Quick start

install, then this

dotnet add package EvaluatedApplications.EvalApp.Neural

var app = Eval.App(...)
    .WithResource(...)
    .WithNeuralTuning()
    .DefineDomain(...);

Why it works

online, not frozen

Online learning

Unlike a frozen prior, it reacts to real-time shifts in contention, load, or workload shape. The built-in heuristic, tuned on static assumptions, can't do this.

Proven on real workloads

Matches the built-in heuristic on lean, single-gate pipelines (no edge there — the optimum is obvious), but beats it by roughly 25% on EvalApp.Neural.Train's Sisyphus benchmark, a coupled DB + CPU + disk workload deliberately built to fight itself.

Opt-in, no refactor

Add .WithNeuralTuning() to your app builder; nothing else about the pipeline changes.

How to use it

one line, or process-wide

var app = Eval.App(...)
    .WithResource(...)
    .WithNeuralTuning()
    .DefineDomain(...);

Install it globally for every pipeline in the process (useful if you're running multiple):

using var _ = NeuralTuning.UseGlobally(NeuralTuning.NewModel());

Or train on your own workload by pointing a shared model file at every runner — the model grows as they train:

using var _ = NeuralTuning.UseGloballyPersisted("model.bin");

Key features

what you get

Warm-start

Ships pre-trained on diverse workloads; nothing to configure before it's useful.

Always learning

Real-time adaptation to non-stationary conditions — no manual retuning needed.

Tiny footprint

A small holographic model (HoloFormer, via AlgFormer) that decides in roughly 0.4ms.

No license gate

Runs at full effect regardless of license tier — no feature here is held back for a license key.

Production ready

Coordinate descent handles multi-gate coupling cleanly; deterministic under a repeated seed.

What you need to know

good to know

Best on coupled, noisy workloads

Clean single-gate CPU-bound pipelines won't see much gain — the optimum there is already obvious. Real multi-gate scenarios are where it shines.

Online by default

Always learning from its own decisions. You can freeze it for reproducibility, but production paths always enable learning.

Dependencies

Depends on EvalApp and AlgFormer — AlgFormer ships built into the DLL, no separate install needed.

Growing the warm-start: the shipped model is retrained periodically on a growing set of realistic, coupled multi-resource workloads, and the improved weights ship in each release — you always start from the best warm-start we've measured, and it keeps learning live from your own workload on top of that. Compatible: .NET 8.0+, Windows/Linux. License: proprietary, same terms as every EA product.