Hello Everyone,
I have been trying to rerun the Lotka_voltera code on the blog: DiffEqFlux.jl – A Julia Library for Neural Differential Equations, but for some reason i never get it running no matter how i tried. Can some please help me.
Thank you
Hi Miraj, welcome to the community. Please read: make it easier to help you.
What did you run? On what version of Julia? With what packages? What error did you get?
Looks like @Miraj_Miraj responded in the wrong thread
1 Like
There is nothing wrong with my question. If you cannot help, you also don’t have to flag my questions.
It looks like you posted it in the wrong thread, which is why no one here saw that there was a response and thus we couldn’t help you. Let me copy it over to here:
Hello ChrisRackauckas,
Thank you very much for coming to my rescue:
This is exactly where my problems start:
cb()
Flux.train!(loss_rd, [p], data, opt, cb = cb)
whenever I run the callback function, I get the following errors:::
MethodError: no method matching init(::Vector{Float64}, ::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; saveat=0.1)
Closest candidates are:
init(!Matched::OptimizationProblem, ::Any, ::Any…; kwargs…) at ~/.julia/packages/SciMLBase/wEAy7/src/solve.jl:144
init(!Matched::SciMLBase.AbstractJumpProblem, ::Any…; kwargs…) at ~/.julia/packages/DiffEqBase/g4OeQ/src/solve.jl:406
init(!Matched::SciMLBase.AbstractDEProblem, ::Any…; sensealg, u0, p, kwargs…) at ~/.julia/packages/DiffEqBase/g4OeQ/src/solve.jl:394
…
solve(::Vector{Float64}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}}) at CommonSolve.jl:23
(::CommonSolve.var”#solve##kw")(::NamedTuple{(:saveat,), Tuple{Float64}}, ::typeof(solve), ::Vector{Float64}, ::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(lotka_volterra), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}) at CommonSolve.jl:23
predict_rd() at Tst.jl:30
loss_rd() at Tst.jl:32
(::var"#32#33")() at Tst.jl:40
top-level scope at Tst.jl:45
eval at boot.jl:368 [inlined]
And I also use julia> v"1.8.1".
Thank you.
From that post, you can see that the issue is init(::Vector{Float64}, ::ODEProblem: there is no dispatch of init where the first thing is a Vector{Float64}. All calls of init always start with the ODEProblem. This is not something seen in the original code. In order to help you debug this, we’ll need to see what code you ran. Can you copy paste and share the code you ran here?
I just ran all of the code in the blog in order. It seemed to work just fine. The code that I ran was:
using DifferentialEquations
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
u0 = [1.0,1.0]
tspan = (0.0,10.0)
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(lotka_volterra,u0,tspan,p)
sol = solve(prob)
using Plots
plot(sol)
u0_f(p,t0) = [p[2],p[4]]
tspan_f(p) = (0.0,10*p[4])
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(lotka_volterra,u0_f,tspan_f,p)
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(lotka_volterra,u0,tspan,p)
sol = solve(prob,Tsit5(),saveat=0.1)
A = sol[1,:] # length 101 vector
plot(sol)
t = 0:0.1:10.0
scatter!(t,A)
using Flux, DiffEqFlux
p = [2.2, 1.0, 2.0, 0.4] # Initial Parameter Vector
params = Flux.params(p)
function predict_rd() # Our 1-layer "neural network"
solve(prob,Tsit5(),p=p,saveat=0.1)[1,:] # override with new parameters
end
loss_rd() = sum(abs2,x-1 for x in predict_rd()) # loss function
data = Iterators.repeated((), 100)
opt = ADAM(0.1)
cb = function () #callback function to observe training
display(loss_rd())
# using `remake` to re-create our `prob` with current parameters `p`
display(plot(solve(remake(prob,p=p),Tsit5(),saveat=0.1),ylim=(0,6)))
