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| ivy | lisp | |
|---|---|---|
| 17 | 2 | |
| 14,022 | 946 | |
| 0.5% | - | |
| 10.0 | 0.0 | |
| 3 days ago | almost 4 years ago | |
| Python | Go | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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.
lisp
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For the LISP 1.5 mainframe fans here...
sure thing https://github.com/robpike/lisp
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Do you recommend learning go for an interpreter project?
Among the listed, Racket stands out to me - it's really on point for the problem, things Racket are organized around implementing languages inside of Racket. That said, Go should be totally fine. I might recommend perusing https://github.com/robpike/lisp, https://github.com/robpike/ivy, there are some talks about these on YouTube. The style is really
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.
Crafting Interpreters - Repository for the book "Crafting Interpreters"
ColossalAI - Making large AI models cheaper, faster and more accessible
go-parsing - A Multi-Package Go Repo Focused on Text Parsing, with Lexers, Parsers, and Related Utils
DeepFaceLive - Real-time face swap for PC streaming or video calls
ivy - ivy, an APL-like calculator
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)
pyright-python - Python command line wrapper for pyright, a static type checker
Kornia - Geometric Computer Vision Library for Spatial AI
lisp-cheney - A mini Lisp in 1k lines of C with Cheney's copying garbage collector, explained. Includes over 40 built-in Lisp primitives, floating point, strings, closures with lexical scope, macros, proper tail recursion, exceptions, execution tracing, file loading, a copying garbage collector and REPL.
devops-exercises - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
IchigoLisp - LISP 1.5(-ish) implementation in WebAssembly