MNIST Classification using Neural ODEs

To understand Neural ODEs, users should look up these lecture notes. We recommend users to directly use DiffEqFlux.jl, instead of implementing Neural ODEs from scratch.

Package Imports

using Lux, ComponentArrays, SciMLSensitivity, AMDGPU, LuxCUDA, Optimisers, OrdinaryDiffEq,
      Random, Statistics, Zygote, OneHotArrays, InteractiveUtils, Printf
import MLDatasets: MNIST
import MLUtils: DataLoader, splitobs

CUDA.allowscalar(false)

Loading MNIST

function loadmnist(batchsize, train_split)
    # Load MNIST: Only 1500 for demonstration purposes
    N = 1500
    dataset = MNIST(; split=:train)
    imgs = dataset.features[:, :, 1:N]
    labels_raw = dataset.targets[1:N]

    # Process images into (H,W,C,BS) batches
    x_data = Float32.(reshape(imgs, size(imgs, 1), size(imgs, 2), 1, size(imgs, 3)))
    y_data = onehotbatch(labels_raw, 0:9)
    (x_train, y_train), (x_test, y_test) = splitobs((x_data, y_data); at=train_split)

    return (
        # Use DataLoader to automatically minibatch and shuffle the data
        DataLoader(collect.((x_train, y_train)); batchsize, shuffle=true),
        # Don't shuffle the test data
        DataLoader(collect.((x_test, y_test)); batchsize, shuffle=false))
end

loadmnist (generic function with 1 method)

Define the Neural ODE Layer

First we will use the @compact macro to define the Neural ODE Layer.

function NeuralODECompact(
        model::Lux.AbstractExplicitLayer; solver=Tsit5(), tspan=(0.0f0, 1.0f0), kwargs...)
    return @compact(; model, solver, tspan, kwargs...) do x, p
        dudt(u, p, t) = vec(model(reshape(u, size(x)), p))
        # Note the `p.model` here
        prob = ODEProblem(ODEFunction{false}(dudt), vec(x), tspan, p.model)
        @return solve(prob, solver; kwargs...)
    end
end

NeuralODECompact (generic function with 1 method)

We recommend using the compact macro for creating custom layers. The below implementation exists mostly for historical reasons when @compact was not part of the stable API. Also, it helps users understand how the layer interface of Lux works.

The NeuralODE is a ContainerLayer, which stores a model. The parameters and states of the NeuralODE are same as those of the underlying model.

struct NeuralODE{M <: Lux.AbstractExplicitLayer, So, T, K} <:
       Lux.AbstractExplicitContainerLayer{(:model,)}
    model::M
    solver::So
    tspan::T
    kwargs::K
end

function NeuralODE(
        model::Lux.AbstractExplicitLayer; solver=Tsit5(), tspan=(0.0f0, 1.0f0), kwargs...)
    return NeuralODE(model, solver, tspan, kwargs)
end

Main.var"##225".NeuralODE

OrdinaryDiffEq.jl can deal with non-Vector Inputs! However, certain discrete sensitivities like ReverseDiffAdjoint can't handle non-Vector inputs. Hence, we need to convert the input and output of the ODE solver to a Vector.

function (n::NeuralODE)(x, ps, st)
    function dudt(u, p, t)
        u_, st = n.model(reshape(u, size(x)), p, st)
        return vec(u_)
    end
    prob = ODEProblem{false}(ODEFunction{false}(dudt), vec(x), n.tspan, ps)
    return solve(prob, n.solver; n.kwargs...), st
end

@views diffeqsol_to_array(l::Int, x::ODESolution) = reshape(last(x.u), (l, :))
@views diffeqsol_to_array(l::Int, x::AbstractMatrix) = reshape(x[:, end], (l, :))

diffeqsol_to_array (generic function with 2 methods)

