robertnishihara

10 ms·

61.
by robertnishihara 3y ago
If you want to try out the Llama-2 models (7B, 13B, 70B), you can get started very easily with Anyscale Endpoints (~2 min). https://app.endpoints.anyscale.com/
62.
by robertnishihara 3y ago
> we believe most companies prefer locally installed solutions to cloud based ones We've also seen a strong desire from businesses to manage models and compute on their own machines or in their own cloud accounts. This is often part
63.
by robertnishihara 3y ago
We (at Anyscale) have benchmarked GPT-4 versus the Llama-2 suite of models on a few problems: functional representation, SQL generation, grade-school math question answering. GPT-4 wins by a lot out of the box. However, surprisingly, fine-t
64.
Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Custom Models(anyscale.com)
308 points by robertnishihara 3y ago|59 comments
65.
Distributed Machine Learning at Instacart(tech.instacart.com)
1 points by robertnishihara 4y ago|0 comments
66.
by robertnishihara 4y ago
Congratulations on the launch, I love the focus on ease of use and making it easy to get started, and it's exciting to see impressive products being built with Ray! I'm one of the Ray developers. It is true that Ray focuses a lot
67.
by robertnishihara 4y ago
Yes, but this will largely come down to whether the deep learning framework that you're using (PyTorch, TensorFlow, Jax, etc) works well in that setting. Ray is pretty framework and hardware agnostic and can be used to schedule /
68.
by robertnishihara 4y ago
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
69.
ChatGPT got all the buzz, but beneath it is a $1B developer framework(businessinsider.com)
2 points by robertnishihara 4y ago|0 comments
70.
Training One Million Machine Learning Models in Record Time with Ray(anyscale.com)
1 points by robertnishihara 4y ago|0 comments
71.
An informal introduction to reinforcement learning(anyscale.com)
3 points by robertnishihara 5y ago|0 comments
72.
Introducing Anyscale: The Future Is Distributed(anyscale.com)
1 points by robertnishihara 5y ago|0 comments
73.
Why Third Generation ML Platforms Are More Performant(anyscale.com)
3 points by robertnishihara 5y ago|0 comments
74.
Analyzing memory management and performance in Dask-on-Ray(anyscale.com)
1 points by robertnishihara 5y ago|0 comments
75.
Elastic Distributed Training with XGBoost on Ray(eng.uber.com)
1 points by robertnishihara 5y ago|0 comments
76.
Parallelizing Python Code(anyscale.com)
2 points by robertnishihara 5y ago|0 comments
77.
Building Highly Available and Scalable Online Applications on Ray at Ant Group(anyscale.com)
1 points by robertnishihara 5y ago|1 comments
78.
The Third Generation of Production ML Architectures(anyscale.com)
3 points by robertnishihara 5y ago|0 comments
79.
Distributed XGBoost on Ray(github.com)
1 points by robertnishihara 5y ago|0 comments
80.
by robertnishihara 5y ago
Thanks for the comments! A few quick notes The term serverless is a bit overloaded. Here's what we want. (1) Ray users should be able to focus only on their application logic and not have to worry about configuring clusters at least in
81.
by robertnishihara 5y ago
Hi all, I'm one of the authors of Ray, thanks for all the comments and discussion! To add to the discussion, I'll mention a few conceptual things that have changed since we wrote the paper. *Emphasis on the library ecosystem* A lo
82.
Ray 1.4.0(github.com)
2 points by robertnishihara 5y ago|0 comments
83.
Data Processing Support in Ray(anyscale.com)
2 points by robertnishihara 6y ago|0 comments
84.
Anyscale raises $40M to launch a managed service for distributed computing(venturebeat.com)
6 points by robertnishihara 6y ago|0 comments
85.
Ray 1.0(anyscale.com)
3 points by robertnishihara 6y ago|0 comments
86.
The Simplest Way to Serve Your NLP Model in Production with Pure Python(medium.com)
1 points by robertnishihara 6y ago|0 comments
87.
How to Scale Python on Every Major Cloud Provider(medium.com)
1 points by robertnishihara 6y ago|0 comments
88.
A Step-by-Step Guide to Scaling Your First Python Application in the Cloud(medium.com)
3 points by robertnishihara 6y ago|0 comments
89.
Understanding the Ray Ecosystem and Community(anyscale.com)
2 points by robertnishihara 6y ago|0 comments
90.
Deploying Ray on Azure for Scalable Distributed Reinforcement Learning(thewindowsupdate.com)
1 points by robertnishihara 6y ago|0 comments

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