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This project forked from sunnysavita10/ml-project-with-docker-mlflow-and-dagshub

0.0 0.0 0.0 10.86 MB

License: MIT License

Shell 0.07% Python 5.46% HTML 0.50% Jupyter Notebook 93.93% Dockerfile 0.03%

airflow's Introduction

this is my end to end project

first initialize the git

git init
git add abc.txt
git add .
git commit -m "this is my first commit"

git pull

bash your_file_name.sh
python setup.py install

another way you can mention -e . in your requirement file and you can run

pip install -r requirements.txt

MLflow

Documentation

local cmd
  • mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/sunny.savita/fsdsmendtoend.mlflow
MLFLOW_TRACKING_USERNAME=sunny.savita
MLFLOW_TRACKING_PASSWORD=3c2c8cd1436ad32b510cfdd84944a528ba4fb650
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/sunny.savita/fsdsmendtoend.mlflow

export MLFLOW_TRACKING_USERNAME=sunny.savita

export MLFLOW_TRACKING_PASSWORD=3c2c8cd1436ad32b510cfdd84944a528ba4fb650

DVC cmd

  • dvc init
  • dvc repro
  • dvc dag

airflow's People

Contributors

sunnysavita10 avatar

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