All Posts

03 . 23 . 2022

Ray Summit 2022 Call for Papers is now open

Ray Summit, the annual Ray user conference, is back this August, and the Call for Papers is open until April 18! In this blog post, we’ll give you all the information you need to prepare a stellar talk proposal.

03 . 22 . 2022

5 reasons to attend this month’s Production RL Summit

We’re holding the first-ever Production RL Summit on March 29. Read more about our featured speakers and sessions and learn why you should join if you’re a reinforcement learning practitioner — or even if you have just a passing interest in RL.

03 . 15 . 2022

Redis in Ray: Past and future

As of version 1.11, Redis is no longer Ray’s default metadata store or pub/sub message broker. In this post, we’ll cover the history of Redis in Ray and how this change allows us to add better support for fault tolerance and high availability.

Ray 1.11 thumb
03 . 09 . 2022

Ray 1.11: Redisless Ray, a docs redesign, and Python 3.9 support

Ray 1.11 is here! Ray no longer starts Redis by default, opening up the possibility for better support of fault tolerance and high availability in future releases. Plus, there’s a new, more intuitive design for the Ray docs.

03 . 03 . 2022

Practical tips for training Deep Q Networks

Implementing RL algorithms without understanding the limitations of Deep Q Networks can lead to poor results. In this post, we'll cover two important limitations that can make Q learning unstable as well as practical ways to resolve these issues.

03 . 02 . 2022

Deploying XGBoost models with Ray Serve

In this article, we’ll cover how to deploy XGBoost with two frameworks: Flask and Ray Serve. We’ll also highlight the advantages of Ray Serve over other serving solutions when comparing models in production.

03 . 01 . 2022

Reinforcement learning with Deep Q Networks

Next in our reinforcement learning series, we'll derive the Q learning algorithm and show how it was applied to yield one of the first breakthroughs that started the field of Deep RL: the Deep Q Network.

02 . 28 . 2022

Sailing to victory with reinforcement learning

McKinsey’s QuantumBlack helps Team New Zealand retool the way they train with reinforcement learning built on Ray.

02 . 24 . 2022

The reinforcement learning framework

Previously, we provided an informal introduction to how reinforcement learning (RL) works and how it’s used in the real world. In this post, we will describe how the RL mathematical framework actually works and derive it from first principles.

02 . 23 . 2022

Serving PyTorch models with FastAPI and Ray Serve

In this article, we will highlight the options available for serving a PyTorch model into production and deploying it with several frameworks, such as TorchServe, Flask, and FastAPI.