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Practical AI: Machine Learning, Data Science, LLM
Deep learning in Rust with Burn 🔥
Oct 24, 2023
40:36
Snipd AI
Nathaniel Simard, creator of burn, joins the podcast to discuss Rust as a deep learning framework. They explore Rust's capabilities, motivations behind using it, challenges in supporting deep learning, working with GPU libraries, importing models & training tools. They discuss the potential of Burn and suggest starting with the burn.dev website for more information.
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Quick takeaways
- Burn is a deep learning framework developed in Rust that aims to provide easy deployment of complex models on any platform.
- Burn is gaining traction among those seeking to deploy models reliably and efficiently, with support for various backends and a training loop library called BurnTrain.
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Overview of Burn's Goals and Capabilities
Burn is a deep learning framework developed in Rust that aims to provide easy deployment of complex models on any platform. It offers a familiar API similar to PyTorch, making it accessible to both machine learning engineers and researchers. Burn's versatile backends, including Torch, WebGPU, NVIDIA Ray, and Gendel, allow for efficient deployment on various hardware configurations. The framework also provides comprehensive training tools, such as metrics, logging, and checkpointing, making it convenient for developers to train and monitor models. With the Burnbook serving as a tutorial and reference guide, users can quickly get started with the framework. The goal for Burn is to be widely used for complex deep learning models, enabling innovative applications and seamless deployment across different platforms. The creator, Nathaniel Samar, hopes to contribute to the research and development of larger models and explore asynchronous neural networks leveraging Burn's concurrency capabilities.
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