richardliaw

7 ms·

1.
by richardliaw 2y ago
> If you have 1 massive job, Ray sucks and doesn't provide table stakes. Can you say more?
2.
LLM Inference Endpoint Performance Benchmarking Tool(github.com)
4 points by richardliaw 3y ago|0 comments
3.
A comprehensive guide to building RAG-based LLM applications for production(github.com)
3 points by richardliaw 3y ago|0 comments
4.
by richardliaw 3y ago
I'm really glad to see a post like this come out. I've seen so many discussions online about customizing models -- this post really does cut through the noise. Really like the evaluation methodology, and seems well-written as well
5.
Ray – an open source project for scaling AI workloads(github.com)
2 points by richardliaw 3y ago|0 comments
6.
Continuous batching enables 23x throughput in LLM inference(anyscale.com)
2 points by richardliaw 3y ago|0 comments
7.
Anyscale's Aviary is a dashboard for evaluating Open Source LLMs(aviary.anyscale.com)
14 points by richardliaw 3y ago|3 comments
8.
Numbers every LLM developer should know(github.com)
428 points by richardliaw 3y ago|103 comments
9.
How to build a LLM search engine using a self-hosted LLM(anyscale.com)
3 points by richardliaw 3y ago|0 comments
10.
Ray is the next generation ML framework(orenleung.super.site)
5 points by richardliaw 4y ago|1 comments
11.
Anyscale raises $40M Series B to revolutionize serverless computing(techcrunch.com)
20 points by richardliaw 6y ago|0 comments
12.
by richardliaw 6y ago
This is a huge release! Weights and Biases support, transformers, Ray support -- it just never stops!
13.
Cloud Computing in Python for all major clouds(medium.com)
1 points by richardliaw 6y ago|0 comments
14.
Large Scale Training at Berkeley AI Research(bair.berkeley.edu)
2 points by richardliaw 7y ago|0 comments
15.
Ray (from UC Berkeley), for the Curious(medium.com)
1 points by richardliaw 7y ago|0 comments
16.
Ray for the Curious(medium.com)
1 points by richardliaw 7y ago|0 comments
17.
by richardliaw 7y ago
Interesting. What do you mean when you say: "versus the authors re-applying some of same research in their own thing." And does "code distribution" just mean to release as open source?
18.
Tune: A scalable framework for hyperparameter tuning from UC Berkeley(medium.com)
5 points by richardliaw 7y ago|0 comments
19.
by richardliaw 8y ago
Very related open source project from the RISE Lab at UC Berkeley: http://ray.readthedocs.io/en/latest/tune.html