start trying to precompile and reduce compile times

[ChrisRackauckas]

using OrdinaryDiffEq, DiffEqSensitivity, Zygote

function lotka_volterra!(du, u, p, t)
  x, y = u
  α, β, δ, γ = p
  du[1] = dx = α*x - β*x*y
  du[2] = dy = -δ*y + γ*x*y
end

# Initial condition
u0 = [1.0, 1.0]

# Simulation interval and intermediary points
tspan = (0.0, 10.0)
tsteps = 0.0:0.1:10.0

# LV equation parameter. p = [α, β, δ, γ]
p = [1.5, 1.0, 3.0, 1.0]

# Setup the ODE problem, then solve
prob = ODEProblem(lotka_volterra!, u0, tspan, p)
sol = solve(prob, Tsit5())

function loss(p)
  sol = solve(prob, Tsit5(), p=p, saveat = tsteps)
  sum(abs2, sol.-1)
end

using SnoopCompile
tinf = @snoopi_deep Zygote.gradient(loss,p)

Before Any Precompile Work:
OrdinaryDiffEq#before_precompile
DiffEqBase#before_precompile
StochasticDiffEq@6.36
RecursiveFactorization@0.2
TriangularSolve@0.1.2

InferenceTimingNode: 12.563108/55.700412 on Core.Compiler.Timings.ROOT() with 928 direct children

Current:
InferenceTimingNode: 11.626459/45.016643 on Core.Compiler.Timings.ROOT() with 925 direct children

This PR:
InferenceTimingNode: 11.704070/43.656520 on Core.Compiler.Timings.ROOT() with 927 direct children

Currently it's not doing all that much.