end
# Display the ODE with the initial parameter values.
cb()
Flux.train!(loss_rd, params, data, opt, cb = cb)
using ParameterizedFunctions
using Sundials
rober = @ode_def Rober begin
dy₁ = -k₁*y₁+k₃*y₂*y₃
dy₂ = k₁*y₁-k₂*y₂^2-k₃*y₂*y₃
dy₃ = k₂*y₂^2
end k₁ k₂ k₃
prob = ODEProblem(rober,[1.0;0.0;0.0],(0.0,1e11),(0.04,3e7,1e4))
solve(prob,CVODE_Adams())
sol = solve(prob,KenCarp4())
using Plots
plot(sol,xscale=:log10,tspan=(0.1,1e11))
function delay_lotka_volterra(du,u,h,p,t)
x, y = u
α, β, δ, γ = p
du[1] = dx = (α - β*y)*h(p,t-0.1)[1]
du[2] = dy = (δ*x - γ)*y
end
h(p,t) = ones(eltype(p),2)
prob = DDEProblem(delay_lotka_volterra,[1.0,1.0],h,(0.0,10.0),constant_lags=[0.1])
p = [2.2, 1.0, 2.0, 0.4]
params = Flux.params(p)
using SciMLSensitivity
function predict_rd_dde()
solve(prob,MethodOfSteps(Tsit5()),p=p,sensealg=TrackerAdjoint(),saveat=0.1)[1,:]
end
loss_rd_dde() = sum(abs2,x-1 for x in predict_rd_dde())
loss_rd_dde()
function lotka_volterra_noise(du,u,p,t)
du[1] = 0.1u[1]
du[2] = 0.1u[2]
end
prob = SDEProblem(lotka_volterra,lotka_volterra_noise,[1.0,1.0],(0.0,5.0))
p = [2.2, 1.0, 2.0, 0.4]
params = Flux.params(p)
function predict_sde()
solve(prob,SOSRI(),p=p,sensealg=TrackerAdjoint(),saveat=0.1,
abstol=1e-1,reltol=1e-1)[1,:]
end
loss_rd_sde() = sum(abs2,x-1 for x in predict_sde())
loss_rd_sde()
data = Iterators.repeated((), 100)
opt = ADAM(0.1)
cb = function ()
display(loss_rd_sde())
display(plot(solve(remake(prob,p=p),SOSRI(),saveat=0.1),ylim=(0,6)))
end
# Display the ODE with the current parameter values.
cb()
Flux.train!(loss_rd_sde, params, data, opt, cb = cb)
dudt = Chain(Dense(2,50,tanh),Dense(50,2))
tspan = (0.0f0,25.0f0)
node = NeuralODE(dudt,tspan,Tsit5(),saveat=0.1)
u0 = Float32[2.; 0.]
datasize = 30
tspan = (0.0f0,1.5f0)
function trueODEfunc(du,u,p,t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end
t = range(tspan[1],tspan[2],length=datasize)
prob = ODEProblem(trueODEfunc,u0,tspan)
ode_data = Array(solve(prob,Tsit5(),saveat=t))
dudt = Chain(x -> x.^3,
Dense(2,50,tanh),
Dense(50,2))
n_ode = NeuralODE(dudt,tspan,Tsit5(),saveat=t,reltol=1e-7,abstol=1e-9)
ps = Flux.params(n_ode)
pred = n_ode(u0) # Get the prediction using the correct initial condition
scatter(t,ode_data[1,:],label="data")
scatter!(t,pred[1,:],label="prediction")
function predict_n_ode()
n_ode(u0)
end
loss_n_ode() = sum(abs2,ode_data .- predict_n_ode())
data = Iterators.repeated((), 1000)
opt = ADAM(0.1)
cb = function () #callback function to observe training
display(loss_n_ode())
# plot current prediction against data
cur_pred = predict_n_ode()
pl = scatter(t,ode_data[1,:],label="data")
scatter!(pl,t,cur_pred[1,:],label="prediction")
display(plot(pl))
