24 Hours – Pancake's Personal Website

说明

所有代码及答案请慎用。

可供c++在线测试平台可供java在线测试平台可供python在线测试平台1 即 vscode 。可供python在线测试平台2 即 jupyter。密码均为 hduitmo

Python 为 10.4 版本。已经安装一些基础的库，python 10.4 arm 暂不支持 geatpy, xeus-python。

可供在线测试，为树莓派，不会长期在线，网速比较慢（没有配置CDN），密码公开，注意隐私。不要乱给 ubuntu 运行命令。不要死循环、一直运行、多进程/线程占用cpu谢谢。

说明

该板块为个人写过的 Timus 题目
该板块都为原创代码，搬运请注明出处，谢谢。
Github上别人写的代码您也可以作为参考1 参考2。

共性问题说明：
cin读取速度慢，但所有题目时间都考虑到cin时间，如果超时，建议换算法，实在不行可以加入：std::ios::sync_with_stdio(false);std::cin.tie(nullptr); 加快cin速度或者改成scanf或scanf_s（最快）。当scanf或scanf_s读入string后不要轻易改变该string赋值否则可能会有很大bug，建议用scanf输入时储存为char。并且建议不要混用cin和scanf。如果你追求极致时间，请用scanf和printf输入和输出。

Contact Form

如果想联系我也可以加QQ：2114496089

个人简介

来自杭州电子科技大学自动化专业的大三理工男一枚。

一只整个暑假都在学校学习的学生。

b站搜索：def__PanCake__

steam好友代码：241282993

喜欢的游戏：明日方舟、csgo、彩虹六号……

合作/友链/联系方式

如果想联系我也可以加QQ：2114496089，加好友都请备注来意。

服务器简介

服务器主要功能为个人博客，于2021.7.1开始正式运营，为wordpress模板。

该博客会不定期更新个人学习到的知识进行分享，你也可以通过我的博客了解我。

请不要在此网站泄露任何个人信息，评论姓名等个人信息请匿名填写，个人不能保证此网站不被攻击，如果出问题请自行负责！请爱护服务器，大佬请不要无故攻击谢谢！请不要对服务器运行爬虫或各种压力测试软件，谢谢，服务器设置每秒访问上限，可能你会被封ip。服务器运行速度及网速可能稍慢，望见谅（有钱了买个好的）。

正则表达式

这东西自己看的时候可谓是眼花缭乱，看到一半我就不想看了，因为本人没用过，看b站的时候刷到了，推荐一下大佬的学习网站

https://r2coding.com/ 里面还有很多计算机学习资料有兴趣的可以看看

遗传算法背包问题

排列编码

import pandas as pd
import geatpy as ea
import numpy as np
import random
from multiprocessing import Pool


class Data:
    market, market_value = None, None

    def __init__(self, df, platform, platform_max):
        self.platform = platform
        self.platform_max = platform_max
        self.x = df['交易量']
        self.y = df["交易费用"]

    def getResult(self, choose, average):
        choose_dict = [{"index": choose[i], "x": self.x[i], "y": self.y[i]} for i in range(len(choose))]
        choose_dict = sorted(choose_dict, key=lambda x: x["index"])
        result = float("inf")
        for i in range(self.platform):
            market_value = 0
            market = 0
            for j in range(average):
                if choose_dict[i * average + j]["x"] + market > self.platform_max:
                    result = min(result, market_value)
                    break
                else:
                    market_value += choose_dict[i * average + j]["y"]
                    market += choose_dict[i * average + j]["x"]
            if market_value <= self.platform_max:
                result = min(result, market_value)
        return result


# GA 参数设置
class My_nsga(ea.Problem):
    def __init__(self, dim, df, platform, platform_max):
        name = 'GA-NET'
        M = 1
        maxormins = [-1] * M
        Dim = dim
        varTypes = [1] * Dim
        lb = [0] * Dim
        ub = [Dim - 1] * Dim
        lbin = [1] * Dim
        ubin = [1] * Dim
        self.count = 1
        self.data = Data(df, platform, platform_max)
        self.average = int(len(df) / platform)

        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

    # 目标函数即神经网络返回值
    def evalVars(self, Vars):
        ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)
        for i in range(len(Vars)):
            ans[i][0] = self.data.getResult(Vars[i], self.average)
        # print(ans.max(axis=0))
        return ans


