MIminimaxir·Nov 27, 2019
Interesting use of ray (https://github.com/ray-project/ray). I think a lot of people are sleeping on that package, as it solves many of the difficulties in parallelizing ML/DL models.
In Dkeras: Distributed Keras Engineray-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.
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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.
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Where it is used
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What people are saying
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This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:03:38 AM UTC.
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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.
* 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
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 3 | 2 | 1 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 6 | 0 | 6 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 5 | 2 | 3 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
3 sourcesWhile I appreciate the efforts of authors and believe in long term mission, they seem to not mention anywhere some key shortcomings of Ray, while marketing it pretty hard (eg see the paper).
I have used ray (a year ago) in one of the advertised basic applications: parallelising the environments for RL. It was unusable back then, as it was clogging up the memory.
The plasma store which is backend for arrow was never cleaned which made the computation stop after 3 hours
Here’s the issue:
https://github.com/ray-project/ray/issues/2128
Or perhaps this has been fixed already?
In Faster Parallel Python Without Python MultiprocessingA fast and simple framework for building and running distributed applications
1 points · Open discussion