end
# Display the ODE with the initial parameter values.
cb()
Flux.train!(loss_n_ode, ps, data, opt, cb = cb)
which produced no errors. This was done with package versions:
(@v1.8) pkg> st
Status `C:\Users\accou\.julia\environments\v1.8\Project.toml`
[aae7a2af] DiffEqFlux v1.52.0
[055956cb] DiffEqPhysics v3.10.0 `C:\Users\accou\.julia\dev\DiffEqPhysics`
[0c46a032] DifferentialEquations v7.6.0
[587475ba] Flux v0.13.9
[98e50ef6] JuliaFormatter v1.0.16
[1dea7af3] OrdinaryDiffEq v6.35.0
[65888b18] ParameterizedFunctions v5.15.0
[91a5bcdd] Plots v1.36.6
[1ed8b502] SciMLSensitivity v7.11.1
[c3572dad] Sundials v4.11.1
and manifest:
(@v1.8) pkg> st
Status `C:\Users\accou\.julia\environments\v1.8\Project.toml`
[aae7a2af] DiffEqFlux v1.52.0
[055956cb] DiffEqPhysics v3.10.0 `C:\Users\accou\.julia\dev\DiffEqPhysics`
[0c46a032] DifferentialEquations v7.6.0
[587475ba] Flux v0.13.9
[98e50ef6] JuliaFormatter v1.0.16
[1dea7af3] OrdinaryDiffEq v6.35.0
[65888b18] ParameterizedFunctions v5.15.0
[91a5bcdd] Plots v1.36.6
[1ed8b502] SciMLSensitivity v7.11.1
[c3572dad] Sundials v4.11.1
(@v1.8) pkg> st -m
Status `C:\Users\accou\.julia\environments\v1.8\Manifest.toml`
[c3fe647b] AbstractAlgebra v0.27.7
[621f4979] AbstractFFTs v1.2.1
[1520ce14] AbstractTrees v0.4.3
[7d9f7c33] Accessors v0.1.22
[79e6a3ab] Adapt v3.4.0
[dce04be8] ArgCheck v2.3.0
[ec485272] ArnoldiMethod v0.2.0
[4fba245c] ArrayInterface v6.0.24
[30b0a656] ArrayInterfaceCore v0.1.26
[6ba088a2] ArrayInterfaceGPUArrays v0.2.2
[015c0d05] ArrayInterfaceOffsetArrays v0.1.7
[b0d46f97] ArrayInterfaceStaticArrays v0.1.5
[dd5226c6] ArrayInterfaceStaticArraysCore v0.1.3
[a2b0951a] ArrayInterfaceTracker v0.1.1
[4c555306] ArrayLayouts v0.8.15
[a9b6321e] Atomix v0.1.0
[15f4f7f2] AutoHashEquals v0.2.0
⌅ [ab4f0b2a] BFloat16s v0.2.0
[aae01518] BandedMatrices v0.17.9
[198e06fe] BangBang v0.3.37
[9718e550] Baselet v0.1.1
[e2ed5e7c] Bijections v0.1.4
[d1d4a3ce] BitFlags v0.1.7
[62783981] BitTwiddlingConvenienceFunctions v0.1.5
[764a87c0] BoundaryValueDiffEq v2.10.0
[fa961155] CEnum v0.4.2
[2a0fbf3d] CPUSummary v0.1.30
[00ebfdb7] CSTParser v3.3.6
[052768ef] CUDA v3.12.0
[72cfdca4] CUDAKernels v0.4.3
[49dc2e85] Calculus v0.5.1
[7057c7e9] Cassette v0.3.11
[082447d4] ChainRules v1.45.0
[d360d2e6] ChainRulesCore v1.15.6
[9e997f8a] ChangesOfVariables v0.1.4
[fb6a15b2] CloseOpenIntervals v0.1.11
[944b1d66] CodecZlib v0.7.0