# 运行 GA
def Run_nsga(dim, df, platform, platform_max, loop_id, ndind=30, maxgen=1500):
    problem = My_nsga(dim, df, platform, platform_max)
    myAlgorithm = ea.soea_EGA_templet(problem, ea.Population(Encoding='P', NIND=ndind), MAXGEN=maxgen, logTras=0)
    # 要得到图把 drawing = 1
    saveFlag = 1
    res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=1, outputMsg=1, drawLog=1, saveFlag=saveFlag, dirName='result{}'.format(loop_id))
    if saveFlag:
        np.savetxt("./result{}/result.csv".format(i), res['Vars'][0], delimiter=",")
    return res['ObjV'][0][0]


if __name__ == "__main__":
    df = pd.read_excel("data.xlsx", sheet_name=None, engine='openpyxl')
    # 设置每张表分成的 platform 数量
    platforms = [2, 4, 10, 20, 10, 50, 50, 100]
    # 设置每张表 platform 最大值
    platform_maxs = [2000, 1000, 1500, 800, 500, 100, 416, 208]
    # 设置是否训练表
    is_train = [0, 0, 1, 1, 0, 0, 0, 0]
    values = list(df.values())[:4]
    pool = Pool(sum(is_train))
    for i in range(len(values)):
        if not is_train[i]:
            continue
        # 最后两个参数：种群数量，最大迭代次数
        pool.apply_async(Run_nsga, args=(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 600))
    pool.close()
    pool.join()

import pandas as pd

import geatpy as ea

import numpy as np

import random

from multiprocessing import Pool

class Data:

market, market_value = None, None

def __init__(self, df, platform, platform_max):

self.platform = platform

self.platform_max = platform_max

self.x = df['交易量']

self.y = df["交易费用"]

def getResult(self, choose, average):

choose_dict = [{"index": choose[i], "x": self.x[i], "y": self.y[i]} for i in range(len(choose))]

choose_dict = sorted(choose_dict, key=lambda x: x["index"])

result = float("inf")

for i in range(self.platform):

market_value = 0

market = 0

for j in range(average):

if choose_dict[i * average + j]["x"] + market > self.platform_max:

result = min(result, market_value)

break

else:

market_value += choose_dict[i * average + j]["y"]

market += choose_dict[i * average + j]["x"]

if market_value <= self.platform_max:

result = min(result, market_value)

return result

# GA 参数设置

class My_nsga(ea.Problem):

def __init__(self, dim, df, platform, platform_max):

name = 'GA-NET'

M = 1

maxormins = [-1] * M

Dim = dim

varTypes = [1] * Dim

lb = [0] * Dim

ub = [Dim - 1] * Dim

lbin = [1] * Dim

ubin = [1] * Dim

self.count = 1

self.data = Data(df, platform, platform_max)

self.average = int(len(df) / platform)

ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

# 目标函数即神经网络返回值

def evalVars(self, Vars):

ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)

for i in range(len(Vars)):

ans[i][0] = self.data.getResult(Vars[i], self.average)

# print(ans.max(axis=0))

return ans

# 运行 GA

def Run_nsga(dim, df, platform, platform_max, loop_id, ndind=30, maxgen=1500):

problem = My_nsga(dim, df, platform, platform_max)

myAlgorithm = ea.soea_EGA_templet(problem, ea.Population(Encoding='P', NIND=ndind), MAXGEN=maxgen, logTras=0)

# 要得到图把 drawing = 1

saveFlag = 1

res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=1, outputMsg=1, drawLog=1, saveFlag=saveFlag, dirName='result{}'.format(loop_id))

if saveFlag:

np.savetxt("./result{}/result.csv".format(i), res['Vars'][0], delimiter=",")

return res['ObjV'][0][0]

if __name__ == "__main__":

df = pd.read_excel("data.xlsx", sheet_name=None, engine='openpyxl')

# 设置每张表分成的 platform 数量

platforms = [2, 4, 10, 20, 10, 50, 50, 100]

# 设置每张表 platform 最大值

platform_maxs = [2000, 1000, 1500, 800, 500, 100, 416, 208]

