# Banyan Julia - large-scale Julia data frames, images, arrays, ML models, and more

**URL:** https://discourse.julialang.org/t/banyan-julia-large-scale-julia-data-frames-images-arrays-ml-models-and-more/83540
**Category:** Package Announcements
**Tags:** announcement, images, distributed, dataframes, machine-learning
**Created:** [June 30, 2022, 12:40am UTC](https://discourse.julialang.org/t/banyan-julia-large-scale-julia-data-frames-images-arrays-ml-models-and-more/83540 "2022-06-30T00:40:58Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![calebwin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/calebwin/32/14432_2.png) [@calebwin](https://discourse.julialang.org/u/calebwin)
#### Post date: [June 30, 2022, 12:40am UTC](https://discourse.julialang.org/t/banyan-julia-large-scale-julia-data-frames-images-arrays-ml-models-and-more/83540/1 "2022-06-30T00:40:58Z")

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I’m excited to make our first public announcement about [Banyan Julia](https://github.com/banyan-team/banyan-julia) - a suite of packages that let you use popular Julia APIs to process massive datasets on and off the cloud (via sampling):

- [BanyanDataFrames.jl](https://www.banyancomputing.com/banyan-data-frames-jl-docs/) for DataFrames.jl
- [BanyanImages.jl](https://www.banyancomputing.com/banyan-images-jl-docs/) for Images.jl
- [BanyanONNXRunTime.jl](https://www.banyancomputing.com/banyan-onnx-run-time-jl-docs/) for ONNXRunTime.jl (for PyTorch/TensorFlow models)
- [BanyanHDF5.jl](https://www.banyancomputing.com/banyan-hdf5-jl-docs/) for HDF5.jl
- [BanyanArrays.jl](https://www.banyancomputing.com/banyan-arrays-jl-docs/) for `Array`

Most recently, we’ve:

1. achieved comparable performance with Dask (Coiled) in a [preliminary benchmark](https://www.banyancomputing.com/benchmarks/) for a common data analytics task
2. put together a [getting started walk-through video](https://www.banyancomputing.com/getting-started/)
3. developed [automatic instant big data sampling](https://www.banyancomputing.com/sampling-data/) to reduce data teams’ reliance on expensive and energy-intensive cloud data centers

TLDR: we’re building a platform for eco-friendly large-scale data science with familiar Julia APIs. More details are on our website - [BanyanComputing.com](http://BanyanComputing.com). (PS - it’s a cloud product so if you want something on-prem then look at Dagger.jl, Distributed, or MPI.jl)

PPS - I want to thank the friendly and helpful Julia community including contributors to DataFrames.jl, Images.jl, ONNXRunTime.jl, etc. Without them, this project would not be possible. ❤