# Flux ready for a beginner deep learning project?

**URL:** https://discourse.julialang.org/t/flux-ready-for-a-beginner-deep-learning-project/9262
**Category:** Machine Learning
**Tags:** flux
**Created:** [February 22, 2018, 7:17pm UTC](https://discourse.julialang.org/t/flux-ready-for-a-beginner-deep-learning-project/9262 "2018-02-22T19:17:25Z")
**Posts on this page:** 1
**Showing post:** 8

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### Author: ![tk3369](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tk3369/32/2824_2.png) [@tk3369](https://discourse.julialang.org/u/tk3369)
#### Post date: [February 23, 2018, 3:09am UTC](https://discourse.julialang.org/t/flux-ready-for-a-beginner-deep-learning-project/9262/8 "2018-02-23T03:09:24Z")

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This is a great docker image and I experimented with it once on AWS.

> [@ANN: Docker image for CUDA packages](https://discourse.julialang.org/t/ann-docker-image-for-cuda-packages/5792):
>
> Hi all, Many JuliaGPU packages are pretty hard to install, so I’ve been working a little on a Docker image which comes with some CUDA packages preinstalled: [maleadt/juliagpu](https://github.com/JuliaGPU/docker), requiring [nvidia-docker](https://github.com/NVIDIA/nvidia-docker/wiki/Installation): $ docker pull maleadt/juliagpu $ nvidia-docker run -it maleadt/juliagpu The image currently comes with CUDAnative.jl and CuArrays.jl preinstalled. Pulling the image requires a single initialization step, after which the image is fully usable, but the image just prompts you to do that (more detail…

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