Create and Initialize the Neural ODE Layer

function create_model(model_fn=NeuralODE; dev=gpu_device(), use_named_tuple::Bool=false,
        sensealg=InterpolatingAdjoint(; autojacvec=ZygoteVJP()))
    # Construct the Neural ODE Model
    model = Chain(FlattenLayer(),
        Dense(784 => 20, tanh),
        model_fn(Chain(Dense(20 => 10, tanh), Dense(10 => 10, tanh), Dense(10 => 20, tanh));
            save_everystep=false, reltol=1.0f-3,
            abstol=1.0f-3, save_start=false, sensealg),
        Base.Fix1(diffeqsol_to_array, 20),
        Dense(20 => 10))

    rng = Random.default_rng()
    Random.seed!(rng, 0)

    ps, st = Lux.setup(rng, model)
    ps = (use_named_tuple ? ps : ComponentArray(ps)) |> dev
    st = st |> dev

    return model, ps, st
end

create_model (generic function with 2 methods)

Define Utility Functions

const logitcrossentropy = CrossEntropyLoss(; logits=Val(true))

function accuracy(model, ps, st, dataloader; dev=gpu_device())
    total_correct, total = 0, 0
    st = Lux.testmode(st)
    cpu_dev = cpu_device()
    for (x, y) in dataloader
        target_class = onecold(y)
        predicted_class = onecold(cpu_dev(first(model(dev(x), ps, st))))
        total_correct += sum(target_class .== predicted_class)
        total += length(target_class)
    end
    return total_correct / total
end

accuracy (generic function with 1 method)

Training

function train(model_function; cpu::Bool=false, kwargs...)
    dev = cpu ? cpu_device() : gpu_device()
    model, ps, st = create_model(model_function; dev, kwargs...)

    # Training
    train_dataloader, test_dataloader = loadmnist(128, 0.9)

    tstate = Training.TrainState(model, ps, st, Adam(0.001f0))

    ### Lets train the model
    nepochs = 9
    for epoch in 1:nepochs
        stime = time()
        for (x, y) in train_dataloader
            x = dev(x)
            y = dev(y)
            _, _, _, tstate = Training.single_train_step!(
                AutoZygote(), logitcrossentropy, (x, y), tstate)
        end
        ttime = time() - stime

        tr_acc = accuracy(model, tstate.parameters, tstate.states, train_dataloader; dev)
        te_acc = accuracy(model, tstate.parameters, tstate.states, test_dataloader; dev)
        @printf "[%d/%d] \t Time %.2fs \t Training Accuracy: %.5f%% \t Test \
                 Accuracy: %.5f%%\n" epoch nepochs ttime tr_acc te_acc
    end
end

train(NeuralODECompact)

[1/9] 	 Time 127.45s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 0.39s 	 Training Accuracy: 0.71333% 	 Test Accuracy: 0.66000%
[3/9] 	 Time 0.41s 	 Training Accuracy: 0.77778% 	 Test Accuracy: 0.70000%
[4/9] 	 Time 0.39s 	 Training Accuracy: 0.81111% 	 Test Accuracy: 0.73333%
[5/9] 	 Time 0.40s 	 Training Accuracy: 0.82444% 	 Test Accuracy: 0.77333%
[6/9] 	 Time 0.41s 	 Training Accuracy: 0.84519% 	 Test Accuracy: 0.79333%
[7/9] 	 Time 0.64s 	 Training Accuracy: 0.85407% 	 Test Accuracy: 0.80000%
[8/9] 	 Time 0.42s 	 Training Accuracy: 0.86815% 	 Test Accuracy: 0.81333%
[9/9] 	 Time 0.42s 	 Training Accuracy: 0.87926% 	 Test Accuracy: 0.82667%

train(NeuralODE)

