pytorch_wavelets
cog
| pytorch_wavelets | cog | |
|---|---|---|
| 1 | 21 | |
| 1,007 | 8,481 | |
| 4.6% | 1.8% | |
| 0.0 | 9.6 | |
| over 1 year ago | 2 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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.
pytorch_wavelets
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How to create a docker environment for model use?
git clone https://github.com/fbcotter/pytorch_wavelets
cog
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Five ways to use Generative AI in JavaScript
If you are working with an ML team that trained their own model or you want to host any model off Huggingface and use the same Docker container approach, you can also check out cog by Replicate. It wraps Docker and is specifically designed for creating Docker containers for ML models.
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AI Grant Traction in OSS Startups
View on GitHub
- Insanely Fast Whisper: Transcribe 300 minutes of audio in less than 98 seconds
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Talk-Llama
I'm in the same situation. I found this cog project to dockerise ML https://github.com/replicate/cog : you write just one python class and a yaml file, and it takes care of the "CUDA hell" and deps. It even creates a flask app in front of your model.
That helps keep your system clean, but someone with big $s please rewrite pytorch to golang or rust or even nodejs / typescript.
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Llama 2 – Meta AI
https://github.com/replicate/cog
Our thinking was just that a bunch of folks will want to fine-tune right away, then deploy the fine-tunes, so trying to make that easy... Or even just deploy the models-as-is on their own infra without dealing with CUDA insanity!
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Handling concurrent requests to ML model API
I have used this tool before: https://github.com/replicate/cog/tree/main
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Opinions on Cog: Containers for machine learning
Then I discovered Cog: Containers for Machine Learning. Looks like a way more flexible solution to plug in the existing infrastructure: you write your custom code and Cog plugs it in a Docker image with FastAPI, no extra ecosystem complexity added.
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can someone teach me how to install the new stable diffusion repo?
Highly recommend using cog https://github.com/replicate/cog
- Run Stable Diffusion on Your M1 Mac’s GPU
- replicate/cog: Containers for machine learning
What are some alternatives?
pywt - PyWavelets - Wavelet Transforms in Python
nixpacks - App source + Nix packages + Docker = Image
ssqueezepy - Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python
sidekiq - Sidekiq worker on Render
WaveDiff - Official Pytorch Implementation of the paper: Wavelet Diffusion Models are fast and scalable Image Generators (CVPR'23)
memray - Memray is a memory profiler for Python