Javascript Integration with Docker and Machine Learning
Task 7.1
First, we should open the Rhel 8 terminal and stop the firewall security. Using these commands
setenforce 0
systemctl stop firewalld
systemctl disable firewalld
make sure first you have to install the httpd server using this command. if you already install then don’t run this command.
yum install httpd
and start the apache server, using this command.
systemctl start httpd
then, go to the directory cgi-bin and create the python file. and make an executable file.
cd /var/www/cgi-bin/
vi home.py
chmod +x home.py
and write the program.
In this program, we have to import the libraries' cgi and subprocess. The CGI library makes possible communication between clients and web servers. Whenever the client browser sends a request to the webserver, the CGI program sends the output back to the web server based on the input provided by the client-server. the content-type line is sent back to the browser and it specifies the content type to be displayed on the browser screen.
and after that, we will go to another directory “/var/www/html” and create an HTML file.
In this program, we will create a responsive web page. we have created a function to the request to the HTTP server and server send back to the response and show the output to the web page.
and after that go to the browser and type your IP and your HTML file name.
for example- 192.168.12.34/index.html
Now we will create a docker container to predict the salary.
First, we will create a new folder and go to this folder and put the dataset csv file, and then create a docker file.
vi Dockerfile
and write the code.
and create one python file “selaryPredict.py” and write the code.
In this program, we have import the warnings library to ignore the FutureWarning and show the clear output. we have read the dataset and split the train test model and fit the model using Linear Regression and predict the salary.
After that, Build the docker image and run it. Follow this commands
docker build . -t ml1:v1
docker run -it ml1
Complete Code in GitHub Repo — https://bit.ly/3daQDKm
and we can run also many commands like —
docker ps
docker run
docker images
docker rm -f
and more
After the Completion web page look like this —