[35d6a980] ColorSchemes v3.20.0
[3da002f7] ColorTypes v0.11.4
[c3611d14] ColorVectorSpace v0.9.9
[5ae59095] Colors v0.12.8
[861a8166] Combinatorics v1.0.2
[a80b9123] CommonMark v0.8.7
[38540f10] CommonSolve v0.2.3
[bbf7d656] CommonSubexpressions v0.3.0
[34da2185] Compat v4.5.0
[b0b7db55] ComponentArrays v0.13.4
[b152e2b5] CompositeTypes v0.1.3
[a33af91c] CompositionsBase v0.1.1
[88cd18e8] ConsoleProgressMonitor v0.1.2
[187b0558] ConstructionBase v1.4.1
[6add18c4] ContextVariablesX v0.1.3
[d38c429a] Contour v0.6.2
[adafc99b] CpuId v0.3.1
[a8cc5b0e] Crayons v4.1.1
[9a962f9c] DataAPI v1.13.0
[82cc6244] DataInterpolations v3.10.1
[864edb3b] DataStructures v0.18.13
[e2d170a0] DataValueInterfaces v1.0.0
[244e2a9f] DefineSingletons v0.1.2
[bcd4f6db] DelayDiffEq v5.40.3
[b429d917] DensityInterface v0.4.0
[2b5f629d] DiffEqBase v6.109.0
[459566f4] DiffEqCallbacks v2.24.3
[aae7a2af] DiffEqFlux v1.52.0
[77a26b50] DiffEqNoiseProcess v5.14.2
[055956cb] DiffEqPhysics v3.10.0 `C:\Users\accou\.julia\dev\DiffEqPhysics`
[163ba53b] DiffResults v1.1.0
[b552c78f] DiffRules v1.12.2
[0c46a032] DifferentialEquations v7.6.0
[b4f34e82] Distances v0.10.7
[31c24e10] Distributions v0.25.79
[ced4e74d] DistributionsAD v0.6.43
[ffbed154] DocStringExtensions v0.9.2
[5b8099bc] DomainSets v0.5.14
[fa6b7ba4] DualNumbers v0.6.8
[7c1d4256] DynamicPolynomials v0.4.5
[da5c29d0] EllipsisNotation v1.6.0
[4e289a0a] EnumX v1.0.4
[7da242da] Enzyme v0.10.12
[f151be2c] EnzymeCore v0.1.0
[d4d017d3] ExponentialUtilities v1.22.0
[e2ba6199] ExprTools v0.1.8
[c87230d0] FFMPEG v0.4.1
[cc61a311] FLoops v0.2.1
[b9860ae5] FLoopsBase v0.1.1
[7034ab61] FastBroadcast v0.2.3
[9aa1b823] FastClosures v0.3.2
[29a986be] FastLapackInterface v1.2.7
[1a297f60] FillArrays v0.13.5
[6a86dc24] FiniteDiff v2.17.0
[53c48c17] FixedPointNumbers v0.8.4
[587475ba] Flux v0.13.9
[9c68100b] FoldsThreads v0.1.1
[59287772] Formatting v0.4.2
[f6369f11] ForwardDiff v0.10.32
[069b7b12] FunctionWrappers v1.1.3
[77dc65aa] FunctionWrappersWrappers v0.1.1
⌅ [d9f16b24] Functors v0.3.0
[0c68f7d7] GPUArrays v8.5.0
[46192b85] GPUArraysCore v0.1.2
[61eb1bfa] GPUCompiler v0.16.7
[28b8d3ca] GR v0.71.1
[c145ed77] GenericSchur v0.5.3
[c27321d9] Glob v1.3.0
[86223c79] Graphs v1.7.4
[42e2da0e] Grisu v1.0.2
[0b43b601] Groebner v0.2.11
[d5909c97] GroupsCore v0.4.0
[cd3eb016] HTTP v1.5.5
[3e5b6fbb] HostCPUFeatures v0.1.13