# 设置是否训练表

is_train = [0, 0, 1, 1, 0, 0, 0, 0]

values = list(df.values())[:4]

pool = Pool(sum(is_train))

for i in range(len(values)):

if not is_train[i]:

continue

# 最后两个参数：种群数量，最大迭代次数

pool.apply_async(Run_nsga, args=(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 600))

pool.close()

pool.join()

格雷编码（选择编码）

import pandas as pd
import geatpy as ea
import numpy as np
import random
from multiprocessing import Pool


class Data:
    market, market_value = None, None

    def __init__(self, df, platform, platform_max):
        self.platform = platform
        self.platform_max = platform_max
        self.x = df['交易量']
        self.y = df["交易费用"]

    def load(self, choose):
        self.market = [[] for _ in range(self.platform)]
        self.market_value = [[] for _ in range(self.platform)]
        for i in range(len(choose)):
            if choose[i] != 0:
                self.market[choose[i] - 1].append(self.x[i])
                self.market_value[choose[i] - 1].append(self.y[i])

    def getResult(self):
        if max(list(map(lambda x: sum(x), self.market))) > self.platform_max:
            return sum(list(map(lambda x: 0 if sum(x) < self.platform_max else self.platform_max - sum(x), self.market)))
        else:
            return min(list(map(lambda x: sum(x), self.market_value)))


# GA 参数设置
class My_nsga(ea.Problem):
    def __init__(self, dim, df, platform, platform_max):
        name = 'GA-NET'
        M = 1
        maxormins = [-1] * M
        Dim = dim
        varTypes = [1] * Dim
        lb = [0] * Dim
        ub = [platform] * Dim
        lbin = [1] * Dim
        ubin = [1] * Dim
        self.count = 1
        self.data = Data(df, platform, platform_max)

        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

    # 目标函数即神经网络返回值
    def evalVars(self, Vars):
        ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)
        for i in range(len(Vars)):
            self.data.load(Vars[i])
            ans[i][0] = self.data.getResult()
        # print(ans.max(axis=0))
        return ans


# 得到初始化种群方式1
def getProphet(dim, ndind, df, platform, platform_max):
    prophet = np.zeros(shape=(ndind, dim), dtype=int)
    x = df['交易量']
    for i in range(ndind):
        # 使得platform尽可能分配均匀
        platforms = [i for i in range(platform + 1)]
        now = [0 for _ in range(platform)]
        for j in range(i % dim, dim):
            if not platforms:
                platforms = [i for i in range(platform + 1)]
            choice = random.choice(platforms)
            platforms.remove(choice)
            if choice != 0:
                now[choice - 1] += x[j]
                if now[choice - 1] < platform_max:
                    prophet[i][j] = choice
        for j in range(0, i % dim):
            if not platforms:
                platforms = [i for i in range(platform + 1)]
            choice = random.choice(platforms)
            platforms.remove(choice)
            if choice != 0:
                now[choice - 1] += x[j]
                if now[choice - 1] < platform_max:
                    prophet[i][j] = choice
    return prophet


# 得到初始化种群方式2
def getProphet2(dim, ndind, df, platform, platform_max):
    prophet = np.zeros(shape=(ndind, dim), dtype=int)
    x = df['交易量']
    for i in range(ndind):
        # 真随机抽取初始化
        now = [0 for _ in range(platform)]
        for j in range(i % dim, dim):
            choice = random.choice([i for i in range(platform + 1)])
            if choice != 0:
                now[choice - 1] += x[j]
                if now[choice - 1] < platform_max:
                    prophet[i][j] = choice
        for j in range(0, i % dim):
            choice = random.choice([i for i in range(platform + 1)])
            if choice != 0:
                now[choice - 1] += x[j]
                if now[choice - 1] < platform_max:
                    prophet[i][j] = choice
    return prophet


