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alpa
Python2022–2023
alpa-projects / alpa · Archived

alpa

Training and serving large-scale neural networks with auto parallelization.

AI & Machine LearningSystems & InfrastructureLanguages & CompilersLearning & ReferenceNetworking
Explore on GitHubRead Hacker News discussions
3Distinct people
3Mentions
0Explicit recommenders
3.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Training and serving large-scale neural networks with auto parallelization.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2022-06-23 18:01:29 UTC to 2023-03-22 15:41:33 UTC.

012022-06-01 00:00:00 UTC to 2022-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 20222022-07-01 00:00:00 UTC to 2022-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-08-01 00:00:00 UTC to 2022-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-09-01 00:00:00 UTC to 2022-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-10-01 00:00:00 UTC to 2022-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20222022-11-01 00:00:00 UTC to 2022-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-12-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20222022-06-01 00:00:00 UTC2022-07-01 00:00:00 UTC110
Jul 20222022-07-01 00:00:00 UTC2022-08-01 00:00:00 UTC000
Aug 20222022-08-01 00:00:00 UTC2022-09-01 00:00:00 UTC000
Sep 20222022-09-01 00:00:00 UTC2022-10-01 00:00:00 UTC000
Oct 20222022-10-01 00:00:00 UTC2022-11-01 00:00:00 UTC000
Nov 20222022-11-01 00:00:00 UTC2022-12-01 00:00:00 UTC000
Dec 20222022-12-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC000
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC101
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC101

What Hacker News says

3 sources
lxe·Mar 22, 2023
> With no real knowledge of LLM and only recently started to understand what LLM terms mean, such as 'model, inference, LLM model, intruction set, fine tuning' whatelse do you think is required to make a took like yours? This was mee a few weeks ago. I got interested in all this when FlexGen (https://github.com/FMInference/FlexGen) was announced, which allowed to run inference using OPT model on consumer hardware. I'm an avid user of Stable Diffusion, and I wanted to see if I can have an SD equivalent of ChatGPT. Not understanding the details of hyperparameters or terminology, I basically asked ChatGPT to explain to me what these things are: Explain to someone who is a software engineer with limited knowledge of ML terms or linear algebra, what is "feed forward" and "self-attention" in the context of ML and large language models. Provi
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de examples when possible. I did the same with all the other terms I didn't understand, like "ADAM optimizer", "gradient", etc. I relied on it very heavily and cross-referenced the answers. Looking at other people's code and just tinkering with things on my own really helped. Through the FlexGen discord I've discovered https://github.com/oobabooga/text-generation-webui where I spent days just playing around with models. This got me into the huggingface ecosystem -- their transformers library is an easy way to get started. I joined a few other discords, like LLaMA Unofficial, RWKV, Eleuther AI, Together, Hivemind and Petals. I bookmarked a bunch of resources but it's very sporadic. Here are some: - https://github.com/zphang/minimal-llama/#peft-fine-tuning-wi... - https://github.com/togethercomputer/OpenChatKit - https://www.cstroik.com/index.php/2023/02/18/finetuning-an-a... - https://github.com/huggingface/peft - https://github.com/kingoflolz/mesh-transformer-jax/blob/mast... - https://github.com/oobabooga/text-generation-webui - https://github.com/hizkifw/WebChatRWKVstic - https://github.com/ggerganov/whisper.cpp - https://github.com/qwopqwop200/GPTQ-for-LLaMa - https://github.com/oobabooga/text-generation-webui/issues/14... - https://github.com/bigscience-workshop/petals - https://github.com/alpa-projects/alpa
In Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
robertnishihara·Feb 12, 2023
I'm one of the Ray developers, thanks for the shoutout :) If you're curious about how Ray is used for LLMs, here are some interesting examples of LLM projects using Ray! - Alpa does training and serving with 175B parameter models https://github.com/alpa-projects/alpa - GPT-J https://github.com/kingoflolz/mesh-transformer-jax - Another HN thread on training LLMs with Ray (on TPUs in this case) https://news.ycombinator.com/item?id=27731168 - OpenAI fireside chat on the evolution of their infrastructure and usage of Ray for training https://www.youtube.com/watch?v=CqiL5QQnN64 - Cohere on their architecture for training LLMs https://www.youtube.com/watch?v=For8yLkZP5w&t=3s Some other thoughts 1. There is a lot more we want to do to make Ray better for working with large language models and for making training, serving, and batch inference wor
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k well out of the box. 2. The original post is about training, but we actually see even more interest in fine-tuning and serving with LLMs, in part because there are good pre-trained models. 3. For LLMs, we see a lot of interest in Ray + Jax or Ray + TPUs relative to what we see in other use cases.
In How to train large models on many GPUs? (2021)

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