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Documentation
MLflow tutorial
mlflow ui
dagshub
MLFLOW_TRACKING_URI=https://dagshub.com/rahulkumar7759/Chest-Cancer-Classification-using-MLOPS.mlflow MLFLOW_TRACKING_USERNAME=rahulkumar7759 MLFLOW_TRACKING_PASSWORD=4a0e85b75d773a5c657426f17d1a8490861c4f3c python script.py
set MLFLOW_TRACKING_URI=https://dagshub.com/rahulkumar7759/Chest-Cancer-Classification-using-MLOPS.mlflow
set MLFLOW_TRACKING_USERNAME=rahulkumar7759
set MLFLOW_TRACKING_PASSWORD=4a0e85b75d773a5c657426f17d1a8490861c4f3c
dvc init dvc repro dvc dag
Its Production Grade Trace all of your expriements Logging & taging your model
Its very lite weight for POC only lite weight expriements tracker It can perform Orchestration (Creating Pipelines)
EC2 access : It is virtual machine
ECR: Elastic Container registry to save your docker image in aws
Build docker image of the source code
Push your docker image to ECR
Launch Your EC2
Pull Your image from ECR in EC2
Lauch your docker image in EC2
AmazonEC2ContainerRegistryFullAccess
AmazonEC2FullAccess
Create ECR repo to store/save docker image
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 6. Configure EC2 as self-hosted runner: setting>actions>runner>new self hosted runner> choose os> then run command one by one 7. Setup github secrets: 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
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