[1/9] 	 Time 36.21s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 0.50s 	 Training Accuracy: 0.71333% 	 Test Accuracy: 0.66000%
[3/9] 	 Time 0.37s 	 Training Accuracy: 0.78000% 	 Test Accuracy: 0.70000%
[4/9] 	 Time 0.52s 	 Training Accuracy: 0.81037% 	 Test Accuracy: 0.74667%
[5/9] 	 Time 0.41s 	 Training Accuracy: 0.82889% 	 Test Accuracy: 0.78000%
[6/9] 	 Time 0.53s 	 Training Accuracy: 0.84222% 	 Test Accuracy: 0.79333%
[7/9] 	 Time 0.41s 	 Training Accuracy: 0.85926% 	 Test Accuracy: 0.80000%
[8/9] 	 Time 0.56s 	 Training Accuracy: 0.86444% 	 Test Accuracy: 0.82000%
[9/9] 	 Time 0.41s 	 Training Accuracy: 0.88000% 	 Test Accuracy: 0.82667%

We can also change the sensealg and train the model! GaussAdjoint allows you to use any arbitrary parameter structure and not just a flat vector (ComponentArray).

train(NeuralODE; sensealg=GaussAdjoint(; autojacvec=ZygoteVJP()), use_named_tuple=true)

[1/9] 	 Time 39.16s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 0.35s 	 Training Accuracy: 0.71111% 	 Test Accuracy: 0.66000%
[3/9] 	 Time 0.35s 	 Training Accuracy: 0.78148% 	 Test Accuracy: 0.70667%
[4/9] 	 Time 0.37s 	 Training Accuracy: 0.80667% 	 Test Accuracy: 0.76000%
[5/9] 	 Time 0.38s 	 Training Accuracy: 0.82519% 	 Test Accuracy: 0.78667%
[6/9] 	 Time 0.57s 	 Training Accuracy: 0.83778% 	 Test Accuracy: 0.79333%
[7/9] 	 Time 0.40s 	 Training Accuracy: 0.85852% 	 Test Accuracy: 0.80000%
[8/9] 	 Time 0.39s 	 Training Accuracy: 0.86593% 	 Test Accuracy: 0.82000%
[9/9] 	 Time 0.57s 	 Training Accuracy: 0.88074% 	 Test Accuracy: 0.82667%

But remember some AD backends like ReverseDiff is not GPU compatible. For a model this size, you will notice that training time is significantly lower for training on CPU than on GPU.

train(NeuralODE; sensealg=InterpolatingAdjoint(; autojacvec=ReverseDiffVJP()), cpu=true)

[1/9] 	 Time 103.66s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 18.40s 	 Training Accuracy: 0.69630% 	 Test Accuracy: 0.66000%
[3/9] 	 Time 21.40s 	 Training Accuracy: 0.77926% 	 Test Accuracy: 0.71333%
[4/9] 	 Time 22.59s 	 Training Accuracy: 0.80741% 	 Test Accuracy: 0.76667%
[5/9] 	 Time 23.41s 	 Training Accuracy: 0.82519% 	 Test Accuracy: 0.78000%
[6/9] 	 Time 23.42s 	 Training Accuracy: 0.84074% 	 Test Accuracy: 0.78667%
[7/9] 	 Time 21.43s 	 Training Accuracy: 0.85333% 	 Test Accuracy: 0.80667%
[8/9] 	 Time 22.16s 	 Training Accuracy: 0.86593% 	 Test Accuracy: 0.81333%
[9/9] 	 Time 19.78s 	 Training Accuracy: 0.87704% 	 Test Accuracy: 0.82000%

For completeness, let's also test out discrete sensitivities!

train(NeuralODE; sensealg=ReverseDiffAdjoint(), cpu=true)

