Deploying machine learning models efficiently is necessary for numerous applications. However, traditional frameworks like PyTorch have their share of problems, including their size, slow instance creation on a cluster, and reliance on Python that can result in performance challenges. There’s a clear need for a minimalistic and efficient solution. Despite the existence of alternative solutions like dfdx and tch-rs, they have their own restrictions, hence require a preferable remediation.
Presenting Candle, a minimalist Machine Learning (ML) framework developed in Rust that intelligently and efficiently addresses these conventional challenges. Candle is designed with a major priority on performance which includes support for GPU, and user-friendliness. It’s intended to enable serverless inference and facilitate the deployment of lightweight binaries by leveraging Rust. Such an approach aids in eliminating Python overhead and the Global Interpreter Lock (GIL), thereby enhancing the performance, and ensuring higher reliability.
Candle incorporates various features to support its objectives. It provides model training capabilities, optimized CPU backends, CUDA support for GPUs, and WebAssembly (WASM) support for running models in web browsers. Additionally, Candle encompasses a range of pre-trained models from multiple domains like language models, computer vision, and audio processing.
Candle enables quick inference times with its optimized CPU backend which makes it suitable for real-time applications. Its CUDA backend allows efficient utilization of GPUs, thereby enabling high-throughput processing of massive datasets. Additionally, the support for WASM helps in lightweight deployment within web environments, broadening its application range.
To sum up, Candle provides a robust solution that combats the challenges of deploying ML models optimally. By capitalizing on the performance advantages of Rust and a minimalist design primed towards user-friendliness, Candle enables developers to improvise their workflows and achieve maximum performance in production environments. Candle provides demos like whisper, LLaMA2, T5, yolo, and Segment Anything. Candle is capturing attention due to its emphasis on performance (including GPU support) and ease of use.
