# tracel-ai/burn **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/tracel-ai-burn).** 14,398 stars · 822 forks · Rust · apache-2.0 ## Links - GitHub: https://github.com/tracel-ai/burn - Homepage: https://burn.dev - awesome-repositories: https://awesome-repositories.com/repository/tracel-ai-burn.md ## Topics `autodiff` `cross-platform` `cuda` `deep-learning` `kernel-fusion` `machine-learning` `metal` `ndarray` `neural-network` `onnx` `pytorch` `rocm` `rust` `scientific-computing` `tensor` `vulkan` `wasm` `webgpu` ## Description Burn is a deep learning framework designed for building, training, and deploying neural networks using a modular architecture. As a machine learning library built in Rust, it provides a backend-agnostic computational engine that enables the execution of models across diverse hardware, including central processors, graphics processors, and web runtimes. The framework distinguishes itself through a highly portable design that allows developers to maintain a single workflow for both training and inference across heterogeneous environments. It incorporates advanced optimization techniques such as just-in-time kernel fusion, asynchronous execution, and static graph compilation to maximize computational efficiency and hardware throughput. The library also functions as a comprehensive model quantization toolkit, offering tools to convert weights and activations into lower-bit representations. These capabilities facilitate the deployment of neural networks on resource-constrained edge devices by reducing memory footprints and accelerating inference tasks without requiring manual code changes for different hardware targets. ## Tags ### Web Development - [Deep Learning Frameworks](https://awesome-repositories.com/f/web-development/state-management-models/state-space-models/deep-learning-frameworks.md) — A modular deep learning framework designed for building, training, and deploying neural networks. ### Artificial Intelligence & ML - [Deep Learning Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-libraries.md) — Implements a high-performance deep learning framework in Rust that leverages memory safety and zero-cost abstractions for model training and inference. - [Backend-Agnostic Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-research/neural-network-toolkits/backend-agnostic-engines.md) — Provides a backend-agnostic computational engine that executes neural networks across diverse hardware including CPUs, GPUs, and web runtimes. - [Cross-Platform Inference Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-platform-inference-frameworks.md) — Facilitates cross-platform machine learning by enabling model execution on diverse hardware and runtimes. - [Model Training and Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/model-training-and-inference-engines.md) — Enables training and inference across diverse hardware including CPUs, GPUs, and web runtimes. ([source](https://burn.dev/docs/burn)) - [Model Quantization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/model-quantization-tools.md) — Provides a comprehensive toolkit for converting model weights to lower-bit representations to accelerate inference. - [Multi-Backend Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/multi-backend-abstractions.md) — Provides a unified interface layer that decouples neural network operations from specific hardware backends. - [Edge AI Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment.md) — Optimizes and manages the deployment of machine learning models for efficient performance on edge devices. - [Neural Network Building Blocks](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-building-blocks.md) — Provides modular building blocks for constructing portable deep learning models. ([source](https://burn.dev/docs/burn)) - [Model Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-networks/model-training-pipelines.md) — Supports end-to-end workflows for training and deploying models across varied hardware environments. ([source](https://burn.dev/docs/burn/)) - [Model Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/quantization/model-quantization.md) — Improves computational efficiency through techniques like kernel fusion, asynchronous execution, and weight quantization. - [Model Quantization Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/quantization/model-quantization-frameworks.md) — Compresses model weights and activations into lower-bit representations to reduce memory and computational requirements. ([source](https://burn.dev/docs/burn)) - [Model Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization.md) — Reduces model precision using static or dynamic methods to improve inference speed and memory usage. ([source](https://burn.dev/docs/burn/)) - [Model Performance Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-performance-optimizations.md) — Optimizes computational performance through kernel fusion and asynchronous execution techniques. ([source](https://burn.dev/docs/burn)) - [Precision Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/precision-quantization.md) — Stores model weights in reduced-bit formats to decrease memory footprint and accelerate arithmetic operations. - [Neural Network Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-optimizers.md) — Accelerates neural network execution across diverse hardware backends using advanced optimization techniques. ([source](https://burn.dev/docs/burn/)) ### Programming Languages & Runtimes - [Static Graph Execution](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/execution-engines/static-graph-execution.md) — Compiles model structures into static execution plans to eliminate runtime overhead and improve inference speed. - [Kernel Fusion Operations](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtimes/graph-symbolic-execution-engines/operation-kernels/kernel-fusion-operations.md) — Combines multiple mathematical operations into single optimized compute kernels at runtime to maximize throughput. ### Data & Databases - [Runtime Hardware Optimizers](https://awesome-repositories.com/f/data-databases/hardware-acceleration/runtime-hardware-optimizers.md) — Automatically selects efficient hardware-specific execution paths for neural network operations. ([source](https://burn.dev/docs/burn/)) ### Software Engineering & Architecture - [Pluggable Backends](https://awesome-repositories.com/f/software-engineering-architecture/pluggable-backends.md) — Implements a pluggable architecture that dynamically swaps between different hardware drivers and compute runtimes.