[34004b35] HypergeometricFunctions v0.3.11
[7869d1d1] IRTools v0.4.7
[615f187c] IfElse v0.1.1
[d25df0c9] Inflate v0.1.3
[83e8ac13] IniFile v0.5.1
[22cec73e] InitialValues v0.3.1
[18e54dd8] IntegerMathUtils v0.1.0
[8197267c] IntervalSets v0.7.4
[3587e190] InverseFunctions v0.1.8
[92d709cd] IrrationalConstants v0.1.1
[42fd0dbc] IterativeSolvers v0.9.2
[82899510] IteratorInterfaceExtensions v1.0.0
[1019f520] JLFzf v0.1.5
[692b3bcd] JLLWrappers v1.4.1
[682c06a0] JSON v0.21.3
[98e50ef6] JuliaFormatter v1.0.16
[b14d175d] JuliaVariables v0.2.4
[ccbc3e58] JumpProcesses v9.2.3
[ef3ab10e] KLU v0.4.0
[63c18a36] KernelAbstractions v0.8.6
[ba0b0d4f] Krylov v0.9.0
[0b1a1467] KrylovKit v0.6.0
[929cbde3] LLVM v4.14.1
[b964fa9f] LaTeXStrings v1.3.0
[2ee39098] LabelledArrays v1.12.5
[984bce1d] LambertW v0.4.5
[23fbe1c1] Latexify v0.15.17
[10f19ff3] LayoutPointers v0.1.12
[50d2b5c4] Lazy v0.15.1
[0fc2ff8b] LeastSquaresOptim v0.8.3
[1d6d02ad] LeftChildRightSiblingTrees v0.2.0
[2d8b4e74] LevyArea v1.0.0
[d3d80556] LineSearches v7.2.0
[7ed4a6bd] LinearSolve v1.31.0
[2ab3a3ac] LogExpFunctions v0.3.19
⌅ [e6f89c97] LoggingExtras v0.4.9
[bdcacae8] LoopVectorization v0.12.141
[b2108857] Lux v0.4.36
[bb33d45b] LuxCore v0.1.0
[82251201] LuxLib v0.1.7
[d8e11817] MLStyle v0.4.16
[f1d291b0] MLUtils v0.3.1
[1914dd2f] MacroTools v0.5.10
[d125e4d3] ManualMemory v0.1.8
[739be429] MbedTLS v1.1.7
[442fdcdd] Measures v0.3.2
[c03570c3] Memoize v0.4.4
[e9d8d322] Metatheory v1.3.5
[128add7d] MicroCollections v0.1.3
[e1d29d7a] Missings v1.0.2
[961ee093] ModelingToolkit v8.36.0
[46d2c3a1] MuladdMacro v0.2.4
[102ac46a] MultivariatePolynomials v0.4.6
[d8a4904e] MutableArithmetics v1.1.0
[d41bc354] NLSolversBase v7.8.3
[2774e3e8] NLsolve v4.5.1
[872c559c] NNlib v0.8.11
[a00861dc] NNlibCUDA v0.2.4
[77ba4419] NaNMath v1.0.1
[71a1bf82] NameResolution v0.1.5
[d8793406] ObjectFile v0.3.7
[6fe1bfb0] OffsetArrays v1.12.8
[0b1bfda6] OneHotArrays v0.2.1
[4d8831e6] OpenSSL v1.3.2
[429524aa] Optim v1.7.4
[3bd65402] Optimisers v0.2.13
[7f7a1694] Optimization v3.9.4
[253f991c] OptimizationFlux v0.1.2
[36348300] OptimizationOptimJL v0.1.5
[42dfb2eb] OptimizationOptimisers v0.1.1
[500b13db] OptimizationPolyalgorithms v0.1.1
[bac558e1] OrderedCollections v1.4.1
[1dea7af3] OrdinaryDiffEq v6.35.0
[90014a1f] PDMats v0.11.16
[65888b18] ParameterizedFunctions v5.15.0
[d96e819e] Parameters v0.12.3
