Update docs

docs/.gitignore

@@ -1,2 +1,3 @@
+Manifest.toml
 build
 site

docs/make.jl

@@ -20,6 +20,7 @@ function main()
             "Test suite"    => "testsuite.md",
         ],
         doctest = true,
+        warnonly=[:missing_docs],
     )
 
     deploydocs(

docs/src/interface.md

@@ -10,7 +10,7 @@ Device functionality is then handled by [KernelAbstractions.jl](https://github.c
 
 You should provide an array type that builds on the `AbstractGPUArray` supertype, such as:
 
-```
+```julia
 mutable struct CustomArray{T, N} <: AbstractGPUArray{T, N}
     data::DataRef{Vector{UInt8}}
     offset::Int
@@ -23,10 +23,18 @@ end
 This will allow your defined type (in this case `JLArray`) to use the GPUArrays interface where available.
 To be able to actually use the functionality that is defined for `AbstractGPUArray`s, you need to define the backend, like so:
 
-```
+```julia
 import KernelAbstractions: Backend
 struct CustomBackend <: KernelAbstractions.GPU
 KernelAbstractions.get_backend(a::CA) where CA <: CustomArray = CustomBackend()
 ```
 
 There are numerous examples of potential interfaces for GPUArrays, such as with [JLArrays](https://github.com/JuliaGPU/GPUArrays.jl/blob/master/lib/JLArrays/src/JLArrays.jl), [CuArrays](https://github.com/JuliaGPU/CUDA.jl/blob/master/src/gpuarrays.jl), and [ROCArrays](https://github.com/JuliaGPU/AMDGPU.jl/blob/master/src/gpuarrays.jl).
+
+## Caching Allocator
+
+```@docs
+GPUArrays.@cache_scope
+GPUArrays.@no_cache_scope
+GPUArrays.invalidate_cache_allocator!
+```