An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. (by ray-project)

Ray Alternatives

Similar projects and alternatives to Ray

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better Ray alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of Ray. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-16.
  • How to deploy a rllib-trained model?
    Currently, rllib's "--export-formats" does nothing; I have folders of checkpoints, but no models. Looks like currently the internal export_model function isn't implemented: https://github.com/ray-project/ray/issues/19021
  • [HELP] Converting many individual workstations into a HPC cluster
    reddit.com/r/HPC | 2021-10-11
    Unless you have infiniband, you might want to build it as a kubernetes cluster and look at something like (ray-project)[https://github.com/ray-project/ray] it has a ton of distributed plugin packages that are Ethernet based.
  • Show HN: SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
    news.ycombinator.com | 2021-10-03
    Neat. Congratulations on the launch!

    Apart from the fact that it could deploy to both GCP and AWS, what does it do differently than AWS Batch [0]?

    When we had a similar problem, we ran jobs on spots with AWS Batch and it worked nicely enough.

    Some suggestions (for a later date):

    1. Add built-in support for Ray [1] (you'd essentially be then competing with Anyscale, which is a VC funded startup, just to contrast it with another comment on this thread) and dbt [2].

    2. Support deploying coin miners (might be good to widen the product's reach; and stand it up against the likes of consensys).

    3. Get in front of many cost optimisation consultants out there, like the Duckbill Group.

    If I may, where are you building this product from? And how many are on the team?


    [0] https://aws.amazon.com/batch/use-cases/

    [1] https://ray.io/

    [2] https://getdbt.com/

  • Writing your First Distributed Python Application with Ray (without multiprocessing)
    reddit.com/r/Python | 2021-08-23
    Here is an older discussion on dask vs ray from the creators of both projects: https://github.com/ray-project/ray/issues/642
  • [D] Kubeflow vs. Argo for ML Pipeline Tool
    Here is link number 1 - Previous text "Ray"
    If you are looking for a developer-friendly tool, I'd ditch the task/workflow orchestration paradigm altogether and use something like Ray. It's made by and for ML practitioners, it's much more versatile, has no unwarranted DSLs (pure python), and you can test locally before deploying with pretty much the same code.
  • Ray 1.4.0
    news.ycombinator.com | 2021-06-08
  • What are the best frameworks data engineers should certainly learn?
    Nice list. Do you know Ray? Where would you place it? I suppose it's compute, but tailored to ML model serving.
  • Attention Nets and More with RLlib’s Trajectory View API
    Just wanted to share a blog post about two new features now stable in RLlib: Support for Attention networks as custom models, and the “trajectory view API” (RLlib is a popular reinforcement learning library that is part of the open-source Ray project).
  • Why is Python popular despite being accused of being slow?
    Perhaps with traditional approaches, but that is changing. Take a look at Ray (from some of the people who originally created Spark). ML usecases are so aggressively focused on Python that there's starting to be a lot of investment in fixing these problems because it's cheaper than shifting the userbase to a "better" language.
  • Deep learning on multiple computers
    Hi there, you can try Anyscale's Ray. It's a really interesting open-source project with an active community and team. It does wonders.
  • Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?
    news.ycombinator.com | 2021-03-26
    Using Ray distributed would be a better stress test. Computing primes this way probably isn't the best way to saturate cores. You are spending a lot of time doing python vm operations vs pure number crunching.

    Using numeric arrays chunked into blocks of number ranges would be more efficient (and therefore "crunchier")


  • [R] ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
    Efficient: the performance is comparable with Ray RLlib.
  • Why do so many of us suck at basic programming?
    Async (added in 3.5) and ray can handle most of the parallelization limitations caused by the GIL.


Basic Ray repo stats
6 days ago

ray-project/ray is an open source project licensed under Apache License 2.0 which is an OSI approved license.

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