ivy
upspin
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| ivy | upspin | |
|---|---|---|
| 17 | 20 | |
| 14,022 | 6,225 | |
| 0.5% | 0.3% | |
| 10.0 | 6.0 | |
| 6 days ago | 7 days ago | |
| Python | Go | |
| 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.
ivy
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Keras 3.0
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
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Show HN: Carton – Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
- Ivy: All in one machine learning framework
- Ivy ML Transpiler and Framework
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[D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
https://unify.ai/ They are trying to do what Ivy is doing already.
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Ask for help: what is the best way to have code both support torch and numpy?
Check Ivy.
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CoreML Stable Diffusion
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...
There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477
It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai
I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.
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DL Frameworks in a nutshell
Won't it all come together with https://lets-unify.ai/ ?
- Unified Machine Learning
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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
upspin
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I Moved My Blog from IPFS to a Server
Super intriguing. Thanks for sharing!
It reminds me a bit of an early Go project called Upspin [1]. And also a bit of Solid [2]. Did you get any inspiration from them?
What excites me about your project is that you're addressing the elephant in the room when it comes to data sovereignty (~nobody wants to self-host a personal database but their personal devices aren't publicly accessible) in an elegant way.
By storing the data on my personal device and (presumably?) paying for a managed relay (and maybe an encrypted backup), I can keep my data in my physical possession, but I won't have to host anything on my own. Is that the idea?
https://upspin.io/
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Educational Codebases
There are a few Go projects meant to be learned from:
- https://github.com/pion/opus for to learn audio
- https://github.com/benbjohnson/wtf for overall production quality
- https://github.com/upspin/upspin difficult to explain, personally I'm not a fan of the errors
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Fundamentals to Learn
You could also take a look at some real-world open-source projects. I like upspin for its idiomatic approach.
- Examples of Good Go Repos
- Examples of an idiomatic API project
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Best practices of validation on web apps?
For example, Rob Pike's upspin places all its validations in the separate package. Do you agree with that approach? Which yet proven options there are?
- Is there a good example of an open source non-trivial (DB connection, authentication, authorization, data validation, tests, etc...) Go API?
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Ask HN: What Are You Working on This Year?
Just a few projects that could perhaps interest you in terms of design of your own solution :
Upspin: https://upspin.io/
- Upspin: A framework for naming everyone's everything.
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proposal: Go 2: error handling: try statement with handler
The early error wrapping work which emerged out of the Upspin project, that eventually made its way into the errors package, included stack traces in the wrap error. This would provide exactly what it appears you seek.
What are some alternatives?
PaddleNLP - 👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
ytcast - cast YouTube videos to your smart TV from command-line
ColossalAI - Making large AI models cheaper, faster and more accessible
mitchellh/cli - A Go library for implementing command-line interfaces.
DeepFaceLive - Real-time face swap for PC streaming or video calls
golang-gin-realworld-example-app - Exemplary real world application built with Golang + Gin
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
fiber-boilerplate - This is the go boilerplate on the top of fiber web framework. With simple setup you can use many features out of the box
lisp - Toy Lisp 1.5 interpreter
Rundeck - Enable Self-Service Operations: Give specific users access to your existing tools, services, and scripts
Kornia - Geometric Computer Vision Library for Spatial AI
nes - NES emulator written in Go.