WGLMakie Casset Zygote

apt-get update
apt-get install build-essential -y
ln -fs /lib/x86_64-linux-gnu/libz.so.1 /usr/local/lib/libz.so
ldconfig

]up; add https://github.com/SimonDanisch/ShaderAbstractions.jl https://github.com/JuliaPlots/WGLMakie.jl#sd-buffers GeometryBasics#master Observables AbstractPlotting#sd-webgl GeometryTypes JSCall#master StaticArrays WebIO#master Zygote PackageCompiler#sd-notomls Cassette Interact; gc

include(
utilities.jl
)
chain = [(rand() * pi) for i in 1:4]
function next_position(position, angle)
    position .+ (sin(angle), cos(angle))
end

# Our "neuronal network" ... or chain of flexible links
function predict(chain, input)
    output = next_position(input,  chain[1]) # Layer 1
    output = next_position(output, chain[2]) # Layer 2
    output = next_position(output, chain[3]) # Layer 3
    output = next_position(output, chain[4]) # Layer 4
    return output
end

function loss(chain, input, target)
    sum((predict(chain, input) .- target) .^ 2)
end

input, target = (0.0, 0.0), (3.0, 3.0)
weights, s = visualize(chain, input, target)
s

using Zygote
function loss_gradient(chain, input, target)
  # first index, to get gradient of first argument
  Zygote.gradient(loss, chain, input, target)[1]
end

for i in 1:100
  # get gradient of loss function
  angle∇= loss_gradient(chain, input, target)
  # update weights with our loss gradients
  # this updates the weights in the direction of smaller loss
  chain .-= 0.01 .* angle∇
  # update visualization
  weights[] = chain
  sleep(0.01)
end;

open("/results/snoop.jl", "w") do io
	write(io, read(
utilities.jl
))
  println(io, """
		function next_position(position, angle)
      position .+ (sin(angle), cos(angle))
    end

    # Our "neuronal network" ... or chain of flexible links
    function predict(chain, input)
        output = next_position(input,  chain[1]) # Layer 1
        output = next_position(output, chain[2]) # Layer 2
        output = next_position(output, chain[3]) # Layer 3
        output = next_position(output, chain[4]) # Layer 4
        return output
    end

    function loss(chain, input, target)
        return sum((predict(chain, input) .- target) .^ 2)
    end
    chain = [(rand() * pi) for i in 1:4]
    input, target = (0.0, 0.0), (3.0, 3.0)
    weights, s = visualize(chain, input, target)
    using Zygote
    repr(MIME"text/html"(), s);
    function loss_gradient(chain, input, target)
      # first index, to get gradient of first argument
      Zygote.gradient(loss, chain, input, target)[1]
    end

    angle∇= loss_gradient(chain, input, target)
    # update weights with our loss gradients
    # this updates the weights in the direction of smaller loss
    chain .-= 0.01 .* angle∇
    # update visualization
    weights[] = chain
    """)
end

using PackageCompiler
open("/results/precompiles.jl", "w") do io
  PackageCompiler.snoop(
snoop.jl
, io)
end

# add the correct usings + imports to the precompile file
using Pkg
packages = ["WGLMakie", "GeometryBasics", "AbstractPlotting", "Zygote", "LinearAlgebra"]
ctx = Pkg.Types.Context()
pkgs = PackageSpec.(packages)
packages = PackageCompiler.flat_deps(ctx, packages)
# remove blacklisted packages from full list of packages
imports = PackageCompiler.to_pkgid.(packages)
filter!(x-> !(x.name in ("WinRPM", "HTTPClient")), imports)
usings = join(["const $(x.name) = Base.require($(PackageCompiler.prepr(x)))" for x in imports], "\n")
open("/results/precompiles2.jl", "w") do io
  println(io, usings)
  open(
precompiles.jl
) do io2
  	write(io, io2)
  end
end

using PackageCompiler
syso, sysold = compile_incremental(
precompiles2.jl
)

cp(syso, sysold, force  = true)