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Machine Learning Application Server for handwritten digit recognition

Principle

  1. Set up a Working Machine to train a model of handwritten digit recognition (and which can be stopped afterward)

  2. Set up a Front-End Machine with a web server for the handwritten digit recognition application

  3. A Back-End Machine with a python server for the predictions (ideally in a private subnet, but not in this tutorial)

Architecture

  1. Create a VPC (CIDR: 192.168.0.0/16)

  2. Create a Public Subnet (CIDR: 192.168.0.0/24)

  3. Create an Internet Gateway and attach it to the VPC

  4. Edit the Route Table

    Public Subnet Route Table
    Destination Target
    10.0.0.0/16 local
    0.0.0.0/0 IGW

  5. Launch three EC2 instances (for example Ubuntu Server 18.04 LTS (HVM), SSD Volume Type - ami-04b9e92b5572fa0d1)

    • for the Working Machine, choose an Instance Type with enough capacity (for example m5.xlarge with 4 vCPUs and 16 GiB memory)
    • for the front and back-end machines a more basic instance type (e.g., t2.micro) is enough

  6. If a Public IP has not been assigned at launch, then allocate three new Elastic IP addresses and associate them to each machine

  7. Edit the Security Groups of the instances

    • for the working machine, open port 8888 for the connection to Jupyter Notebooks
    • for the front-end machine, open port 80 (HTTP) for the connection with the web server
    • for the back-end machine, open port 5000 used by Flask Python web framework

    Working Machine Security Group
    Type Ports Protocol Source
    Inbound SSH 22 TCP 0.0.0.0/0
    Custom TCP Rule 8888 TCP 0.0.0.0/0
    Outbound all traffic all all 0.0.0.0/0
    Front-End Machine Security Group
    Type Ports Protocol Source
    Inbound SSH 22 TCP 0.0.0.0/0
    HTTP 80 TCP 0.0.0.0/0
    Outbound all traffic all all 0.0.0.0/0
    Back-End Machine Security Group
    Type Ports Protocol Source
    Inbound SSH 22 TCP 0.0.0.0/0
    Custom TCP Rule 5000 TCP 0.0.0.0/0
    Outbound all traffic all all 0.0.0.0/0

Training the model on the Working Machine

  1. Connect to the machine via ssh

    PS> ssh -i myKeyPair.pem ubuntu@<wm_public_dns>

  2. First thing first, we need to update the package tool

    $ sudo apt-get update

  3. Install Miniconda (and restart the shell)

    $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ sh Miniconda3-latest-Linux-x86_64.sh
$ exec $SHELL

    If conda command is not found after installation: $ export PATH=~/miniconda3/bin:$PATH

  4. Create a new environment with Python 3, install packages ipykernel, jupyter, tensorflow, keras (with conda), opencv-python (with pip) and then install a Python kernel in the new environment

    $ conda create -n myenv python=3.6
$ conda activate myenv
$ conda install ipykernel jupyter
$ conda install tensorflow 
$ conda install keras
$ pip install opencv-python
$ python -m ipykernel install --user --name myenv --display-name "Python 3 (myenv)"

    See Files/wm_requirements.txt for precise versions of packages

  5. From local machine, export Files/00-mnist-cnn.ipynb (and Files/9.png) to remote working machine

    PS> scp -i myKeyPair.pem ./00-mnist-cnn.ipynb ubuntu@<wm_public_ip>:/home/ubuntu/

  6. In the remote host, run Jupyter Notebook in background, allowing any distant machine to connect to it

    $ nohup jupyter notebook --ip=0.0.0.0 &

    (To stop the notebook: $ jupyter notebook stop)

  7. Connect to the notebook by typing in any browser <wm_public_ip>:8888/?token=<token> (the token can be accessed in nohup.out file) and then launch the notebook (it saves the model in cnn-mnist)

Front-End Web Server

  1. Connect to the machine via ssh

    PS> ssh -i myKeyPair.pem ubuntu@<frontend_public_dns>

  2. Update the package tool

    $ sudo apt-get update

  3. Install apache web server

    $ sudo apt-get install apache2

  4. From local machine, export Files/index.html and Files/static/* to remote machine

    PS> scp -i myKeyPair.pem ./index.html ubuntu@<frontend_public_ip>:/home/ubuntu/
PS> scp -i myKeyPair.pem ./static/* ubuntu@<frontend_public_ip>:/home/ubuntu/static/

  5. Move index.html and static/ to the default Ubuntu document root /var/www/html

    $ sudo mv index.html /var/www/html
$ sudo mv static /var/www/html

  6. In the index.html file, change the target IP (public or private IP) of the back-end machine

  7. We can now access the web application by typing in any browser http:\\<frontend_public_ip>

Back-End Python Server

  1. Connect to the machine via ssh

    PS> ssh -i myKeyPair.pem ubuntu@<backend_public_dns>

  2. Update the package tool

    $ sudo apt-get update

  3. Install Miniconda (and restart the shell)

    $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$ sh Miniconda3-latest-Linux-x86_64.sh
$ exec $SHELL

    If conda command is not found after installation: $ export PATH=~/miniconda3/bin:$PATH

  4. Create a new environment with Python 3, install packages tensorflow then keras, imageio, Flask

    $ conda create -n myenv python=3.6
$ conda activate myenv
$ conda install tensorflow
$ conda install keras imageio Flask

    See Files/backend_requirements.txt for precise versions of packages

  5. Install Flask extension for handling Cross Origin Resource Sharing (CORS)

    conda install flask-cors

  6. From local machine, export Python script keras_flask.py to remote backend machine

    PS> scp -i myKeyPair.pem ./keras_flask.py ubuntu@<backend_public_ip>:/home/ubuntu/

    and from remote working machine, export the model cnn-mnist to remote backend machine

    $ scp -i myKeyPair.pem ./cnn-mnist ubuntu@<backend_private_ip>:/home/ubuntu/

  7. Et voilà!

    digit-prediction