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On-device AI across mobile, embedded and edge for PyTorch
ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices.
Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.
Meta Lingua is a minimal and fast LLM training and inference library designed for research. Meta Lingua uses easy-to-modify PyTorch components in order to try new architectures, losses, data, etc. We aim for this code to enable end to end training, inference and evaluation as well as provide tools to better understand speed and stability. While Meta Lingua is currently under development, we provide you with multiple apps to showcase how to use this codebase.
PyTorch implementation of PerCo (Towards Image Compression with Perfect Realism at Ultra-Low Bitrates, ICLR 2024)
An array framework for Apple silicon.
MLX is an array framework for machine learning research on Apple silicon, brought to you by Apple machine learning research.
A platform for the machine learning lifecycle.
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:
ImageBind One Embedding Space to Bind Them All.
PyTorch implementation and pretrained models for ImageBind. For details, see the paper: ImageBind: One Embedding Space To Bind Them All.
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:
YOLOv5 in PyTorch > ONNX > CoreML > TFLite.
YOLOv5 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
VToonify: Controllable High-Resolution Portrait Video Style Transfer.
[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer.
This repository provides the official PyTorch implementation for the following paper:
An open source platform for the machine learning lifecycle.
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).