Win10+Anaconda+RTX3070+Pytorch-GPU deep learning environment construction

1. CUDA installation

1. Query the CUDA driver version:

 

 2. Download and install:

CUDA Toolkit Archive | NVIDIA DeveloperPrevious releases of the CUDA Toolkit, GPU Computing SDK, documentation and developer drivers can be found using the links below. Please select the release you want from the list below, and be sure to check www.nvidia.com/drivers for more recent production drivers appropriate for your hardware configuration.https://developer.nvidia.com/cuda-toolkit-archive

3. Check the version of CUDA in the environment

nvcc -V

Two, Pytorch installation

1. pytorch official website:

 PyTorch An open source machine learning framework that accelerates the path from research prototyping to production deployment.https://pytorch.org/

2. Select the installation method:

3. Localized installation method:

1. Tsinghua mirror source

Index of /anaconda/cloud/pytorch/win-64/ | Tsinghua University open source software mirror station | Tsinghua Open Source Mirror Index of /anaconda/cloud/pytorch/win-64/ | Tsinghua University open source software mirror station, dedicated to domestic Provide high-quality open source software mirroring and Linux mirroring source services to campus users to help users obtain open source software more conveniently. This mirror station is operated and maintained by Tsinghua University TUNA Association. https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/

2. Local installation

 

3. Post-installation test

(pytorch-gpu) C:\Users\u_name>python
>>>import torch
>>>torch.__version__
'2.0.1'
>>>torch.cuda.is_available()
True