如何创建机器学习环境-基于瑞芯微米尔RK3576开发板

本篇源自：优秀创作者 lulugl

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本文将介绍基于米尔电子MYD-LR3576开发板（米尔基于瑞芯微 RK3576开发板）的创建机器学习环境方案测试。

【前言】
【米尔-瑞芯微RK3576核心板及开发板】具有6TpsNPU以及GPU，因此是学习机器学习的好环境，为此结合《深度学习的数学——使用Python语言》
1、使用vscode 连接远程开发板

2、使用conda新建虚拟环境：

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root@myd-lr3576x-debian:/home/myir/pro_learn# conda create --name myenv python=3.9

执行结果如下：

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root@myd-lr3576x-debian:/home/myir/pro_learn# conda create --name myenv python=3.9

Channels:

- defaults

Platform: linux-aarch64

Collecting package metadata (repodata.json): done

Solving environment: done



## Package Plan ##



environment location: /root/miniconda3/envs/myenv



added / updated specs:

- python=3.9





The following packages will be downloaded:



package | build

---------------------------|-----------------

_libgcc_mutex-0.1 | main 2 KB defaults

_openmp_mutex-5.1 | 51_gnu 1.4 MB defaults

ca-certificates-2024.11.26 | hd43f75c_0 131 KB defaults

ld_impl_linux-aarch64-2.40 | h48e3ba3_0 848 KB defaults

libffi-3.4.4 | h419075a_1 140 KB defaults

libgcc-ng-11.2.0 | h1234567_1 1.3 MB defaults

libgomp-11.2.0 | h1234567_1 466 KB defaults

libstdcxx-ng-11.2.0 | h1234567_1 779 KB defaults

ncurses-6.4 | h419075a_0 1.1 MB defaults

openssl-3.0.15 | h998d150_0 5.2 MB defaults

pip-24.2 | py39hd43f75c_0 2.2 MB defaults

python-3.9.20 | h4bb2201_1 24.7 MB defaults

readline-8.2 | h998d150_0 381 KB defaults

setuptools-75.1.0 | py39hd43f75c_0 1.6 MB defaults

sqlite-3.45.3 | h998d150_0 1.5 MB defaults

tk-8.6.14 | h987d8db_0 3.5 MB defaults

tzdata-2024b | h04d1e81_0 115 KB defaults

wheel-0.44.0 | py39hd43f75c_0 111 KB defaults

xz-5.4.6 | h998d150_1 662 KB defaults

zlib-1.2.13 | h998d150_1 113 KB defaults

------------------------------------------------------------

Total: 46.2 MB



The following NEW packages will be INSTALLED:



_libgcc_mutex anaconda/pkgs/main/linux-aarch64::_libgcc_mutex-0.1-main

_openmp_mutex anaconda/pkgs/main/linux-aarch64::_openmp_mutex-5.1-51_gnu

ca-certificates anaconda/pkgs/main/linux-aarch64::ca-certificates-2024.11.26-hd43f75c_0

ld_impl_linux-aar~ anaconda/pkgs/main/linux-aarch64::ld_impl_linux-aarch64-2.40-h48e3ba3_0

libffi anaconda/pkgs/main/linux-aarch64::libffi-3.4.4-h419075a_1

libgcc-ng anaconda/pkgs/main/linux-aarch64::libgcc-ng-11.2.0-h1234567_1

libgomp anaconda/pkgs/main/linux-aarch64::libgomp-11.2.0-h1234567_1

libstdcxx-ng anaconda/pkgs/main/linux-aarch64::libstdcxx-ng-11.2.0-h1234567_1

ncurses anaconda/pkgs/main/linux-aarch64::ncurses-6.4-h419075a_0

openssl anaconda/pkgs/main/linux-aarch64::openssl-3.0.15-h998d150_0

pip anaconda/pkgs/main/linux-aarch64::pip-24.2-py39hd43f75c_0

python anaconda/pkgs/main/linux-aarch64::python-3.9.20-h4bb2201_1

readline anaconda/pkgs/main/linux-aarch64::readline-8.2-h998d150_0

setuptools anaconda/pkgs/main/linux-aarch64::setuptools-75.1.0-py39hd43f75c_0

sqlite anaconda/pkgs/main/linux-aarch64::sqlite-3.45.3-h998d150_0

tk anaconda/pkgs/main/linux-aarch64::tk-8.6.14-h987d8db_0

tzdata anaconda/pkgs/main/noarch::tzdata-2024b-h04d1e81_0

wheel anaconda/pkgs/main/linux-aarch64::wheel-0.44.0-py39hd43f75c_0

xz anaconda/pkgs/main/linux-aarch64::xz-5.4.6-h998d150_1

zlib anaconda/pkgs/main/linux-aarch64::zlib-1.2.13-h998d150_1





Proceed ([y]/n)? y





Downloading and Extracting Packages:



Preparing transaction: done

Verifying transaction: done

Executing transaction: done

#

# To activate this environment, use

#

# $ conda activate myenv

#

# To deactivate an active environment, use

#

# $ conda deactivate



root@myd-lr3576x-debian:/home/myir/pro_learn#

然后再激活环境：

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root@myd-lr3576x-debian:/home/myir/pro_learn# conda activate myenv

(myenv) root@myd-lr3576x-debian:/home/myir/pro_learn#

2、查看python版本号：

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(myenv) root@myd-lr3576x-debian:/home/myir/pro_learn# python --version

Python 3.9.20

3、使用conda install numpy等来安装组件，安装好后用pip list查看

编写测试代码：

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import numpy as np

from sklearn.datasets import load_digits

from sklearn.neural_network import MLPClassifier

d = load_digits()

digits = d["data"]

labels = d["target"]



N = 200

idx = np.argsort(np.random.random(len(labels)))

xtest, ytest = digits[idx[:N]], labels[idx[:N]]

xtrain, ytrain = digits[idx[N:]], labels[idx[N:]]

clf = MLPClassifier(hidden_layer_sizes=(128, ))

clf.fit(xtrain, ytrain)



score = clf.score(xtest, ytest)

pred = clf.predict(xtest)

err = np.where(pred != ytest)[0]

print("score:", score)

print("err:", err)

print("actual:", ytest[err])

print("predicted:", pred[err])

在代码中，使用MLPClassifier对象进行建模，训练测试，训练数据集非常快，训练4次后可以达到0.99:

【总结】
米尔的这款开发板，搭载3576这颗强大的芯片，搭建了深度学习的环境，进行了基础的数据集训练，效果非常好！在书中记录训练要几分钟，但是这在这款开发板上测试，只要几秒钟就训练完毕，书中说总体准确率为0.97，但是我在这款开发板上有0.99的良好效果！