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ray-project
ray
Python2017–2026
ray-project / ray

ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

AI & Machine LearningSystems & InfrastructureLanguages & CompilersScience & EngineeringLearning & ReferenceDevOps & CloudLibraries & SDKs
Explore on GitHubRead Hacker News discussions
25Distinct people
27Mentions
0Explicit recommenders
44.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

GitHub

Where it is used

No documented product use was verified in the last source check.

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

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:17:07 AM 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 year27 dated mentions

Observed mentions: 2017-03-16 16:43:36 UTC to 2026-01-30 19:51:20 UTC.

02462017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC321
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC606
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC312
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC110
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC110
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC202
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC312
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC523
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

6 sources
JPKab·Mar 3, 2018
As a person who LOVES pandas, numpy, scikit, and all things SciPy, I really wish these kinds of posts would take a few seconds to include a link, or maybe just a quick paragraph, to answer one question: What the %$#@ is Ray? I make a habit of doing this myself whenever I do a post like this. Sure, I was able to look up Ray from the Riselab and figure this out myself, but I wish I didn't have to. From the Ray homepage: Ray is a high-performance distributed execution framework targeted at large-scale machine learning and reinforcement learning applications. It achieves scalability and fault tolerance by abstracting the control state of the system in a global control store and keeping all other components stateless. It uses a shared-memory distributed object store to efficiently handle large data through shared memory, and it uses a bottom-
Read the rest
up hierarchical scheduling architecture to achieve low-latency and high-throughput scheduling. It uses a lightweight API based on dynamic task graphs and actors to express a wide range of applications in a flexible manner. Check out the following links! Codebase: https://github.com/ray-project/ray Documentation: http://ray.readthedocs.io/en/latest/index.html Tutorial: https://github.com/ray-project/tutorial Blog: https://ray-project.github.io Mailing list: ray-dev@googlegroups.com
In Pandas on Ray – Make Pandas faster
chrisaycock·Mar 3, 2018
The one line of code is import ray.dataframe as pd They've replaced many pandas functions with an identical API that runs actions in parallel on top of Ray, a task-parallel library: https://github.com/ray-project/ray Unlike Dask, Ray can communicate between processes without serializing and copying data. It uses a shared-memory object store within Apache Arrow: http://arrow.apache.org/blog/2017/08/08/plasma-in-memory-obj... Worker processes (scheduled by Ray's computation graph) simply map the required memory region into their address space.
In Pandas on Ray – Make Pandas faster

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