Tomorrow
Ray
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| Tomorrow | Ray | |
|---|---|---|
| - | 42 | |
| 1,459 | 31,101 | |
| - | 3.4% | |
| 0.0 | 10.0 | |
| about 4 years ago | 1 day ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Tomorrow
We haven't tracked posts mentioning Tomorrow yet.
Tracking mentions began in Dec 2020.
Ray
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Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
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Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Custom Models
Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...
- Ray – an open source project for scaling AI workloads
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Methods to keep agents inside grid world.
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
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TransformerXL + PPO Baseline + MemoryGym
RLlib
- Is dynamic action masking possible in Rllib?
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AWS re:Invent 2022 Recap | Data & Analytics services
⦿ 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
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Think about it for a second
https://ray.io (just dropping the link)
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Elixir Livebook now as a desktop app
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
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Learn various techniques to reduce data processing time by using multiprocessing, joblib, and tqdm concurrent
Adding these for anyone who had a similar question about Ray vs dask 1, 2, 3
What are some alternatives?
deco
optuna - A hyperparameter optimization framework
gevent - Coroutine-based concurrency library for Python
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Faust - Python Stream Processing
pyeventbus - Python Eventbus
aiochan - CSP-style concurrency for Python
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
eventlet - Concurrent networking library for Python
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)