Deep Learning in Rust: Burn 0.4.0 released and plans for 2023

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  • burn

    Discontinued Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn] (by burn-rs)

  • Last year, I announced Burn (https://github.com/burn-rs/burn), a new deep learning framework written in Rust. The response from the community was very positive, which encouraged me to continue working on the project and solidify the core architecture.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • neuronika

    Tensors and dynamic neural networks in pure Rust.

  • Also perhaps comparing to Neuronika.

  • Graphite

    2D vector & raster editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.

  • Would this mean it could be possible to, say, port the inference code for Stable Diffusion to pure Rust using the NdArrayBackend? (Out of curiosity, what performance reduction percent would it likely mean over the CUDA backend?) I'm working on a pure Rust 2D graphics editor (Graphite) that will integrate lots of ML models for image processing and synthesis, but it's unfortunately looking like we'll have to very messily bundle a bunch of Python projects and somehow call into them. I dream of a pure Rust solution someday, and I'm wondering how feasible that might be over time.

  • diffusers-rs

    An implementation of the diffusers api in Rust

  • Are you aware of https://github.com/LaurentMazare/diffusers-rs

  • ort

    A Rust wrapper for ONNX Runtime (by pykeio)

  • I would't try to distribute your ml models with the typical frameworks, especially not with python. Have you looked in to ONNX?For example: https://github.com/pykeio/ort

  • dfdx

    Deep learning in Rust, with shape checked tensors and neural networks

  • A question I have is: what are the philosophical/design differences with dfdx? As someone who's played around with dfdx and only skimmed the README of burn, it seems like dfdx leans into Rust's type system/type inference for compile time checking of as much as is possible to check at compile time. I wonder if you've gotten a chance to look at dfdx and would like to outline what you think the differences are. Thanks!

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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