PyCUDA November 2014

pycuda@tiker.net

6 participants
10 discussions

by Andreas Kloeckner

Mike, Mike McFarlane <mike.mcfarlane(a)iproov.com> writes: >> I'm using Numpy 1.4.1. Thanks for your help. First, please keep the list cc'd. That makes answers searchable. Second, could you try upgrading that? You don't even need to mess with your system to do so--just use a virtualenv. Andreas

5 years, 9 months

Re: [PyCUDA] Install issue

by Andreas Kloeckner

Mike McFarlane <mike.mcfarlane(a)iproov.com> writes: > Hi > > I've installed pycuda following > http://wiki.tiker.net/PyCuda/Installation/Linux > > When I try to run test/test_driver.py it fails many tests, mainly with > 'TypeError: 'numpy.ndarray' does not have the buffer interface'. The output > is below for test_driver.py and the initial make. > > Can anyone explain what is wrong please? > > Thanks, and apologies if this mailing list isn't the right place for such > Qs. It is. What's your version of numpy? Andreas

5 years, 9 months

Install issue

by Mike McFarlane

Hi I've installed pycuda following http://wiki.tiker.net/PyCuda/Installation/Linux When I try to run test/test_driver.py it fails many tests, mainly with 'TypeError: 'numpy.ndarray' does not have the buffer interface'. The output is below for test_driver.py and the initial make. Can anyone explain what is wrong please? Thanks, and apologies if this mailing list isn't the right place for such Qs. Mike python test_driver.py ================================================= test session starts ================================================= platform linux2 -- Python 2.6.6 -- pytest-2.3.5 collected 23 items test_driver.py FFF..F.F.FFFF.FFsFFF... ====================================================== FAILURES ======================================================= ___________________________________________ TestDriver.test_simple_kernel_2 ___________________________________________ args = (<test_driver.TestDriver instance at 0x22ceea8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x229bb18>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x22ceea8> @mark_cuda_test def test_simple_kernel_2(self): mod = SourceModule(""" __global__ void multiply_them(float *dest, float *a, float *b) { const int i = threadIdx.x; dest[i] = a[i] * b[i]; } """) multiply_them = mod.get_function("multiply_them") a = np.random.randn(400).astype(np.float32) b = np.random.randn(400).astype(np.float32) > a_gpu = drv.to_device(a) test_driver.py:66: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bf_obj = array([-0.4856573 , 0.45444968, 1.2984767 , -0.02761065, 0.41128042, ...0.56486648, -0.43269619, 0.55279583, -1.11403978, -0.85410458], dtype=float32) def to_device(bf_obj): import sys if sys.version_info >= (2, 7): bf = memoryview(bf_obj).tobytes() else: bf = buffer(bf_obj) result = mem_alloc(len(bf)) > memcpy_htod(result, bf) E TypeError: 'buffer' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:776: TypeError _______________________________________________ TestDriver.test_memory ________________________________________________ args = (<test_driver.TestDriver instance at 0x228dd88>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x23031b8>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x228dd88> @mark_cuda_test def test_memory(self): z = np.random.randn(400).astype(np.float32) > new_z = drv.from_device_like(drv.to_device(z), z) test_driver.py:28: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bf_obj = array([ -4.73744690e-01, 1.29374540e+00, -6.42129302e-01, 3.951009...1, -1.27567184e+00, 1.12105417e+00, -6.89846277e-01], dtype=float32) def to_device(bf_obj): import sys if sys.version_info >= (2, 7): bf = memoryview(bf_obj).tobytes() else: bf = buffer(bf_obj) result = mem_alloc(len(bf)) > memcpy_htod(result, bf) E TypeError: 'buffer' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:776: TypeError ______________________________________________ TestDriver.test_gpuarray _______________________________________________ args = (<test_driver.TestDriver instance at 0x2487b90>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x23030c8>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x2487b90> @mark_cuda_test def test_gpuarray(self): a = np.arange(200000, dtype=np.float32) b = a + 17 import pycuda.gpuarray as gpuarray > a_g = gpuarray.to_gpu(a) test_driver.py:148: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ary = array([ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, ..., 1.99997000e+05, 1.99998000e+05, 1.99999000e+05], dtype=float32) allocator = <Boost.Python.function object at 0x1a8f7c0> def to_gpu(ary, allocator=drv.mem_alloc): """converts a numpy array to a GPUArray""" result = GPUArray(ary.shape, ary.dtype, allocator, strides=ary.strides) > result.set(ary) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/gpuarray.py:913: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <[TypeError("'numpy.ndarray' does not have the buffer interface") raised in repr()] SafeRepr object at 0x2477ea8> ary = array([ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, ..., 1.99997000e+05, 1.99998000e+05, 1.99999000e+05], dtype=float32) def set(self, ary): assert ary.size == self.size assert ary.dtype == self.dtype if ary.strides != self.strides: from warnings import warn warn("Setting array from one with different strides/storage order. " "This will cease to work in 2013.x.", stacklevel=2) assert self.flags.forc if self.size: > drv.memcpy_htod(self.gpudata, ary) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/gpuarray.py:228: