•2 min read•from Machine Learning

Unlock LLM Training: A Practical Guide to Distributed Algorithms

Unlock LLM Training: A Practical Guide to Distributed Algorithms
A Little Guide to Learning Distributed Algorithms for LLMS Training and Inference [D]

Distributed Training and Inference both involves having a fundamental understanding of how distributed systems work in general

  • Distributed Parallelism
  • Tensor Parallelism
  • Pipeline Parallelism
  • Model Parallelism

Reading and reading and reading or even worse, not knowing where to start ;(

That’s boring!

We want to read what’s just needed and quickly get started with applications and that’s what exactly what I have for you all today

Here’s the list of a few initial papers I have read for the past three months that is enough to understand

And a few basics too!

Read them Code them Play with them

I have implemented a few at basic level which you could use as a reference too (the repo is a bit all over the place but I actively trying to maintain and love your feedback too)

Link: https://alphaxiv.org/shared/folder/019de088-28f7-7f02-acd4-c22459fe153e

Gh repo: https://github.com/YuvrajSingh-mist/smolcluster

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Tagged with

#Distributed Algorithms
#Distributed Training
#Distributed Inference
#LLMs
#Parallelism
#Tensor Parallelism
#Pipeline Parallelism
#Model Parallelism
#Distributed Systems
#Machine Learning
#smolcluster
#GitHub
#alphaxiv
#Training
#Inference
#Code
#Implementation
#Repo
#Feedback