Ecosystem Extensions — Neutron Docs

Neutron for Julia integrates with the scientific-computing ecosystem through package extensions. Each loads automatically when you import both NeutronJulia and the target package (the Julia weak-dependency pattern) — no extra setup.

DataFrames

using NeutronJulia, DataFrames

rows = query(sql(client), "SELECT * FROM sales")
df = DataFrame(rows)  # Automatic conversion

DifferentialEquations

Store ODE/SDE solutions directly in Nucleus TimeSeries:

using NeutronJulia, DifferentialEquations

# Solve a system
prob = ODEProblem(lorenz!, u0, tspan, p)
sol = solve(prob, Tsit5(), saveat=0.01)

# Store solution — each variable becomes a separate series
ts = timeseries(client)
store!(ts, sol, "lorenz:run1"; variable_names=["x", "y", "z"])

# Retrieve later
t, u = load_solution(ts, "lorenz:run1", ["x", "y", "z"])
# t::Vector{Float64} (seconds)
# u::Matrix{Float64} (n_vars x n_points)

ModelingToolkit

Symbolic variable names flow through to TimeSeries storage:

using NeutronJulia, ModelingToolkit

# Variable names from an MTK system are used as series names automatically

Flux (ML)

Generate embeddings with Flux models and store them in Nucleus Vector:

using NeutronJulia, Flux

model = Chain(Dense(768, 384), relu, Dense(384, 128))
embedding = model(input_data)

v = vector(client)
# Store and search embeddings

CUDA

GPU-accelerated vector similarity search:

using NeutronJulia, CUDA

# Vector operations accelerated on NVIDIA GPUs

Makie

Plot TimeSeries data directly:

using NeutronJulia, CairoMakie

ts = timeseries(client)
fig = plot_timeseries!(ts, "cpu_usage", start_ms, end_ms)
save("cpu.png", fig)

Each extension lives in ext/ (NeutronJuliaDataFramesExt.jl, NeutronJuliaDiffEqExt.jl, NeutronJuliaMTKExt.jl, NeutronJuliaGraphsExt.jl, NeutronJuliaFluxExt.jl, NeutronJuliaCUDAExt.jl, NeutronJuliaMakieExt.jl) and requires Julia 1.9+.