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x), x * (rho - z) - y, x * y - beta * z]\n\nu0 = [1.0,0.0,0.0]\ntspan = (0., 100.)\np = [10.0,28.0,8/3]\nprob = de.ODEProblem(f, u0, tspan, p)\nfast_prob = de.jit(prob)\nsol = de.solve(fast_prob,saveat=0.01)\n\nimport random\ndef prob_func(prob,i,rep):\n  de.remake(prob,u0=[random.uniform(0, 1)*u0[i] for i in range(0,3)],\n            p=[random.uniform(0, 1)*p[i] for i in range(0,3)])\n\nensembleprob = de.EnsembleProblem(fast_prob, prob_func = prob_func, safetycopy=False)\nsol = de.solve(ensembleprob,de.Tsit5(),de.EnsembleSerial(),trajectories=10000,saveat=0.01)\n\nfrom diffeqpy import cuda\n\nsol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(cuda.CUDABackend()),trajectories=10000,saveat=0.01)\n```\n\nCurrently fails at the first ensemble solve:\n\n```py\nsol = de.solve(ensembleprob,de.Tsit5(),de.EnsembleSerial(),trajectories=10000,saveat=0.01)\n```\n\n```\n>>> sol = de.solve(ensembleprob,de.Tsit5(),de.EnsembleSerial(),trajectories=10000,saveat=0.01)\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\n  File \"C:\\Users\\accou\\.julia\\packages\\PythonCall\\qTEA1\\src\\jlwrap\\any.jl\", line 208, in __call__\n    return self._jl_callmethod($(pyjl_methodnum(pyjlany_call)), args, kwargs)\n       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\njuliacall.JuliaError: MethodError: no method matching init(::Py, ::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; saveat::Float64)\n\nClosest candidates are:\n  init(!Matched::SciMLBase.OptimizationProblem, ::Any, !Matched::Any...; kwargs...)\n   @ SciMLBase C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\solve.jl:146\n  init(!Matched::SciMLBase.PDEProblem, ::SciMLBase.AbstractDEAlgorithm, !Matched::Any...; kwargs...)\n   @ DiffEqBase C:\\Users\\accou\\.julia\\packages\\DiffEqBase\\xSmHR\\src\\solve.jl:1116\n  init(!Matched::SciMLBase.AbstractJumpProblem, ::Any...; kwargs...)\n   @ DiffEqBase C:\\Users\\accou\\.julia\\packages\\DiffEqBase\\xSmHR\\src\\solve.jl:499\n  ...\n\nStacktrace:\n  [1] solve(::Py, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})\n    @ CommonSolve C:\\Users\\accou\\.julia\\packages\\CommonSolve\\JfpfI\\src\\CommonSolve.jl:23\n  [2] batch_func(i::Int64, prob::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var\"#k#545\"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var\"#637#generated_observed#555\"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Py, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})\n    @ SciMLBase C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:100\n  [3] (::SciMLBase.var\"#604#605\"{Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}}, SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var\"#k#545\"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var\"#637#generated_observed#555\"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Py, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}})(i::Int64)\n    @ SciMLBase C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:154\n  [4] responsible_map\n    @ C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:147 [inlined]\n  [5] #solve_batch#603\n    @ C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:153 [inlined]\n  [6] solve_batch\n    @ C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:152 [inlined]\n  [7] macro expansion\n    @ .\\timing.jl:393 [inlined]\n  [8] __solve(prob::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var\"#k#545\"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var\"#637#generated_observed#555\"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Py, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ensemblealg::SciMLBase.EnsembleSerial; trajectories::Int64, batch_size::Int64, pmap_batch_size::Int64, kwargs::Base.Pairs{Symbol, Float64, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{Float64}}})\n    @ SciMLBase C:\\Users\\accou\\.julia\\packages\\SciMLBase\\McEqc\\src\\ensemble\\basic_ensemble_solve.jl:64\n  [9] solve(::SciMLBase.EnsembleProblem{SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, SciMLBase.ODEFunction{true, SciMLBase.AutoSpecialize, ModelingToolkit.var\"#k#545\"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0x946926fe, 0xab7b8dc2, 0x707dbba2, 0x8b060826, 0xe63fdf5b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :ˍ₋arg1, :ˍ₋arg2, :t), ModelingToolkit.var\"#_RGF_ModTag\", ModelingToolkit.var\"#_RGF_ModTag\", (0xaf9b47a5, 0x8d86fb18, 0x65c64d40, 0xf8480c5a, 0x8614cffa), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Symbol}, Symbol, Vector{Symbol}, ModelingToolkit.var\"#637#generated_observed#555\"{Bool, ModelingToolkit.ODESystem, Dict{Any, Any}, Vector{SymbolicUtils.BasicSymbolic{Real}}}, Nothing, ModelingToolkit.ODESystem}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Py, typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, ::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Real, Tuple{Symbol, Symbol}, NamedTuple{(:saveat, :trajectories), Tuple{Float64, Int64}}})\n    @ DiffEqBase C:\\Users\\accou\\.julia\\packages\\DiffEqBase\\xSmHR\\src\\solve.jl:1053\n [10] pyjlany_call(self::typeof(CommonSolve.solve), args_::Py, kwargs_::Py)\n    @ PythonCall C:\\Users\\accou\\.julia\\packages\\PythonCall\\qTEA1\\src\\jlwrap\\any.jl:34\n [11] _pyjl_callmethod(f::Any, self_::Ptr{PythonCall.C.PyObject}, args_::Ptr{PythonCall.C.PyObject}, nargs::Int64)\n    @ PythonCall C:\\Users\\accou\\.julia\\packages\\PythonCall\\qTEA1\\src\\jlwrap\\base.jl:69\n [12] _pyjl_callmethod(o::Ptr{PythonCall.C.PyObject}, args::Ptr{PythonCall.C.PyObject})\n    @ PythonCall.C C:\\Users\\accou\\.julia\\packages\\PythonCall\\qTEA1\\src\\cpython\\jlwrap.jl:47\n```","shortMessageHtmlLink":"[WIP] Setup DiffEqGPU 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