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Top 23 pytorch-lightning Open-Source Projects
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Dreambooth-Stable-Diffusion
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
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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.
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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 -
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SUPIR
SUPIR aims at developing Practical Algorithms for Photo-Realistic Image Restoration In the Wild
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)
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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 | 2023-06-04So, 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.
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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.
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solo-learn
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
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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].
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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?
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machin
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
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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.
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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.
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Stock-Prediction-Neural-Network-and-Machine-Learning-Examples
Examples of python neural net and ML stock prediction methods with sample stock data.
Code on GitHub
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Three: This one technically came out early march, but we didn't hear about it till the 12th. [2303.01469] Consistency Models (arxiv.org)
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pytorch-lightning related posts
- User-friendly PyTorch Lightning and Hydra template for ML experimentation
- Hyperparameter tuning neural networks on financial data
- stock prediction NN and ML examples
- Best practice for saving logits/activation values of model in PyTorch Lightning
- Where can I train my own LoRA?
- I miss the old days where people asked me to recreate “Facebook” or “Twitter”
- I am having an error with ControlNet (RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`)
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A note from our sponsor - SaaSHub
www.saashub.com | 28 Mar 2024
Index
What are some of the best open-source pytorch-lightning projects? This list will help you:
Project | Stars | |
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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 |