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
git init
dvc init
dvc add data_given/mushrooms.csv
dvc repro -> to run the pipeline
dvc metrics show -> to see params.json and metrics.json
dvc metrics diff -> to see difference old and new metrics
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/pranav-c01/Ineuron_internhip_proj.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 . # installs local packages present in dir (setup.py file)
build your own package commands-
python setup.py sdist bdist_wheel
create an artifcats folder
mkdir artifacts
mlflow server command -
mlflow server
--backend-store-uri sqlite:///mlflow.db
--default-artifact-root ./artifacts
--host 0.0.0.0 -p 1234
mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts --host 0.0.0.0 -p 1234
#with specific access
1. EC2 access : It is virtual machine
2. ECR: Elastic Container registry to save your docker image in aws
#Description: About the deployment
1. Build docker image of the source code
2. Push your docker image to ECR
3. Launch Your EC2
4. Pull Your image from ECR in EC2
5. Lauch your docker image in EC2
#Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
- Save the URI (Eg-> : 136566696263.dkr.ecr.us-east-1.amazonaws.com/mlproject)
#optinal
sudo apt-get update -y
sudo apt-get upgrade
#required
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
setting>actions>runner>new self hosted runner> choose os> then run command one by one
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION = us-east-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app