google-research/scenic
Scenic
Scenic is a research framework designed for the development and training of deep learning models, with a specific focus on computer vision and multimodal transformer architectures. It provides a comprehensive toolkit for defining neural network structures, managing large-scale data pipelines, and executing training workflows across distributed hardware environments.
The framework is built upon a functional programming paradigm that utilizes hardware-agnostic tensor abstractions and just-in-time compilation to maximize computational efficiency. By employing modular layer composition, it allows researchers to construct and customize complex model architectures through abstract interfaces, facilitating rapid prototyping and experimentation.
Beyond architecture design, the project includes integrated utilities for tracking experimental metrics and performance data to ensure research reproducibility. It supports high-throughput training by orchestrating the loading and preprocessing of large datasets across multiple compute nodes, enabling the scaling of attention-based models for intensive research tasks.
Features
- Deep Learning Frameworks - Serves as a comprehensive research framework for defining neural network architectures and managing large-scale training pipelines.
- Automatic Differentiation Engines - Provides a core engine for computing gradients of mathematical functions by traversing computational graphs.
- Large-Scale Model Training - Provides specialized methodologies for training massive transformer architectures that exceed single-device capacity.
- Complex Architecture Design - Enables the design of complex neural networks using abstract interfaces for rapid prototyping.
- Computer Vision Research - Facilitates the development and testing of novel vision models through high-performance research workflows.
- Data Input Pipelines - Manages the loading and preprocessing of large datasets across multiple devices to ensure consistent data flow.
- Data-Parallel Training - Distributes model training workloads across multiple hardware units by synchronizing gradients and parameters.
- Hardware Abstraction Layers - Provides a unified interface for multi-dimensional array manipulation that executes seamlessly on different hardware backends.
- Modular Layer Compositions - Constructs complex neural network architectures by chaining reusable functional blocks.
- Vision Model Training - Executes optimized training loops for vision and multimodal transformer models with high computational efficiency.
- Computer Vision Frameworks - Provides an integrated environment for building and training modular vision and multimodal transformer models.
- Transformer Training Toolkits - Offers a toolkit for executing optimized training loops and scaling complex attention-based architectures.
- Data Pipeline Scaling - Distributes data processing pipelines across compute nodes to ensure high-speed data delivery during training.
- XLA Backend Compilations - Compiles high-level mathematical operations into optimized machine code for hardware accelerators.
- Computer Vision - JAX-based library for computer vision research.
- Multimodal and Specialized Tasks - Learned token processing for images and videos.
- Neural Network Libraries - Library focused on computer vision research and beyond.
- Perception Models - Vision transformer framework for video processing tasks.
