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Python wrappers for the NVIDIA cudnn 6.5 R1 libraries.

Project description

Python wrappers for the NVIDIA cuDNN libraries.

This is a set of minimal Python wrappers for the NVIDIA cuDNN library of convolutional neural network primitives. NVIDIA cuDNN is available free of charge, but requires an NVIDIA developer account to download. Users should follow the cuDNN API documentation to use these wrappers, as they faithfully replicate the cuDNN C API.

These wrappers expose the full cuDNN API as Python functions, but are minimalistic in that they don’t implement any higher order functionality, such as operating directly on data structures like PyCUDA GPUArray or cudamat CUDAMatrix. Since the interface faithfully replicates the C API, the user is responsible for allocating and deallocating handles to all cuDNN data structures and passing references to arrays as pointers. However, cuDNN status codes are translated to Python exceptions. The most common application for these wrappers will be to be used along PyCUDA, but they will work equally well with other frameworks such as CUDAMat.

Users need to make sure that they pass all arguments as the correct data type, that is ctypes.c_void_p for all handles and array pointers and ctypes.c_int for all integer arguments and enums. Here is an example on how to perform forward convolution on a PyCUDA GPUArray:

import pycuda.autoinit
from pycuda import gpuarray
import libcudnn, ctypes
import numpy as np

# Create a cuDNN context
cudnn_context = libcudnn.cudnnCreate()

# Set some options and tensor dimensions
accumulate = libcudnn.cudnnAccumulateResults['CUDNN_RESULT_NO_ACCUMULATE']
tensor_format = libcudnn.cudnnTensorFormat['CUDNN_TENSOR_NCHW']
data_type = libcudnn.cudnnDataType['CUDNN_DATA_FLOAT']
convolution_mode = libcudnn.cudnnConvolutionMode['CUDNN_CROSS_CORRELATION']
convolution_path = libcudnn.cudnnConvolutionPath['CUDNN_CONVOLUTION_FORWARD']

n_input = 100
filters_in = 10
filters_out = 8
height_in = 20
width_in = 20
height_filter = 5
width_filter = 5
pad_h = 4
pad_w = 4
vertical_stride = 1
horizontal_stride = 1
upscalex = 1
upscaley = 1

# Input tensor
X = gpuarray.to_gpu(np.random.rand(n_input, filters_in, height_in, width_in)

# Filter tensor
filters = gpuarray.to_gpu(np.random.rand(filters_out,
    filters_in, height_filter, width_filter).astype(np.float32))

#Descriptor for input
X_desc = libcudnn.cudnnCreateTensor4dDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(X_desc, tensor_format, data_type,
    n_input, filters_in, height_in, width_in)

# Filter descriptor
filters_desc = libcudnn.cudnnCreateFilterDescriptor()
libcudnn.cudnnSetFilterDescriptor(filters_desc, data_type, filters_out,
    filters_in, height_filter, width_filter)

# Convolution descriptor
conv_desc = libcudnn.cudnnCreateConvolutionDescriptor()
libcudnn.cudnnSetConvolutionDescriptor(conv_desc, X_desc, filters_desc,
    pad_h, pad_w, vertical_stride, horizontal_stride, upscalex, upscaley,

# Get output dimensions (first two values are n_input and filters_out)
_, _, height_output, width_output = libcudnn.cudnnGetOutputTensor4dDim(
    conv_desc, convolution_path)

# Output tensor
Y = gpuarray.empty((n_input, filters_out, height_output, width_output), np.float32)
Y_desc = libcudnn.cudnnCreateTensor4dDescriptor()
libcudnn.cudnnSetTensor4dDescriptor(Y_desc, tensor_format, data_type, n_input,
    filters_out, height_output, width_output)

# Get pointers to GPU memory
X_data = ctypes.c_void_p(int(X.gpudata))
filters_data = ctypes.c_void_p(int(filters.gpudata))
Y_data = ctypes.c_void_p(int(Y.gpudata))

# Perform convolution
libcudnn.cudnnConvolutionForward(cudnn_context, X_desc, X_data,
    filters_desc, filters_data, conv_desc,
    Y_desc, Y_data, accumulate)

# Clean up


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pip install cudnn-python-wrappers

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