TypeError ____________________________________________ TestDriver.test_simple_kernel ____________________________________________ args = (<test_driver.TestDriver instance at 0x1e73908>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b2488>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x1e73908> @mark_cuda_test def test_simple_kernel(self): mod = SourceModule(""" __global__ void multiply_them(float *dest, float *a, float *b) { const int i = threadIdx.x; dest[i] = a[i] * b[i]; } """) multiply_them = mod.get_function("multiply_them") a = np.random.randn(400).astype(np.float32) b = np.random.randn(400).astype(np.float32) dest = np.zeros_like(a) multiply_them( drv.Out(dest), drv.In(a), drv.In(b), > block=(400, 1, 1)) test_driver.py:49: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ func = <pycuda._driver.Function object at 0x229bd70> args = (<pycuda.driver.Out object at 0x227dcd0>, <pycuda.driver.In object at 0x227ded0>, <pycuda.driver.In object at 0x227ddd0>) kwargs = {}, grid = (1, 1), stream = None, block = (400, 1, 1), shared = 0, texrefs = [], time_kernel = False handlers = [<pycuda.driver.Out object at 0x227dcd0>, <pycuda.driver.In object at 0x227ded0>, <pycuda.driver.In object at 0x227ddd0>] arg_buf = '\x00\x00!\x00\x00\x00\x00\x00\x00\x07!\x00\x00\x00\x00\x00\x00\x0e!\x00\x00\x00\x00\x00' handler = <pycuda.driver.In object at 0x227ded0> def function_call(func, *args, **kwargs): grid = kwargs.pop("grid", (1, 1)) stream = kwargs.pop("stream", None) block = kwargs.pop("block", None) shared = kwargs.pop("shared", 0) texrefs = kwargs.pop("texrefs", []) time_kernel = kwargs.pop("time_kernel", False) if kwargs: raise ValueError( "extra keyword arguments: %s" % (",".join(kwargs.iterkeys()))) if block is None: raise ValueError("must specify block size") func._set_block_shape(*block) handlers, arg_buf = _build_arg_buf(args) for handler in handlers: > handler.pre_call(stream) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:380: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <pycuda.driver.In object at 0x227ded0>, stream = None def pre_call(self, stream): if stream is not None: memcpy_htod(self.get_device_alloc(), self.array) else: > memcpy_htod(self.get_device_alloc(), self.array) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:82: TypeError _______________________________________ TestDriver.test_multichannel_2d_texture _______________________________________ args = (<test_driver.TestDriver instance at 0x2478d88>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b2f50>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x2478d88> @mark_cuda_test def test_multichannel_2d_texture(self): mod = SourceModule(""" #define CHANNELS 4 texture<float4, 2, cudaReadModeElementType> mtx_tex; __global__ void copy_texture(float *dest) { int row = threadIdx.x; int col = threadIdx.y; int w = blockDim.y; float4 texval = tex2D(mtx_tex, row, col); dest[(row*w+col)*CHANNELS + 0] = texval.x; dest[(row*w+col)*CHANNELS + 1] = texval.y; dest[(row*w+col)*CHANNELS + 2] = texval.z; dest[(row*w+col)*CHANNELS + 3] = texval.w; } """) copy_texture = mod.get_function("copy_texture") mtx_tex = mod.get_texref("mtx_tex") shape = (5, 6) channels = 4 a = np.asarray( np.random.randn(*((channels,)+shape)), dtype=np.float32, order="F") drv.bind_array_to_texref( > drv.make_multichannel_2d_array(a, order="F"), mtx_tex) test_driver.py:261: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ndarray = array([[[-0.87390321, 0.78430617, 0.80751866, 0.0188568 , 0.31150779, ... -1.94733143, -1.69470119, 0.88077658, 0.6898424 ]]], dtype=float32) order = 'F' def make_multichannel_2d_array(ndarray, order): """Channel count has to be the first dimension of the C{ndarray}.""" descr = ArrayDescriptor() if order.upper() == "C": h, w, num_channels = ndarray.shape stride = 0 elif order.upper() == "F": num_channels, w, h = ndarray.shape stride = 2 else: raise LogicError("order must be either F or C") descr.width = w descr.height = h descr.format = dtype_to_array_format(ndarray.dtype) descr.num_channels = num_channels ary = Array(descr) copy = Memcpy2D() > copy.set_src_host(ndarray) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:744: TypeError _____________________________________________ TestDriver.test_2d_texture ______________________________________________ args = (<test_driver.TestDriver instance at 0x2478bd8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b25f0>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x2478bd8> @mark_cuda_test def test_2d_texture(self): mod = SourceModule(""" texture<float, 2, cudaReadModeElementType> mtx_tex; __global__ void copy_texture(float *dest) { int row = threadIdx.x; int col = threadIdx.y; int w = blockDim.y; dest[row*w+col] = tex2D(mtx_tex, row, col); } """) copy_texture = mod.get_function("copy_texture") mtx_tex = mod.get_texref("mtx_tex") shape = (3, 4) a = np.random.randn(*shape).astype(np.float32) > drv.matrix_to_texref(a, mtx_tex, order="F") test_driver.py:188: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ matrix = array([[ 1.00075412, -1.55745828, -0.64481455, 1.14575994], [-3.110294...], [-2.00341797, 0.95837516, 0.69810289, -0.08028961]], dtype=float32) texref = <pycuda._driver.TextureReference object at 0x24b0910>, order = 'F' def matrix_to_texref(matrix, texref, order): > bind_array_to_texref(matrix_to_array(matrix, order), texref) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:764: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ matrix = array([[ 1.00075412, -1.55745828, -0.64481455, 