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https://github.com/yashwanth1119/mlops_project
End-to-end Machine Learning Project with MLops
https://github.com/yashwanth1119/mlops_project
Last synced: about 2 months ago
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End-to-end Machine Learning Project with MLops
- Host: GitHub
- URL: https://github.com/yashwanth1119/mlops_project
- Owner: Yashwanth1119
- License: mit
- Created: 2023-09-05T07:53:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-16T15:56:54.000Z (over 1 year ago)
- Last Synced: 2023-09-16T19:16:59.710Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 85 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# End-to-end-Machine-Learning-Project-with-MLflow
## Workflows
1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the app.py
# How to run?
### STEPS:
Clone the repository
```bash
https://github.com/Yashwanth1119/MLops_Project.git
```
### STEP 01- Create a conda environment after opening the repository
```bash
conda create -n mlproj python=3.8 -y
```
```bash
conda activate env/
```
### STEP 02- install the requirements
```bash
pip install -r requirements.txt
```
```bash
# Finally run the following command
python app.py
```
Now,
```bash
open up you local host and port
```
## MLflow
[Documentation](https://mlflow.org/docs/latest/index.html)
##### cmd
- mlflow ui
### dagshub
[dagshub](https://dagshub.com/)
MLFLOW_TRACKING_URI=https://dagshub.com/Yashwanth1119/MLops_Project.mlflow \
MLFLOW_TRACKING_USERNAME=Yashwanth1119 \
MLFLOW_TRACKING_PASSWORD=bd5a42aef3a34fd357639b3d7e8d719a21b4ecd6 \
python script.py
Run this to export as env variables:
```bash
export MLFLOW_TRACKING_URI=https://dagshub.com/Yashwanth1119/MLops_Project.mlflow
export MLFLOW_TRACKING_USERNAME=Yashwanth1119
export MLFLOW_TRACKING_PASSWORD=bd5a42aef3a34fd357639b3d7e8d719a21b4ecd6
```
# AWS-CICD-Deployment-with-Github-Actions
## 1. Login to AWS console.
## 2. Create IAM user for deployment
#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
## 3. Create ECR repo to store/save docker image
- Save the URI: 566373416292.dkr.ecr.ap-south-1.amazonaws.com/mlproj
## 4. Create EC2 machine (Ubuntu)
## 5. Open EC2 and Install docker in EC2 Machine:
#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
# 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
## About MLflow
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & tagging your model