PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)日萌社

人工智能AI:Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战(不定时更新)


Ubuntu 安装 CUDA、cuDNN、anaconda、pycharm、tensorflow、pytorch、JDK1.8

PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)

window 安装 PyTorch


pytorch gpu 安装
	1.官网下载安装:https://pytorch.org/get-started/locally/
	  安装命令:conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
	2.国内源直接下载链接
		网站链接:
                    window:https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/
                    linux:https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/linux-64/
		选择下载:
                    window:pytorch-1.5.0-py3.7_cpu_0.tar.bz2
                    linux:pytorch-1.5.0-py3.7_cuda10.1.243_cudnn7.6.3_0.tar.bz2
		下载链接:
                    window:https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/pytorch-1.5.0-py3.7_cpu_0.tar.bz2
                    linux:https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/linux-64/pytorch-1.5.0-py3.7_cuda10.1.243_cudnn7.6.3_0.tar.bz2
		安装:
                    window:conda install pytorch-1.5.0-py3.7_cpu_0.tar.bz2
                    linux:conda install pytorch-1.5.0-py3.7_cuda10.1.243_cudnn7.6.3_0.tar.bz2   
		pip list
			torch  1.5.0
	3.测试GPU版本的代码
		import torch
		#测试 CUDA
		print("Support CUDA ?: ", torch.cuda.is_available()) #返回True
		x = torch.tensor([10.0])
		x = x.cuda()
		print(x)
		y = torch.randn(2, 3)
		y = y.cuda()
		print(y)
		z = x + y
		print(z)
		# 测试 CUDNN
		from torch.backends import cudnn
		print("Support cudnn ?: ",cudnn.is_acceptable(x)) #返回True

PyTorch 安装 GPU版本(CUDA/cuDNN)


1.官网下载安装:https://pytorch.org/get-started/locally/
2.执行命令:conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

PyTorch 安装 GPU版本(CUDA/cuDNN)

3.比如我的是CUDA 10.0的话,需要到https://pytorch.org/get-started/previous-versions/选择其他PyTorch版本
	执行命令:conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
4.添加 清华源 下载
		conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
		conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
		conda config --set show_channel_urls yes
		conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
5.国内下载
	https://download.pytorch.org/whl/torch_stable.html

PyTorch 安装 GPU版本(CUDA/cuDNN)

4.测试GPU版本的代码

import torch
if __name__ == '__main__':
    #测试 CUDA
    print("Support CUDA ?: ", torch.cuda.is_available())
    x = torch.tensor([10.0])
    x = x.cuda()
    print(x)
 
    y = torch.randn(2, 3)
    y = y.cuda()
    print(y)
 
    z = x + y
    print(z)
 
    # 测试 CUDNN
    from torch.backends import cudnn
    print("Support cudnn ?: ",cudnn.is_acceptable(x))


PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)

PyTorch 安装 GPU版本(CUDA/cuDNN)