1.14575994], [-3.110294...], [-2.00341797, 0.95837516, 0.69810289, -0.08028961]], dtype=float32) order = 'F', allow_double_hack = False def matrix_to_array(matrix, order, allow_double_hack=False): if order.upper() == "C": h, w = matrix.shape stride = 0 elif order.upper() == "F": w, h = matrix.shape stride = -1 else: raise LogicError("order must be either F or C") matrix = np.asarray(matrix, order=order) descr = ArrayDescriptor() descr.width = w descr.height = h if matrix.dtype == np.float64 and allow_double_hack: descr.format = array_format.SIGNED_INT32 descr.num_channels = 2 else: descr.format = dtype_to_array_format(matrix.dtype) descr.num_channels = 1 ary = Array(descr) copy = Memcpy2D() > copy.set_src_host(matrix) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:712: TypeError _____________________________________________ TestDriver.test_fp_textures _____________________________________________ args = (<test_driver.TestDriver instance at 0x2582b00>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b2500>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x2582b00> @mark_cuda_test def test_fp_textures(self): if drv.Context.get_device().compute_capability() < (1, 3): return for tp in [np.float32, np.float64]: from pycuda.tools import dtype_to_ctype tp_cstr = dtype_to_ctype(tp) mod = SourceModule(""" #include <pycuda-helpers.hpp> texture<fp_tex_%(tp)s, 1, cudaReadModeElementType> my_tex; __global__ void copy_texture(%(tp)s *dest) { int i = threadIdx.x; dest[i] = fp_tex1Dfetch(my_tex, i); } """ % {"tp": tp_cstr}) copy_texture = mod.get_function("copy_texture") my_tex = mod.get_texref("my_tex") import pycuda.gpuarray as gpuarray shape = (384,) a = np.random.randn(*shape).astype(tp) > a_gpu = gpuarray.to_gpu(a) test_driver.py:522: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ary = array([ 4.60776985e-01, 7.17113495e-01, 1.01722872e+00, -1.146432...1, 2.04511788e-02, 1.11698084e-01, -2.13677096e+00], dtype=float32) allocator = <Boost.Python.function object at 0x1a8f7c0> def to_gpu(ary, allocator=drv.mem_alloc): """converts a numpy array to a GPUArray""" result = GPUArray(ary.shape, ary.dtype, allocator, strides=ary.strides) > result.set(ary) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/gpuarray.py:913: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <[TypeError("'numpy.ndarray' does not have the buffer interface") raised in repr()] SafeRepr object at 0x2300dd0> ary = array([ 4.60776985e-01, 7.17113495e-01, 1.01722872e+00, -1.146432...1, 2.04511788e-02, 1.11698084e-01, -2.13677096e+00], dtype=float32) def set(self, ary): assert ary.size == self.size assert ary.dtype == self.dtype if ary.strides != self.strides: from warnings import warn warn("Setting array from one with different strides/storage order. " "This will cease to work in 2013.x.", stacklevel=2) assert self.flags.forc if self.size: > drv.memcpy_htod(self.gpudata, ary) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/gpuarray.py:228: TypeError ____________________________________________ TestDriver.test_vector_types _____________________________________________ args = (<test_driver.TestDriver instance at 0x25a02d8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b29b0>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x25a02d8> @mark_cuda_test def test_vector_types(self): mod = SourceModule(""" __global__ void set_them(float3 *dest, float3 x) { const int i = threadIdx.x; dest[i] = x; } """) set_them = mod.get_function("set_them") a = gpuarray.vec.make_float3(1, 2, 3) dest = np.empty((400), gpuarray.vec.float3) > set_them(drv.Out(dest), a, block=(400,1,1)) test_driver.py:98: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ func = <pycuda._driver.Function object at 0x24b27d0> args = (<pycuda.driver.Out object at 0x1e6fcd0>, array((1.0, 2.0, 3.0), dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])) kwargs = {}, grid = (1, 1), stream = None, block = (400, 1, 1), shared = 0, texrefs = [], time_kernel = False handlers = [<pycuda.driver.Out object at 0x1e6fcd0>] arg_buf = '\x00\x00!\x00\x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00@\x00\x00@ @' handler = <pycuda.driver.Out object at 0x1e6fcd0> def function_call(func, *args, **kwargs): grid = kwargs.pop("grid", (1, 1)) stream = kwargs.pop("stream", None) block = kwargs.pop("block", None) shared = kwargs.pop("shared", 0) texrefs = kwargs.pop("texrefs", []) time_kernel = kwargs.pop("time_kernel", False) if kwargs: raise ValueError( "extra keyword arguments: %s" % (",".join(kwargs.iterkeys()))) if block is None: raise ValueError("must specify block size") func._set_block_shape(*block) handlers, arg_buf = _build_arg_buf(args) for handler in handlers: handler.pre_call(stream) for texref in texrefs: func.param_set_texref(texref) post_handlers = [handler for handler in handlers if hasattr(handler, "post_call")] if stream is None: if time_kernel: Context.synchronize() from time import time start_time = time() func._launch_kernel(grid, block, arg_buf, shared, None) if post_handlers or time_kernel: Context.synchronize() if time_kernel: run_time = time()-start_time for handler in post_handlers: > handler.post_call(stream) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:405: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <pycuda.driver.Out object at 0x1e6fcd0>, stream = None def post_call(self, stream): if stream is not None: memcpy_dtoh(self.array, self.get_device_alloc()) else: > memcpy_dtoh(self.array, self.get_device_alloc()) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:90: