koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code. (by rentruewang)
gpt-j-api-huggingface
By TensorBox
| koila | gpt-j-api-huggingface | |
|---|---|---|
| 7 | 3 | |
| 1,817 | 19 | |
| - | - | |
| 6.8 | 0.0 | |
| 20 days ago | over 2 years ago | |
| Python | Python | |
| 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.
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.
-
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.
-
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.
-
[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
-
[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
-
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
gpt-j-api-huggingface
Posts with mentions or reviews of gpt-j-api-huggingface.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-03.
-
[P] Dynamic batching for GPT-J API
Github link: https://github.com/TensorBox/gpt-j-api-huggingface
-
[P] Dynamic batching increases throughput 7x for HuggingFace models
I created an implementation dynamic batching for HuggingFace models hosted on FastAPI: https://github.com/TensorBox/gpt-j-api-huggingface. From our tests, it increases throughput 7x for GPT-J on Nvidia 3090.
-
[P] Inference server for GPT-J 6B (on HuggingFace) with optimizations to fit into G4dn.xlarge
Hi, I created an inference server for GPT-J 6B (on HuggingFace) using FastAPI: https://github.com/TensorBox/gpt-j-api-huggingface . The main challenge here was making sure that the model fits in the smallest G4dn instance (with 16Gb of RAM and VRAM) to save on the server costs.
What are some alternatives?
When comparing koila and gpt-j-api-huggingface you can also consider the following projects:
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
torchsynth - A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.
bittensor - Internet-scale Neural Networks
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.
ADOP