Project Goal - Experimental

Create end to end time series feature validation.
Will plug in BTCUSDT price+price_features from Binance for feature importance.
Use this for experimental iteration.
Build on MFLow Pipeline. Deploy on AWS EC2 with GitHUb Actions.

Then use XGBoost/LSTM for forward looking price (for 15-30 minutes).
Add a live data feed vs the forecasted dashboard.

dagshub tracking

MLFLOW_TRACKING_URI=https://dagshub.com/dennismutugi/Feature_Eng.mlflow
MLFLOW_TRACKING_USERNAME=dennismutugi
MLFLOW_TRACKING_PASSWORD= XXXXXXXXXXXXXXXXXXXX
python script.py

How to run?

STEPS:

Clone the repository

https://github.com/MutugiD/Feature_Eng

STEP 01- Create a conda environment after opening the repository

pip install virtualenv
python -m venv features  

features\Scripts\activate

STEP 02- install the requirements

pip install -r requirements.txt

MLflow

Documentation

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/dennismutugi/Feature_Eng.mlflow
MLFLOW_TRACKING_USERNAME=dennismutugi
MLFLOW_TRACKING_PASSWORD=XXXXXXXXXXXXXXX
python script.py

Run this to export as env variables:

Ubunut-based
export MLFLOW_TRACKING_URI=https://dagshub.com/dennismutugi/Feature_Eng.mlflow
export MLFLOW_TRACKING_USERNAME=dennismutugi
export MLFLOW_TRACKING_PASSWORD=XXXXXXXXXXXXXXXXXXXX

Windows-based:
set MLFLOW_TRACKING_URI=https://dagshub.com/dennismutugi/Feature_Eng.mlflow
set MLFLOW_TRACKING_USERNAME=dennismutugi
set MLFLOW_TRACKING_PASSWORD=XXXXXXXXXXXXXXXXXXXXXXX

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:	897569202249.dkr.ecr.ap-northeast-2.amazonaws.com/ml_runner

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

Activate inactive runner (In Ubuntu)

Under active runners, should be marked inactive, after shutting down a EC2. access via SSH, then CD to the root DIR

~/actions-runner$ ./run.sh

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = ap-northeast-2

AWS_ECR_LOGIN_URI = 897569202249.dkr.ecr.ap-northeast-2.amazonaws.com/ml_runner

ECR_REPOSITORY_NAME = ml_runner

About MLflow

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & tagging your model