TypeError ________________________________________ TestDriver.test_multiple_2d_textures _________________________________________ args = (<test_driver.TestDriver instance at 0x280f950>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b2e60>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x280f950> @mark_cuda_test def test_multiple_2d_textures(self): mod = SourceModule(""" texture<float, 2, cudaReadModeElementType> mtx_tex; texture<float, 2, cudaReadModeElementType> mtx2_tex; __global__ void copy_texture(float *dest) { int row = threadIdx.x; int col = threadIdx.y; int w = blockDim.y; dest[row*w+col] = tex2D(mtx_tex, row, col) + tex2D(mtx2_tex, row, col); } """) copy_texture = mod.get_function("copy_texture") mtx_tex = mod.get_texref("mtx_tex") mtx2_tex = mod.get_texref("mtx2_tex") shape = (3,4) a = np.random.randn(*shape).astype(np.float32) b = np.random.randn(*shape).astype(np.float32) > drv.matrix_to_texref(a, mtx_tex, order="F") test_driver.py:223: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ matrix = array([[-0.60509264, -0.69680727, -1.962533 , 0.67714691], [-1.094154...], [ 0.40417606, -0.97287714, 0.10696917, -1.54577947]], dtype=float32) texref = <pycuda._driver.TextureReference object at 0x24b0d00>, order = 'F' def matrix_to_texref(matrix, texref, order): > bind_array_to_texref(matrix_to_array(matrix, order), texref) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:764: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ matrix = array([[-0.60509264, -0.69680727, -1.962533 , 0.67714691], [-1.094154...], [ 0.40417606, -0.97287714, 0.10696917, -1.54577947]], dtype=float32) order = 'F', allow_double_hack = False def matrix_to_array(matrix, order, allow_double_hack=False): if order.upper() == "C": h, w = matrix.shape stride = 0 elif order.upper() == "F": w, h = matrix.shape stride = -1 else: raise LogicError("order must be either F or C") matrix = np.asarray(matrix, order=order) descr = ArrayDescriptor() descr.width = w descr.height = h if matrix.dtype == np.float64 and allow_double_hack: descr.format = array_format.SIGNED_INT32 descr.num_channels = 2 else: descr.format = dtype_to_array_format(matrix.dtype) descr.num_channels = 1 ary = Array(descr) copy = Memcpy2D() > copy.set_src_host(matrix) E TypeError: 'numpy.ndarray' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:712: TypeError ___________________________________________ TestDriver.test_streamed_kernel ___________________________________________ args = (<test_driver.TestDriver instance at 0x23007e8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24b2e60>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x23007e8> @mark_cuda_test def test_streamed_kernel(self): # this differs from the "simple_kernel" case in that *all* computation # and data copying is asynchronous. Observe how this necessitates the # use of page-locked memory. mod = SourceModule(""" __global__ void multiply_them(float *dest, float *a, float *b) { const int i = threadIdx.x*blockDim.y + threadIdx.y; dest[i] = a[i] * b[i]; } """) multiply_them = mod.get_function("multiply_them") shape = (32, 8) a = drv.pagelocked_zeros(shape, dtype=np.float32) b = drv.pagelocked_zeros(shape, dtype=np.float32) a[:] = np.random.randn(*shape) b[:] = np.random.randn(*shape) a_gpu = drv.mem_alloc(a.nbytes) b_gpu = drv.mem_alloc(b.nbytes) strm = drv.Stream() > drv.memcpy_htod_async(a_gpu, a, strm) E TypeError: 'numpy.ndarray' does not have the buffer interface test_driver.py:127: TypeError _________________________________________ TestDriver.test_prepared_invocation _________________________________________ args = (<test_driver.TestDriver instance at 0x24951b8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24a20c8>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x24951b8> @mark_cuda_test def test_prepared_invocation(self): a = np.random.randn(4,4).astype(np.float32) a_gpu = drv.mem_alloc(a.size * a.dtype.itemsize) > drv.memcpy_htod(a_gpu, a) E TypeError: 'numpy.ndarray' does not have the buffer interface test_driver.py:450: TypeError ___________________________________________ TestDriver.test_constant_memory ___________________________________________ args = (<test_driver.TestDriver instance at 0x24963f8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24a2500>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x24963f8> @mark_cuda_test def test_constant_memory(self): # contributed by Andrew Wagner module = SourceModule(""" __constant__ float const_array[32]; __global__ void copy_constant_into_global(float* global_result_array) { global_result_array[threadIdx.x] = const_array[threadIdx.x]; } """) copy_constant_into_global = module.get_function("copy_constant_into_global") const_array, _ = module.get_global('const_array') host_array = np.random.randint(0,255,(32,)).astype(np.float32) global_result_array = drv.mem_alloc_like(host_array) > drv.memcpy_htod(const_array, host_array) E TypeError: 'numpy.ndarray' does not have the buffer interface test_driver.py:551: TypeError _____________________________________ TestDriver.test_multichannel_linear_texture _____________________________________ args = (<test_driver.TestDriver instance at 0x1e61cb0>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24a2398>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x1e61cb0> @mark_cuda_test def test_multichannel_linear_texture(self): mod = SourceModule(""" #define CHANNELS 4 texture<float4, 1, cudaReadModeElementType> mtx_tex; __global__ void copy_texture(float *dest) { int i = threadIdx.x+blockDim.x*threadIdx.y; float4 