candle-wasm-examples
A minimalist, high-performance machine learning framework for Rust with a focus on serverless inference and GPU support.
What is candle-wasm-examples?
Candle is a minimalist machine learning framework written in Rust, designed for high-performance inference and training. It provides a PyTorch-like API for tensor operations and model building, with support for CPU, GPU, and WebAssembly backends. The framework solves the problem of deploying lightweight, efficient ML models in serverless environments without Python overhead.
Rust developers and ML engineers who need to deploy efficient, production-ready machine learning models, particularly those focused on serverless inference, embedded systems, or browser-based applications.
Developers choose Candle for its minimal footprint, performance optimizations (including GPU support), and ability to create standalone binaries that eliminate Python dependencies. It offers a familiar API while leveraging Rust's safety and speed, making it ideal for resource-constrained deployments.
Overview
Minimalist ML framework for Rust
Use Cases
Best For
- Deploying ML models in serverless or edge computing environments
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