ivy
leptonai
| ivy | leptonai | |
|---|---|---|
| 1 | 2 | |
| 10,475 | 2,461 | |
| - | 5.4% | |
| 10.0 | 9.7 | |
| about 1 year ago | 3 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.
ivy
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Need internship/part time role in AI/ML/Data Science field.
As a volunteer developer at Ivy, I also been actively contributing to the project for several months now. I extended the Ivy Functional API by adding some linear algebra functions - matrix_exp (https://github.com/ivy-dl/ivy/blob/dev/ivy/linalg/matrix_exp.py) and diff (https://github.com/ivy-dl/ivy/blob/dev/ivy/linalg/diff.py) - to all four popular deep learning frameworks: NumPy, TensorFlow, PyTorch, and JAX. These functions allowed developers to perform matrix exponentiation and differentiation operations more efficiently and easily using any of these frameworks.
leptonai
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Show HN: Running LLMs in one line of Python without Docker
Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image.
We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that.
Imagine if you are a developer who sees a good model on github, or HuggingFace. To make it a production ready service, the current solution usually requires you to build a docker image. But think about it - I have a few python code and a few python dependencies. That sounds like a huge overhead, right?
lepton.ai is really a pythonic way to free you from such difficulties. You write a simple python scaffold around your PyTorch / TensorFlow code, and lepton launches it as a full-fledged service callable via python, javascript, or any language that understands OpenAPI. We use containers under the hood, but you don't need to worry about all the infrastructure nuts and bolts.
We have made the python library open-source at https://github.com/leptonai/leptonai/. With it, launching a common HuggingFace model is as simple as a one liner. For example, if you have a GPU, Stable Diffusion XL is as simple as:
```
- Lepton: An open-source library (Apache 2.0) for scaling model inference
What are some alternatives?
machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
examples - Lepton Examples
tensorboardX - tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
foolbox - A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
chainer - A flexible framework of neural networks for deep learning
pennylane - PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
ivy - The Unified AI Framework
tensor-sensor - The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
PDEBench - PDEBench: An Extensive Benchmark for Scientific Machine Learning
ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities