ubuntu安装pyCUDA

0. 写在前面

安装环境:ubuntu18.04(16和18差不多，但是18太爽了)和python3(具体版本忘了，应该是3.6)

参考链接：参考了验证程序

1. 安装pyCUDA之前必须安装CUDA

    参考本人上一个博客

2.安装pyCUDA

    首先用pip3安装一般服务器会超时，这个时候也可以用清华源或者其他国内源安装，标准命令是"pip3 install pycuda"，但是这种安装方式我没试过。

   如果用sudo apt install python3-pycuda会发现这个包并不是最新包，除非降低显卡驱动版本，反正我不愿意。

   以下是本人实测的方法。

   pyCUDA各版本的变化可以参考这个，最新的编译包贴在这，python.org上也有，github有各版本压缩包，安装步骤参考这个的第一步和第三步(如下图)，第二步忽略(一般都是安装numpy了的)，注意对于python3第三步中的"python configure.py ..."改成"python3 ..."

 

3. 检验pyCUDA安装

    新建py脚本如下

import pycuda
import pycuda.driver as drv
drv.init()
print('CUDA device query (PyCUDA version) \n')
print('Detected {} CUDA Capable device(s) \n'.format(drv.Device.count()))
for i in range(drv.Device.count()):
    
    gpu_device = drv.Device(i)
    print('Device {}: {}'.format( i, gpu_device.name() ) )
    compute_capability = float( '%d.%d' % gpu_device.compute_capability() )
    print('\t Compute Capability: {}'.format(compute_capability))
    print('\t Total Memory: {} megabytes'.format(gpu_device.total_memory()//(1024**2)))
    
    # The following will give us all remaining device attributes as seen 
    # in the original deviceQuery.
    # We set up a dictionary as such so that we can easily index
    # the values using a string descriptor.
    
    device_attributes_tuples = gpu_device.get_attributes().items() 
    device_attributes = {}
    
    for k, v in device_attributes_tuples:
        device_attributes[str(k)] = v
    
    num_mp = device_attributes['MULTIPROCESSOR_COUNT']
    
    # Cores per multiprocessor is not reported by the GPU!  
    # We must use a lookup table based on compute capability.
    # See the following:
    # http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
    
    cuda_cores_per_mp = { 5.0 : 128, 5.1 : 128, 5.2 : 128, 6.0 : 64, 6.1 : 128, 6.2 : 128}[compute_capability]
    
    print('\t ({}) Multiprocessors, ({}) CUDA Cores / Multiprocessor: {} CUDA Cores'.format(num_mp, cuda_cores_per_mp, num_mp*cuda_cores_per_mp))
    
    device_attributes.pop('MULTIPROCESSOR_COUNT')
    
    for k in device_attributes.keys():
        print('\t {}: {}'.format(k, device_attributes[k]))

    应该得到输出结果如下

CUDA device query (PyCUDA version) 

Detected 1 CUDA Capable device(s) 

Device 0: GeForce GTX 1060
	 Compute Capability: 6.1
	 Total Memory: 6078 megabytes
	 (10) Multiprocessors, (128) CUDA Cores / Multiprocessor: 1280 CUDA Cores
	 ASYNC_ENGINE_COUNT: 2
	 CAN_MAP_HOST_MEMORY: 1
	 CLOCK_RATE: 1733000
	 COMPUTE_CAPABILITY_MAJOR: 6
	 COMPUTE_CAPABILITY_MINOR: 1
	 COMPUTE_MODE: DEFAULT
	 CONCURRENT_KERNELS: 1
	 ....
	 ....
	 TEXTURE_PITCH_ALIGNMENT: 32
	 TOTAL_CONSTANT_MEMORY: 65536
	 UNIFIED_ADDRESSING: 1
	 WARP_SIZE: 32