aiomultiprocess
Ray
aiomultiprocess | Ray | |
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2 | 43 | |
1,674 | 31,179 | |
1.1% | 1.8% | |
6.6 | 10.0 | |
6 days ago | 4 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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aiomultiprocess
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What's New in Python 3.11?
> Why not just use multi processing?
Multiprocessing provides parallelism up to what the machine supports, but no additional degree of concurrency, asyncio provides a fairly high degree of concurrency, but no parallelism.
OF course, you can use them together to get both.
https://github.com/omnilib/aiomultiprocess
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Standalone electrical circuit simulation framework
Take a look at aiomultiprocess. It combines multiprocessing and asynchio to bypass the GIL for greatly increased performance.
Ray
- Ray: Unified framework for scaling AI and Python applications
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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
What are some alternatives?
think-async - 🌿 Exploring cooperative concurrency primitives in Python
optuna - A hyperparameter optimization framework
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
aiopath - 📁 Asynchronous pathlib for Python
Faust - Python Stream Processing
example-hftish - Example Order Book Imbalance Algorithm
gevent - Coroutine-based concurrency library for Python
bunny-storm - RabbitMQ asynchronous connector library for Python with built in RPC support
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
cookiecutter-django - Cookiecutter Django + PostGres + Docker + DramatiQ
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)