maed
[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation (by ziniuwan)
pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes. (by Lightning-AI)
| maed | pytorch-lightning | |
|---|---|---|
| 1 | 9 | |
| 201 | 26,952 | |
| - | 1.3% | |
| 0.0 | 9.9 | |
| about 2 years ago | 6 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
maed
Posts with mentions or reviews of maed.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-01.
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An elegant and strong PyTorch Trainer
I opened source some works (AAAI 21 SeqNet, ICCV 21 MAED, etc) and earned more than 500 stars. After referring to some popular projects (detectron2, pytorch-image-models, and mmcv), based on my personal development experience, I developed a SIMPLE enough, GENERIC enough, and STRONG enough PyTorch Trainer: core-pytorch-utils, also named CPU. CPU covers most details in the process of training a deep neural network, including:
pytorch-lightning
Posts with mentions or reviews of pytorch-lightning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-29.
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SB-1047 will stifle open-source AI and decrease safety
It's very easy to get started, right in your Terminal, no fees! No credit card at all.
And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.
You don't need OpenAI - nobody does.
- Lightning AI Studios – A persistent GPU cloud environment
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Como empezar con inteligencia artificial?
https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/
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Best practice for saving logits/activation values of model in PyTorch Lightning
I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.
- New to ML, which is easier to learn - Tensorflow or PyTorch?
- PyTorch Lightning – DL framework to train, deploy, and ship AI fast
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We just release a complete open-source solution for accelerating Stable Diffusion pretraining and fine-tuning!
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!
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An elegant and strong PyTorch Trainer
For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.
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[D] Mixed Precision Training: Difference between BF16 and FP16
For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).