How to compile Quantlib-Python for Raspberry Pi 4B arm32 and arm64

Raspberry Pi has default gcc-8 and Python 3.7 for its 32 bit / 64 bit buster image. And compiling QuantLib-Python on this machine could have out of memeory error. Cross compiling on docker might have different python version which is not compatible. The trick to compile on Raspberry Pi is to setup swap say 2G and 4G Ram and turn off debug -g flag when compiling as Python package.

Shell script for building arm32 version   Select all

# install necessary packages for building sudo apt update sudo apt install -y build-essential wget libbz2-dev libboost-test1.67.0 libboost-test-dev # Get QuantLib-1.22 and build static library cd ${HOME} wget https://github.com/lballabio/QuantLib/releases/download/QuantLib-v1.22/QuantLib-1.22.tar.gz tar xzf QuantLib-1.22.tar.gz cd QuantLib-1.22/ ./configure --prefix=/usr --disable-shared CXXFLAGS=-O3 make -j 4 && make install sudo ldconfig # Setup and enable swap and check it for at least 2GB. sudo dphys-swapfile setup sudo dphys-swapfile swapon free -mh sudo apt install -y python3 python3-pip python-dev libgomp1 # Get QuantLib-SWIG-1.22 and compile it cd ${HOME} wget --no-check-certificate https://github.com/lballabio/QuantLib-SWIG/releases/download/QuantLib-SWIG-v1.22/QuantLib-SWIG-${quantlib_swig_version}.tar.gz tar xfz QuantLib-SWIG-1.22.tar.gz cd QuantLib-SWIG-1.22/ ./configure CXXFLAGS="-O2 --param ggc-min-expand=1 --param ggc-min-heapsize=32768 -Wno-deprecated-declarations -Wno-misleading-indentation" PYTHON=/usr/bin/python3 # manual compile it and remove the -g flag cd Python/ mkdir -p build/temp.linux-armv7l-3.7/QuantLib export CXX="echo gcc"; python3 setup.py bdist_wheel g++ -fwrapv -O2 -Wall -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNDEBUG -I/usr/include/python3.7m -I/usr/include -c QuantLib/quantlib_wrap.cpp -o build/temp.linux-armv7l-3.7/QuantLib/quantlib_wrap.o -Wno-unused --param ggc-min-expand=1 --param ggc-min-heapsize=32768 -Wno-deprecated-declarations -Wno-misleading-indentation mkdir -p build/lib.linux-armv7l-3.7/QuantLib/ g++ -shared -Wl,-z,relro -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-armv7l-3.7/QuantLib/quantlib_wrap.o -lQuantLib -o build/lib.linux-armv7l-3.7/QuantLib/_QuantLib.cpython-37m-arm-linux-gnueabihf.so # create wheel file python3 setup.py bdist_wheel # Upgrade PIP and install the wheel file /usr/bin/python3 -m pip install --upgrade pip pip3 install dist/QuantLib-1.22-cp37-cp37m-linux_armv7l.whl # Or alternatively install as site-package sudo python3 setup.py install # Test examples after installation pip3 install pandas python3 examples/bonds.py . . . .

Compiling for Rapberry Pi arm64 is very similar but has to add -fPIC flag for the QuantLib when building static library

