natural-posterior-network
Ray
| natural-posterior-network | Ray | |
|---|---|---|
| 1 | 43 | |
| 71 | 31,566 | |
| - | 1.5% | |
| 0.0 | 10.0 | |
| about 1 year ago | 2 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
natural-posterior-network
-
[R] PyTorch Implementation of the Natural Posterior Network
Therefore, we put serious effort in the publicly available implementation to facilitate usage of NatPN: we (1) provide an intuitive interface that enables using the model as easily as Scikit-learn estimators and (2) follow a modular design that allows you to customize and build upon the model at different levels of abstraction. Check it out on GitHub!
Ray
- Ray: Unified framework for scaling AI and Python applications
-
Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
-
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
-
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
-
TransformerXL + PPO Baseline + MemoryGym
RLlib
- Is dynamic action masking possible in Rllib?
-
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
-
Think about it for a second
https://ray.io (just dropping the link)
-
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?
ngboost - Natural Gradient Boosting for Probabilistic Prediction
optuna - A hyperparameter optimization framework
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Faust - Python Stream Processing
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
gevent - Coroutine-based concurrency library for Python
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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