国内下载
https://download.pytorch.org/whl/torch_stable.html 

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torch-0.1-cp35-cp35m-macosx_10_6_x86_64.whl
torch-0.1.10.post1-cp27-none-macosx_10_7_x86_64.whl
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cu100/torchvision-0.4.0-cp36-cp36m-manylinux1_x86_64.whl
cu100/torchvision-0.4.0-cp36-cp36m-win_amd64.whl
cu100/torchvision-0.4.0-cp37-cp37m-manylinux1_x86_64.whl
cu100/torchvision-0.4.0-cp37-cp37m-win_amd64.whl
cu100/torchvision-0.4.1%2Bcu100-cp27-cp27m-linux_x86_64.whl
cu100/torchvision-0.4.1%2Bcu100-cp27-cp27mu-linux_x86_64.whl
cu100/torchvision-0.4.1%2Bcu100-cp35-cp35m-linux_x86_64.whl
cu100/torchvision-0.4.1%2Bcu100-cp36-cp36m-linux_x86_64.whl
cu100/torchvision-0.4.1%2Bcu100-cp37-cp37m-linux_x86_64.whl
cu100/torchvision-0.4.2%2Bcu100-cp27-cp27m-linux_x86_64.whl
cu100/torchvision-0.4.2%2Bcu100-cp27-cp27mu-linux_x86_64.whl
cu100/torchvision-0.4.2%2Bcu100-cp35-cp35m-linux_x86_64.whl
cu100/torchvision-0.4.2%2Bcu100-cp36-cp36m-linux_x86_64.whl
cu100/torchvision-0.4.2%2Bcu100-cp37-cp37m-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp27-cp27m-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp27-cp27mu-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp35-cp35m-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp36-cp36m-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp37-cp37m-linux_x86_64.whl
cu100/torchvision-0.5.0%2Bcu100-cp38-cp38-linux_x86_64.whl
cu101/torch-1.3.0-cp27-cp27m-manylinux1_x86_64.whl
cu101/torch-1.3.0-cp27-cp27mu-manylinux1_x86_64.whl
cu101/torch-1.3.0-cp35-cp35m-manylinux1_x86_64.whl
cu101/torch-1.3.0-cp35-cp35m-win_amd64.whl
cu101/torch-1.3.0-cp36-cp36m-manylinux1_x86_64.whl
cu101/torch-1.3.0-cp36-cp36m-win_amd64.whl
cu101/torch-1.3.0-cp37-cp37m-manylinux1_x86_64.whl
cu101/torch-1.3.0-cp37-cp37m-win_amd64.whl
cu101/torch-1.3.1-cp27-cp27m-linux_x86_64.whl
cu101/torch-1.3.1-cp27-cp27mu-linux_x86_64.whl
cu101/torch-1.3.1-cp35-cp35m-linux_x86_64.whl
cu101/torch-1.3.1-cp35-cp35m-win_amd64.whl
cu101/torch-1.3.1-cp36-cp36m-linux_x86_64.whl
cu101/torch-1.3.1-cp36-cp36m-win_amd64.whl
cu101/torch-1.3.1-cp37-cp37m-linux_x86_64.whl
cu101/torch-1.3.1-cp37-cp37m-win_amd64.whl
cu101/torch-1.4.0-cp27-cp27m-linux_x86_64.whl
cu101/torch-1.4.0-cp27-cp27mu-linux_x86_64.whl
cu101/torch-1.4.0-cp35-cp35m-linux_x86_64.whl
cu101/torch-1.4.0-cp35-cp35m-win_amd64.whl
cu101/torch-1.4.0-cp36-cp36m-linux_x86_64.whl
cu101/torch-1.4.0-cp36-cp36m-win_amd64.whl
cu101/torch-1.4.0-cp37-cp37m-linux_x86_64.whl
cu101/torch-1.4.0-cp37-cp37m-win_amd64.whl
cu101/torch-1.4.0-cp38-cp38-linux_x86_64.whl
cu101/torch-1.4.0-cp38-cp38-win_amd64.whl
cu101/torchvision-0.4.1-cp27-cp27m-linux_x86_64.whl
cu101/torchvision-0.4.1-cp27-cp27mu-linux_x86_64.whl
cu101/torchvision-0.4.1-cp35-cp35m-linux_x86_64.whl
cu101/torchvision-0.4.1-cp35-cp35m-win_amd64.whl
cu101/torchvision-0.4.1-cp36-cp36m-linux_x86_64.whl
cu101/torchvision-0.4.1-cp36-cp36m-win_amd64.whl
cu101/torchvision-0.4.1-cp37-cp37m-linux_x86_64.whl
cu101/torchvision-0.4.1-cp37-cp37m-win_amd64.whl
cu101/torchvision-0.4.2-cp27-cp27m-linux_x86_64.whl
cu101/torchvision-0.4.2-cp27-cp27mu-linux_x86_64.whl
cu101/torchvision-0.4.2-cp35-cp35m-linux_x86_64.whl
cu101/torchvision-0.4.2-cp35-cp35m-win_amd64.whl
cu101/torchvision-0.4.2-cp36-cp36m-linux_x86_64.whl
cu101/torchvision-0.4.2-cp36-cp36m-win_amd64.whl
cu101/torchvision-0.4.2-cp37-cp37m-linux_x86_64.whl
cu101/torchvision-0.4.2-cp37-cp37m-win_amd64.whl
cu101/torchvision-0.5.0-cp27-cp27m-linux_x86_64.whl
cu101/torchvision-0.5.0-cp27-cp27mu-linux_x86_64.whl
cu101/torchvision-0.5.0-cp35-cp35m-linux_x86_64.whl
cu101/torchvision-0.5.0-cp35-cp35m-win_amd64.whl
cu101/torchvision-0.5.0-cp36-cp36m-linux_x86_64.whl
cu101/torchvision-0.5.0-cp36-cp36m-win_amd64.whl
cu101/torchvision-0.5.0-cp37-cp37m-linux_x86_64.whl
cu101/torchvision-0.5.0-cp37-cp37m-win_amd64.whl
cu101/torchvision-0.5.0-cp38-cp38-linux_x86_64.whl
cu101/torchvision-0.5.0-cp38-cp38-win_amd64.whl 

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