Shell script for building arm64 version   Select all

# install necessary packages for building sudo apt update sudo apt install -y build-essential wget libbz2-dev sudo apt install -y libboost-test1.67.0 libboost-test-dev cd ${HOME} wget https://github.com/lballabio/QuantLib/releases/download/1.22/QuantLib-1.22.tar.gz tar xzf QuantLib-1.22.tar.gz cd QuantLib-1.22/ # enable -fPIC flag for building static library ./configure --prefix=/usr --disable-shared CXXFLAGS="-O3 -fPIC" make -j 4 && make install sudo ldconfig # If Raspbeery Pi has 8GB Ram, no need to setup and enable swap sudo apt install -y python3 python3-pip python-dev libgomp1 # Get QuantLib-SWIG-1.22 and compile it cd {HOME} wget https://github.com/lballabio/QuantLib-SWIG/releases/download/QuantLib-SWIG-v1.22/QuantLib-SWIG-1.22.tar.gz tar xzf QuantLib-SWIG-1.22.tar.gz cd QuantLib-SWIG-1.22/ cd Python/ ./configure CXXFLAGS="--param ggc-min-expand=1 --param ggc-min-heapsize=32768 -fPIC -Wno-deprecated-declarations -Wno-misleading-indentation" PYTHON=/usr/bin/python3 # manual compile it and remove the -g flag cd Python/ mkdir -p build/temp.linux-aarch64-3.7/QuantLib/ g++ -fwrapv -O2 -Wall -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/python3.7m -I/usr/include -c QuantLib/quantlib_wrap.cpp -o build/temp.linux-aarch64-3.7/QuantLib/quantlib_wrap.o -Wno-unused --param ggc-min-expand=1 --param ggc-min-heapsize=32768 -fno-strict-aliasing -Wno-unused -Wno-uninitialized -Wno-sign-compare -Wno-write-strings -Wno-deprecated-declarations -Wno-misleading-indentation mkdir -p build/lib.linux-aarch64-3.7/QuantLib/ g++ -shared -Wl,-z,relro -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-aarch64-3.7/QuantLib/quantlib_wrap.o -lQuantLib -o build/lib.linux-aarch64-3.7/QuantLib/_QuantLib.cpython-37m-aarch64-linux-gnu.so # create wheel file python3 setup.py bdist_wheel # Upgrade PIP and install the wheel file /usr/bin/python3 -m pip install --upgrade pip pip3 install dist/QuantLib-1.22-cp37-cp37m-linux_aarch64.whl # Or alternatively install as site-package sudo python3 setup.py install # Test examples after installation pip3 install pandas python3 examples/bonds.py

File Download QuantLib-1.22-cp37-cp37m-linux_armv7l.whl https://mega.nz/file/mtJSxZTT#fzDDHw0AIqz-2LIspBGNZLoyW4_MT9qjft_b-ITTA8w

File Download QuantLib-1.22-cp37-cp37m-linux_aarch64.whl https://mega.nz/file/WlAEXJCZ#UKFnlTrfQfRNzFW-OJbXHLFIHwzCw_189HvMa_xU4Oo

How to install docker client and connect to docker desktop engine on another machine macOS or Windows.

Use Docker Desktop for Windows 10 / macOS as engine and docker client for linux/macos/Android to connect.
Why use docker client ? Because don't want to / cannot install docker engine in the client environment and just want to connect to the docker engine on local LAN.
(1) For Windows 10 Host, after installation of docker desktop
# C:\ProgramData\Docker\config\daemon.json and add

"hosts" : ["tcp://0.0.0.0:2375"],

# change port forwarding to docker wsl backend, run this in powershell admin mode

netsh interface portproxy add v4tov4 listenport=2375 listenaddress=192.168.64.1 connectaddress=127.0.0.1 connectport=2375

# change Windows defender firewall and add incoming rule to enable port 2375 # Refer to this for setting openssh server authorized_keys, with proper file permission https://superuser.com/questions/1445976/windows-ssh-server-refuses-key-based-authentication-from-client

(2) For macOS Host, after installation of docker desktop
# ssh-keygen in client and ssh-copy-id to host, e.g. remote host username with ip address of 192.168.64.1

ssh-keygen -t rsa
ssh-copy-id username@192.168.64.1

# edit sshd_config

sudo vi /private/etc/ssh/sshd_config

# and add

PermitUserEnvironment PATH,LANG

# edit ~/.docker/daemon.json and add

"hosts" : ["tcp://0.0.0.0:2375"],

# add .ssh/environment

PATH=$PATH:/usr/local/bin

# restarting sshd using macOS System Preferences -> Sharing -> File Sharing

(3) To install docker client for Linux using tcp

cd ~/
wget https://download.docker.com/linux/static/stable/x86_64/docker-20.10.6.tgz
tar -xzvf docker-20.10.6.tgz
cd docker
./docker -H tcp://<remote host ip address>:2375 images

(3.1) To install docker client for Termux app of Android and using tcp

wget https://download.docker.com/linux/static/stable/aarch64/docker-20.10.6.tgz

tar xzvf docker-20.10.6.tgz
mv docker/docker /data/data/com.termux/files/usr/bin/
./docker -H tcp://<remote host ip address>:2375 images