texval = tex1Dfetch(mtx_tex, i); dest[i*CHANNELS + 0] = texval.x; dest[i*CHANNELS + 1] = texval.y; dest[i*CHANNELS + 2] = texval.z; dest[i*CHANNELS + 3] = texval.w; } """) copy_texture = mod.get_function("copy_texture") mtx_tex = mod.get_texref("mtx_tex") shape = (16, 16) channels = 4 a = np.random.randn(*(shape+(channels,))).astype(np.float32) > a_gpu = drv.to_device(a) test_driver.py:297: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ bf_obj = array([[[ 0.06351238, -1.50826311, 0.99232352, -0.19848856], [ 0.1630... [-0.59885794, 0.58485585, 0.93704814, 0.02101008]]], dtype=float32) def to_device(bf_obj): import sys if sys.version_info >= (2, 7): bf = memoryview(bf_obj).tobytes() else: bf = buffer(bf_obj) result = mem_alloc(len(bf)) > memcpy_htod(result, bf) E TypeError: 'buffer' does not have the buffer interface /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/driver.py:776: TypeError _____________________________________________ TestDriver.test_3d_texture ______________________________________________ args = (<test_driver.TestDriver instance at 0x2585ea8>,), kwargs = {} pycuda = <module 'pycuda' from '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/__init__.pyc'> ctx = <pycuda._driver.Context object at 0x24a2848>, clear_context_caches = <function clear_context_caches at 0x1af6ed8> collect = <built-in function collect> def f(*args, **kwargs): import pycuda.driver # appears to be idempotent, i.e. no harm in calling it more than once pycuda.driver.init() ctx = make_default_context() try: assert isinstance(ctx.get_device().name(), str) assert isinstance(ctx.get_device().compute_capability(), tuple) assert isinstance(ctx.get_device().get_attributes(), dict) > inner_f(*args, **kwargs) /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg/pycuda/tools.py:453: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <test_driver.TestDriver instance at 0x2585ea8> @mark_cuda_test def test_3d_texture(self): # adapted from code by Nicolas Pinto w = 2 h = 4 d = 8 shape = (w, h, d) a = np.asarray( np.random.randn(*shape), dtype=np.float32, order="F") descr = drv.ArrayDescriptor3D() descr.width = w descr.height = h descr.depth = d descr.format = drv.dtype_to_array_format(a.dtype) descr.num_channels = 1 descr.flags = 0 ary = drv.Array(descr) copy = drv.Memcpy3D() > copy.set_src_host(a) E TypeError: 'numpy.ndarray' does not have the buffer interface test_driver.py:412: TypeError =================================== 14 failed, 8 passed, 1 skipped in 13.38 seconds =================================== -- ------------------------- make output ----------------- *** WARNING: nvcc not in path. running install running bdist_egg running egg_info writing requirements to pycuda.egg-info/requires.txt writing pycuda.egg-info/PKG-INFO writing top-level names to pycuda.egg-info/top_level.txt writing dependency_links to pycuda.egg-info/dependency_links.txt reading manifest file 'pycuda.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no files found matching '*.cpp' under directory 'bpl-subset/bpl_subset/boost' warning: no files found matching '*.html' under directory 'bpl-subset/bpl_subset/boost' warning: no files found matching '*.inl' under directory 'bpl-subset/bpl_subset/boost' warning: no files found matching '*.txt' under directory 'bpl-subset/bpl_subset/boost' warning: no files found matching '*.h' under directory 'bpl-subset/bpl_subset/libs' warning: no files found matching '*.ipp' under directory 'bpl-subset/bpl_subset/libs' warning: no files found matching '*.pl' under directory 'bpl-subset/bpl_subset/libs' writing manifest file 'pycuda.egg-info/SOURCES.txt' installing library code to build/bdist.linux-x86_64/egg running install_lib running build_py running build_ext creating build/bdist.linux-x86_64/egg creating build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/autoinit.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/compiler.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/tools.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/__init__.py -> build/bdist.linux-x86_64/egg/pycuda creating build/bdist.linux-x86_64/egg/pycuda/compyte copying build/lib.linux-x86_64-2.6/pycuda/compyte/__init__.py -> build/bdist.linux-x86_64/egg/pycuda/compyte copying build/lib.linux-x86_64-2.6/pycuda/compyte/array.py -> build/bdist.linux-x86_64/egg/pycuda/compyte copying build/lib.linux-x86_64-2.6/pycuda/compyte/dtypes.py -> build/bdist.linux-x86_64/egg/pycuda/compyte copying build/lib.linux-x86_64-2.6/pycuda/gpuarray.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/_mymako.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/debug.py -> build/bdist.linux-x86_64/egg/pycuda creating build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/__init__.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/pkt_build.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/cg.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/packeted.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/coordinate.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/operator.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/sparse/inner.py -> build/bdist.linux-x86_64/egg/pycuda/sparse copying build/lib.linux-x86_64-2.6/pycuda/_pvt_struct.so -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/cumath.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/_cluda.