spaceopt
Ray
spaceopt | Ray | |
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1 | 43 | |
42 | 31,322 | |
- | 2.3% | |
3.6 | 10.0 | |
almost 2 years ago | 4 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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spaceopt
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[D] What kind of Hyperparameter Optimisation do you use?
I had exactly the same problem: How do I naturally integrate human expertise or manual interventions into automatic hyperparameter optimization process? I ended up writing my own algorithm that helps me achieve that: https://github.com/ar-nowaczynski/spaceopt
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?
optuna - A hyperparameter optimization framework
Spearmint - Spearmint Bayesian optimization codebase
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
optuna-examples - Examples for https://github.com/optuna/optuna
Faust - Python Stream Processing
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
gevent - Coroutine-based concurrency library for Python
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)
Thespian Actor Library - Python Actor concurrency library