(4) To install docker client for macOS using ssh

cd ~/
wget https://download.docker.com/mac/static/stable/x86_64/docker-20.10.6.tgz
#or curl -OL https://download.docker.com/mac/static/stable/x86_64/docker-20.10.6.tgz
#or curl -OL https://download.docker.com/mac/static/stable/aarch64/docker-20.10.6.tgz
tar xzvf docker-20.10.6.tgz
xattr -rc docker
cd docker
sudo mkdir -p /usr/local/bin
sudo mv * /usr/local/bin/
docker -H ssh://username@<remote host ip address> images

(5) Or simply add the corresponding variables to ~/.bashrc

unset DOCKER_HOST
# for tcp connection to Windows 10 host
export DOCKER_HOST=tcp://192.168.64.1:2375

# for ssh connection to macOS host using SSH
export DOCKER_HOST=ssh://user@192.168.64.1

How to install openssh server in wsl2

(1) This is the guide to install openssh server and connect to wsl2 shell when ssh login from other machines in LAN network.

https://www.hanselman.com/blog/the-easy-way-how-to-ssh-into-bash-and-wsl2-on-windows-10-from-an-external-machine

(2) However, if still cannot connect from external machines from LAN network after setting all the firewall rules. Do this in powershell with admin right.

Set-ExecutionPolicy Unrestricted -Force

(3) If want to setup jupyter-notebook server in wsl2 and connect from other machines in the LAN network. Follow this guide.
https://medium.com/swlh/how-to-set-up-a-jupiter-notebook-server-and-access-it-from-a-local-or-remote-network-on-windows-d335c5ba490d

(4) The important setup steps are to open the Windows firewall rule and the script to port forward to the vm of the wsl2 as in.

wget https://gist.githubusercontent.com/david-littlefield/f45999c069e0b6b68bdae829d8616727/raw/80a60968f1bdda598eaf275bf1300bb3451d45ab/jupyter_notebook_port_wsl2.ps1

(5) It is advised to login from external internet to local LAN machines via VPN Server only rather than exposing or forwarding the local ports directly via the router.

(6) Refer to this for setting openssh server authorized_keys, with proper file permission https://superuser.com/questions/1445976/windows-ssh-server-refuses-key-based-authentication-from-client

How to use Chrome browser to scrape website using javascript

(1) This is to demo how to scrape website using Chrome Browser and save the json text results to local drive.

(2) First launch Desktop version of Chrome Browser and goto this website "http://aastocks.com/en/stocks/market/calendar.aspx?type=5"

(3) Install "JQuery Inject" as Chrome Extension and enable it in current browser session.

(4) Open Chrome Developer tools (Ctrl-Shift-I) and select console tab to enter the following code. Enter the code in 3 steps.

Chrome Browser console code : Step 1   Select all

// Step 1 // define arrays for scraped objects and should be store as global variable var scrapeResults = [];

Console Code : Step 2   Select all

//Step 2 // function to scrape page $("table.CalendarResultTable > tbody > .crtRow").each((index, element) => { const tds = $(element).find("td"); if (index===0) { previousDate = $(tds[0]).text(); } if ($(tds[0]).text().trim()==='') { } else { previousDate = $(tds[0]).text(); } const date = previousDate; const namecell = $(tds[1]).find("a"); const name = $(tds[1]).text().replace('\n','').split(/[0-9]+.HK/)[0].trim(); const stockcode = $(namecell).text(); const stockurl = $(namecell).attr("href"); const dividend = $(tds[2]).text().trim().split('D:')[1]; const dividenddate = $(tds[3]).text().trim().split(/Ex-Date: | Payable: | Book Close: /); const exdate = dividenddate[1] const payable = dividenddate[2] const bookclose = dividenddate[3] const scrapeResult = { date, name, stockcode, stockurl, dividend, exdate, payable, bookclose }; //console.log(scrapeResult); if (!scrapeResults.find(({stockcode}) => stockcode === scrapeResult.stockcode)) { scrapeResults.push(scrapeResult); } }); // copy to clipboard. copy(scrapeResults);

If there are more than 1 page, click to goto next page and Repeat Step 2 to scrape again. After finished with all the pages then run Step 3 code to download to local drive. scrapeResults should be stored as Global variable for this to work,

