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SciML / ModelingToolkit.jl

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
https://mtk.sciml.ai/dev/
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Building ODEProblem From ODESystem Causes AutoDiff To Fail #2856

Closed nrummel closed 1 month ago

nrummel commented 3 months ago

Sorry for bothering you again Chris. I really want to use ModelingToolKit in package I am developing. The idea of it seems to fit perfectly with my use case, but I am running into a couple bugs. I'll make two different Issues so they can be handled separately.

When I create a ODEProblem from an ODESystem, rather than from a function, initial condition, and time span, then try to use AutoDiff (through ForwardDiff.jl), I am getting all zeros for the gradient (and hessian), rather than seeing something true. I updated to the most recent of ModelingToolKit to v9.24.0 to make sure that was not a factor.

Here contained example that illustrates the issue: 

@info "Loading external dependencies"
using OrdinaryDiffEq, ModelingToolkit
using ModelingToolkit: D_nounits, t_nounits as t
using DiffEqParamEstim, Optimization, OptimizationOptimJL
using Statistics, Random 
@info "Build test problem"
## Call from ODEProblem Directly
function f(du, u, w, t)
   du[1] = w[1] * u[2] + w[2] * u[1]^3 + w[3] * u[1]^2 + w[4] * u[3] 
   du[2] = w[5] + w[6] * u[1]^2 + w[7] * u[2] 
   du[3] = w[8] * u[1] + w[9] + w[10] * u[3]
end
u0= [-1.31; -7.6; -0.2]
tspan = (0.0, 10.0)
wTrue = [10,-10,30,-10,10,-50,-10,0.04,0.0319,-0.01]
D = length(u0)
J = length(wTrue)
M = 1024
σ = 0.1 # snr for noise to data
μ = 0.1 # snr for initCond 
Random.seed!(1)
w0 = wTrue + μ .* abs.(wTrue) .* rand(J)
opt = OptimizationOptimJL.NewtonTrustRegion();
##
prob = ODEProblem(f, u0, tspan, wTrue)
sol = solve(prob, Rosenbrock23())
tt = collect(range(tspan[1], stop = tspan[end], length = M))
U_exact = reduce(hcat, sol(tt[i]) for i in 1:M)
U = U_exact + σ*sqrt(mean(U_exact.^2))*rand(D,M);
##
obj = build_loss_objective(prob, Rosenbrock23(), L2Loss(tt, U), Optimization.AutoForwardDiff())
optprob = Optimization.OptimizationProblem(obj, w0);
##
@info "Solving ODE param estimation problem with default ODEProb construction"
res = solve(optprob, opt, show_trace=true, show_every=100);
## try to use ModelingToolkit
## See Wendy paper
@mtkmodel HindmarshRoseModel begin
    @variables begin
        u1(t) = -1.31
        u2(t) = -7.6
        u3(t) = -0.2
    end
    @parameters begin
        w1 = 10
        w2 = -10
        w3 = 30
        w4 = -10
        w5 = 10
        w6 = -50
        w7 = -10
        w8 = 0.04
        w9 = 0.0319
        w10= -0.01
    end
    @equations begin
        D_nounits(u1) ~ w1 * u2 + w2 * u1^3 + w3 * u1^2 + w4 * u3 
        D_nounits(u2) ~ w5 + w6 * u1^2 + w7 * u2 
        D_nounits(u3) ~ w8 *u1 + w9 + w10 * u3
    end
end
@mtkbuild HINDMARSH_ROSE_SYSTEM = HindmarshRoseModel()
mtk_prob = ODEProblem(
    HINDMARSH_ROSE_SYSTEM, 
    ModelingToolkit.getdefault.(unknowns(HINDMARSH_ROSE_SYSTEM)), 
    tspan, 
    ModelingToolkit.getdefault.(parameters(HINDMARSH_ROSE_SYSTEM))
);
##
mtk_obj = build_loss_objective(mtk_prob, Rosenbrock23(), L2Loss(tt, U), Optimization.AutoForwardDiff())
mtk_optprob = Optimization.OptimizationProblem(mtk_obj, w0);
##
@info "Solving ODE param estimation problem with default MTK construction"
res = solve(mtk_optprob, opt, show_trace=true, show_every=100);