# 运行 GA
def Run_nsga(dim, df, platform, platform_max, loop_id, ndind=30, maxgen=1500):
    problem = My_nsga(dim, df, platform, platform_max)
    myAlgorithm = ea.soea_EGA_templet(problem, ea.Population(Encoding='RI', NIND=ndind), MAXGEN=maxgen, logTras=0)
    prophet = getProphet(dim, ndind, df, platform, platform_max)
    # 要得到图把 drawing = 1
    saveFlag = 1
    res = ea.optimize(myAlgorithm, prophet=prophet, seed=1, verbose=False, drawing=1, outputMsg=1, drawLog=1, saveFlag=saveFlag, dirName='result{}'.format(loop_id))
    if saveFlag:
        np.savetxt("./result{}/result.csv".format(i), res['Vars'][0], delimiter=",")
    return res['ObjV'][0][0]


if __name__ == "__main__":
    df = pd.read_excel("data.xlsx", sheet_name=None, engine='openpyxl')
    # 设置每张表分成的 platform 数量
    platforms = [2, 4, 10, 20, 10, 50, 50, 100]
    # 设置每张表 platform 最大值
    platform_maxs = [2000, 1000, 1500, 800, 500, 100, 416, 208]
    # platform_maxs[4] = [100, 200, 300, 400, 500, 500, 600, 700, 800, 900]
    # 设置是否训练表
    # is_train = [1, 1, 1, 1, 1, 1, 1, 1]
    is_train = [1, 1, 1, 1, 0, 0, 0, 0]
    result = []
    result_value = []
    values = list(df.values())[:8]
    pool = Pool(sum(is_train))
    for i in range(len(values)):
        if not is_train[i]:
            continue
        # 最后两个参数：种群数量，最大迭代次数
        pool.apply_async(Run_nsga, args=(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 1000))
    pool.close()
    pool.join()
    #     now_value = Run_nsga(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 1000)
    #     result_value.append(now_value)
    # np.savetxt("result_values.csv", result_value, delimiter=",")

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import pandas as pd

import geatpy as ea

import numpy as np

import random

from multiprocessing import Pool

class Data:

market, market_value = None, None

def __init__(self, df, platform, platform_max):

self.platform = platform

self.platform_max = platform_max

self.x = df['交易量']

self.y = df["交易费用"]

def load(self, choose):

self.market = [[] for _ in range(self.platform)]

self.market_value = [[] for _ in range(self.platform)]

for i in range(len(choose)):

if choose[i] != 0:

self.market[choose[i] - 1].append(self.x[i])

self.market_value[choose[i] - 1].append(self.y[i])

def getResult(self):

if max(list(map(lambda x: sum(x), self.market))) > self.platform_max:

return sum(list(map(lambda x: 0 if sum(x) < self.platform_max else self.platform_max - sum(x), self.market)))

else:

return min(list(map(lambda x: sum(x), self.market_value)))

# GA 参数设置

class My_nsga(ea.Problem):

def __init__(self, dim, df, platform, platform_max):

name = 'GA-NET'

M = 1

maxormins = [-1] * M

Dim = dim

varTypes = [1] * Dim

lb = [0] * Dim

ub = [platform] * Dim

lbin = [1] * Dim

ubin = [1] * Dim

self.count = 1

self.data = Data(df, platform, platform_max)

ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)

# 目标函数即神经网络返回值

def evalVars(self, Vars):

ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1)

for i in range(len(Vars)):

self.data.load(Vars[i])

ans[i][0] = self.data.getResult()

# print(ans.max(axis=0))

return ans

# 得到初始化种群方式1

def getProphet(dim, ndind, df, platform, platform_max):

prophet = np.zeros(shape=(ndind, dim), dtype=int)

x = df['交易量']

for i in range(ndind):

# 使得platform尽可能分配均匀

platforms = [i for i in range(platform + 1)]

now = [0 for _ in range(platform)]

for j in range(i % dim, dim):

if not platforms:

platforms = [i for i in range(platform + 1)]

choice = random.choice(platforms)

platforms.remove(choice)

if choice != 0:

now[choice - 1] += x[j]

if now[choice - 1] < platform_max:

prophet[i][j] = choice

for j in range(0, i % dim):

if not platforms:

platforms = [i for i in range(platform + 1)]

choice = random.choice(platforms)

platforms.remove(choice)

if choice != 0:

now[choice - 1] += x[j]

if now[choice - 1] < platform_max:

prophet[i][j] = choice

return prophet

# 得到初始化种群方式2

def getProphet2(dim, ndind, df, platform, platform_max):

prophet = np.zeros(shape=(ndind, dim), dtype=int)

x = df['交易量']

for i in range(ndind):

