koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code. (by rentruewang)
ADOP
By darglein
koila | ADOP | |
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7 | 13 | |
1,817 | 2,013 | |
- | - | |
6.8 | 5.3 | |
20 days ago | 3 months ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.
koila
Posts with mentions or reviews of koila.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-17.
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How to fix CUDA out of memory with Koila?
but I always get CUDA out of memory . Long story short I found koila which should fix this issue, but I'm not sure how to add this to my code. in their page they have (input, label) = lazy(input, label, batch=0) but i kinda feel lost. can you help me please.
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Pytorch CUDA out of memory persists after lowering batch size and clearing gpu cache
Having 53760 neurons takes much memory. Try adding more Conv2D layers or play with stride. Also, try .detach() to data and labels after training. Lastly, I would suggest to take a look at https://github.com/rentruewang/koila. Have not tried yet but it should be helpful.
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[D] Would the 8gb VRAM of the 3060ti mean that some models in computer vision cannot be trained with it at all?
Tools like this can help: https://github.com/rentruewang/koila
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[P] Dynamic batching for GPT-J API
You could take a look at how these guys are determining memory batch size limits... https://github.com/rentruewang/koila
- Koila: Prevent PyTorch's out of memory error with lazy evaluation
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Solve PyTorch's `CUDA error: out of memory` in 1 line of code
Project Link
- Show HN: Solve `CUDA error: out of memory` in one line of code
ADOP
Posts with mentions or reviews of ADOP.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-14.
- 인공지능에 대한 이해 : https://youtu.be/g1ARrNTwBHg 1편 - 딥러닝의 원리 https://youtu.be/CA5Ggqg5x6o 2편 - 인공지능의 창의성과 테슬라 AI https://youtu.be/jHYYggG7qq8 3편 - 코딩, 과학, 수학 난제를 해결하려는 A.I. https://youtu.be/BWJWAdMZGNY ---------------------------------------------------- 영상에 등장하는 링크 : ADOP(2021) https://arxiv.org
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[D] Would the 8gb VRAM of the 3060ti mean that some models in computer vision cannot be trained with it at all?
To train something like https://github.com/darglein/ADOP , can 8gb VRAM in a 3060ti prove to be a hard limit? If it can still train the model by using cpu RAM, how much of a performance hit will it suffer? Should I go with 3060 12gb instead?
- ADOP: New AI Rendering Pipeline that generates incredible results
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[D] Does this even exist?
I recommend taking a look at this repository: https://github.com/darglein/ADOP There's cool stuff happening in this area!
- ADOP: Approximate Differentiable One-Pixel Point Rendering
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New AI: Photos Go In, Reality Comes Out! | Two Minute Papers. More latent space synthesis from photos!
code here: https://github.com/darglein/ADOP
- AI pre-processing image sets
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Create your own 3D scenes from a set of photos with this cool neural network-based algorithm.
Any hope for a compiled file executable under Windows? They only released the bare code https://github.com/darglein/ADOP/releases/tag/v1.0
- AI Synthesizes Smooth Videos from a Couple of Images! Rückert, D. et al., (2021), ADOP
What are some alternatives?
When comparing koila and ADOP you can also consider the following projects:
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
omnimatte
torchsynth - A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.
glide-text2im - GLIDE: a diffusion-based text-conditional image synthesis model
bittensor - Internet-scale Neural Networks
gpt-j-api-huggingface
tributary - Streaming reactive and dataflow graphs in Python
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
thrash-protect - Simple-Stupid user-space program doing "kill -STOP" and "kill -CONT" to protect from thrashing. It works a bit like the ABS break on the car.