[ Info: Loading external dependencies
[ Info: Build test problem
[ Info: Solving ODE param estimation problem with default ODEProb construction
Iter     Function value   Gradient norm 
     0     1.312220e+04     1.305949e+05
 * time: 0.00011086463928222656
 * g(x): [-11835.392502819925, -9691.874869015533, 156.1049423257333, -663.0827228604223, 4414.979301648392, 3401.8983455768353, -16562.305669189358, 69040.26000225743, -130594.92982876718, 22732.5495097705]
 * reached_subproblem_solution: true
 * h(x): [-62845.94819434995 -97384.48440634544 -16156.682787035135 -3302.967251268705 33588.53008405125 22425.22567804772 -119028.39406901498 992829.1622789137 -2.061453836668006e6 324160.5471088927; -97384.48440634586 -120374.86852514069 -13103.651990135877 -5530.567493433912 45842.81050147195 31672.0700236485 -163122.6526739951 1.1653614367854204e6 -2.373263733398785e6 377015.87573420757; -16156.68278703505 -13103.651990136053 524.5852754025087 -1009.3018417674406 6380.571877247003 4705.357018856951 -22804.799125601785 113023.25181513408 -214290.95594199694 35299.40014421789; -3302.9672512683173 -5530.567493433798 -1009.3018417674479 -173.67182381047246 1896.2266218368597 1211.5760467176715 -6515.854304714365 51269.5393485764 -109817.9273281088 16916.77013686628; 33588.53008404843 45842.810501475775 6380.571877247027 1896.2266218369043 -17101.481071005666 -11314.851881762375 59577.716386724955 -460222.10215340357 954277.8226441568 -150437.95908093377; 22425.22567804585 31672.070023649158 4705.357018856973 1211.576046717452 -11314.851881762303 -7561.185197110738 40193.457567402365 -318409.4406011705 659266.1723043135 -103908.85994089546; -119028.39406900956 -163122.65267399285 -22804.799125602 -6515.85430471422 59577.71638672538 40193.45756739892 -211596.17710204114 1.6263770592364336e6 -3.3557797033448154e6 529773.325127917; 992829.1622788592 1.1653614367854064e6 113023.25181513846 51269.53934857463 -460222.10215342394 -318409.44060118176 1.626377059236409e6 -1.0725105955977047e7 2.179154503796496e7 -3.2896352567722457e6; -2.0614538366680928e6 -2.3732637333989474e6 -214290.95594200576 -109817.9273281094 954277.822644195 659266.1723043174 -3.3557797033447195e6 2.1791545037964992e7 -4.410993875327773e7 6.721912218517469e6; 324160.54710888927 377015.8757341983 35299.40014421667 16916.770136865394 -150437.95908093167 -103908.85994088984 529773.3251279388 -3.2896352567724423e6 6.721912218517537e6 -1.0059845601990005e6]
 * x: [10.049171822148121, -9.880921183592493, 31.17981306967584, -9.975905689475471, 10.691857287534221, -46.162409729563045, -9.912746951087257, 0.043422870736438295, 0.034460168664655934, -0.009338574648315232]
 * lambda: NaN
 * interior: true
 * hard case: false
 * delta: 1.0
[ Info: Solving ODE param estimation problem with default MTK construction
┌ Warning: At t=0.01483221772898653, dt was forced below floating point epsilon 1.734723475976807e-18, and step error estimate = 1.8687259587471354. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{OptimizationForwardDiffExt.var"#37#55"{OptimizationFunction{true, AutoForwardDiff{nothing, Nothing}, DiffEqParamEstim.var"#29#30"{Nothing, typeof(DiffEqParamEstim.STANDARD_PROB_GENERATOR), Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, ModelingToolkit.MTKParameters{Tuple{Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x9c845606, 0xf2c37616, 0x9c88dfd9, 0xf11cefcd, 0x25a942b6), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1,), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe76332ee, 0x2e783b89, 0x43ba34c1, 0x38322bf4, 0x12861fd3), Nothing}}, ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#f#717"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe6a61356, 0x434e4e39, 0x499c058e, 0x64876f67, 0xadde982b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xcc0ab0a6, 0x935170f1, 0x1d2f9bbf, 0x7646761d, 0xa0789215), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{ODESystem}, Nothing, ODESystem, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Rosenbrock23{0, true, Nothing, typeof(OrdinaryDiffEq.DEFAULT_PRECS), Val{:forward}, true, nothing, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!)}, L2Loss{Vector{Float64}, Matrix{Float64}, Nothing, Nothing, Nothing}, Nothing, Tuple{}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, OptimizationBase.ReInitCache{Vector{Float64}, SciMLBase.NullParameters}}, Float64}, Float64, 10}).
