Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
91e7185ba2
workflow modified
6 months ago
1de4364aae
model evaluation and mlflow added
6 months ago
4a036fa31b
app and html css added
6 months ago
src
4a036fa31b
app and html css added
6 months ago
4a036fa31b
app and html css added
6 months ago
4a036fa31b
app and html css added
6 months ago
5bcb0d2980
data ingestion added
6 months ago
4a036fa31b
app and html css added
6 months ago
f4808877dc
Initial commit
6 months ago
4a036fa31b
app and html css added
6 months ago
8e89e7cb13
cicd modified port
6 months ago
1de4364aae
model evaluation and mlflow added
6 months ago
1de4364aae
model evaluation and mlflow added
6 months ago
1de4364aae
model evaluation and mlflow added
6 months ago
af85df570f
model trainer added
6 months ago
030bd71f26
requirments added
6 months ago
d21da23c77
folder structure added
6 months ago
57218dfc7f
data uploaded
6 months ago
Storage Buckets

README.md

You have to be logged in to leave a comment. Sign In

Wine-Quality-Tester

Workflows

  1. Update Config.yaml
  2. Update schema.yaml
  3. Update params.yaml
  4. Update entity
  5. Update the configuration manager in src config
  6. Update Components
  7. Update the Pipeline
  8. Update the main.py
  9. Update the App.py

How to Run?

STEPS:

Clone the repository

https://github.com/ItzmeAkash/Wine-Quality-Tester/raw/main/winequality-data.zip

STEP 01 - Create a conda environment after opening the repository

conda create -n winetester python=3.8 -y
conda activate winetester

STEP 02 - Install the requirements

pip install -r requirements.txt
# Finally run the following Command
python app.py

Now,

open up you local host and port

MLflow

Documentation

cmd
  • mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/itzmeakashps/Wine-Quality-Tester.mlflow MLFLOW_TRACKING_USERNAME=itzmeakashps MLFLOW_TRACKING_PASSWORD=d587244e64e62ea9ab9a724d252a21ca83c524fe python script.py

Run this to export as env variables


export MLFLOW_TRACKING_URL=https://dagshub.com/itzmeakashps/Wine-Quality-Tester.mlflow

export MLFLOW_TRACKING_USERNAME = itzmeakashps

export MLFLOW_TRACKING_PASSWORD = d587244e64e62ea9ab9a724d252a21ca83c524fe 

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: 479278133245.dkr.ecr.us-east-1.amazonaws.com/wine

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
Tip!

Press p or to see the previous file or, n or to see the next file

About

No description

Collaborators 1

Comments

Loading...