snapmath

Project TOC

1. Just want to run the project;
2. The app in action;
3. Design Document;
4. Jupyter Notebook of model;
5. App local development (Python);
6. App local development (React);
7. Saving a built and trained model;
8. Exposing the model on Tensorflow serving;
9. Next steps;
10. Contribution;

Running from docker

docker run -p 5000:5000 -e MODEL_PATH="./models/1703825980" buarki/snapmath-app

Once running go to http://localhost:5000. You can find some images to use at the ML directory.

App in action

Running Python app from source code

To develop the app you can follow this steps:

1. enter the app directory:

cd app

2. Initialize virtual env:

python3 -m venv app/snapmath

3. Activate the virtual env:

source snapmath/bin/activate

4. Install dependencies:

pip3 install -r requirements.txt

5. To Run the app:

MODEL_PATH="../models/1703825980" python3 -m flask run --host=0.0.0.0

MODEL_PATH is pointing to the saved model 1703825980.

Once running go to http://localhost:5000. You can find some images to use at the ML directory.

Running React app from source code

To develop the app you can follow this steps:

1. enter the app directory:

cd app-js

2. Set Node version to 19.0.0:

nvm 19.0.0

3. Install dependencies:

npm i

4. Run

npm run dev

Once running go to http://localhost:3000. You can find some images to use at the ML directory.

For more info, see the full project here.

Saving a built and trained mode

You can collect and save your built model by using this script. Just follow bellow steps:

1. Collect the path of the model inside jupyter notebook. You can do so by running:

docker-compose exec jupyter-notebook sh -c "ls -td snapmath-model/* | head -n 1"

2. Get the base path of the jupyter container:

docker-compose exec jupyter-notebook pwd

3. Get the container id of jupyter notebook:

docker ps | grep "jupyter-notebook" 

If you are using docker-compose you can also use the container name;

4. Now call the script collect-model.sh proving these arguments:

./collect-model.sh jupyter-notebook CONTAINER_BASE_PATH/MODEL_PATH

A concrete example is bellow one:

./collect-model.sh 2c40e8ce7197 /home/jovyan/snapmath-model/1703825980

where:

• 2c40e8ce7197 is the container jupyter notebook container id
• /home/jovyan/snapmath-model/1703825980 is the new model to be saved

Exposing the model using TensorFlow Serving

In order to see a trained model running performing the inferences, we can leverage TensorFlow Serving, which is a prepared docker image able to load the model and make it available via REST or gRPC. The Makefile already has a command to expose a collected model in it. First, we need to build the image based on the provided Dockerfile adding the collected models inside of it:

make build-tf-serving

Then we can run:

make run-tf-service

The REST API will be available at localhost:8501, and you can easily call this API using the provided client using Jupyter Notebook.

Next steps

For now I see two interesting points to inspect:

• Check if is it possible to run this model using Go;

Contribution

Contributions are welcome! If you find something interesting to improve just open a PR :)