[69de0a69] Parsers v2.5.1
[b98c9c47] Pipe v1.3.0
[ccf2f8ad] PlotThemes v3.1.0
[995b91a9] PlotUtils v1.3.1
[91a5bcdd] Plots v1.36.6
[e409e4f3] PoissonRandom v0.4.3
[f517fe37] Polyester v0.6.18
[1d0040c9] PolyesterWeave v0.1.11
[85a6dd25] PositiveFactorizations v0.2.4
[d236fae5] PreallocationTools v0.4.5
[21216c6a] Preferences v1.3.0
[8162dcfd] PrettyPrint v0.2.0
[27ebfcd6] Primes v0.5.3
[33c8b6b6] ProgressLogging v0.1.4
[92933f4c] ProgressMeter v1.7.2
[1fd47b50] QuadGK v2.6.0
[74087812] Random123 v1.6.0
[fb686558] RandomExtensions v0.4.3
[e6cf234a] RandomNumbers v1.5.3
[c1ae055f] RealDot v0.1.0
[3cdcf5f2] RecipesBase v1.3.2
[01d81517] RecipesPipeline v0.6.11
[731186ca] RecursiveArrayTools v2.32.3
[f2c3362d] RecursiveFactorization v0.2.12
[189a3867] Reexport v1.2.2
[42d2dcc6] Referenceables v0.1.2
[29dad682] RegularizationTools v0.6.0
[05181044] RelocatableFolders v1.0.0
[ae029012] Requires v1.3.0
[ae5879a3] ResettableStacks v1.1.1
[37e2e3b7] ReverseDiff v1.14.4
[79098fc4] Rmath v0.7.0
[7e49a35a] RuntimeGeneratedFunctions v0.5.5
[3cdde19b] SIMDDualNumbers v0.1.1
[94e857df] SIMDTypes v0.1.0
[476501e8] SLEEFPirates v0.6.37
[0bca4576] SciMLBase v1.77.0
[1ed8b502] SciMLSensitivity v7.11.1
[6c6a2e73] Scratch v1.1.1
[efcf1570] Setfield v1.1.1
[605ecd9f] ShowCases v0.1.0
[992d4aef] Showoff v1.0.3
[777ac1f9] SimpleBufferStream v1.1.0
[727e6d20] SimpleNonlinearSolve v0.1.2
[699a6c99] SimpleTraits v0.9.4
[66db9d55] SnoopPrecompile v1.0.1
[a2af1166] SortingAlgorithms v1.1.0
[47a9eef4] SparseDiffTools v1.29.0
[276daf66] SpecialFunctions v2.1.7
[171d559e] SplittablesBase v0.1.15
[aedffcd0] Static v0.8.2
[90137ffa] StaticArrays v1.5.11
[1e83bf80] StaticArraysCore v1.4.0
[82ae8749] StatsAPI v1.5.0
[2913bbd2] StatsBase v0.33.21
[4c63d2b9] StatsFuns v1.1.0
[9672c7b4] SteadyStateDiffEq v1.9.1
[789caeaf] StochasticDiffEq v6.57.3
⌅ [7792a7ef] StrideArraysCore v0.3.17
[09ab397b] StructArrays v0.6.13
[53d494c1] StructIO v0.3.0
[c3572dad] Sundials v4.11.1
[d1185830] SymbolicUtils v0.19.11
[0c5d862f] Symbolics v4.13.0
[3783bdb8] TableTraits v1.0.1
[bd369af6] Tables v1.10.0
[62fd8b95] TensorCore v0.1.1
⌅ [8ea1fca8] TermInterface v0.2.3
[5d786b92] TerminalLoggers v0.1.6
[8290d209] ThreadingUtilities v0.5.0
[ac1d9e8a] ThreadsX v0.1.11
[a759f4b9] TimerOutputs v0.5.22
[0796e94c] Tokenize v0.5.24
[9f7883ad] Tracker v0.2.22
[3bb67fe8] TranscodingStreams v0.9.10
