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Bankn8II©$A edited this page Sep 9, 2025 · 8 revisions

Welcome to the moc.orbiter wiki!

lokalt: L:\Clones_github\APpollo_11

https://uoxyc.github.io/moc.orbiter/orbit.html

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Why Soviet Union Shows Artists instead of real Astronauts? Because all was hided and sereted for few decinees and they using artists because a children of real writers, astronauts or composers will understood in the future, whos gen they whear! Even gen was created so, that to nun't understand was nun't possible!

So looking on Djanibekows POST MARK I thinking about Sjuåtta bÅRostaveli and I guess we arrived to Ross128 and it is good! Do not need to lie about things ... the real world is too nice and belive me you will find more your own stuff! I could help too, but my unsver are delay'd but hope arrive soon! I have to do things good for everyone, but after egelity!

uoxyc/moc.orbiter

releases:

https://github.com/uoxyc/moc.orbiter/releases/tag/060929

based on template: Composarc barionleg Ci TemP

Ci TEMP

https://github.com/uoxyc/moc.orbiter/app.html

image

ci_temp_dond21 Retemplate

CiT bARQ

image

för dagshub

Modelkreation: https://www.mlflow.org/docs/latest/model-registry.html#adding-an-mlflow-model-to-the-model-registry

*data pipline: https://dagshub.com/docs/tutorial/ *

Resten är 🏛 ett Tem⛩lATE

Kidney-Disease-Classification-MLflow-DVC Workflows Update config.yaml Update secrets.yaml [Optional] Update params.yaml Update the entity Update the configuration manager in src config Update the components Update the pipeline Update the main.py Update the dvc.yaml app.py How to run? STEPS: Clone the repository

https://github.com/ibanknatoPrad/Kidney-Disease-Classification-Deep-Learning-Project STEP 01- Create a conda environment after opening the repository conda create -n cnncls python=3.8 -y conda activate cnncls 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

MLflow tutorial

cmd mlflow ui dagshub dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/Kidney-Disease-Classification-MLflow-DVC.mlflow MLFLOW_TRACKING_USERNAME=entbappy MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0 python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/Kidney-Disease-Classification-MLflow-DVC.mlflow

export MLFLOW_TRACKING_USERNAME=entbappy

export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0

DVC cmd dvc init dvc repro dvc dag About MLflow & DVC MLflow

Its Production Grade Trace all of your expriements Logging & taging your model DVC

Its very lite weight for POC only lite weight expriements tracker It can perform Orchestration (Creating Pipelines) AWS-CICD-Deployment-with-Github-Actions

  1. Login to AWS console.

  2. Create IAM user for deployment #with specific access

  3. EC2 access : It is virtual machine

  4. 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: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken
  1. Create EC2 machine (Ubuntu)
  2. 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

Clone this wiki locally