# \`Conv\` is 2x slow than pytorch \`Conv\` on cpu

**URL:** <https://discourse.julialang.org/t/conv-is-2x-slow-than-pytorch-conv-on-cpu/39802>\
**Category:** Machine Learning\
**Created:** [May 20, 2020, 5:46am UTC](https://discourse.julialang.org/t/conv-is-2x-slow-than-pytorch-conv-on-cpu/39802 "2020-05-20T05:46:41Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![LeoKoo](https://avatars.discourse-cdn.com/v4/letter/l/ccd318/32.png) [@LeoKoo](https://discourse.julialang.org/u/LeoKoo)\
**Post date:** [May 21, 2020, 10:37am UTC](https://discourse.julialang.org/t/conv-is-2x-slow-than-pytorch-conv-on-cpu/39802/4 "2020-05-21T10:37:11Z")

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This is probably not responsible for the entire 2x speed difference, but I have noticed Flux.Conv layers are not type-stable, which I guess would reduce performance. Opened a github issue [here](https://github.com/FluxML/Flux.jl/issues/1178) and a thread [here](https://discourse.julialang.org/t/flux-convolutional-layer-not-type-stable/39454).

Because of the lack of response I am unsure whether this type-instability is actually a big deal though, I am quite new to Julia.

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