight_decay') rate = self.rate for name, param in opt.target.namedparams(): if name == 'b' or name.endswith('/b'): continue p, g = param.data, param.grad with cuda.get_device(p) as dev: if int(dev) == -1: g += rate * p else: kernel(p, rate, g)
def sample(self, trainer): x = trainer.updater.sample() x = x.data if cuda.available and isinstance(x, cuda.ndarray): x = cuda.to_cpu(x) return x
def __call__(self, opt): if cuda.available: kernel = cuda.elementwise( 'T p, T decay', 'T g', 'g += decay * p', 'weight_decay') rate = self.rate for param in opt.target.params(): p, g = param.data, param.grad with cuda.get_device(p) as dev: if int(dev) == -1: g += rate * p else: kernel(p, rate, g)
def __call__(self, opt): if cuda.available: kernel = cuda.elementwise( 'T s, T decay', 'T g', 'g += decay * s', 'lasso') rate = self.rate for param in opt.target.params(): p, g = param.data, param.grad xp = cuda.get_array_module(p) sign = xp.sign(p) with cuda.get_device(p) as dev: if int(dev) == -1: g += rate * sign else: kernel(sign, rate, g)
def empty_like(x): if cuda.available and isinstance(x, cuda.ndarray): return cuda.cupy.empty_like(x) else: return numpy.empty_like(x)
def test_get_dummy_device(self): if not cuda.available: self.assertIs(cuda.get_device(), cuda.DummyDevice)
def test_to_gpu_unavailable(self): x = numpy.array([1]) if not cuda.available: with self.assertRaises(RuntimeError): cuda.to_gpu(x)
def test_empy_like_unavailable(self): x = numpy.array([1]) if not cuda.available: with self.assertRaises(RuntimeError): cuda.empty_like(x)
def __init__(self, blocking_method='non_block'): if not cuda.available: raise RuntimeError('CUDA must be available to use GPUTimer.') if not (blocking_method == 'non_block' or blocking_method == 'block_first_time' or blocking_method == 'block_every_time'): raise ValueError( 'Invalid blocking method:{}'.format(blocking_method)) self.blocking_method = blocking_method self.reset()
def gpu_enabled(self): if cuda.available is False: return False return self._gpu
def gpu(self): if cuda.available is False: return False return True if self.xp is cuda.cupy else False
