Python code to test PyTorch for CUDA GPU (NVIDIA card) capability

PyTorch is a machine learning package for Python. This code sample will test if it access to your Graphical Processing Unit (GPU) to use “CUDA


<pre>from __future__ import print_function
import torch

x = torch.rand(5, 3)
print(x)

if not torch.cuda.is_available():
   print ("Cuda is available")
   device_id = torch.cuda.current_device()
   gpu_properties = torch.cuda.get_device_properties(device_id)
   print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
          "%.1fGb total memory.\n" % 
          (torch.cuda.device_count(),
          device_id,
          gpu_properties.name,
          gpu_properties.major,
          gpu_properties.minor,
          gpu_properties.total_memory / 1e9))
else:    
   print ("Cuda is not available")
</pre>

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords.

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