This is a sample Bank Loan Classification
project template where we are using a binary classification model to classify whether to grant loans to applicants or not.
The Bank Loan Classification
dataset used during the workshop is taken from Kaggle and can be found here
.
- Python3
- git
- Docker
Loan Classification Project
├── models
│ └── RF_Model_V1.pkl
├── Dockerfile
├── requirements.txt
├── deployment.yaml
├── model_api.py
└── streamlit_app.py
After completing the tasks your deployed model API can be accessed from the ai service endpoint in your cluster. You can also append /docs
to the endpoint to have access to the Swagger UI for API Testing.
Step 1
: Cloning the repo
git clone https://github.com/DigitalProductschool/react-spring-template.git
Step 2
: Changing working directory to ai
cd react-spring-template/ai/
Step 3
: Installing dependencies using pip3
pip3 install -r requirements.txt
Step 4
: Running uvicorn server locally
python3 model_api.py
Note: Go to /docs
route to test the api.
Step 5
: Building the runner container image locally
docker build -t ai-run-image -f run.Dockerfile .
Step 6
: Building the container image for our ai app using the Google Cloud Run builder locally
pack build ai:dev --env-file ./pack_envfile --builder gcr.io/buildpacks/builder:v1 --run-image ai-run-image
--env-file ./pack_envfile pass an env file to the build
--run-image ai-run-image use the custom run image called ai-run-image
Step 7
: Running the container locally
docker run --rm -d -p 8000:8000 -p 8501:8501 ai:dev
Note: Visit port 8501
to access Streamlit Application and 8000/docs
route to test the api.