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PyTorch View used GPU

Unleash GPU Power for Deep Learning! Master PyTorch View to optimize training on your graphics card. Accelerate model development and conquer complex tasks

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Pytorch
Pytorch

TrickOrTip.com – In deep learning, it is common practice to use GPUs for model training and inference because GPUs can provide faster computing speeds than CPUs. PyTorch, as a popular deep learning framework, provides convenient tools to manage GPU resources. This article will introduce how to use PyTorch to view the GPU used to help developers who are new to the industry better understand and use this feature.

Step analysis

Check GPU availability

Before using the GPU, we first need to check if the system has an available GPU. PyTorch provides torch.cuda.is_available()functions to determine whether the current system has an available GPU. Here is the corresponding code:

import torch

if torch.cuda.is_available():
    print("GPU is available")
else:
    print("GPU is not available")

This code first imports the PyTorch library and then uses torch.cuda.is_available()a function to check whether the current system has an available GPU. If the return result is True, it means that the GPU is available; if the return result is False, it means that there is no available GPU.

Check current GPU device

If the GPU is available, we need to further determine the currently used GPU device. PyTorch provides torch.cuda.current_device()functions to get the index of the currently used GPU device. Here is the corresponding code:

import torch

if torch.cuda.is_available():
    device = torch.cuda.current_device()
    print(f"Current GPU device: {device}")
else:
    print("GPU is not available")

torch.cuda.current_device()This code first imports the PyTorch library and uses a function to get the index of the currently used GPU device if the GPU is available and stores it in a variable device. Then, print out the index of the currently used GPU device.

View GPU memory usage

In addition to getting the index of the currently used GPU device, we can also view the memory usage of the GPU. PyTorch provides torch.cuda.memory_allocated()functions to get the currently allocated GPU memory in bytes. Here is the corresponding code:

import torch

if torch.cuda.is_available():
    device = torch.cuda.current_device()
    print(f"Current GPU device: {device}")

    memory_allocated = torch.cuda.memory_allocated(device) / 1024**3  # Convert to GB
    print(f"GPU memory allocated: {memory_allocated:.2f} GB")
else:
    print("GPU is not available")

This code first imports the PyTorch library and gets the index of the currently used GPU device if the GPU is available. Then, use torch.cuda.memory_allocated(device)a function to get the currently allocated GPU memory and convert it to GB units. Finally, print out the GPU memory usage.

Summarize

With the above steps, we can easily see which GPU is being used as well as the memory usage of the GPU. In deep learning tasks, it is very important to understand and manage GPU resources, because rational allocation and use of GPU can improve the efficiency of model training and inference. I hope this article will be helpful to developers who are new to the industry and enable them to better use PyTorch for deep learning work.

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