Check PyTorch version, CPU and GPU(CUDA) in PyTorch

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*My post explains how to create and acceess a tensor.

__version__ can check PyTorch version as shown below. *__version__ can be used with torch but not with a tensor:

import torch

torch.__version__ # 2.2.1+cu121

cpu.is_available(), cpu.device_count() or cpu.current_device() can check if CPU is available, getting a scalar as shown below:

*Memos:

• cpu.is_available(), cpu.device_count() or cpu.current_device() can be used with torch but not with a tensor.
• cpu.device_count() can get the number of CPUs. *It always gets 1:
• cpu.current_device() can get the index of a currently selected CPU. *It always gets cpu:

import torch

torch.cpu.is_available() # True

torch.cpu.device_count() # 1

torch.cpu.current_device() # cpu

cuda.is_available() or cuda.device_count() can check if GPU(CUDA) is available, getting a scalar as shown below:

*Memos:

• cuda.is_available() or cuda.device_count() can be used with torch but not with a tensor.
• cuda.device_count() can get the number of GPUs.

import torch

torch.cuda.is_available() # True

torch.cuda.device_count() # 1

In addition, you can use cuda.current_device(), cuda.get_device_name() or cuda.get_device_properties(), getting a scalar as shown below:

*Memos:

• cuda.current_device(), cuda.get_device_name() or cuda.get_device_properties() can be used with torch but not with a tensor.
• cuda.current_device() can get the index of a currently selected GPU.
• cuda.get_device_name() can get the name of a GPU. *Memos:
  • The 1st argument with torch is device(Optional-Default:None-Type:(str, int or device().
  • If it's None, cuda.current_device() is used.
  • The number must be zero or positive.
  • Only cuda can be set to device.
  • My post explains device().
• cuda.get_device_properties() can get the properties of a GPU. *Memos:
  • The 1st argument with torch is device(Required-Type:str, int or device()).
  • The number must be zero or positive.
  • Only cuda can be set to device.
  • My post explains device().

torch.cuda.current_device() # 0

torch.cuda.get_device_name()
torch.cuda.get_device_name(device='cuda:0')
torch.cuda.get_device_name(device='cuda')
torch.cuda.get_device_name(device=0)
torch.cuda.get_device_name(device=torch.device(device='cuda:0'))
torch.cuda.get_device_name(device=torch.device(device='cuda'))
torch.cuda.get_device_name(device=torch.device(device=0))
torch.cuda.get_device_name(device=torch.device(type='cuda'))
torch.cuda.get_device_name(device=torch.device(type='cuda', index=0))
# Tesla T4

torch.cuda.get_device_properties(device='cuda:0')
torch.cuda.get_device_properties(device='cuda')
torch.cuda.get_device_properties(device=0)
torch.cuda.get_device_properties(device=torch.device(device='cuda:0'))
torch.cuda.get_device_properties(device=torch.device(device='cuda'))
torch.cuda.get_device_properties(device=torch.device(device=0))
torch.cuda.get_device_name(device=torch.device(type='cuda'))
torch.cuda.get_device_name(device=torch.device(type='cuda', index=0))
# _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, 
# total_memory=15102MB, multi_processor_count=40)

!nvidia-smi can get the information about GPUs as shown below:

!nvidia-smi

Wed May 15 13:18:15 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |
| N/A   56C    P0              28W /  70W |    105MiB / 15360MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+