# Improve performance in Flux.jl

**URL:** <https://discourse.julialang.org/t/improve-performance-in-flux-jl/127939>\
**Category:** Performance\
**Tags:** performance, flux, lstm\
**Created:** [April 10, 2025, 4:42pm UTC](https://discourse.julialang.org/t/improve-performance-in-flux-jl/127939 "2025-04-10T16:42:33Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Wuaphi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/wuaphi/32/216250_2.png) [@Wuaphi](https://discourse.julialang.org/u/Wuaphi)\
**Post date:** [April 10, 2025, 4:42pm UTC](https://discourse.julialang.org/t/improve-performance-in-flux-jl/127939/1 "2025-04-10T16:42:33Z")

</div>

I tried to train a LSTM model in Flux.jl, but whatever I do, the gpu usage only reaches about 50%. But I can’t make the batchsize bigger, as the vram is nearly full. I would appreciate any kind of help.

```julia
module Lstm
    using Flux
    using CUDA
    using cuDNN
    using CSV
    using DataFrames
    using ProgressMeter
    using Zygote
    using Plots
    using JLD2
    using Statistics

    CUDA.allowscalar(false)

    function predicter()
        device = gpu_device()
        epochs = 1_000_000
        batchsize = 1000
        initLearn = 0.001
        clipping = 1
        plotting_mean_threshold = 2

        LSTM_NN = Chain(
            LSTM(14 => 256),
            LSTM(256 => 128),
            Dense(128 => 64, relu),
            Dense(64 => 1)
        ) |> device
        
        opt_state = Flux.setup(OptimiserChain(ClipNorm(clipping), AdamW(initLearn)), LSTM_NN)

        inloc = "data"
        losses = []
        averaged_losses = []

        train_data = Float32.(load(inloc * "/big_train_data.jld2", "collected_train_data"))
        true_data = Float32.(load(inloc * "/big_true_data.jld2", "collected_true_data"))
        println(size(train_data), typeof(train_data), size(true_data), typeof(true_data))

        loader = Flux.DataLoader((data=train_data, label=true_data); batchsize=batchsize, shuffle=true)

        @showprogress for epoch_idx in 1:epochs

            for batch in loader
                X, Y = batch |> device
                loss, grads = Zygote.withgradient(LSTM_NN) do m 
                    output = m(X)[1, end, :]
                    Flux.Losses.mse(output, Y)
                end
                Flux.update!(opt_state, LSTM_NN, grads[1]) 
                push!(losses, cpu(loss))
            end
            
            states = Flux.state(cpu(LSTM_NN))
            println("Finished_epoch_$epoch_idx")

            M = mean(losses)
            S = std(losses)
            filtered_losses = filter(x -> abs(x - M) <= plotting_mean_threshold * S, losses)
            push!(averaged_losses, mean(filtered_losses))
            resize!(losses, 0)
            display(plot(1:length(averaged_losses), averaged_losses))

  
        end
    end

    predicter()
end

```