warp-drive
cog
| warp-drive | cog | |
|---|---|---|
| 1 | 20 | |
| 435 | 7,186 | |
| 1.1% | 3.2% | |
| 8.1 | 9.4 | |
| 26 days ago | 5 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
warp-drive
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[N] Salesforce Open-Sources ‘WarpDrive’, A Light Weight Reinforcement Learning (RL) Framework That Implements End-To-End Multi-Agent RL On A Single GPU
4 Min Read | Codes | Paper | SalesForce Blog
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?
Simple-MADRL-Chess - MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.
nixpacks - App source + Nix packages + Docker = Image
TransformerEngine - A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
pytorch_wavelets - Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
simba-ps - Fast deterministic all-Python Lennard-Jones particle simulator that utilizes Numba for GPU-accelerated computation.
piku - The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.
torchrec - Pytorch domain library for recommendation systems
heroku-review-app-actions - GitHub action to automate managing review apps on your Heroku account
chainer - A flexible framework of neural networks for deep learning
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
jittor - Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
memray - Memray is a memory profiler for Python