[28d57a85] Transducers v0.4.75
[a2a6695c] TreeViews v0.3.0
[d5829a12] TriangularSolve v0.1.15
[410a4b4d] Tricks v0.1.6
[5c2747f8] URIs v1.4.1
[3a884ed6] UnPack v1.0.2
[d9a01c3f] Underscores v3.0.0
[1cfade01] UnicodeFun v0.4.1
[1986cc42] Unitful v1.12.2
[013be700] UnsafeAtomics v0.2.1
[d80eeb9a] UnsafeAtomicsLLVM v0.1.0
[41fe7b60] Unzip v0.2.0
[3d5dd08c] VectorizationBase v0.21.56
[19fa3120] VertexSafeGraphs v0.2.0
[e88e6eb3] Zygote v0.6.51
[700de1a5] ZygoteRules v0.2.2
[6e34b625] Bzip2_jll v1.0.8+0
[83423d85] Cairo_jll v1.16.1+1
⌅ [7cc45869] Enzyme_jll v0.0.43+0
[2e619515] Expat_jll v2.4.8+0
[b22a6f82] FFMPEG_jll v4.4.2+2
[a3f928ae] Fontconfig_jll v2.13.93+0
[d7e528f0] FreeType2_jll v2.10.4+0
[559328eb] FriBidi_jll v1.0.10+0
[0656b61e] GLFW_jll v3.3.8+0
[d2c73de3] GR_jll v0.71.1+0
[78b55507] Gettext_jll v0.21.0+0
[7746bdde] Glib_jll v2.74.0+1
[3b182d85] Graphite2_jll v1.3.14+0
[2e76f6c2] HarfBuzz_jll v2.8.1+1
[aacddb02] JpegTurbo_jll v2.1.2+0
[c1c5ebd0] LAME_jll v3.100.1+0
[88015f11] LERC_jll v3.0.0+1
[dad2f222] LLVMExtra_jll v0.0.16+0
[dd4b983a] LZO_jll v2.10.1+0
⌅ [e9f186c6] Libffi_jll v3.2.2+1
[d4300ac3] Libgcrypt_jll v1.8.7+0
[7e76a0d4] Libglvnd_jll v1.6.0+0
[7add5ba3] Libgpg_error_jll v1.42.0+0
[94ce4f54] Libiconv_jll v1.16.1+1
[4b2f31a3] Libmount_jll v2.35.0+0
[89763e89] Libtiff_jll v4.4.0+0
[38a345b3] Libuuid_jll v2.36.0+0
[e7412a2a] Ogg_jll v1.3.5+1
[458c3c95] OpenSSL_jll v1.1.19+0
[efe28fd5] OpenSpecFun_jll v0.5.5+0
[91d4177d] Opus_jll v1.3.2+0
[30392449] Pixman_jll v0.40.1+0
[ea2cea3b] Qt5Base_jll v5.15.3+2
[f50d1b31] Rmath_jll v0.3.0+0
[fb77eaff] Sundials_jll v5.2.1+0
[a2964d1f] Wayland_jll v1.19.0+0
[2381bf8a] Wayland_protocols_jll v1.25.0+0
[02c8fc9c] XML2_jll v2.9.14+0
[aed1982a] XSLT_jll v1.1.34+0
[4f6342f7] Xorg_libX11_jll v1.6.9+4
[0c0b7dd1] Xorg_libXau_jll v1.0.9+4
[935fb764] Xorg_libXcursor_jll v1.2.0+4
[a3789734] Xorg_libXdmcp_jll v1.1.3+4
[1082639a] Xorg_libXext_jll v1.3.4+4
[d091e8ba] Xorg_libXfixes_jll v5.0.3+4
[a51aa0fd] Xorg_libXi_jll v1.7.10+4
[d1454406] Xorg_libXinerama_jll v1.1.4+4
[ec84b674] Xorg_libXrandr_jll v1.5.2+4
[ea2f1a96] Xorg_libXrender_jll v0.9.10+4
[14d82f49] Xorg_libpthread_stubs_jll v0.1.0+3
[c7cfdc94] Xorg_libxcb_jll v1.13.0+3
[cc61e674] Xorg_libxkbfile_jll v1.1.0+4
[12413925] Xorg_xcb_util_image_jll v0.4.0+1
[2def613f] Xorg_xcb_util_jll v0.4.0+1
