chicken-disease-classification

End-to-end project CNN based project, also demonstrating the MLOps practices, along with deployment.

Workflows

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity # to create some return type/classes
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml

How to run?

STEPS:

Clone the repository

git clone https://github.com/satyamsc0/chicken-disease-classification

STEP 01- Create a virtual environment after opening the repository

python -m venv venv

source venv/bin/activate

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

DVC cmd

1. dvc init
2. dvc repro
3. dvc dag

Tensorboard:

tensorboard --logdir artifacts/prepare_callbacks/tensorboard_log_dir/

MLflow

mlflow ui

Dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/satyamsc0/chicken-disease.mlflow
MLFLOW_TRACKING_USERNAME=satyamsc0
MLFLOW_TRACKING_PASSWORD=5a7753b233a8bdd1a304ece714880751be9fda6e
python script.py

export MLFLOW_TRACKING_URI=https://dagshub.com/satyamsc0/chicken-disease.mlflow
export MLFLOW_TRACKING_USERNAME=satyamsc0
export MLFLOW_TRACKING_PASSWORD=5a7753b233a8bdd1a304ece714880751be9fda6e

AZURE-CICD-Deployment-with-Github-Actions

Save pass:

EZKH5yytiVnJ3h+eI3qhhzf9q1vNwEi6+q+WGdd+ACRCZ7J

Run from terminal:

docker build -t chickenreg.azurecr.io/chicken:latest .

docker login chickenreg.azurecr.io

docker push chickenreg.azurecr.io/chicken:latest

Deployment Steps:

1. Build the Docker image of the Source Code
2. Push the Docker image to Container Registry
3. Launch the Web App Server in Azure
4. Pull the Docker image from the container registry to Web App server and run