# 真随机抽取初始化

now = [0 for _ in range(platform)]

for j in range(i % dim, dim):

choice = random.choice([i for i in range(platform + 1)])

if choice != 0:

now[choice - 1] += x[j]

if now[choice - 1] < platform_max:

prophet[i][j] = choice

for j in range(0, i % dim):

choice = random.choice([i for i in range(platform + 1)])

if choice != 0:

now[choice - 1] += x[j]

if now[choice - 1] < platform_max:

prophet[i][j] = choice

return prophet

# 运行 GA

def Run_nsga(dim, df, platform, platform_max, loop_id, ndind=30, maxgen=1500):

problem = My_nsga(dim, df, platform, platform_max)

myAlgorithm = ea.soea_EGA_templet(problem, ea.Population(Encoding='RI', NIND=ndind), MAXGEN=maxgen, logTras=0)

prophet = getProphet(dim, ndind, df, platform, platform_max)

# 要得到图把 drawing = 1

saveFlag = 1

res = ea.optimize(myAlgorithm, prophet=prophet, seed=1, verbose=False, drawing=1, outputMsg=1, drawLog=1, saveFlag=saveFlag, dirName='result{}'.format(loop_id))

if saveFlag:

np.savetxt("./result{}/result.csv".format(i), res['Vars'][0], delimiter=",")

return res['ObjV'][0][0]

if __name__ == "__main__":

df = pd.read_excel("data.xlsx", sheet_name=None, engine='openpyxl')

# 设置每张表分成的 platform 数量

platforms = [2, 4, 10, 20, 10, 50, 50, 100]

# 设置每张表 platform 最大值

platform_maxs = [2000, 1000, 1500, 800, 500, 100, 416, 208]

# platform_maxs[4] = [100, 200, 300, 400, 500, 500, 600, 700, 800, 900]

# 设置是否训练表

# is_train = [1, 1, 1, 1, 1, 1, 1, 1]

is_train = [1, 1, 1, 1, 0, 0, 0, 0]

result = []

result_value = []

values = list(df.values())[:8]

pool = Pool(sum(is_train))

for i in range(len(values)):

if not is_train[i]:

continue

# 最后两个参数：种群数量，最大迭代次数

pool.apply_async(Run_nsga, args=(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 1000))

pool.close()

pool.join()

# now_value = Run_nsga(len(values[i]), values[i], platforms[i], platform_maxs[i], i, 500, 1000)

# result_value.append(now_value)

# np.savetxt("result_values.csv", result_value, delimiter=",")

两个编码方式有差异，我个人认为排列编码会比较好一点，逻辑更加高一些。代码中为EGA模板（即带有精英选择机制的遗传算法），测试下来可能NSGA-ii模板会更优一些，可以在测试的时候选择你想要的算法及结果。data.xlsx

2022.8.5 余杭径山

图片均压缩<500kb

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IMG_0421-Pano

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IMG_0457

Python 多线程与多进程

python多线程因为GIL锁的原因可以说是假的，但是多进程是可以使用多核 cpu。

测试代码如下：

from threading import Thread

def loop1():
    a = 0
    while 1:
        a += 1

def loop2(a):
    while 1:
        a += 1

for i in range(3):
    t = Thread(target=loop1())
    t.start()

for i in range(3):
    t = Thread(target=loop2(), args=(1,))
    t.start()

from threading import Thread

def loop1():

a = 0

while 1:

a += 1

def loop2(a):

while 1:

a += 1

for i in range(3):

t = Thread(target=loop1())

t.start()

for i in range(3):

t = Thread(target=loop2(), args=(1,))

t.start()

from multiprocessing import Process, Pool

def loop1():
    a = 0
    while 1:
        a += 1

def loop2(a):
    while 1:
        a += 1

pool = Pool(processes=6)

for i in range(3):
    pool.apply_async(loop1)

for i in range(3):
    pool.apply_async(loop2, args=(1,))

pool.close()
pool.join()

from multiprocessing import Process, Pool

def loop1():

a = 0

while 1:

a += 1

def loop2(a):

while 1:

a += 1

pool = Pool(processes=6)

for i in range(3):

pool.apply_async(loop1)

for i in range(3):

pool.apply_async(loop2, args=(1,))

pool.close()

pool.join()

