# Flux: Hard to use train! and DataLoader for minibatched NamedTuple dataset with GPU

**URL:** <https://discourse.julialang.org/t/flux-hard-to-use-train-and-dataloader-for-minibatched-namedtuple-dataset-with-gpu/47189>\
**Category:** Machine Learning\
**Tags:** flux\
**Created:** [September 24, 2020, 7:37am UTC](https://discourse.julialang.org/t/flux-hard-to-use-train-and-dataloader-for-minibatched-namedtuple-dataset-with-gpu/47189 "2020-09-24T07:37:26Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![paalon](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/paalon/32/5784_2.png) [@paalon](https://discourse.julialang.org/u/paalon)\
**Post date:** [September 24, 2020, 7:37am UTC](https://discourse.julialang.org/t/flux-hard-to-use-train-and-dataloader-for-minibatched-namedtuple-dataset-with-gpu/47189/1 "2020-09-24T07:37:26Z")

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I want to write the following neural networks machine learning code on GPU but I need to write complicated and dirty something within `dataloader`’s for loop to pass `data` to `train!`:

```julia
# This GPU based program works but very slow than CPU

using Flux
using Flux.Data: DataLoader
using Printf

xpu = gpu
# xpu = cpu

n = 100
dataset = (
    input = (
        data1 = rand(1, n),
        data2 = rand(1, n),
        data3 = rand(1, n),
        data4 = rand(5, 10, n)
    ),
    output = rand(1, n)
) |> xpu
dataloader = DataLoader(dataset.input, dataset.output, batchsize = 4)
model = Chain(
    (input) -> cat(dims=1, input.data1, input.data2), # Here I ignore data3 and data4 for simplicity. In real model, I'll use these data.
    Dense(2, 10, relu),
    Dense(10, 1)
) |> xpu

loss(input, output) = Flux.mse(model(input), output)
optimizer = ADAM()
epoch_length = 100
for epoch in 1:epoch_length
    for (input, output) in dataloader
        # complicated and maybe slow, I need to reconstruct minibatched dataset
        data = [((data1 = input.data1[:, i], data2 = input.data2[:, i], data3 = input.data3[:, i], data4 = input.data4[:, :, i]), output[:, i]) for i in size(output)[2]]
        Flux.train!(loss, params(model), data, optimizer)
    end
    loss(dataset.input, dataset.output) |> println
end

```

This code is very slower than CPU. How should I do to run it fast? What is the good practice?

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**Author:** ![nuclear718](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nuclear718/32/16163_2.png) [@nuclear718](https://discourse.julialang.org/u/nuclear718)\
**Post date:** [September 24, 2020, 8:26am UTC](https://discourse.julialang.org/t/flux-hard-to-use-train-and-dataloader-for-minibatched-namedtuple-dataset-with-gpu/47189/2 "2020-09-24T08:26:04Z")

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If the batchsize is too small, it is hard to gain performance from GPU.

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<div class="post-metadata">

**Author:** ![paalon](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/paalon/32/5784_2.png) [@paalon](https://discourse.julialang.org/u/paalon)\
**Post date:** [September 24, 2020, 8:54am UTC](https://discourse.julialang.org/t/flux-hard-to-use-train-and-dataloader-for-minibatched-namedtuple-dataset-with-gpu/47189/3 "2020-09-24T08:54:55Z")

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The batch size is small and the complexity of the model is too small, and it seems there is another working GPU process by another user. Thanks!