pytorch_wavelets
cog
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| pytorch_wavelets | cog | |
|---|---|---|
| 1 | 20 | |
| 857 | 7,133 | |
| - | 8.2% | |
| 0.0 | 9.4 | |
| 9 months ago | 7 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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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
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Why companies move off Heroku (besides the cost)
Dokku Maintainer here.
Dokku also supports Dockerfiles, Docker Images, Tarballs (similar to heroku slugs), and Cloud Native Buildpacks. I'm also actively working on AWS Lambda support (both for simple usage without much config as well as SAM-based usage) and investigating Replicate's Cog[1] and Railways Nixpacks[2] functionalities for building apps.
There are quite a few options in the OSS space (as well as Commercial offerings from new startups and popular incumbents). It's an interesting space to be in, and its always fun to see how new offerings innovate on existing solutions.
[1] https://github.com/replicate/cog
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
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
mish-cuda - Mish Activation Function for PyTorch
heroku-review-app-actions - GitHub action to automate managing review apps on your Heroku account
mish-cuda - Mish Activation Function for PyTorch
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
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
sidekiq - Sidekiq worker on Render