https://github.com/codewithcharan/end-to-end-mlops-project
https://github.com/codewithcharan/end-to-end-mlops-project
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/codewithcharan/end-to-end-mlops-project
- Owner: CodeWithCharan
- License: mit
- Created: 2024-07-10T10:24:52.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-18T15:25:44.000Z (almost 2 years ago)
- Last Synced: 2025-04-03T03:42:42.895Z (about 1 year ago)
- Language: CSS
- Size: 1.69 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# End-to-End-MLOps-Project
## 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
## Pipelines
1. Data Ingestion
2. Data Validation
3. Data Transformation
4. Model Trainer
5. Model Evaluation
## STEPS
Clone the repository
```bash
git clone https://github.com/CodeWithCharan/End-to-End-MLOps-Project.git
```
### STEP 01: Create a conda environment after opening the repository
```bash
conda create -n mlopsenv python=3.8 -y
```
```bash
conda activate mlopsenv
```
### STEP 02: install the requirements
```bash
pip install -r requirements.txt
```
### STEP 03: run app.py
```bash
python app.py
```
### STEP 04: After running the app.py, it will be available at:
- `http://127.0.0.1:8080`
- `http://localhost:8080`
## MLFlow
[Documentation](https://mlflow.org/docs/latest/index.html)
### CMD
```bash
mlflow ui
```
## DagsHub
[Documentation](https://dagshub.com/)
### Tracking URI:
- MLFLOW_TRACKING_URI = https://dagshub.com/CodeWithCharan/End-to-End-MLOps-Project.mlflow
- MLFLOW_TRACKING_USERNAME = CodeWithCharan
- MLFLOW_TRACKING_PASSWORD = YourAccessToken
### Run this to export as env variables:
```bash
export MLFLOW_TRACKING_URI=https://dagshub.com/CodeWithCharan/End-to-End-MLOps-Project.mlflow
```
```bash
export MLFLOW_TRACKING_USERNAME=CodeWithCharan
```
```bash
export MLFLOW_TRACKING_PASSWORD=YourAccessToken
```
# AWS-CICD-Deployment-with-Github-Actions
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
## 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
#Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
## 3. Create ECR repo to store/save docker image
- Save the URI: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
## 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 = ap-south-1
AWS_ECR_LOGIN_URI =
ECR_REPOSITORY_NAME = mlproj
# About MLflow
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & tagging your model