The Julia Ecosystem

One of the main strengths of Julia lies in an ecosystem of packages globally providing a rich and consistent user experience.

This is a non-exhaustive list of Julia packages, nicely complementing Flux in typical machine learning and deep learning workflows:

• ArgParse.jl: package for parsing command-line arguments to Julia programs.
• Augmentor.jl: a fast image augmentation library in Julia for machine learning.
• BSON.jl: package for working with the Binary JSON serialisation format
• DataFrames.jl: in-memory tabular data in Julia
• DrWatson.jl: a scientific project assistant software
• MLDatasets.jl: utility package for accessing common machine learning datasets
• OnlineStats.jl: single-pass algorithms for statistics
• Parameters.jl: types with default field values, keyword constructors and (un-)pack macros
• ProgressMeters.jl: progress meters for long-running computations
• TensorBoardLogger.jl: easy peasy logging to tensorboard in Julia
• ParameterSchedulers.jl: standard scheduling policies for machine learning

This tight integration among Julia packages is shown in some of the examples in the model-zoo repository.