└ @ SciMLBase ~/.julia/packages/SciMLBase/rR75x/src/integrator_interface.jl:600
┌ Warning: At t=0.014818212783429985, dt was forced below floating point epsilon 1.734723475976807e-18, and step error estimate = 53.2934187330348. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of Float64).
└ @ SciMLBase ~/.julia/packages/SciMLBase/rR75x/src/integrator_interface.jl:600
┌ Warning: At t=0.014850966833559073, dt was forced below floating point epsilon 1.734723475976807e-18, and step error estimate = 1.7472423420602798. Aborting. There is either an error in your model specification or the true solution is unstable (or the true solution can not be represented in the precision of ForwardDiff.Dual{ForwardDiff.Tag{OptimizationForwardDiffExt.var"#37#55"{OptimizationFunction{true, AutoForwardDiff{nothing, Nothing}, DiffEqParamEstim.var"#29#30"{Nothing, typeof(DiffEqParamEstim.STANDARD_PROB_GENERATOR), Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, ModelingToolkit.MTKParameters{Tuple{Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x9c845606, 0xf2c37616, 0x9c88dfd9, 0xf11cefcd, 0x25a942b6), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1,), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe76332ee, 0x2e783b89, 0x43ba34c1, 0x38322bf4, 0x12861fd3), Nothing}}, ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#f#717"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe6a61356, 0x434e4e39, 0x499c058e, 0x64876f67, 0xadde982b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xcc0ab0a6, 0x935170f1, 0x1d2f9bbf, 0x7646761d, 0xa0789215), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{ODESystem}, Nothing, ODESystem, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Rosenbrock23{0, true, Nothing, typeof(OrdinaryDiffEq.DEFAULT_PRECS), Val{:forward}, true, nothing, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!)}, L2Loss{Vector{Float64}, Matrix{Float64}, Nothing, Nothing, Nothing}, Nothing, Tuple{}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, OptimizationBase.ReInitCache{Vector{Float64}, SciMLBase.NullParameters}}, Float64}, ForwardDiff.Dual{ForwardDiff.Tag{OptimizationForwardDiffExt.var"#37#55"{OptimizationFunction{true, AutoForwardDiff{nothing, Nothing}, DiffEqParamEstim.var"#29#30"{Nothing, typeof(DiffEqParamEstim.STANDARD_PROB_GENERATOR), Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, ModelingToolkit.MTKParameters{Tuple{Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x9c845606, 0xf2c37616, 0x9c88dfd9, 0xf11cefcd, 0x25a942b6), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1,), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe76332ee, 0x2e783b89, 0x43ba34c1, 0x38322bf4, 0x12861fd3), Nothing}}, ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var"#f#717"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xe6a61356, 0x434e4e39, 0x499c058e, 0x64876f67, 0xadde982b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xcc0ab0a6, 0x935170f1, 0x1d2f9bbf, 0x7646761d, 0xa0789215), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{ODESystem}, Nothing, ODESystem, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Rosenbrock23{0, true, Nothing, typeof(OrdinaryDiffEq.DEFAULT_PRECS), Val{:forward}, true, nothing, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!)}, L2Loss{Vector{Float64}, Matrix{Float64}, Nothing, Nothing, Nothing}, Nothing, Tuple{}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, OptimizationBase.ReInitCache{Vector{Float64}, SciMLBase.NullParameters}}, Float64}, Float64, 10}, 10}).
└ @ SciMLBase ~/.julia/packages/SciMLBase/rR75x/src/integrator_interface.jl:600
Iter     Function value   Gradient norm 
     0              Inf     0.000000e+00
 * time: 5.698204040527344e-5
 * g(x): [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
 * reached_subproblem_solution: true
 * h(x): [0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]
 * x: [10.049171822148121, -9.880921183592493, 31.17981306967584, -9.975905689475471, 10.691857287534221, -46.162409729563045, -9.912746951087257, 0.043422870736438295, 0.034460168664655934, -0.009338574648315232]
 * lambda: NaN
 * interior: true
 * hard case: false
 * delta: 1.0

