客户端,站长工具seo综合查询降级,wordpress侧边栏自定义,Git page wordpressconda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#xff0c;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winR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-gpu2.4.2 cuda10.2 conda install paddlepaddle-gpu2.4.2 cudatoolkit10.2 -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CtrlshiftP,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! Lets 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 time705.907865047454811.75分钟#opt paddle.optimizer.SGD(learning_rate0.01, parametersmodel.parameters()) #运行时间cost time: 705.9078650474548#opt paddle.optimizer.SGD(learning_rate0.001, parametersmodel.parameters()) #运行时间Cost time: 705.5072021484375#opt paddle.optimizer.SGD(learning_rate0.0001, parametersmodel.parameters()) #运行时间Cost time: 708.7056725025177#opt paddle.optimizer.Momentum(learning_rate0.01, momentum0.9, parametersmodel.parameters()) #运行时间Cost time: 707.7317929267883#opt paddle.optimizer.Adagrad(learning_rate0.01, parametersmodel.parameters()) #运行时间Cost time: 756.807531118393opt paddle.optimizer.Adam(learning_rate0.01, parametersmodel.parameters()) #运行时间Cost time: 702.2813944816589PS 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—the—end—
