# MNIST GPU CuArrays error

**URL:** <https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695>\
**Category:** GPU\
**Created:** [January 16, 2019, 1:13pm UTC](https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695 "2019-01-16T13:13:52Z")\
**Posts on this page:** 4\
**Page:** 2

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**Author:** ![Fadi\_Nader](https://avatars.discourse-cdn.com/v4/letter/f/a587f6/32.png) [@Fadi\_Nader](https://discourse.julialang.org/u/Fadi_Nader)\
**Post date:** [January 22, 2019, 9:35am UTC](https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695/21 "2019-01-22T09:35:12Z")

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Hi dear,

I did the same comparison using tensorflow, and the GPU performed better, so maybe Flux is not optimized on that type of GPU.  
Anyways, thanks a lot for your support and guidance for this issue 🙂

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**Author:** ![kristoffer.carlsson](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/kristoffer.carlsson/32/22_2.png) [@kristoffer.carlsson](https://discourse.julialang.org/u/kristoffer.carlsson)\
**Post date:** [January 22, 2019, 9:37am UTC](https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695/22 "2019-01-22T09:37:26Z")

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How much better?

If you feel up to it, you can profile the run, using e.g.

```julia
nvprof path/to/julia myfile.jl

```

and see what is taking time for the Flux model.

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<div class="post-metadata">

**Author:** ![maleadt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maleadt/32/10097_2.png) [@maleadt](https://discourse.julialang.org/u/maleadt)\
**Post date:** [January 22, 2019, 10:50am UTC](https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695/23 "2019-01-22T10:50:27Z")

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> [@kristoffer.carlsson](#):
>
> If you feel up to it

If not, it would make a valuable issue now that you have side-by-side Flux/TF implementations.

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<div class="post-metadata">

**Author:** ![Fadi\_Nader](https://avatars.discourse-cdn.com/v4/letter/f/a587f6/32.png) [@Fadi\_Nader](https://discourse.julialang.org/u/Fadi_Nader)\
**Post date:** [January 22, 2019, 3:06pm UTC](https://discourse.julialang.org/t/mnist-gpu-cuarrays-error/19695/24 "2019-01-22T15:06:40Z")

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Hi,

I ran MNIST training for 45 epochs, with different batch sizes and got below results:

# Flux:

CPU:

batch size 100: 479.395901 seconds (52.17 M allocations: 303.536 GiB, 4.77% gc time)  
batch size 512: 160.653196 seconds (6.71 M allocations: 184.139 GiB, 10.34% gc time)  
batch size1024: 256.346342 seconds (3.39 M allocations: 169.667 GiB, 53.28% gc time)  
batch size 2048: 250.305340 seconds (1.73 M allocations: 162.432 GiB, 55.05% gc time)

GPU:

batch size 100 483.669281 seconds (33.77 M allocations: 302.615 GiB, 4.81% gc time)  
batch size 512: 159.605954 seconds (6.78 M allocations: 184.142 GiB, 10.33% gc time)  
batch size1024: 255.784214 seconds (3.45 M allocations: 169.670 GiB, 53.21% gc time)  
batch size 2048: 246.802858 seconds (1.80 M allocations: 162.434 GiB, 55.49% gc time)

# Tensorflow:

GPU:

batch size 100: 368 seconds  
batch size 512: 125 seconds  
batch size1024: 111 seconds  
batch size 2048: 97 seconds

@kristoffer, actually I’m using notebook, not a file.jl so anything you need me to do by this command ?

```julia-auto
nvprof path/to/julia myfile.jl

```

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