- Update config.yaml
- Update secrets.yaml [Optional]
- Update params.yaml
- Update the entity
- Update the configuration manager in src config
- Update the components
- Update the pipeline
- Update the main.py
- Update the dvc.yaml
- app.py
Clone the repository
https://github.com/AkashKulkarni4444/KidneyDiseaseClassification
conda create -n kidney python=3.8 -y
conda activate kidney
pip install -r requirements.txt
# Finally run the following command
python app.py
Now,
open up you local host and port
MLFLOW_TRACKING_URI= MLFLOW_TRACKING_USERNAME= MLFLOW_TRACKING_PASSWORD= python script.py
Run this to export as env variables:
export MLFLOW_TRACKING_URI=
export MLFLOW_TRACKING_USERNAME=
export MLFLOW_TRACKING_PASSWORD=
- dvc init
- dvc repro
- dvc dag
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & taging your model
DVC
- 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
#Policy:
-
AmazonEC2ContainerRegistryFullAccess
-
AmazonEC2FullAccess
- Save the URI: 638821426924.dkr.ecr.ap-south-1.amazonaws.com/kidney
#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 =
AWS_ECR_LOGIN_URI =
ECR_REPOSITORY_NAME =