pytorch-lightning

Open-source projects categorized as pytorch-lightning

Top 23 pytorch-lightning Open-Source Projects

  • so-vits-svc-fork

    so-vits-svc fork with realtime support, improved interface and more features.

    Project mention: Zade - Çaresizim | /r/zfam | 2023-06-21
  • Dreambooth-Stable-Diffusion

    Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

    Project mention: Where can I train my own LoRA? | /r/StableDiffusionInfo | 2023-06-21
  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • lightning-hydra-template

    PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

    Project mention: User-friendly PyTorch Lightning and Hydra template for ML experimentation | news.ycombinator.com | 2024-02-05
  • pytorch-forecasting

    Time series forecasting with PyTorch

    Project mention: FLaNK Stack Weekly for 14 Aug 2023 | dev.to | 2023-08-14
  • SUPIR

    SUPIR aims at developing Practical Algorithms for Photo-Realistic Image Restoration In the Wild

    Project mention: Compressing Images with Neural Networks | news.ycombinator.com | 2024-03-18

    Current SOTA open source is I believe SUPIR (Example - https://replicate.com/p/okgiybdbnlcpu23suvqq6lufze), but it needs a lot of VRAM, or you can run it through replicate, or here's the repo (https://github.com/Fanghua-Yu/SUPIR)

  • uvadlc_notebooks

    Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

    Project mention: I miss the old days where people asked me to recreate “Facebook” or “Twitter” | /r/ProgrammerHumor | 2023-06-04

    So, I don’t have anything simple that’s readily available, and I don’t know how much you’d get from the code itself without some background. But I would recommend the UVA Deep Learning tutorials. Particularly, I’d recommend trying the autoencoder as a good start (tutorial 9). Autoencoders are very easy and fast models to train.

  • labml

    🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • Pointnet2_PyTorch

    PyTorch implementation of Pointnet2/Pointnet++

  • solo-learn

    solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning

  • traingenerator

    🧙 A web app to generate template code for machine learning

  • detoxify

    Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].

  • open-metric-learning

    Library for metric learning pipelines and models.

  • quaterion

    Blazing fast framework for fine-tuning similarity learning models

  • nn-template

    Generic template to bootstrap your PyTorch project.

  • norse

    Deep learning with spiking neural networks (SNNs) in PyTorch.

    Project mention: Neuromorphic learning, working memory, and metaplasticity in nanowire networks | news.ycombinator.com | 2023-04-24

    This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...

    Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.

    Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.

    I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?

  • torchmd

    End-To-End Molecular Dynamics (MD) Engine using PyTorch

  • machin

    Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

  • minLoRA

    minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.

    Project mention: [D] Is it possible to train the same LLM instance on different users' data? | /r/MachineLearning | 2023-04-11

    This repository seems to be doing it. Basically, you want to take the weights/biases that were trained during the LoRA training process and include them in the compute graph for the larger network, or remove them.

  • hydra-zen

    Create powerful Hydra applications without the yaml files and boilerplate code.

  • Renate

    Library for automatic retraining and continual learning

    Project mention: Renate: A Library for Real-World Continual Learning | /r/BotNews | 2023-04-25

    Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indication of the use of state-of-the-art continual learning algorithms in practical machine learning deployment. This paper presents Renate, a continual learning library designed to build real-world updating pipelines for PyTorch models. We discuss requirements for the use of continual learning algorithms in practice, from which we derive design principles for Renate. We give a high-level description of the library components and interfaces. Finally, we showcase the strengths of the library by presenting experimental results. Renate may be found at https://github.com/awslabs/renate.

  • Stock-Prediction-Neural-Network-and-Machine-Learning-Examples

    Examples of python neural net and ML stock prediction methods with sample stock data.

    Project mention: Hyperparameter tuning neural networks on financial data | /r/quant | 2023-10-19

    Code on GitHub

  • pytorch_tempest

    My repo for training neural nets using pytorch-lightning and hydra

  • consistency-models

    A Toolkit for OpenAI's Consistency Models.

    Project mention: AI is getting scary | /r/ChatGPT | 2023-04-19

    Three: This one technically came out early march, but we didn't hear about it till the 12th. [2303.01469] Consistency Models (arxiv.org)

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020). The latest post mention was on 2024-03-18.

pytorch-lightning related posts

Index

What are some of the best open-source pytorch-lightning projects? This list will help you:

Project Stars
1 so-vits-svc-fork 8,193
2 Dreambooth-Stable-Diffusion 7,383
3 lightning-hydra-template 3,611
4 pytorch-forecasting 3,533
5 SUPIR 2,975
6 uvadlc_notebooks 2,053
7 labml 1,811
8 Pointnet2_PyTorch 1,364
9 solo-learn 1,343
10 traingenerator 1,342
11 detoxify 818
12 open-metric-learning 748
13 quaterion 610
14 nn-template 604
15 norse 601
16 torchmd 498
17 machin 381
18 minLoRA 378
19 hydra-zen 269
20 Renate 263
21 Stock-Prediction-Neural-Network-and-Machine-Learning-Examples 259
22 pytorch_tempest 201
23 consistency-models 192
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