django ml server for compute document similarity web app

Deploy machine learning Django App

Window set up for localhost

1. Install python 3.10.6
   • Windows installer (32-bit) https://www.python.org/ftp/python/3.10.6/python-3.10.6.exe
   • Windows installer (64-bit) https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
   • Should select install for all users, and Add to PATH
   • Check if python installed with command python --version
2. Create python virtual environment
   • Open Terminal or Powershell
   • mkdir DATN
   • cd DATN
   • git clone https://github.com/npsang/django-ml-server.git
   • cd django-ml-server
   • python -m pip install --upgrade pip setuptools wheel
   • python -m venv ml_env
   • ml_env\Scripts\activate The terminal should show something like "(ml_env) ~\DATN\django-ml-server\"
   • python -m pip install --upgrade pip setuptools wheel
   • pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
   • pip install -r requirements.txt
3. Download Machine Learning Model
   • Download https://drive.google.com/drive/folders/1KzWmS_CIP_ddDotJwIUY9PwU3Urk7x-B?usp=share_link
   • Unzip the download you get the folder name make-multilingual-sys-2023-01-12_03-33-43
   • Copy the folder into the project in path_to_project\DATN\django-ml-server\MLRestAPI\ml\compute_similarity\models\
4. Create Database MySQL
   • Open MySQL Workbench
   • Create new connection or use already connection with below infomation:
     • HOST: 127.0.0.1
     • PORT: 3306
     • USER: root
     • Password
   • Connect above connection and create database:
     • Select Create a new scheme in the connected server button
     • Name: ml_restapi_db
     • Charset: utf8mb4
     • Collation: utf8mb4_unicode_ci
     • Select Apply then select Apply
5. Run
   • cd MLRestAPI
   • python manage.py makemigrations
   • python manage.py migrate
   • python manage.py runserver
6. Next time run
   • ml_env\Scripts\activate
   • cd MLRestAPI
   • python manage.py makemigrations
   • python manage.py migrate
   • python manage.py runserver

Window Set up with IIS

1. Create python virutal environment and active

   cd C:\ mkdir pyenv cd pyenv python -m venv djangoML .\djangoML\Scripts\activate Now environment is djangoML python -m pip install --upgrade pip

2. Clone project

   git clone https://github.com/npsang/django-ml-aws-ec2.git cd django-ml-aws-ec2

3. pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
4. pip install -r .\requirements.txt

Ubuntu 22 LTS set up

1. sudo apt-get update

2. sudo apt-get upgrade

3. sudo apt install python3-pip

4. sudo apt-get install python3-dev default-libmysqlclient-dev build-essential

5. pip install -r .\requirements.txt

Django ML AWS EC2 project set up

1. git clone https://github.com/npsang/django-ml-aws-ec2.git

2. cd django-ml-aws-ec2

3. pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu

4. pip3 install -r requirements.txt

Run project

1. cd MLRestAPI

2. python3 manage.py runserver 0.0.0.0:8000 Then you can access project with url: http://35.77.218.136:8000/

       #         """  

        #         # 1. set the field to Django BinaryField
        #         from django.db import models
        #         np_field = models.BinaryField()
        #         # 2. transform numpy array to python byte using pickle dumps, then encoded by base64
        #         # np_bytes = pickle.dumps(np_array)
        #         np_base64 = base64.b64encode(np_bytes)
        #         model.np_field = np_base64
        #         # 3. get the numpy array from django model
        #         np_bytes = base64.b64decode(model.np_field)
        #         np_array = pickle.loads(np_bytes)
        #         """`