Flux [17] is a machine learning framework, written using the numerical computing language Julia[4]. The framework makes writing layers as simple as writing mathematical formulae, and it's advanced AD, Zygote [11] , applies automatic differentiation (AD) to calculate derivatives and train the model. It makes heavy use of Julia's language and compiler features to carry out code analysis and make optimisations. For example, Julia's GPU compilation support [3] can be used to JIT-compile custom GPU kernels for model layers [19]. Flux also supports a number of a hardware options, from CPUs, GPUs and even TPUs via XLA.jl, that compiles Julia code to XLA: an advanced compiler for linear algebra that is capable of greatly optimizing speed and memory usage in large deep learning models. ONNX.jl is an Open Neural Network Exchange backend for the Flux.jl deep learning framework. ONNX.jl supports directly importing high quality ONNX standard models into Flux, thus saving time and reducing the need for additional computation resources. This paper aims at introducing ONNX.jl and explaining how it fits into the bigger picture: How we can use the Julia Language, specifically Flux.jl and ONNX.jl as a starting for high quality transfer learning of large deep learning models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.