[1/9] 	 Time 38.85s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 23.75s 	 Training Accuracy: 0.69630% 	 Test Accuracy: 0.66000%
[3/9] 	 Time 20.51s 	 Training Accuracy: 0.77926% 	 Test Accuracy: 0.71333%
[4/9] 	 Time 22.30s 	 Training Accuracy: 0.80741% 	 Test Accuracy: 0.76667%
[5/9] 	 Time 26.19s 	 Training Accuracy: 0.82519% 	 Test Accuracy: 0.78000%
[6/9] 	 Time 23.35s 	 Training Accuracy: 0.84074% 	 Test Accuracy: 0.78667%
[7/9] 	 Time 26.31s 	 Training Accuracy: 0.85333% 	 Test Accuracy: 0.80667%
[8/9] 	 Time 26.00s 	 Training Accuracy: 0.86593% 	 Test Accuracy: 0.81333%
[9/9] 	 Time 26.28s 	 Training Accuracy: 0.87704% 	 Test Accuracy: 0.82000%

Alternate Implementation using Stateful Layer

Starting v0.5.5, Lux provides a StatefulLuxLayer which can be used to avoid the Boxing of st. Using the @compact API avoids this problem entirely.

struct StatefulNeuralODE{M <: Lux.AbstractExplicitLayer, So, T, K} <:
       Lux.AbstractExplicitContainerLayer{(:model,)}
    model::M
    solver::So
    tspan::T
    kwargs::K
end

function StatefulNeuralODE(
        model::Lux.AbstractExplicitLayer; solver=Tsit5(), tspan=(0.0f0, 1.0f0), kwargs...)
    return StatefulNeuralODE(model, solver, tspan, kwargs)
end

function (n::StatefulNeuralODE)(x, ps, st)
    st_model = StatefulLuxLayer(n.model, ps, st)
    dudt(u, p, t) = st_model(u, p)
    prob = ODEProblem{false}(ODEFunction{false}(dudt), x, n.tspan, ps)
    return solve(prob, n.solver; n.kwargs...), st_model.st
end

Train the new Stateful Neural ODE

train(StatefulNeuralODE)

[1/9] 	 Time 36.53s 	 Training Accuracy: 0.50963% 	 Test Accuracy: 0.43333%
[2/9] 	 Time 0.35s 	 Training Accuracy: 0.71630% 	 Test Accuracy: 0.66667%
[3/9] 	 Time 0.35s 	 Training Accuracy: 0.77926% 	 Test Accuracy: 0.70000%
[4/9] 	 Time 0.38s 	 Training Accuracy: 0.80370% 	 Test Accuracy: 0.74667%
[5/9] 	 Time 0.61s 	 Training Accuracy: 0.82741% 	 Test Accuracy: 0.77333%
[6/9] 	 Time 0.38s 	 Training Accuracy: 0.84148% 	 Test Accuracy: 0.79333%
[7/9] 	 Time 0.39s 	 Training Accuracy: 0.85407% 	 Test Accuracy: 0.80000%
[8/9] 	 Time 0.39s 	 Training Accuracy: 0.86667% 	 Test Accuracy: 0.80667%
[9/9] 	 Time 0.40s 	 Training Accuracy: 0.88000% 	 Test Accuracy: 0.82000%

We might not see a significant difference in the training time, but let us investigate the type stabilities of the layers.

Type Stability

model, ps, st = create_model(NeuralODE)

model_stateful, ps_stateful, st_stateful = create_model(StatefulNeuralODE)

x = gpu_device()(ones(Float32, 28, 28, 1, 3));

NeuralODE is not type stable due to the boxing of st

@code_warntype model(x, ps, st)

MethodInstance for (::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".NeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing})(::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}, ::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}, ::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}})
  from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-3/julialang/lux-dot-jl/src/layers/containers.jl:518
Arguments
  c::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".NeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}
  x::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}
  ps::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}
  st::Core.Const((layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = (layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = NamedTuple()), layer_4 = NamedTuple(), layer_5 = NamedTuple()))
Body::TUPLE{CUDA.CUARRAY{FLOAT32, 2, CUDA.DEVICEMEMORY}, NAMEDTUPLE{(:LAYER_1, :LAYER_2, :LAYER_3, :LAYER_4, :LAYER_5), <:TUPLE{@NAMEDTUPLE{}, @NAMEDTUPLE{}, ANY, @NAMEDTUPLE{}, @NAMEDTUPLE{}}}}
1 ─ %1 = Base.getproperty(c, :layers)::@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".NeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}
│   %2 = Lux.applychain(%1, x, ps, st)::TUPLE{CUDA.CUARRAY{FLOAT32, 2, CUDA.DEVICEMEMORY}, NAMEDTUPLE{(:LAYER_1, :LAYER_2, :LAYER_3, :LAYER_4, :LAYER_5), <:TUPLE{@NAMEDTUPLE{}, @NAMEDTUPLE{}, ANY, @NAMEDTUPLE{}, @NAMEDTUPLE{}}}}
└──      return %2

