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广州知名网站建设哪家好,网络推广费用一般多少,网站百度突然不收录了,洛宁网站建设conda模式安装paddlepaddle2.4.2版本一、下载anaconda 2022.10 window-x86-x64.exe 清华镜像源Index of /anaconda/archive/ | 清华大学开源软件镜像站 | Tsinghua Open Source Mirror，进去后根据自己的需要选择对应的版本下载下载安装到磁盘空闲空间要大的D:\Pr…

conda模式安装paddlepaddle2.4.2版本
一、下载anaconda 2022.10 window-x86-x64.exe
清华镜像源Index of /anaconda/archive/ | 清华大学开源软件镜像站 | Tsinghua Open Source Mirror，进去后根据自己的需要选择对应的版本下载

下载安装到磁盘空闲空间要大的D:\ProgramData\Anaconda3

设置Path

path+=
D:\ProgramData\Anaconda3
D:\ProgramData\Anaconda3\Scripts
D:\ProgramData\Anaconda3\Library\bin
D:\ProgramData\Anaconda3\Library\mingw-w64\bin

查看版本：
conda --version
conda 22.9.0

python
Python 3.9.12 (tags/v3.9.12:b28265d, Mar 23 2022, 23:52:46) [MSC v.1929 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.

二、国内镜像源配置
通过win+R输入cmd进入命令行，通过输入下面命令配置为清华源：
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 --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/

三、conda模式安装paddlepaddle-gpu==2.4.2 cuda10.2
conda install paddlepaddle-gpu==2.4.2 cudatoolkit=10.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge

四、vscode测试

# 查看 paddle能够调用 gpu
import paddle
paddle.fluid.is_compiled_with_cuda()
paddle.utils.run_check()'''
特别注意：
在vscode中使用创建的虚拟环境
选择python的解释器：在vscode中按住快捷键Ctrl+shift+P,输入Python然后执行如下图：
Python:Select Interpreter
select D:\ProgramData\Anaconda3\python.exe
'''

测试结果：安装成功

PS E:\project\python> & D:/ProgramData/Anaconda3/python.exe e:/project/python/gpu-install.py
Running verify PaddlePaddle program ...
W0731 20:45:35.723439  3984 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 12.2, Runtime API Version: 10.2
W0731 20:45:35.740528  3984 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
PaddlePaddle works well on 1 GPU.
PaddlePaddle works well on 1 GPUs.
PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.

五、用GPU测试手写数字识别模型情况
结论：GPU执行训练时间从702秒变成69秒，从11分钟变成1分钟

GPU测试结果：运行时间Cost time: 69.26997494697571
本次预测的数字是: 0

原CPU测试结果：#运行时间Cost time: 702.2813944816589
本次预测的数字是: 0

# 使用SGD优化器，learning_rate设置为0.01时loss下降明显最优   #cost time=705.9078650474548=11.75分钟#opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters())     #运行时间cost time: 705.9078650474548#opt = paddle.optimizer.SGD(learning_rate=0.001, parameters=model.parameters())    #运行时间Cost time: 705.5072021484375#opt = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())    #运行时间Cost time: 708.7056725025177#opt = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9, parameters=model.parameters())   #运行时间Cost time: 707.7317929267883#opt = paddle.optimizer.Adagrad(learning_rate=0.01, parameters=model.parameters())    #运行时间Cost time: 756.807531118393opt = paddle.optimizer.Adam(learning_rate=0.01, parameters=model.parameters())        #运行时间Cost time: 702.2813944816589

PS E:\project\python> & D:/ProgramData/Anaconda3/python.exe e:/project/python/MNIST_Good.py
9.2.0
2.4.2
视觉相关数据集： ['DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']
自然语言相关数据集： []
数据处理方法： ['BaseTransform', 'Compose', 'Resize', 'RandomResizedCrop', 'CenterCrop', 'RandomHorizontalFlip', 'RandomVerticalFlip', 'Transpose', 'Normalize', 'BrightnessTransform', 'SaturationTransform', 'ContrastTransform', 'HueTransform', 'ColorJitter', 'RandomCrop', 'Pad', 'RandomAffine', 'RandomRotation', 'RandomPerspective', 'Grayscale', 'ToTensor', 'RandomErasing', 'to_tensor', 'hflip', 'vflip', 'resize', 'pad', 'affine', 'rotate', 'perspective', 'to_grayscale', 'crop', 'center_crop', 'adjust_brightness', 'adjust_contrast', 'adjust_hue', 'normalize', 'erase']
训练数据集数量: 60000
第一个图片为 (28, 28) [5]
W0731 20:48:01.011943 20788 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 12.2, Runtime API Version: 10.2
W0731 20:48:01.021474 20788 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
loading mnist dataset from ./work/mnist.json.gz ......
mnist dataset load done
训练数据集数量:  50000
epoch: 0, batch: 0, loss is: 2.6393485069274902
epoch: 0, batch: 200, loss is: 0.03634800389409065
epoch: 0, batch: 400, loss is: 0.06814056634902954
epoch: 1, batch: 0, loss is: 0.028553010895848274
epoch: 1, batch: 200, loss is: 0.044877491891384125
epoch: 1, batch: 400, loss is: 0.021085500717163086
epoch: 2, batch: 0, loss is: 0.09579199552536011
epoch: 2, batch: 200, loss is: 0.040042776614427567
epoch: 2, batch: 400, loss is: 0.04694807901978493
epoch: 3, batch: 0, loss is: 0.039875153452157974
epoch: 3, batch: 200, loss is: 0.012882424518465996
epoch: 3, batch: 400, loss is: 0.013728930614888668
epoch: 4, batch: 0, loss is: 0.004363641142845154
epoch: 8, batch: 0, loss is: 0.005882401950657368
epoch: 8, batch: 200, loss is: 0.06317561864852905
epoch: 8, batch: 400, loss is: 0.05743202567100525
epoch: 9, batch: 0, loss is: 0.014044929295778275
epoch: 9, batch: 200, loss is: 0.032656021416187286
epoch: 9, batch: 400, loss is: 0.002203206531703472
运行时间Cost time: 69.26997494697571
e:\project\python\MNIST_Good.py:247: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.im = im.resize((28, 28), Image.LANCZOS)
本次预测的数字是:  0
PS E:\project\python>