Project Description
The aim of the project is to run a sample microservice app in Kubernetes. A simple app will be written in Python and work as an online store comprising of frontend, orders, and products services. (could be more!!)
- a frontend (a simple web page, using flask)
- a product service (an inventory of the products with description and cost)
- an orders service (recording the orders with order numbers, items and cost)
Further questions to answer/explore:
- How this app is going to look
- Which components to setup in k8s (a deployment and service for each microservice, what more?)
- How the APIs are going to be exposed (so the services can talk to each other. Right now, I only know how to expose the frontend on 8080 for user interaction).
Goals for this Hackweek
The project will have several learning goals:
- How to breakdown a monolith to microservices.
- Understand how Kubernetes works.
- Learn how to design Kubernetes topology for containerized applications.
Looking for hackers with the skills:
This project is part of:
Hack Week 20
Activity
Comments
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over 4 years ago by epromislow | Reply
I've been reading https://learning.oreilly.com/library/view/cloud-native-patterns/9781617294297/ but not working through it because the examples are all in java, and I don't want to just use the spring boot platform to hide all the details. Would be interested in the points you've listed, as well as implementing a quick-and-dirty chaos monkey to kill off random/selected connections and nodes and monitor what happens, as well as see what works for fast recoveries.
I'm at UTC-0700
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The Problem
Running LLMs can get expensive and complex pretty quickly.
Today there are typically two choices:
- Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
- Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.
What if there was a middle ground?
What if infrastructure scaled itself instead of making you scale it?
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A complete, self-scaling LLM infrastructure that:
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