nimbo VS Ray

You should really mention / give attribution / emphasize more that this is a fork of https://spotty.cloud and you took a lot from https://github.com/nimbo-sh/nimbo as well.

Hey everyone,

I (Miguel) am an ML PhD from the University of Edinburgh and Juozas is a Software Engineer also from Edinburgh.

Together we developed Nimbo, a dead-simple CLI that wraps the AWS CLI, allowing you to run code on AWS as if you were running it locally. You can find the source code here (https://github.com/nimbo-sh/nimbo) and the docs here (https://docs.nimbo.sh).

We decided to build this because we were frustrated with how cumbersome using AWS was, and we just wanted to be able to run jobs on AWS as easily as we run them locally. At the same time, we wanted to make use of the cheap spot instances (on Nimbo, this is a single parameter). All in all, we didn't like the current user experience of working with AWS, and we believed it was possible to vastly improve it.

For this reason, we also provide many useful commands to make it faster and easier to work with AWS, such as launching notebooks on EC2, easily checking prices, logging onto an instance, or syncing data to/from S3 (you can see some useful commands at https://docs.nimbo.sh/useful-commands).

Unlike other similar services, we are solely client-side, meaning that the code runs on your EC2 instances and data is stored in your S3 buckets (we don't have a server; all the infrastructure orchestration happens in the Nimbo package). We are also open contribution, meaning that all the source code is publicly available on our GitHub, and we welcome community contribution.

We have tons of ideas for Nimbo, like adding docker support, and providing instances with preloaded datasets like ImageNet, so that you don't have to download and store it yourself - you simply spin the instance, and the dataset is available at /datasets. We are currently working on adding GCP support, so that you can use AWS or GCP with the same config file.

We are happy to receive any feedback and suggestions you have.

My friend and I just launched Nimbo, a dead-simple CLI that wraps AWS CLI, allowing you to run code on AWS as if you were running it locally. GitHub: https://github.com/nimbo-sh/nimbo. Docs: https://docs.nimbo.sh.

22. Ray | Github | tutorial

Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...

Here's a reference from RLlib that points to docs and an example, and here's one from one of my projects that includes all my own implementations

RLlib

⦿ AWS Glue Data Quality - Automatic data quality rule recommendations based on your data AWS Glue for Ray - Data integration with Ray (ray.io), a popular new open-source compute framework that helps you scale Python workloads

https://ray.io (just dropping the link)

I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.

By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.

Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).

[0] https://github.com/ray-project/ray

Adding these for anyone who had a similar question about Ray vs dask 1, 2, 3