We avoid the problem entirely by using StatefulNeuralODE

@code_warntype model_stateful(x, ps_stateful, st_stateful)

MethodInstance for (::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".StatefulNeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing})(::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}, ::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}, ::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}})
  from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-3/julialang/lux-dot-jl/src/layers/containers.jl:518
Arguments
  c::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".StatefulNeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}
  x::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}
  ps::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}
  st::Core.Const((layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = (layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = NamedTuple()), layer_4 = NamedTuple(), layer_5 = NamedTuple()))
Body::Tuple{CUDA.CuArray{Float32, 2, CUDA.DeviceMemory}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}}}
1 ─ %1 = Base.getproperty(c, :layers)::@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Main.var"##225".StatefulNeuralODE{Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}, OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}
│   %2 = Lux.applychain(%1, x, ps, st)::Tuple{CUDA.CuArray{Float32, 2, CUDA.DeviceMemory}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}}}
└──      return %2

Note, that we still recommend using this layer internally and not exposing this as the default API to the users.

Finally checking the compact model

model_compact, ps_compact, st_compact = create_model(NeuralODECompact)

@code_warntype model_compact(x, ps_compact, st_compact)

MethodInstance for (::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.CompactLuxLayer{:₋₋₋no_special_dispatch₋₋₋, Main.var"##225".var"#2#3", Nothing, @NamedTuple{model::Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}}, Lux.CompactMacroImpl.ValueStorage{@NamedTuple{}, @NamedTuple{solver::Returns{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}}, tspan::Returns{Tuple{Float32, Float32}}}}, Tuple{Tuple{Symbol}, Tuple{Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing})(::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}, ::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(model = ViewAxis(1:540, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))),)), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}, ::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{model::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, solver::OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, tspan::Tuple{Float32, Float32}, ₋₋₋kwargs₋₋₋::@NamedTuple{kwargs::Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}})
  from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-3/julialang/lux-dot-jl/src/layers/containers.jl:518
Arguments
  c::Lux.Chain{@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.CompactLuxLayer{:₋₋₋no_special_dispatch₋₋₋, Main.var"##225".var"#2#3", Nothing, @NamedTuple{model::Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}}, Lux.CompactMacroImpl.ValueStorage{@NamedTuple{}, @NamedTuple{solver::Returns{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}}, tspan::Returns{Tuple{Float32, Float32}}}}, Tuple{Tuple{Symbol}, Tuple{Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}
  x::CUDA.CuArray{Float32, 4, CUDA.DeviceMemory}
  ps::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.DeviceMemory}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784))), bias = ViewAxis(15681:15700, ShapedAxis((20, 1))))), layer_3 = ViewAxis(15701:16240, Axis(model = ViewAxis(1:540, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10))), bias = ViewAxis(201:220, ShapedAxis((20, 1))))))),)), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20))), bias = ViewAxis(201:210, ShapedAxis((10, 1))))))}}}
  st::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{model::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, solver::OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, tspan::Tuple{Float32, Float32}, ₋₋₋kwargs₋₋₋::@NamedTuple{kwargs::Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}}
Body::Tuple{CUDA.CuArray{Float32, 2, CUDA.DeviceMemory}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{model::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, solver::OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, tspan::Tuple{Float32, Float32}, ₋₋₋kwargs₋₋₋::@NamedTuple{kwargs::Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}}}
1 ─ %1 = Base.getproperty(c, :layers)::@NamedTuple{layer_1::Lux.FlattenLayer{Nothing}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.CompactLuxLayer{:₋₋₋no_special_dispatch₋₋₋, Main.var"##225".var"#2#3", Nothing, @NamedTuple{model::Lux.Chain{@NamedTuple{layer_1::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_2::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}, layer_3::Lux.Dense{typeof(NNlib.tanh_fast), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}, Nothing}}, Lux.CompactMacroImpl.ValueStorage{@NamedTuple{}, @NamedTuple{solver::Returns{OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}}, tspan::Returns{Tuple{Float32, Float32}}}}, Tuple{Tuple{Symbol}, Tuple{Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}}, layer_4::Lux.WrappedFunction{:direct_call, Base.Fix1{typeof(Main.var"##225".diffeqsol_to_array), Int64}}, layer_5::Lux.Dense{typeof(identity), Int64, Int64, typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32), Static.True}}
│   %2 = Lux.applychain(%1, x, ps, st)::Tuple{CUDA.CuArray{Float32, 2, CUDA.DeviceMemory}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{model::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}, solver::OrdinaryDiffEqTsit5.Tsit5{typeof(OrdinaryDiffEqCore.trivial_limiter!), typeof(OrdinaryDiffEqCore.trivial_limiter!), Static.False}, tspan::Tuple{Float32, Float32}, ₋₋₋kwargs₋₋₋::@NamedTuple{kwargs::Base.Pairs{Symbol, Any, NTuple{5, Symbol}, @NamedTuple{save_everystep::Bool, reltol::Float32, abstol::Float32, save_start::Bool, sensealg::SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}}}}}, layer_4::@NamedTuple{}, layer_5::@NamedTuple{}}}
└──      return %2

