Differential Equations Solve - top not defined

General Usage
, ,
1

When running

using DifferentialEquations
f(u,p,t) = 1.01*u
u0 = 1/2
tspan = (0.0,1.0)
prob = ODEProblem(f,u0,tspan)
sol = solve(prob, Tsit5(), reltol=1e-8, abstol=1e-8)

As taken off the DifferentialEquations.jl Documentation, I get:

ERROR: LoadError: UndefVarError: top not defined
Stacktrace:
[1] solve!(::OrdinaryDiffEq.ODEIntegrator{Tsit5,false,Float64,Nothing,Float64,DiffEqBase.NullParameters,Float64,Float64,Float64,Array{Float64,1},ODESolution{Float64,1,Array{Float64,1},Nothing,Nothing,Array{Float64,1},Array{Array{Float64,1},1},ODEProblem{Float64,Tuple{Float64,Float64},false,DiffEqBase.NullParameters,ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{Float64,1},Array{Float64,1},Array{Array{Float64,1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},DiffEqBase.DEStats},ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,typeof(DiffEqBase.ODE_DEFAULT_NORM),typeof(LinearAlgebra.opnorm),CallbackSet{Tuple{},Tuple{}},typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN),typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE),typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK),DataStructures.BinaryHeap{Float64,DataStructures.LessThan},DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Nothing,Nothing,Int64,Tuple{},Tuple{},Tuple{}},Float64,Float64,Nothing,OrdinaryDiffEq.DefaultInit}) at /home/tobiasharvey/.julia/packages/OrdinaryDiffEq/yn1YB/src/solve.jl:421
[2] #__solve#382 at /home/tobiasharvey/.julia/packages/OrdinaryDiffEq/yn1YB/src/solve.jl:5 [inlined]
[3] solve_call(::ODEProblem{Float64,Tuple{Float64,Float64},false,DiffEqBase.NullParameters,ODEFunction{false,typeof(f),LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem}, ::Tsit5; merge_callbacks::Bool, kwargs::Base.Iterators.Pairs{Symbol,Float64,Tuple{Symbol,Symbol},NamedTuple{(:reltol, :abstol),Tuple{Float64,Float64}}}) at /home/tobiasharvey/.julia/packages/DiffEqBase/T5smF/src/solve.jl:92
[4] #solve_up#461 at /home/tobiasharvey/.julia/packages/DiffEqBase/T5smF/src/solve.jl:114 [inlined]
[5] #solve#460 at /home/tobiasharvey/.julia/packages/DiffEqBase/T5smF/src/solve.jl:102 [inlined]
[6] top-level scope at /home/tobiasharvey/Desktop/sbpwaveprop/test.jl:6
[7] include(::String) at ./client.jl:457
[8] top-level scope at REPL[2]:1
in expression starting at /home/tobiasharvey/Desktop/sbpwaveprop/test.jl:6

Looking at my pkg status I have:

Status ~/.julia/environments/v1.5/Project.toml
[0c46a032] DifferentialEquations v6.15.0
[1dea7af3] OrdinaryDiffEq v5.42.1

What is going here? There are no warning messages or anything only this crash.

2

I can’t reproduce. What’s your full ]st -m?

3

Just went ahead and deleted everything julia related on my computer, and reinstalled…now it works. Not sure what was up…not really a solution, but I guess id call this “closed”.

4

Having the same problem after installing v1.5.1 and running code. I have deleted the compiled cache folder but did not help. What exatly did you delete/reinstall?

5

What happens if you do ]add DataStructures@0.18?

6

Then it works.

7

When you did that, what packages decreased their versions? They’re the ones to blame and we need to go talk to their authors.

moesphere 8

grafik

9

The issue on your setup is due to Queryverse blocking DataStructures v0.18 through TextParse in https://github.com/queryverse/TextParse.jl/pull/159 . I alerted the author and it should all get updated soon enough. Why this is an issue is a weird deep thing but hopefully it all blows over soon! Sorry about that but thanks for helping pinpoint where it is.