测试结果如下：

结果：我们可以看到 threading 多线程库只可以跑满一核 cpu，但是 multiprocessing 多进程库可以跑满 cpu

关于树莓派重新配置所有命令

作为 backup 使用，为 ubuntu server 22.04，首先 sd 卡配置好 config.txt 以及 network-config。树莓派主要用途为 jupyter code-server frp 映射到公网作为计算服务器。

config.txt 备份文件

[all]
kernel=vmlinuz
cmdline=cmdline.txt
initramfs initrd.img followkernel

[pi4]
max_framebuffers=2
arm_boost=1

[all]
# Enable the audio output, I2C and SPI interfaces on the GPIO header. As these
# parameters related to the base device-tree they must appear *before* any
# other dtoverlay= specification
dtparam=audio=on
dtparam=i2c_arm=on
dtparam=spi=on
gpu_mem=256
arm_freq=2100
over_voltage=6
arm_freq_min=1500
# Comment out the following line if the edges of the desktop appear outside
# the edges of your display
disable_overscan=1

# If you have issues with audio, you may try uncommenting the following line
# which forces the HDMI output into HDMI mode instead of DVI (which doesn't
# support audio output)
#hdmi_drive=2

# Enable the serial pins
enable_uart=1

# Autoload overlays for any recognized cameras or displays that are attached
# to the CSI/DSI ports. Please note this is for libcamera support, *not* for
# the legacy camera stack
camera_auto_detect=1
display_auto_detect=1

# Config settings specific to arm64
arm_64bit=1
dtoverlay=dwc2

[cm4]
# Enable the USB2 outputs on the IO board (assuming your CM4 is plugged into
# such a board)
dtoverlay=dwc2,dr_mode=host

[all]
start_x=1

[all]

kernel=vmlinuz

cmdline=cmdline.txt

initramfs initrd.img followkernel

[pi4]

max_framebuffers=2

arm_boost=1

[all]

# Enable the audio output, I2C and SPI interfaces on the GPIO header. As these

# parameters related to the base device-tree they must appear *before* any

# other dtoverlay= specification

dtparam=audio=on

dtparam=i2c_arm=on

dtparam=spi=on

gpu_mem=256

arm_freq=2100

over_voltage=6

arm_freq_min=1500

# Comment out the following line if the edges of the desktop appear outside

# the edges of your display

disable_overscan=1

# If you have issues with audio, you may try uncommenting the following line

# which forces the HDMI output into HDMI mode instead of DVI (which doesn't

# support audio output)

#hdmi_drive=2

# Enable the serial pins

enable_uart=1

# Autoload overlays for any recognized cameras or displays that are attached

# to the CSI/DSI ports. Please note this is for libcamera support, *not* for

# the legacy camera stack

camera_auto_detect=1

display_auto_detect=1

# Config settings specific to arm64

arm_64bit=1

dtoverlay=dwc2

[cm4]

# Enable the USB2 outputs on the IO board (assuming your CM4 is plugged into

# such a board)

dtoverlay=dwc2,dr_mode=host

[all]

start_x=1

基本操作设置

sudo passwd root
su -
apt update
apt upgrade
adduser code
adduser code-jupyter
apt install python3-pip net-tools
pip3 config set global.index-url https://pypi.douban.com/simple
pip3 install numpy pandas torch torchvision jupyter bs4 requests openpyxl matplotlib scrapy
apt install python3-opencv

sudo passwd root

su -

apt update

apt upgrade

adduser code

adduser code-jupyter

apt install python3-pip net-tools

pip3 config set global.index-url https://pypi.douban.com/simple

pip3 install numpy pandas torch torchvision jupyter bs4 requests openpyxl matplotlib scrapy

apt install python3-opencv

Python 及 jupyter 的配置

su code-jupyter
mkdir code
python3
    from notebook.auth import passwd
    passwd()
    exit()
jupyter notebook --generate-config
vim .jupyter/jupyter_notebook_config.py
    c.NotebookApp.ip = '0.0.0.0'
    c.NotebookApp.password = ******
    c.NotebookApp.notebook_dir = '/home/code-jupyter/code'
    c.NotebookApp.open_browser = False
    c.NotebookApp.allow_remote_access = True
    c.NotebookApp.terminals_enabled = False
su -
vim /etc/systemd/system/jupyter.service
systemctl start jupyter
systemctl status jupyter
systemctl enable jupyter
pip3 config set global.index-url https://pypi.douban.com/simple
pip3 install jupyter_contrib_nbextensions
pip3 install jupyter_nbextensions_configurator
jupyter contrib nbextension install --user
systemctl restart jupyter.service

