SoFunction
Updated on 2025-04-28

Summary of the method to determine whether PyTorch is GPU version or CPU version

Preface

As one of the most popular deep learning frameworks at present, PyTorch supports running on CPUs and GPUs (NVIDIA CUDA). For deep learning developers, it is crucial to correctly identify the PyTorch version, because the GPU version can bring 10-100 times performance improvement. This article will provide a comprehensive introduction to how to judge your PyTorch installation version and provide detailed case analysis and problem solutions.

Why do we need to distinguish between GPU and CPU versions?

Performance differences

The GPU version of PyTorch can use the CUDA core of the NVIDIA graphics card for parallel computing:

  • Training speed is usually 10-100 times faster than CPU
  • Ability to handle larger batch size
  • Supports more complex model architectures

Hardware requirements

The GPU version needs to meet the following conditions:

  • Compatible NVIDIA graphics cards (such as RTX 30/40 series, Tesla series, etc.)
  • Correctly installed NVIDIA driver and CUDA toolkit
  • Hardware-matched version of PyTorch GPU

How to check the PyTorch version?

Method 1: Quick check using the command line

Run the following command to get basic information:

python -c "import torch; print(torch.__version__); print('CUDA available:', .is_available()); print('Device count:', .device_count())"

Output Case 1: GPU version works normally

2.3.0+cu121
CUDA available: True
Device count: 1

Interpretation：

  • +cu121Indicates that the CUDA version used by PyTorch is 12.1
  • CUDA available: TrueIndicates that CUDA is available
  • Device count: 1Indicates that 1 available GPU was detected

Output Case 2: CPU Version

2.3.0
CUDA available: False
Device count: 0

Interpretation：

  • No version number+cuxxSuffix, indicating that it is the CPU version
  • CUDA available: FalseConfirm that CUDA is not supported

Method 2: Use detailed check scripts

import torch

print(f"PyTorchVersion: {torch.__version__}")
print(f"CUDAAvailable: {.is_available()}")

if .is_available():
    print(f"CUDAVersion: {}")
    print(f"GPUNumber of equipment: {.device_count()}")
    print(f"Current equipment: {.current_device()}")
    print(f"Device name: {.get_device_name(0)}")
    print(f"Device memory: {.get_device_properties(0).total_memory/1024**3:.2f} GB")
else:
    print("The CPU version of PyTorch or CUDA is not available")

Output case: Detailed GPU information

PyTorchVersion: 2.3.0+cu121
CUDAAvailable: True
CUDAVersion: 12.1
GPUNumber of equipment: 1
Current equipment: 0
Device name: NVIDIA GeForce RTX 4090
Device memory: 24.00 GB

FAQs and Solutions

Problem 1: GPU version is installed but it is not available

Possible Causes：

  • NVIDIA driver is not installed correctly
  • CUDA toolkit version mismatch
  • PyTorch version is incompatible with CUDA version

Solution：

  • Check NVIDIA driver: Runnvidia-smi
  • Check CUDA version:nvcc --version
  • Reinstall the matching version of PyTorch

This is the article about the method of judging whether PyTorch is GPU version or CPU version. For more related content on judging whether PyTorch is GPU version or CPU version, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!