machine_learning_examples
dm_env
| machine_learning_examples | dm_env | |
|---|---|---|
| 3 | 2 | |
| 8,102 | 329 | |
| - | 0.0% | |
| 5.3 | 0.0 | |
| 3 days ago | over 1 year ago | |
| Python | Python | |
| - | Apache License 2.0 |
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machine_learning_examples
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Doubt about numpy's eigen calculation
Does that mean that the example I found on the internet is wrong (I think it comes from a DL Course, so I'd imagine it is not wrong)? or does it mean that I am comparing two different things? I guess this has to deal with right and left eigen vectors as u/JanneJM pointed out in her comment?
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How to save an attention model for deployment/exposing to an API?
I've been following a course teaching how to make an attention model for neural machine translation, This is the file inside the repo. I know that I'll have to use certain functions to make the textual input be processed in encodings and tokens, but those functions use certain instances of the model, which I don't know if I should keep or not. If anyone can please take a look and help me out here, it'd be really really appreciated.
dm_env
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Worthwhile to convert custom env to be dm_env compatible?
Can anyone speak to their experience using acme (https://github.com/deepmind/acme) and by extension dm_env (https://github.com/deepmind/dm_env)? I'm wondering if it would be worthwhile for me to invest the time into converting my custom environment (which loosely follows the standard RL setup) over to this format.
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[D] What would a "Production" RL stack look like in terms of tooling?
An interface based loosely on the standard RL setup. I'm thinking about adapting it to fit dm_env (https://github.com/deepmind/dm_env) to let it do more heavy lifting since I quite like Haiku, rlax and the rest of what they do.
What are some alternatives?
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
panda-gym - Set of robotic environments based on PyBullet physics engine and gymnasium.
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
acme - A library of reinforcement learning components and agents
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
neptune-client - 📘 The MLOps stack component for experiment tracking
maze - Maze Applied Reinforcement Learning Framework
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
neptune-contrib - This library is a location of the LegacyLogger for PyTorch Lightning.