su code-jupyter

mkdir code

python3

from notebook.auth import passwd

passwd()

exit()

jupyter notebook --generate-config

vim .jupyter/jupyter_notebook_config.py

c.NotebookApp.ip = '0.0.0.0'

c.NotebookApp.password = ******

c.NotebookApp.notebook_dir = '/home/code-jupyter/code'

c.NotebookApp.open_browser = False

c.NotebookApp.allow_remote_access = True

c.NotebookApp.terminals_enabled = False

su -

vim /etc/systemd/system/jupyter.service

systemctl start jupyter

systemctl status jupyter

systemctl enable jupyter

pip3 config set global.index-url https://pypi.douban.com/simple

pip3 install jupyter_contrib_nbextensions

pip3 install jupyter_nbextensions_configurator

jupyter contrib nbextension install --user

systemctl restart jupyter.service

code-server 的配置

cd /home/code
wget https://ghproxy.com/https://github.com/coder/code-server/releases/download/v4.5.1/code-server_4.5.1_arm64.deb
dpkg -i code-server_4.5.1_arm64.deb
rm -f code-server_4.5.1_arm64.deb
su code
code-server
vim .config/code-server/config.yaml
su -
vim /etc/systemd/system/code.service
systemctl start code
systemctl status code
systemctl enable code

cd /home/code

wget https://ghproxy.com/https://github.com/coder/code-server/releases/download/v4.5.1/code-server_4.5.1_arm64.deb

dpkg -i code-server_4.5.1_arm64.deb

rm -f code-server_4.5.1_arm64.deb

su code

code-server

vim .config/code-server/config.yaml

su -

vim /etc/systemd/system/code.service

systemctl start code

systemctl status code

systemctl enable code

frp 的配置

su ubuntu
cd /home/ubuntu
wget https://ghproxy.com/https://github.com/fatedier/frp/releases/download/v0.44.0/frp_0.44.0_linux_arm64.tar.gz
tar -xvzf frp_0.44.0_linux_arm64.tar.gz
rm -f frp_0.44.0_linux_arm64.tar.gz
mv frp_0.44.0_linux_arm64/ frp/
cd frp/
vim frpc.ini
sudo vim /etc/systemd/system/code.service
systemctl start code
systemctl status code
systemctl enable code

su ubuntu

cd /home/ubuntu

wget https://ghproxy.com/https://github.com/fatedier/frp/releases/download/v0.44.0/frp_0.44.0_linux_arm64.tar.gz

tar -xvzf frp_0.44.0_linux_arm64.tar.gz

rm -f frp_0.44.0_linux_arm64.tar.gz

mv frp_0.44.0_linux_arm64/ frp/

cd frp/

vim frpc.ini

sudo vim /etc/systemd/system/code.service

systemctl start code

systemctl status code

systemctl enable code

frp code-server jupyter 自启脚本

[Unit]
Description=jupyter
After=network.target

[Service]
User=code-jupyter
ExecStart=/usr/local/bin/jupyter-notebook

[Install]
WantedBy=multi-user.target

[Unit]

Description=jupyter

After=network.target

[Service]

User=code-jupyter

ExecStart=/usr/local/bin/jupyter-notebook

[Install]

WantedBy=multi-user.target

[Unit]
Description=code-server
After=network.target

[Service]
User=code
ExecStart=/bin/code-server

[Install]
WantedBy=multi-user.target

[Unit]

Description=code-server

After=network.target

[Service]

User=code

ExecStart=/bin/code-server

[Install]

WantedBy=multi-user.target

[Unit]
Description=frp
After=network.target

[Service]
User=ubuntu
ExecStart=/home/ubuntu/frp/frpc -c /home/ubuntu/frp/frpc.ini

[Install]
WantedBy=multi-user.target

[Unit]

Description=frp

After=network.target

[Service]

User=ubuntu

ExecStart=/home/ubuntu/frp/frpc -c /home/ubuntu/frp/frpc.ini

[Install]

WantedBy=multi-user.target