Console Code : Step 3   Select all

// Step 3 // define download function for webAPI function download(content, fileName, contentType) { var a = document.createElement("a"); var file = new Blob([content], {type: contentType}); a.href = URL.createObjectURL(file); a.download = fileName; a.click(); } // download json to local folder var jsonData = JSON.stringify(scrapeResults); var currentdate = new Date(); download(jsonData, currentdate.getFullYear() + ('0'+(currentdate.getMonth()+1)).slice(-2) + ('0'+currentdate.getDate()).slice(-2) + '_' + ('0'+currentdate.getHours()).slice(-2) + ('0'+currentdate.getMinutes()).slice(-2) + ('0'+currentdate.getSeconds()).slice(-2)+'_stockjson.txt', 'text/plain');

(5) The same jquery code function above can be used in nodejs script for automation. Just add "request request-promise cheerio" packages to the project

(6) For nodejs, the save function should be

nodejs script   Select all

var fs = require('fs'); fs.writeFile("json.txt", jsonData, function(err) { if (err) { console.log(err); } });

(7) For browser console code without jQuery inject or don't want to import the jQuery library, and have to use querySelectorAll() function and use Object.values to convert to object as demo below.

console code   Select all

// Goto http://www.aastocks.com/en/stocks/market/calendar.aspx?type=1 and then open Browser (Chrome, Firefox, Safari) developer tools using ( Cmd + Opt + I in mac or Ctrl + Shift + I in win) and enter the following console code to run. var scrapeResults = ''; document.querySelectorAll('tr.crtRow').forEach(function(item) { const first = Object.values(item.querySelectorAll('.first'))[0]; if (typeof first !== 'undefined' && first !== null) { console.log(first.textContent??first.textContent.trim()); scrapeResults = scrapeResults.concat(first.textContent??first.textContent.trim(), ' '); }; const second = Object.values(item.querySelectorAll('td.second'))[0]; if (typeof second !== 'undefined' && second !== null) { console.log(second.textContent??second.textContent.trim()); scrapeResults = scrapeResults.concat(second.textContent??second.textContent.trim(), ' '); }; const third = Object.values(item.querySelectorAll('td.minw4'))[0]; if (typeof third !== 'undefined' && third !== null) { console.log(third.textContent??third.textContent.trim()); scrapeResults = scrapeResults.concat(third.textContent??third.textContent.trim(), ' '); }; const last = Object.values(item.querySelectorAll('td.last.minw1'))[0]; if (typeof last !== 'undefined' && last !== null) { console.log(last.textContent??last.textContent.trim()); scrapeResults = scrapeResults.concat(last.textContent??last.textContent.trim(), '\n'); }; }); // copy results to clipboard. copy(scrapeResults);

(7.1) For browser console code and use Array.from and map function to return json data.

console code   Select all

// //// Goto http://www.aastocks.com/en/stocks/market/calendar.aspx?type=1 and then open Browser (Chrome, Firefox, Safari) developer tools using ( Cmd + Opt + I in mac or Ctrl + Shift + I in win) and enter the following console code to run. var data = Array.from( document.querySelectorAll('.crtRow') ).map( row => Array.from(row.children).map(node => node.textContent.trim()) ).map( (row) => row[0].length === 0 ? [...row.slice(1)] : row ).map( (row, idx, arr) => { if (row.length === 1) return null; const getLastMatch = (idx, arr) => arr[idx].length === 4 ? arr[idx] : getLastMatch(idx - 1, arr); const match = getLastMatch(idx, arr); const isSameDate = row.length === 3; console.log(''.concat(match[0],' ',row[1 - isSameDate *1],' ',row[2 - isSameDate *1],' ',row[3 - isSameDate *1],'\n')); return { date:match[0], stock:row[1- isSameDate *1], code:row[1 - isSameDate *1].slice(-8).slice(0,5), industry:row[2 - isSameDate *1], period:row[3 - isSameDate *1 -3] } }).filter(Boolean); console.log(data); copy(data);

(8) Another example for nodejs scrapping code as demo below.

