Ray is a flexible, high-performance distributed execution framework.
Achieves parallelism in Python with simple and consistent API.
Particularly suited for machine learning (agnostic to the machine learning library of choice)
Forms the base of Ray's own distributed libraries for deep and reinforcement learning, processing of Pandas dataframes, and hyper parameter search.
Uses Plasma as its object store which allows to efficiently share large numpy arrays (or objects serializable with Apache Arrow) between the processes, avoiding unnecessary data copies and with only minimal deserialization
What's the difference between this Python project and similar ones?
Less overhead and lower latency with bottom up scheduling than similar projects
Actors - allow sharing mutable state between tasks
Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/1170
**Author:** [@jmargeta](https://github.com/jmargeta)
**Created:** 10/22/2018
**Status:** ✅ Merged
**Merged:** 2/18/2019
**Merged by:** [@vinta](https://github.com/vinta)
**Base:** `master` ← **Head:** `patch-1`
---
### 📝 Commits (1)
- [`39fe666`](https://github.com/vinta/awesome-python/commit/39fe666fc7d15307c35183dd0ae40570cf743a81) Add Ray to Cluster computing
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## What is this Python project?
Ray is a flexible, high-performance distributed execution framework.
- Achieves parallelism in Python with simple and consistent API.
- Particularly suited for machine learning (agnostic to the machine learning library of choice)
- Forms the base of Ray's own distributed libraries for deep and reinforcement learning, processing of Pandas dataframes, and hyper parameter search.
- Uses Plasma as its object store which allows to efficiently share large numpy arrays (or objects serializable with Apache Arrow) between the processes, avoiding unnecessary data copies and with only minimal deserialization
## What's the difference between this Python project and similar ones?
- Less overhead and lower latency with bottom up scheduling than similar projects
- Actors - allow sharing mutable state between tasks
- Similar to Dask, for more details see a comparison here: https://github.com/ray-project/ray/issues/642
--
Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/1170
Author: @jmargeta
Created: 10/22/2018
Status: ✅ Merged
Merged: 2/18/2019
Merged by: @vinta
Base:
master← Head:patch-1📝 Commits (1)
39fe666Add Ray to Cluster computing📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
What is this Python project?
Ray is a flexible, high-performance distributed execution framework.
What's the difference between this Python project and similar ones?
--
Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.