Appendix

using InteractiveUtils
InteractiveUtils.versioninfo()

if @isdefined(LuxDeviceUtils)
    if @isdefined(CUDA) && LuxDeviceUtils.functional(LuxCUDADevice)
        println()
        CUDA.versioninfo()
    end

    if @isdefined(AMDGPU) && LuxDeviceUtils.functional(LuxAMDGPUDevice)
        println()
        AMDGPU.versioninfo()
    end
end

Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 48 × AMD EPYC 7402 24-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 4 default, 0 interactive, 2 GC (on 2 virtual cores)
Environment:
  JULIA_CPU_THREADS = 2
  JULIA_DEPOT_PATH = /root/.cache/julia-buildkite-plugin/depots/01872db4-8c79-43af-ab7d-12abac4f24f6
  LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64
  JULIA_PKG_SERVER = 
  JULIA_NUM_THREADS = 4
  JULIA_CUDA_HARD_MEMORY_LIMIT = 100%
  JULIA_PKG_PRECOMPILE_AUTO = 0
  JULIA_DEBUG = Literate

CUDA runtime 12.5, artifact installation
CUDA driver 12.5
NVIDIA driver 555.42.6

CUDA libraries: 
- CUBLAS: 12.5.3
- CURAND: 10.3.6
- CUFFT: 11.2.3
- CUSOLVER: 11.6.3
- CUSPARSE: 12.5.1
- CUPTI: 2024.2.1 (API 23.0.0)
- NVML: 12.0.0+555.42.6

Julia packages: 
- CUDA: 5.4.3
- CUDA_Driver_jll: 0.9.2+0
- CUDA_Runtime_jll: 0.14.1+0

Toolchain:
- Julia: 1.10.5
- LLVM: 15.0.7

Environment:
- JULIA_CUDA_HARD_MEMORY_LIMIT: 100%

1 device:
  0: NVIDIA A100-PCIE-40GB MIG 1g.5gb (sm_80, 3.951 GiB / 4.750 GiB available)

---

This page was generated using Literate.jl.