ronaldo.js   Select all

// need to install npm install request // run with node ronaldo.js const request = require("request-promise"); const url = "https://www.transfermarkt.com/cristiano-ronaldo/alletore/spieler/8198/plus/1" headers = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.93 Safari/537.36' } ; const jsdom = require("jsdom"); const { JSDOM } = jsdom; const options = { url: url, timeout: 300000, headers:headers }; async function scrape() { try { const htmlResult = await request.get(options); const dom = new JSDOM(htmlResult); const { document } = dom.window; var data = Array.from( document.querySelectorAll('.responsive-table table tbody tr') ).map( row => Array.from(row.children).map(node => node.textContent.trim()) ).map( (row) => row.length === 15 ? [...row.slice(0, 5), ...row.slice(6)] : row ).map( (row, idx, goals) => { if (row.length === 1) return null; const getLastMatch = (idx, goals) => goals[idx].length === 14 ? goals[idx] : getLastMatch(idx - 1, goals); const match = getLastMatch(idx, goals); const isSameMatch = row.length === 14; return { competition: match[1], matchday: match[2], date: match[3], venue: match[4], opponent: match[7], result: match[8], position: match[9], minute: row[1 + isSameMatch * 9], atScore: row[2 + isSameMatch * 9], goalType: row[3 + isSameMatch * 9], assist: row[4 + isSameMatch * 9], } } ).filter(Boolean) // filter null .filter(x => (new Date(x.date)).getFullYear() >= 2021) // filter year console.log(data); } catch (err) { // try catch console.error(err); } } scrape();

Google colab - keras-learn and Logistic Regression example

(1) Following the previous post, this demo the keras sample from how-to-install-tensorflow-with-gpu.html
Please take note that google only allow one active session for the free service. If you need faster GPU, more RAM and sessions, please consider to subscribe colab pro.

keraslearn.ipynb   Select all

# Step 1 mount google drive if data is from google drive import os from google.colab import drive drive.mount('/content/drive') # Step 2 if using tensorflow GPU #%tensorflow_version 2.x #import tensorflow as tf #print('TensorFlow: {}'.format(tf.__version__)) #tf.test.gpu_device_name() # Step 3 from keras.models import Sequential from keras.layers import Dense import numpy import time # fix random seed for reproducibility numpy.random.seed(7) # Step 4 # download pima indians dataset to google drive !curl -L https://tinyurl.com/tensorflowwin | grep -A768 pima-indians-diabetes.data.nbsp | sed '1d' > 'drive/MyDrive/Colab Notebooks/pima-indians-diabetes.data' # or download to local data directory !mkdir -p ./data !curl -L https://tinyurl.com/tensorflowwin | grep -A768 pima-indians-diabetes.data.nbsp | sed '1d' > './data/pima-indians-diabetes.data' # Step 5 load dataset from google drive dataset = numpy.loadtxt("drive/MyDrive/Colab Notebooks/pima-indians-diabetes.data", delimiter=",") # or load data from local data directory dataset = numpy.loadtxt("./data/pima-indians-diabetes.data", delimiter=",") # Step 6 # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # Step 7 # create model model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Step 8 # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Step 9 start_time=time.time() # Fit the model model.fit(X, Y, batch_size=10, epochs=1500) # parameters for keras 1.2.2 # evaluate the model scores = model.evaluate(X, Y) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) print("\nTraining took %.2f seconds\n" %(time.time()-start_time))

(2) For large training data set, consider to zip them and upload to google drive. Mount the google drive, then unzip it in local session. e.g.

!mkdir -p ./data
!unzip -o './drive/MyDrive/Colab Notebooks/mydata.zip' -d ./data/

(3) To stop the running cell in Google Colab use Ctrl-M I

(4) How to quickly run an ipynb example from github ?
4.1) Go to https://colab.research.google.com/, after login gmail and choose GitHub tab and enter search say "clareyan/From-Linear-to-Logistic-Regression-Explained-Step-by-Step"
4.2) In Step 2 cell box change the importing of dataset to
df = pd.read_csv('https://raw.githubusercontent.com/clareyan/From-Linear-to-Logistic-Regression-Explained-Step-by-Step/master/Social_Network_Ads.csv')
4.3) Then choose menu -> Runtime -> Run All. After that, use menu -> File -> Save a copy in Drive.

How to setup google colab and start linear regression with tensorflow.

(1) You only need a chrome browser, google gmail account and google drive account to start cloud tensorflow computing. And it is free to use and learn.