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/characterize.py -> build/bdist.linux-x86_64/egg/pycuda creating build/bdist.linux-x86_64/egg/pycuda/cuda copying build/lib.linux-x86_64-2.6/pycuda/cuda/pycuda-complex.hpp -> build/bdist.linux-x86_64/egg/pycuda/cuda copying build/lib.linux-x86_64-2.6/pycuda/cuda/pycuda-complex-impl.hpp -> build/bdist.linux-x86_64/egg/pycuda/cuda copying build/lib.linux-x86_64-2.6/pycuda/cuda/pycuda-helpers.hpp -> build/bdist.linux-x86_64/egg/pycuda/cuda copying build/lib.linux-x86_64-2.6/pycuda/reduction.py -> build/bdist.linux-x86_64/egg/pycuda creating build/bdist.linux-x86_64/egg/pycuda/gl copying build/lib.linux-x86_64-2.6/pycuda/gl/autoinit.py -> build/bdist.linux-x86_64/egg/pycuda/gl copying build/lib.linux-x86_64-2.6/pycuda/gl/__init__.py -> build/bdist.linux-x86_64/egg/pycuda/gl copying build/lib.linux-x86_64-2.6/pycuda/driver.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/curandom.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/elementwise.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/scan.py -> build/bdist.linux-x86_64/egg/pycuda copying build/lib.linux-x86_64-2.6/pycuda/_driver.so -> build/bdist.linux-x86_64/egg/pycuda byte-compiling build/bdist.linux-x86_64/egg/pycuda/autoinit.py to autoinit.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/compiler.py to compiler.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/tools.py to tools.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/__init__.py to __init__.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/compyte/__init__.py to __init__.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/compyte/array.py to array.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/compyte/dtypes.py to dtypes.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/gpuarray.py to gpuarray.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/_mymako.py to _mymako.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/debug.py to debug.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/__init__.py to __init__.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/pkt_build.py to pkt_build.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/cg.py to cg.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/packeted.py to packeted.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/coordinate.py to coordinate.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/operator.py to operator.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/sparse/inner.py to inner.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/cumath.py to cumath.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/_cluda.py to _cluda.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/characterize.py to characterize.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/reduction.py to reduction.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/gl/autoinit.py to autoinit.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/gl/__init__.py to __init__.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/driver.py to driver.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/curandom.py to curandom.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/elementwise.py to elementwise.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/scan.py to scan.pyc creating stub loader for pycuda/_driver.so creating stub loader for pycuda/_pvt_struct.so byte-compiling build/bdist.linux-x86_64/egg/pycuda/_driver.py to _driver.pyc byte-compiling build/bdist.linux-x86_64/egg/pycuda/_pvt_struct.py to _pvt_struct.pyc creating build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/not-zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO copying pycuda.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt creating 'dist/pycuda-2014.1-py2.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it removing 'build/bdist.linux-x86_64/egg' (and everything under it) Processing pycuda-2014.1-py2.6-linux-x86_64.egg removing '/usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg' (and everything under it) creating /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg Extracting pycuda-2014.1-py2.6-linux-x86_64.egg to /usr/lib64/python2.6/site-packages pycuda 2014.1 is already the active version in easy-install.pth Installed /usr/lib64/python2.6/site-packages/pycuda-2014.1-py2.6-linux-x86_64.egg Processing dependencies for pycuda==2014.1 Searching for decorator==3.4.0 Best match: decorator 3.4.0 Processing decorator-3.4.0-py2.6.egg decorator 3.4.0 is already the active version in easy-install.pth Using /usr/lib64/python2.6/site-packages/decorator-3.4.0-py2.6.egg Searching for pytest==2.3.5 Best match: pytest 2.3.5 Adding pytest 2.3.5 to easy-install.pth file Installing py.test script to /usr/bin Installing py.test-2.6 script to /usr/bin Using /usr/lib/python2.6/site-packages Searching for pytools==2014.3.4 Best match: pytools 2014.3.4 Processing pytools-2014.3.4-py2.6.egg pytools 2014.3.4 is already the active version in easy-install.pth Using /usr/lib64/python2.6/site-packages/pytools-2014.3.4-py2.6.egg Searching for py==1.4.18 Best match: py 1.4.18 Adding py 1.4.18 to easy-install.pth file Using /usr/lib/python2.6/site-packages Searching for appdirs==1.4.0 Best match: appdirs 1.4.0 Processing appdirs-1.4.0-py2.6.egg appdirs 1.4.0 is already the active version in easy-install.pth Using /usr/lib64/python2.6/site-packages/appdirs-1.4.0-py2.6.egg Finished processing dependencies for pycuda==2014.1 ****************************************************************************************** *Mike McFarlane* mike.mcfarlane(a)iproov.com +44 7557 780175 ******************************************************************************************