10

Thank you for taking care of it in no time!

11

Im getting the same error.
(first time using julia)
i have tried using https://github.com/MikeInnes/zygote-paper but got error with 1.5.1 so i tried downgrading to 1.3 and got error again (different kind of errors from error in prepocessing to “top not defined”) now im using 1.3.2 and i tried running a very simple example from the documentation (with using Flux, DiffEqFlux, DifferentialEquations, Plots) and got error again “top not defined” at “ODEProblem”

i tried also updating and nothing seems to work.
if someone could at least suggest me a possible solution (either a version of julia that isnt full of bugs or a combination of flux+ DiffEqFlux ecc.) it would be nice

12

What exactly did you run? Did you ]add DataStructures@0.18? Everything should be fine on a standard installation if a package doesn’t hold that back.

13

i manged to fix it by reinstalling 1.5.1 again.

please update the “using CuArray” here: https://diffeqflux.sciml.ai/dev/GPUs/
probably before i tried following one of these outdated tutorials (adding CuArray instead of CUDA) which downgraded other pkgs
i have tried using CUDA instead of CuArray but now im getting ArgumentError: cannot take the CPU address of a CuArray{Float32,1} (in DiffEqFlux.sciml_train)

also im getting an error here GPU-based MNIST Neural ODE Classifier ¡ DiffEqFlux.jl
Error evaluating MNISTodeClassification.jl
LoadError: MethodError: no method matching eachcol(::Array{Float32,4})
also:
LoadError: MethodError: no method matching loss(::Array{CuArray,1})
Closest candidates are:
loss(::Any, ::Any)
i have checked on github the MNISTodeClassification has not been updated in 16 months…

14

Thanks. Done https://github.com/SciML/DiffEqFlux.jl/commit/4b49eace0b3b1f5d17b4c9a656712c5883fbcf82

15

@avikpal do you know about this one?

16

i checked the wrong the github repo.

tried it again and im getting only error at loss
but i think its due to the code itself since loss(x, y) is there

i still get the same error in the ode/gpu example though

gpu(prob_neuralode(u0,p))
…
pred = predict_neuralode(p)
…
result_neuralode = DiffEqFlux.sciml_train(loss_neuralode, p,
ADAM(0.05), cb = callback,
maxiters = 300) ArgumentError: cannot take the CPU address of a CuArray{Float32,1}

(im only copy-pasting and it should be fine without gpu)

17

https://github.com/SciML/DiffEqFlux.jl/pull/398 should fix this. Probably something changed in the way the DataLoader expects the data to be fed.

18

I am getting the same top not defined error when trying the example ODE problem. Following the thread, I think I have tracked down the source to an older version of DataStructures (v0.17.20), which was being held back this time by BinaryBuilder.

@ChrisRackauckas - Thanks for your previous posts, they helped me find track down the problem very quickly.

19

Yeah sorry about this whole DataStructures change: it was a PITA but it’s mostly passed now. The actual reason it became a huge issue is that DataStructures accidentally didn’t have a deprecation warning, as in it has a deprecation and some dispatches but not the ones we used, so two years later the dispatches we use were deleted and so there was this version gap without a middleground :man_shrugging:, but actually the transition versions did exist for a long time but we never got the warning. Good times @oxinabox :laughing:. Moral of the story is, there is a version set of DiffEq+DataStructures that hiccups when it doesn’t exactly resolve everything but that should be relatively in the past now.

20

Hi, thanks for the previous replies on this issue. Could you also have a look of my issue please? thanks in advance.

I have the same problem when using DifferetialEquations and Flux. I am getting warning of top is deprecated, use first instead. I’ve checked, it is a function inside OrdinaryDiffEq.jl, and then the training broke after one iteration with error ‘type BinaryHeap has no field comparer’. I actually have reported this issue perviously on github and it was solved aftering I did ]add DataStructures@0.18. But now the same error occurs.

Now my package status looks like this and I am pretty struggling at the moment. Is it anything wrong with package versions? Thank you very much.
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