Environment (please complete the following information):

Status `~/.julia/dev/WENDy.jl/Project.toml`
  [fbb218c0] BSON v0.3.9
  [6e4b80f9] BenchmarkTools v1.5.0
  [1130ab10] DiffEqParamEstim v2.2.0
  [31c24e10] Distributions v0.25.109
  [7a1cc6ca] FFTW v1.8.0
  [6a86dc24] FiniteDiff v2.23.1
  [f6369f11] ForwardDiff v0.10.36
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  [23992714] MAT v0.10.7
  [961ee093] ModelingToolkit v9.24.0
  [8913a72c] NonlinearSolve v3.13.1
  [429524aa] Optim v1.9.4 `~/.julia/dev/Optim`
  [7f7a1694] Optimization v3.27.0
  [36348300] OptimizationOptimJL v0.3.2
  [1dea7af3] OrdinaryDiffEq v6.85.0
  [f0f68f2c] PlotlyJS v0.18.13
  [91a5bcdd] Plots v1.40.5
  [d236fae5] PreallocationTools v0.4.22
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  [56ddb016] Logging
  [9a3f8284] Random
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Project WENDy v1.0.0-DEV
Status `~/.julia/dev/WENDy.jl/Manifest.toml`
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⌅ [4297ee4d] SymbolicAnalysis v0.1.0
  [2efcf032] SymbolicIndexingInterface v0.3.26
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  [d1185830] SymbolicUtils v2.1.0
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  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.11.1
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⌅ [8ea1fca8] TermInterface v0.4.1
  [5d786b92] TerminalLoggers v0.1.7
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  [a759f4b9] TimerOutputs v0.5.24
  [0796e94c] Tokenize v0.5.29
  [3bb67fe8] TranscodingStreams v0.11.0
  [d5829a12] TriangularSolve v0.2.1
  [410a4b4d] Tricks v0.1.8
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  [bc48ee85] Tullio v0.3.7
  [5c2747f8] URIs v1.5.1
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  [1986cc42] Unitful v1.20.0
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  [cc8bc4a8] Widgets v0.6.6
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  [700de1a5] ZygoteRules v0.2.5
⌅ [68821587] Arpack_jll v3.5.1+1
  [6e34b625] Bzip2_jll v1.0.8+1
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  [cd4c43a9] Dierckx_jll v0.1.0+0
  [7cc45869] Enzyme_jll v0.0.133+0
  [2702e6a9] EpollShim_jll v0.0.20230411+0
  [2e619515] Expat_jll v2.6.2+0
⌅ [b22a6f82] FFMPEG_jll v4.4.4+1
  [f5851436] FFTW_jll v3.3.10+0
  [a3f928ae] Fontconfig_jll v2.13.96+0
  [d7e528f0] FreeType2_jll v2.13.2+0
  [559328eb] FriBidi_jll v1.0.14+0
  [0656b61e] GLFW_jll v3.4.0+0
  [d2c73de3] GR_jll v0.73.6+0
  [78b55507] Gettext_jll v0.21.0+0
  [7746bdde] Glib_jll v2.80.2+0
  [3b182d85] Graphite2_jll v1.3.14+0
  [0234f1f7] HDF5_jll v1.14.3+3
  [2e76f6c2] HarfBuzz_jll v2.8.1+1
  [e33a78d0] Hwloc_jll v2.11.0+0
  [1d5cc7b8] IntelOpenMP_jll v2024.2.0+0
  [aacddb02] JpegTurbo_jll v3.0.3+0
  [f7e6163d] Kaleido_jll v0.2.1+0
  [c1c5ebd0] LAME_jll v3.100.2+0
⌅ [88015f11] LERC_jll v3.0.0+1
  [dad2f222] LLVMExtra_jll v0.0.30+0
  [1d63c593] LLVMOpenMP_jll v15.0.7+0
  [dd4b983a] LZO_jll v2.10.2+0
  [81d17ec3] L_BFGS_B_jll v3.0.1+0
⌅ [e9f186c6] Libffi_jll v3.2.2+1
  [d4300ac3] Libgcrypt_jll v1.8.11+0
  [7e76a0d4] Libglvnd_jll v1.6.0+0
  [7add5ba3] Libgpg_error_jll v1.49.0+0
  [94ce4f54] Libiconv_jll v1.17.0+0
  [4b2f31a3] Libmount_jll v2.40.1+0
⌅ [89763e89] Libtiff_jll v4.5.1+1
  [38a345b3] Libuuid_jll v2.40.1+0
  [856f044c] MKL_jll v2024.2.0+0
  [7cb0a576] MPICH_jll v4.2.1+1
  [f1f71cc9] MPItrampoline_jll v5.4.0+0
  [9237b28f] MicrosoftMPI_jll v10.1.4+2
  [e7412a2a] Ogg_jll v1.3.5+1
⌅ [fe0851c0] OpenMPI_jll v4.1.6+0
  [458c3c95] OpenSSL_jll v3.0.14+0
  [efe28fd5] OpenSpecFun_jll v0.5.5+0
  [91d4177d] Opus_jll v1.3.2+0
  [30392449] Pixman_jll v0.43.4+0
  [c0090381] Qt6Base_jll v6.7.1+1
  [f50d1b31] Rmath_jll v0.4.2+0
  [a44049a8] Vulkan_Loader_jll v1.3.243+0
  [a2964d1f] Wayland_jll v1.21.0+1
  [2381bf8a] Wayland_protocols_jll v1.31.0+0
  [02c8fc9c] XML2_jll v2.13.1+0
  [aed1982a] XSLT_jll v1.1.41+0
  [ffd25f8a] XZ_jll v5.4.6+0
  [f67eecfb] Xorg_libICE_jll v1.1.1+0
  [c834827a] Xorg_libSM_jll v1.2.4+0
  [4f6342f7] Xorg_libX11_jll v1.8.6+0
  [0c0b7dd1] Xorg_libXau_jll v1.0.11+0
  [935fb764] Xorg_libXcursor_jll v1.2.0+4
  [a3789734] Xorg_libXdmcp_jll v1.1.4+0
  [1082639a] Xorg_libXext_jll v1.3.6+0
  [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 v0.9.11+0
  [14d82f49] Xorg_libpthread_stubs_jll v0.1.1+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.0+0
  [cc61e674] Xorg_libxkbfile_jll v1.1.2+0
  [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.4+0
  [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 v1.4.6+0
  [33bec58e] Xorg_xkeyboard_config_jll v2.39.0+0
  [c5fb5394] Xorg_xtrans_jll v1.5.0+0
  [3161d3a3] Zstd_jll v1.5.6+0
  [35ca27e7] eudev_jll v3.2.9+0
⌅ [214eeab7] fzf_jll v0.43.0+0
  [1a1c6b14] gperf_jll v3.1.1+0
  [477f73a3] libaec_jll v1.1.2+0
  [a4ae2306] libaom_jll v3.9.0+0
  [0ac62f75] libass_jll v0.15.1+0
  [2db6ffa8] libevdev_jll v1.11.0+0
  [f638f0a6] libfdk_aac_jll v2.0.2+0
  [36db933b] libinput_jll v1.18.0+0
  [b53b4c65] libpng_jll v1.6.43+1
  [f27f6e37] libvorbis_jll v1.3.7+1
  [009596ad] mtdev_jll v1.1.6+0
  [1317d2d5] oneTBB_jll v2021.12.0+0
  [1270edf5] x264_jll v2021.5.5+0
  [dfaa095f] x265_jll v3.5.0+0
  [d8fb68d0] xkbcommon_jll v1.4.1+1
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [9abbd945] Profile
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.8.0+1
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
julia> versioninfo()
Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 8 × Intel(R) Core(TM) i7-7820HQ CPU @ 2.90GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 4 default, 0 interactive, 2 GC (on 8 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 4
ChrisRackauckas commented 1 month ago

@AayushSabharwal this is handled now?

AayushSabharwal commented 1 month ago

Yes, this runs as-is