5 years, 9 months

Re: [PyCUDA] Windows installation instructions have been deleted from the wiki

by Andreas Kloeckner

Daniel Pineo <daniel(a)pineo.net> writes: > The instructions to build and install PyCUDA on Windows have been removed > from the wiki. There was a very detailed set of instructions that I had > previously used to successfully install. Can these still be found > somewhere? Is there a way to restore them to the wiki? I try to catch vandalism of this nature and undo it right away, but this seems to have slipped through the cracks. Sorry about that, it should be back up. Andreas

5 years, 10 months

Windows installation instructions have been deleted from the wiki

by Daniel Pineo

The instructions to build and install PyCUDA on Windows have been removed from the wiki. There was a very detailed set of instructions that I had previously used to successfully install. Can these still be found somewhere? Is there a way to restore them to the wiki?

5 years, 10 months

Re: [PyCUDA] Question How to Safely Use pycuda with mpi4py

by kjs

Eric Larson wrote: > Hey Kevin, > > Not sure about the CUDA limitations, I'll let others speak to that... > > But in developing the mne-python CUDA filtering code, IIRC the primary > limitation was (by far) transferring the data to and from the GPU. The FFT > computations themselves were a fraction of the total time. I suspect using > multiple jobs won't help CUDA filtering very much since the jobs would > presumably compete for the same memory bandwidth, but I would love to be > wrong about this. If it works better, it would be great to open an > mne-python issue for it, as we are always looking for speedups :) > > Cheers, > Eric > On Nov 1, 2014 7:21 PM, "kjs" <bfb(a)riseup.net> wrote: > >> Hello, >> >> I have written an MPI routine in Python that sends jobs to N worker >> processes. The root process handles file IO and the workers do >> computation. In the worker processes calls are made to the cuda enabled >> GPU to do FFTs. >> >> Is it safe to have N processes potentially making calls to the same GPU >> at the same time? I have not made any amendments to the cuda code[0], >> and have little knowledge of what could possibly go wrong. >> >> Thanks much, >> Kevin >> >> [0] I am using python-mne with cuda enabled to call scikits.cuda.fft >> https://github.com/mne-tools/mne-python/blob/master/mne/cuda.py >> >> _______________________________________________ >> PyCUDA mailing list >> PyCUDA(a)tiker.net >> http://lists.tiker.net/listinfo/pycuda >> >> > Thanks Andreas, this is good to know. I noticed that even though pycuda is currently only using one of two GPUs, that GPU is only ever at ~35% memory and ~22% processing utilization. This could be related to Eric's observation that the PCI-e 16x bus bandwidth reaches capacity while the GPU is pushing out fast FFT'ed arrays. Thus allowing for only one or two arrays in the GPU at the same time. From what I have seen, using cuda speeds up my FFTs ~2x. Though, the workers do many other computations on the CPU. It's a worst case scenario that all N workers are trying to send data to the GPU at the same time. -Kevin

