create env
conda create -n wineq python=3.7 -y
activate env
conda activate wineq
created a req file
install the req
pip install -r requirements.txt
download the data from
https://drive.google.com/drive/folders/18zqQiCJVgF7uzXgfbIJ-04zgz1ItNfF5?usp=sharing
git init
dvc init
dvc add data_given/winequality.csv
git add .
git commit -m "first commit"
oneliner updates for readme
git add . && git commit -m "update Readme.md"
git remote add origin https://github.com/shivamordanny/dvc-mlflow-actions.git
git branch -M main
git push origin main
tox command -
tox
for rebuilding -
tox -r
pytest command
pytest -v
setup commands -
pip install -e .
Hyperparameter tuning:
Change the hyperparameter for the elasticnet model from params.yaml
Run the mlflow server:
mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts --host 0.0.0.0 -p 1234
Perform steps to start training:
dvc repro
Commit the changes to trigger the CI/CD pipeline:
git add . && git commit -m "version-8 ElasticNet" && git push origin main
Check the Deployment status under Github actions on the git repository. Check the mlflow server to compare different models.
Webapp: