# DiffEqFlux tutorial example not running out-of-the-box

**URL:** <https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113>\
**Category:** New to Julia\
**Created:** [July 26, 2023, 11:50am UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113 "2023-07-26T11:50:23Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![poplarShift](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/poplarshift/32/51826_2.png) [@poplarShift](https://discourse.julialang.org/u/poplarShift)\
**Post date:** [July 26, 2023, 11:50am UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/1 "2023-07-26T11:50:23Z")

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Hi, thanks for all the great work!

I’ve been trying to run this example: [Neural Ordinary Differential Equations · DiffEqFlux.jl](https://docs.sciml.ai/DiffEqFlux/stable/examples/neural_ode/) (I attach it at the bottom for future reference)

So I’ve installed the DiffEqFlux 1.53.0 version, then the rest of the dependencies of the script, but still the script won’t run:

1. `ComponentArray` is not part of `Lux` namespace (sorry for python lingo) in the versions I have installed. Taking it from `ComponentArrays.jl` seems to work
2. I need to add `using OptimizationOptimisers` for the `ADAM` optimizer to work.

These are simple fixes, but am I maybe misunderstanding how version pinning works in julia?

- Should this example have worked but versions were pinned in the wrong way, or
- are these typos in the docs, or
- is it common in Julia that code is moved around between different libraries, or are these just mutual imports and the real code lives somewhere else?

```julia
(@v1.9) pkg> st
Status `~/.julia/environments/v1.9/Project.toml`
⌃ [aae7a2af] DiffEqFlux v1.53.0
  [0c46a032] DifferentialEquations v7.8.0
  [7073ff75] IJulia v1.24.2
⌅ [b2108857] Lux v0.4.58
⌃ [7f7a1694] Optimization v3.14.0
⌃ [36348300] OptimizationOptimJL v0.1.8
  [91a5bcdd] Plots v1.38.17
  [9a3f8284] Random

```

```julia
using Lux, DiffEqFlux, DifferentialEquations, Optimization, OptimizationOptimJL, Random, Plots

rng = Random.default_rng()
u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan[1], tspan[2], length = datasize)

function trueODEfunc(du, u, p, t)
    true_A = [-0.1 2.0; -2.0 -0.1]
    du .= ((u.^3)'true_A)'
end

prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))

dudt2 = Lux.Chain(x -> x.^3,
                  Lux.Dense(2, 50, tanh),
                  Lux.Dense(50, 2))
p, st = Lux.setup(rng, dudt2)
prob_neuralode = NeuralODE(dudt2, tspan, Tsit5(), saveat = tsteps)

function predict_neuralode(p)
  Array(prob_neuralode(u0, p, st)[1])
end

function loss_neuralode(p)
    pred = predict_neuralode(p)
    loss = sum(abs2, ode_data .- pred)
    return loss, pred
end

# Do not plot by default for the documentation
# Users should change doplot=true to see the plots callbacks
callback = function (p, l, pred; doplot = false)
  println(l)
  # plot current prediction against data
  if doplot
    plt = scatter(tsteps, ode_data[1,:], label = "data")
    scatter!(plt, tsteps, pred[1,:], label = "prediction")
    display(plot(plt))
  end
  return false
end

pinit = Lux.ComponentArray(p)
callback(pinit, loss_neuralode(pinit)...; doplot=true)

# use Optimization.jl to solve the problem
adtype = Optimization.AutoZygote()

optf = Optimization.OptimizationFunction((x, p) -> loss_neuralode(x), adtype)
optprob = Optimization.OptimizationProblem(optf, pinit)

result_neuralode = Optimization.solve(optprob,
                                       ADAM(0.05),
                                       callback = callback,
                                       maxiters = 300)

optprob2 = remake(optprob,u0 = result_neuralode.u)

result_neuralode2 = Optimization.solve(optprob2,
                                        Optim.BFGS(initial_stepnorm=0.01),
                                        callback=callback,
                                        allow_f_increases = false)

callback(result_neuralode2.u, loss_neuralode(result_neuralode2.u)...; doplot=true)

```

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<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [July 26, 2023, 2:14pm UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/2 "2023-07-26T14:14:22Z")

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> [@poplarShift](#):
>
> Should this example have worked but versions were pinned in the wrong way, or

The answer is likely yes - note that your environment shows that you’re not on current versions of packages, I get:

```julia
  [aae7a2af] DiffEqFlux v2.1.0
  [0c46a032] DifferentialEquations v7.8.0
  [b2108857] Lux v0.5.0
  [7f7a1694] Optimization v3.15.2
  [36348300] OptimizationOptimJL v0.1.9
  [91a5bcdd] Plots v1.38.17

```

but still see the same error. The docs are automatically built so should work as-is if you copy/paste, it looks like something went wrong here with Lux updating what it exports (in this case `ComponentArray`). Worth @ChrisRackauckas looking at this I guess, but as I’m typing this he’s giving a talk at JuliaCon on another tab in my browser so he won’t get to it immediately 😉

> [@poplarShift](#):
>
> are these typos in the docs, or

Based on what I said above: unlikely,

> [@poplarShift](#):
>
> is it common in Julia that code is moved around between different libraries, or are these just mutual imports and the real code lives somewhere else?

Not sure I understand the question, but it seems to me like what’s happening here is that there were changes in what packages re-export (e.g. Lux is using ComponentArrays internally and might have done `export ComponentArray` before, and stopped doing that at some point). The SciML universe is a bit unusual maybe in that it has loads of these glue packages like `OptimizationOptimJL` which I don’t really understand.

Also Julia 1.9 brought a new way to specify optional dependencies as “extensions”, so especially in tightly coupled ecosystems like SciML you would have seen a lot of refactoring of dependencies into extensions which might lead to more than usual code churn with possibility for error.

---

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**Author:** ![poplarShift](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/poplarshift/32/51826_2.png) [@poplarShift](https://discourse.julialang.org/u/poplarShift)\
**Post date:** [July 28, 2023, 7:29am UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/3 "2023-07-28T07:29:01Z")

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Hi @nilshg , thanks for the detailed answer!

> The answer is likely yes - note that your environment shows that you’re not on current versions of packages, I get:

The docs do not seem to exist for the latest version (dropdown menu on the bottom left) - the latest I found was for 1.53.0, which is why I used that one. I had initially tried with the latest, but then figured that the transition to 2.1 might have introduced breaking changes, so rolled back.

> Not sure I understand the question, but it seems to me like what’s happening here is that there were changes in what packages re-export (e.g. Lux is using ComponentArrays internally and might have done `export ComponentArray` before, and stopped doing that at some point). The SciML universe is a bit unusual maybe in that it has loads of these glue packages like `OptimizationOptimJL` which I don’t really understand.

In which case I would have thought that DiffEqFlux 1.53.0 should have pinned earlier versions of Lux and `OptimizationOptimJl` or wherever `ADAM` lived before than the ones I got when installing `DiffEqFlux`.

Thanks for the heads up on the Julia 1.9 “extensions”, I’ll read up on that, maybe that’s at fault here.

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**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [July 28, 2023, 7:53am UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/4 "2023-07-28T07:53:47Z")

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Just to be clear I meant versions of dependencies - you have `DiffEqFlux@1.53.0` like me, but `Lux@0.4.58` while I got `0.5` for that. In any case the problem persists with 0.5 so like I said @ChrisRackauckas or @avikpal or someone else from the SciML team probably needs to have a look at what’s going on here.

To your point about “pinning” - the entire Julia ecosystem usually follows SemVer, so pinning isn’t generally done as people rely on compat bounds. I think what happened here is that Lux moved ComponentArrays into an extension in this commit [Move CA stuff into an extension · LuxDL/Lux.jl@91619bc · GitHub](https://github.com/LuxDL/Lux.jl/commit/91619bcd94ddbe78fd14c85274b8f62ebb52595f) and bumped the version from 0.4.37 to 0.4.38, i.e. a patch release. With this, the docs would have to do `using ComponentArrays` explicitly - that’s the idea of an extension, you get to define behaviour that relies on `ComponentArrays` being loaded in `Lux` without having to pull in `ComponentArrays` for those users which don’t need that bit of functionality, making the overall package more lightweight.

What puzzles me is that the docs are building - normally this stuff should be caught when the docs get generated in CI as it should error out with the same issue that you stumbled upon, but as I said maybe Chris or Avik can clarify.

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**Author:** ![poplarShift](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/poplarshift/32/51826_2.png) [@poplarShift](https://discourse.julialang.org/u/poplarShift)\
**Post date:** [July 28, 2023, 8:20am UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/5 "2023-07-28T08:20:50Z")

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Aah, that makes sense, thank you so much! So apart from the docs building, the ComponentArray issue seems to boil down to two things I could be tempted to call sloppy, at least with my python background:

1. changing the Lux API (in the widest sense) in a patch release, and
2. relying on using `ComponentArray` from Lux where the code does not actually live.

I’ll look at the `OptimizationOptimisers` thing along the same lines 🙂

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**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [July 30, 2023, 7:59pm UTC](https://discourse.julialang.org/t/diffeqflux-tutorial-example-not-running-out-of-the-box/102113/6 "2023-07-30T19:59:17Z")

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The docs just haven’t built in a long time and we’re working to fix them as part of the JuliaCon hackathon [Revive docs by ErikQQY · Pull Request #848 · SciML/DiffEqFlux.jl · GitHub](https://github.com/SciML/DiffEqFlux.jl/pull/848)