5 years, 10 months

Re: [PyCUDA] Problem with PyCUDA on Ubuntu 14.10

by Tomasz Rybak

Dnia 2014-11-01, sob o godzinie 17:01 -0700, Eric Larson pisze: > Before I left work on Friday I checked to ensure that the packages in > Synaptic were all NVIDIA version 331 (I think). Even if the numbers > are right in Synaptic, I suppose there could still be a version > mismatch somewhere, so I'll check "dmesg" specifically on Monday. > > > FWIW I am pretty sure that the CUDA version jumped up from 5.5 (or > 5.0?) in 14.04 up to CUDA 6.0 in 14.10. But I assume that PyCUDA is > designed (and tested) to be forward-compatible, so I doubt that's the > problem. It depends on how Ubuntu builds PyCUDA. Check pycuda package dependencies: $ apt-cache show python-pycuda whether there is any dependency on package with 5.5 in its name, or only 6.0 packages. Regards -- Tomasz Rybak GPG/PGP key ID: 2AD5 9860 Fingerprint A481 824E 7DD3 9C0E C40A 488E C654 FB33 2AD5 9860 http://member.acm.org/~tomaszrybak

5 years, 10 months

Question How to Safely Use pycuda with mpi4py

by kjs

Hello, I have written an MPI routine in Python that sends jobs to N worker processes. The root process handles file IO and the workers do computation. In the worker processes calls are made to the cuda enabled GPU to do FFTs. Is it safe to have N processes potentially making calls to the same GPU at the same time? I have not made any amendments to the cuda code[0], and have little knowledge of what could possibly go wrong. Thanks much, Kevin [0] I am using python-mne with cuda enabled to call scikits.cuda.fft https://github.com/mne-tools/mne-python/blob/master/mne/cuda.py

5 years, 10 months

Re: [PyCUDA] Problem with PyCUDA on Ubuntu 14.10

by Andreas Kloeckner

Eric Larson <larson.eric.d(a)gmail.com> writes: > PyCUDA worked perfectly on Ubuntu 14.04, but after upgrade to 14.10 I get > the following in both Python 2 and Python 3: > >>>> import pycuda.autoinit > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > File > "/home/larsoner/.local/lib/python2.7/site-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/autoinit.py", > line 4, in <module> > cuda.init() > pycuda._driver.Error: cuInit failed: unknown > > This is the same on latest `master` (shown above) and using the version in > the Ubuntu repos. I can manually compile and run at least this example > (devicequery): > > http://www.cac.cornell.edu/vw/gpu/example_submit.aspx > > and my system is using the proprietary NVIDIA drivers, so CUDA appears to > be configured properly. Has anyone else experienced this? I've reported the > bug here as well: > > https://bugs.launchpad.net/ubuntu/+source/pycuda/+bug/1388217 Check if 'dmesg' reveals anything. You might have a mismatch between libcuda.so and you driver. (If so, good job, Ubuntu.) Andreas

5 years, 10 months

Problem with PyCUDA on Ubuntu 14.10

by Eric Larson

PyCUDA worked perfectly on Ubuntu 14.04, but after upgrade to 14.10 I get the following in both Python 2 and Python 3: >>> import pycuda.autoinit Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/larsoner/.local/lib/python2.7/site-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/autoinit.py", line 4, in <module> cuda.init() pycuda._driver.Error: cuInit failed: unknown This is the same on latest `master` (shown above) and using the version in the Ubuntu repos. I can manually compile and run at least this example (devicequery): http://www.cac.cornell.edu/vw/gpu/example_submit.aspx and my system is using the proprietary NVIDIA drivers, so CUDA appears to be configured properly. Has anyone else experienced this? I've reported the bug here as well: https://bugs.launchpad.net/ubuntu/+source/pycuda/+bug/1388217 Cheers, Eric

5 years, 10 months

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PyCUDA November 2014