(2) Go to https://colab.research.google.com/
(3) Create a new notebook rename it and then Copy to Drive
(4) Type the following into notebook and run it step by step (Press Alt-Enter to run after each step)

LinearRegression.ipynb   Select all

#Step 1 # mount Google Drive, will ask for authorization code import numpy as np import os from google.colab import drive drive.mount('/content/drive') #Step 2 # choose the notebook settings to use GPU, via Menu -> Edit -> Notebook Settings. %tensorflow_version 2.x import tensorflow as tf ＃ will show GPU if successful tf.test.gpu_device_name() #Step 3 # load data import pandas as pd # either download the linear_data.csv and upload to google drive, or direct download it via the shell command as below !curl -L https://tinyurl.com/lineardatacsv | grep -A200 START_OF_LINEAR_DATA.CSV | sed '1d' | sed -n "/END_OF_LINEAR_DATA.CSV/q;p" | sed 's/&gt;/\>/g;s/&lt;/\</g' > 'drive/MyDrive/Colab Notebooks/linear_data.csv' df = pd.read_csv('drive/MyDrive/Colab Notebooks/linear_data.csv') df.head() #Step 4 # split into independent and dependent X = df[['X']].values y = df[['Y']].values X.shape, y.shape #Step 5 # visualize data import matplotlib.pyplot as plt %matplotlib inline plt.scatter(X,y) plt.xlabel('independent') plt.ylabel('dependent') plt.show() # Use Text box to enter ＃ # Linear Regression $ \hat y = a + b * X $ #Step 6 # Linear Regression # define regression model class regression(): def __init__(self): self.a = tf.Variable(initial_value=0,dtype=tf.float32) self.b = tf.Variable(initial_value=0,dtype=tf.float32) def __call__(self, X): x = tf.convert_to_tensor(X,dtype=tf.float32) y_est = tf.add(self.a, tf.multiply(self.b,x)) return y_est model = regression() # Use Text box to enter ＃ # loss = sum of square error (sse) = $ \sum (y_t - y_p) ^ 2 $ # step 7 # define loss function def loss_func(y_true, y_pred): # both values are in tensors sse = tf.reduce_sum(tf.square(tf.subtract(y_true,y_pred))) return sse # Use Text box to enter ＃ # Gradient Descent $ a = a_i - \nabla(sse) | a * LR $ $ b = b_i - \nabla(sse) | b * LR $ # step 8 # define train function def train(model, inputs, outputs, learning_rate): # convert outputs into tensor y_true = tf.convert_to_tensor(outputs,dtype=tf.float32) # GradientTape cal gradient distance with tf.GradientTape() as g: y_pred = model(inputs) current_loss = loss_func(y_true,y_pred) da,db = g.gradient(current_loss,[model.a,model.b]) # update the values model.a.assign_sub(da*learning_rate) model.b.assign_sub(db*learning_rate) # Step 9 def plot_scatter(x,y): plt.scatter(x,y) # scatter plt.plot(x,model(x),'r--') #line plot_scatter(X,y) # step 10 # model fitting model = regression() a_values = [] b_values = [] cost_values = [] # epochs, no of steps epochs = 100 # learning_rate learning_rate = 0.0001 for epoch in range(epochs): a_values.append(model.a) b_values.append(model.b) # prediction values and error y_pred = model(X) cost_value = loss_func(y,y_pred) cost_values.append(cost_value) # training train(model,X,y,learning_rate) # visual the scatter plot_scatter(X,y) plt.show #print the value print('Epoch: %d, Loss: %0.2f, a: %0.2f, b: %0.2f' %(epoch,cost_value,a_values[-1],b_values[-1])) # step 11 plt.plot(cost_values)

(5) Linear_data.csv , download and upload to google drive

linear_data.csv   Select all

X,Y 4,2 4,10 7,4 7,22 8,16 9,10 10,18 10,26 10,34 11,17 11,28 12,14 12,20 12,24 12,28 13,25 13,34 13,24 13,46 14,26 14,36 14,60 14,80 15,20 15,26 15,54 16,32 16,40 17,32 17,40 17,50 18,42 18,56 18,76 18,84 19,36 19,45 19,68 20,32 20,48 20,52

HelloWorld Assembler Code for x86_64, arm64 and for linux or macOS

(1) Following the previous post, this post demo the assembler code for command line program HelloWorld for x86_64, arm64 and for linux or macOS.

