malib
A parallel framework for population-based multi-agent reinforcement learning. (by sjtu-marl)
runhouse
Fast, Pythonic AI services and workflows on your own infra. Unobtrusive, debuggable, PyTorch-like APIs. (by run-house)
| malib | runhouse | |
|---|---|---|
| 2 | 6 | |
| 464 | 711 | |
| 1.7% | 2.4% | |
| 3.4 | 9.8 | |
| 5 months ago | 3 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
malib
Posts with mentions or reviews of malib.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-07-23.
-
MALib: A parallel framework for population-based multi-agent reinforcement learning
Code for https://arxiv.org/abs/2106.07551 found: https://github.com/sjtu-marl/malib
runhouse
Posts with mentions or reviews of runhouse.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-27.
- Runhouse
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Better GPU Cluster Scheduling with Runhouse
With Runhouse, it’s easy to send code to your compute no matter where it lives, and efficiently utilize your resources across multiple callers scheduling jobs (e.g. researchers, pipelines, inference services, etc). We believe less is more when it comes to AI DevOps, so we don’t make any assumptions about the structure of your code or the infrastructure to which you’re sending it.
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The Great MLOps Hoax: Is It Just Data Engineering in Disguise?
You may want to look at run.house [0] for a pretty powerful solution to many of these problems.
[0] https://github.com/run-house/runhouse
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Who uses Apache Airflow for MLOps? Enlighten me.
I was the product lead for PyTorch and was seeing the same problem all over, so I've been working on a new tool for exactly this: https://github.com/run-house/runhouse
- Run-house/runhouse: Programmable remote compute and data across environments
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How easy is it to migrate from one MLOps tool to another? And what SaaS platform would you recommend?
I've been working on a very flexible and low-lift OSS ML platform that sounds like it would suit your needs: https://github.com/run-house/runhouse
What are some alternatives?
When comparing malib and runhouse you can also consider the following projects:
Python-Raytracer - A basic Ray Tracer that exploits numpy arrays and functions to work fast.
omegaml - MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
modin - Modin: Scale your Pandas workflows by changing a single line of code
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
IC3Net - Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks