% reset -f
from __future__ import print_function
from __future__ import division
import math
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import torch
import sys
print('__Python VERSION:', sys.version)
print('__pyTorch VERSION:', torch.__version__)
print('__CUDA VERSION')
from subprocess import call
# call(["nvcc", "--version"]) does not work
! nvcc --version
print('__CUDNN VERSION:', torch.backends.cudnn.version())
print('__Number CUDA Devices:', torch.cuda.device_count())
print('__Devices')
call(["nvidia-smi", "--format=csv", "--query-gpu=index,name,driver_version,memory.total,memory.used,memory.free"])
print('Active CUDA Device: GPU', torch.cuda.current_device())

print ('Available devices ', torch.cuda.device_count())
print ('Current cuda device ', torch.cuda.current_device())



__Python VERSION: 3.6.1 |Anaconda custom (64-bit)| (default, May 11 2017, 13:09:58) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
__pyTorch VERSION: 0.2.0_4
__CUDA VERSION
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61
__CUDNN VERSION: 6021
__Number CUDA Devices: 3
__Devices
Active CUDA Device: GPU 0
Available devices  3
Current cuda device  0

use_cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
Tensor = FloatTensor


import pycuda
from pycuda import compiler
import pycuda.driver as drv

drv.init()
print("%d device(s) found." % drv.Device.count())
           
for ordinal in range(drv.Device.count()):
    dev = drv.Device(ordinal)
    print (ordinal, dev.name())

3 device(s) found.
0 GeForce GTX 1080 Ti
1 GeForce GTX 1080 Ti
2 GeForce GTX 1080 Ti

from pycuda import gpuarray
from pycuda.curandom import rand as curand
 # -- initialize the device
import pycuda.autoinit

height = 100
width = 200
X = curand((height, width), np.float32)
X.flags.c_contiguous 
print (type(X))

<class 'pycuda.gpuarray.GPUArray'>

torch.cuda.device_count()

3