All the usual [Julia performance tips apply](https://docs.julialang.org/en/v1/manual/performance-tips/).
As always [profiling your code](https://docs.julialang.org/en/v1/manual/profile/#Profiling-1) is generally a useful way of finding bottlenecks.
Below follow some Flux specific tips/reminders.
## Don't use more precision than you need.
Flux works great with all kinds of number types.
But often you do not need to be working with say `Float64` (let alone `BigFloat`).
Switching to `Float32` can give you a significant speed up,
not because the operations are faster, but because the memory usage is halved.
Which means allocations occur much faster.
And you use less memory.
## Make sure your custom activation functions preserve the type of their inputs
Not only should your activation functions be [type-stable](https://docs.julialang.org/en/v1/manual/performance-tips/#Write-%22type-stable%22-functions-1),
they should also preserve the type of their inputs.