[975044d2] Xorg_xcb_util_keysyms_jll v0.4.0+1
[0d47668e] Xorg_xcb_util_renderutil_jll v0.3.9+1
[c22f9ab0] Xorg_xcb_util_wm_jll v0.4.1+1
[35661453] Xorg_xkbcomp_jll v1.4.2+4
[33bec58e] Xorg_xkeyboard_config_jll v2.27.0+4
[c5fb5394] Xorg_xtrans_jll v1.4.0+3
[3161d3a3] Zstd_jll v1.5.2+0
⌅ [214eeab7] fzf_jll v0.29.0+0
[a4ae2306] libaom_jll v3.4.0+0
[0ac62f75] libass_jll v0.15.1+0
[f638f0a6] libfdk_aac_jll v2.0.2+0
[b53b4c65] libpng_jll v1.6.38+0
[f27f6e37] libvorbis_jll v1.3.7+1
[1270edf5] x264_jll v2021.5.5+0
[dfaa095f] x265_jll v3.5.0+0
[d8fb68d0] xkbcommon_jll v1.4.1+0
[0dad84c5] ArgTools v1.1.1
[56f22d72] Artifacts
[2a0f44e3] Base64
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[f43a241f] Downloads v1.6.0
[7b1f6079] FileWatching
[9fa8497b] Future
[b77e0a4c] InteractiveUtils
[4af54fe1] LazyArtifacts
[b27032c2] LibCURL v0.6.3
[76f85450] LibGit2
[8f399da3] Libdl
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[d6f4376e] Markdown
[a63ad114] Mmap
[ca575930] NetworkOptions v1.2.0
[44cfe95a] Pkg v1.8.0
[de0858da] Printf
[3fa0cd96] REPL
[9a3f8284] Random
[ea8e919c] SHA v0.7.0
[9e88b42a] Serialization
[1a1011a3] SharedArrays
[6462fe0b] Sockets
[2f01184e] SparseArrays
[10745b16] Statistics
[4607b0f0] SuiteSparse
[fa267f1f] TOML v1.0.0
[a4e569a6] Tar v1.10.1
[8dfed614] Test
[cf7118a7] UUIDs
[4ec0a83e] Unicode
[e66e0078] CompilerSupportLibraries_jll v0.5.2+0
[deac9b47] LibCURL_jll v7.84.0+0
[29816b5a] LibSSH2_jll v1.10.2+0
[c8ffd9c3] MbedTLS_jll v2.28.0+0
[14a3606d] MozillaCACerts_jll v2022.2.1
[4536629a] OpenBLAS_jll v0.3.20+0
[05823500] OpenLibm_jll v0.8.1+0
[efcefdf7] PCRE2_jll v10.40.0+0
[bea87d4a] SuiteSparse_jll v5.10.1+0
[83775a58] Zlib_jll v1.2.12+3
[8e850b90] libblastrampoline_jll v5.1.1+0
[8e850ede] nghttp2_jll v1.48.0+0
[3f19e933] p7zip_jll v17.4.0+0
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
on the computer:
julia> versioninfo()
Julia Version 1.8.2
Commit 36034abf26 (2022-09-29 15:21 UTC)
Platform Info:
OS: Windows (x86_64-w64-mingw32)
CPU: 8 × Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-13.0.1 (ORCJIT, skylake)
Threads: 1 on 8 virtual cores
Environment:
JULIA_EDITOR = code
JULIA_NUM_THREADS =
Could you similarly share what you ran?
1 Like
Thank you very much Chris Rackauckas. I really appreciate your help. I don’t know what actually happened, but for some reason everything is now working without any errors. Thanks