https://github.com/am-ankitgit/mlops_with_aws_deployment
creating project from scratched to implement the MLOPs with Deployment on AWS
https://github.com/am-ankitgit/mlops_with_aws_deployment
Last synced: about 1 year ago
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creating project from scratched to implement the MLOPs with Deployment on AWS
- Host: GitHub
- URL: https://github.com/am-ankitgit/mlops_with_aws_deployment
- Owner: AM-Ankitgit
- License: mit
- Created: 2024-08-10T14:11:06.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-07T14:17:52.000Z (over 1 year ago)
- Last Synced: 2025-02-15T20:52:06.701Z (over 1 year ago)
- Language: CSS
- Size: 172 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Wine_Qauitiy_MLOPS_with_AWS_Deployment_With_CDCI Pipleine
Creating project from scratched to implement the MLOPs with Deployment on AWS
## Workflows
1. Update config.yaml
2. Update schema.yaml #schema of your data
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/AM-Ankitgit/MLOPS_with_AWS_Deployment.git
```
### STEP 01- Create a conda environment after opening the repository
```bash
conda create -n venv python=3.8 -y
```
```bash
conda activate mlproj
```
### 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
mlflow tracking url (this url is created by dagshub)
for windows user set url and you can see the dashboard of mlflow
set MLFLOW_TRACKING_URI=https://dagshub.com/AM-Ankitgit/MLOPS_with_AWS_Deployment.mlflow
set MLFLOW_TRACKING_USERNAME=AM-Ankitgit
set MLFLOW_TRACKING_PASSWORD=9a0cfdb8c9f9890d8d9e80455ae5918fcb9f4cb6
```
import dagshub
dagshub.init(repo_owner='AM-Ankitgit', repo_name='MLOPS_with_AWS_Deployment', mlflow=True)
# example script :
import mlflow
with mlflow.start_run():
mlflow.log_param('parameter name', 'value')
mlflow.log_metric('metric name', 1)
```
Run this to export as env variables:
```bash
export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow.mlflow
export MLFLOW_TRACKING_USERNAME=entbappy
export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
```
# 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
# Name of Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
## 3. Create ECR repo to store/save docker image
- Save the URI: 322848076327.dkr.ecr.us-east-1.amazonaws.com/mlproject
## 4. Create EC2 machine (Ubuntu)
## 5. Open EC2 and Install docker in EC2 Machine:
open the ec2 terminal
#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
# check your docker is installed
docker --version
# 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 = mlproj
ECR_REPOSITORY_NAME diamond
## About MLflow
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & tagging your model