HelloWorld.S   Select all

// // Assembler program to print "Hello World!" // to stdout. For amr64, x86_64, linux and macOS // #define STDIN 0 // standard input device #define STDOUT 1 // standard output device #ifdef __APPLE__ #define SYS_read 0x2000003 // system call to read input macOS #define SYS_write 0x2000004 // system call to write message macOS #define SYS_exit 0x2000001 // system call to terminate program macOS #define SVC_write 4 // SVC write arm64 macOS #define SVC_exit 1 // SVC exit arm64 macOS #endif #ifdef __linux__ #define SYS_read 0 // system call to read input #define SYS_write 1 // system call to write message #define SYS_exit 60 // system call to terminate program #define SVC_write 64 // SVC write arm64 linux #define SVC_exit 93 // SVC exit arm64 linux #endif #define EXIT_OK 0 // OK exit status .globl _start // Provide program starting address to linker #ifdef __APPLE__ .align 4 #endif .text _start: #if defined __arm64__ || defined __ARM_ARCH_ISA_A64 mov X0, #STDOUT // 1 = StdOut #ifdef __linux__ ldr X1, =helloworld // string to print mov X8, #SVC_write // linux write system call #endif #ifdef __APPLE__ // adr X1, helloworld // string to print //(adr calculates an address from the PC plus an offset, but for local) adrp X1, helloworld@PAGE // adrp can be used to access relative address of 4GB range add X1, X1, helloworld@PAGEOFF // string to print mov X16, #SVC_write // linux write system call #endif ldr X2, =len // length of our string svc #0 // Call linux to output the string // Setup the parameters to exit the program // and then call Linux to do it. mov X0, #0 // Use 0 return code #ifdef __linux__ mov X8, #SVC_exit // Service command code 93 terminates this program #endif #ifdef __APPLE__ mov X16, #1 // Service command terminates this program #endif svc #0 // Call linux to terminate the program #endif #if defined __x86_64__ movq $STDOUT, %rdi #ifdef __linux__ movq $helloworld, %rsi // char * #endif #ifdef __APPLE__ leaq helloworld(%rip), %rsi #endif movq $len, %rdx // length of our string movq $SYS_write, %rax // write system call syscall movq $EXIT_OK, %rdi // Use 0 return code movq $SYS_exit, %rax // exit system call syscall #endif .data helloworld: .ascii "Hello World!\n" len = . - helloworld // len = start - end

(2) To compile and debug for different systems

shell scripts   Select all

# To download the above code using command line. curl -L https://tinyurl.com/helloworld-gas | grep -A200 START_OF_HELLOWORLD.S | sed '1d' | sed -n "/END_OF_HELLOWORLD.S/q;p" | sed 's/&gt;/\>/g;s/&lt;/\</g' > HelloWorld.S # To compile with debug symbols under linux, e.g. Win10 WSL2 or Linux or Android Termux App clang -g -c HelloWorld.S -o HelloWorld.o ; ld HelloWorld.o -o HelloWorld # To compile under macOS (e.g. with M1 cpu) clang -g HelloWorld.S -o HelloWorld_x86_64 -e _start -arch x86_64 clang -g HelloWorld.S -o HelloWorld_arm64 -e _start -arch arm64

(3) To debug using lldb

shell scripts   Select all

# To start program debug lldb HelloWorld_x86_64 # or lldb HelloWorld_arm64 # lldb debug session for arm64 - useful commands (lldb) breakpoint set --name _start (lldb) breakpoint list (lldb) run (lldb) step (lldb) reg read x0 x1 x2 x8 lr pc (lldb) reg read -f t cpsr # lldb debug session for x86_64 - useful commands (lldb) reg read -f d rax rdi rsi rdx rflags (lldb) reg read -f t rflags # print the address value in the stackpointer for x86_64 (lldb) p *(int **)$sp # hint: to search lldb command history use ctrl-r

(4) Summary of differences
4.1) In order to preprocess the assembler file using clang compiler, the filename extension should be capital letter S in linux. Subroutine name between C and global asm labels should prefix by underscore for macOS.
4.2) A64 (arm64) parameter/ results registers are X0-7. If the function has a return value, it will be stored in X0.
4.3) x86_64 parameter registers for integer or pointer are %rdi. %rsi, %rdx, %rcx, %r8, %r9. If the function has a return value, it will be stored in %rax.
4.4) Linux and macOS has different syscall number (x86_64) or Service call number (for arm64). They are defined in this source code.
4.5) Absolute addressing is not allowed for arm64. For macOS, adr instruction can be used for accessing readonly local data. But for non-local data section (which is a buffer in RAM), adrp instruction and @PAGE and @PAGEOFF operators should be used as demo in the code.