ivy
snakeware
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ivy | snakeware | |
---|---|---|
17 | 8 | |
13,980 | 1,717 | |
0.5% | - | |
10.0 | 0.0 | |
5 days ago | 3 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT 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 β Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
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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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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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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
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The coolest Python projects you've ever seen?
Ivy is seeking to unify all ML frameworks: https://lets-unify.ai/
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The 10 Trending Python Repositories on GitHub (May 2022)
IVY
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[P] Kornia: Differential Computer Vision
(*Differentiable) It's a great project. Wish they had a JAX version! Maybe something like Ivy would help make that possible without a manual port.
snakeware
- Snakeware β Linux distro with Python userspace inspired by Commodore 64
- The coolest Python projects you've ever seen?
- Snakeware β Linux distro with Python userspace inspired by Commodore 64 (2020)
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Which FOSS project do you recommend donating to and why?
look at https://github.com/joshiemoore/snakeware where almost everything besides the Linux Kernel will be replaced by Python(They will find a way to init via Python.) Also http.server(a http server python module) and more.
- Vegans of the programming world
What are some alternatives?
PaddleNLP - π Easy-to-use and powerful NLP and LLM library with π€ Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including πText Classification, π Neural Search, β Question Answering, βΉοΈ Information Extraction, π Document Intelligence, π Sentiment Analysis etc.
ColossalAI - Making large AI models cheaper, faster and more accessible
DeepFaceLive - Real-time face swap for PC streaming or video calls
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)
lisp - Toy Lisp 1.5 interpreter
Kornia - Geometric Computer Vision Library for Spatial AI
material-design-icons-adt-template - Android Studio / Eclipse ADT template for material-design-icons resources
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
machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
upspin - Upspin: A framework for naming everyone's everything.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.