WireML

Teachable Machine, re-imagined as a terminal TUI on modern foundation models. Auto-detects CUDA · MPS · MLX · ROCm · DirectML · XPU · CPU.

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Install

One line. Installs uv if needed, then installs WireML as an isolated global tool.

curl -fsSL https://raw.githubusercontent.com/tejasnaladala/wireml/main/install.sh | sh

Or, if you already have uv:

uv tool install git+https://github.com/tejasnaladala/wireml

To pull the ML extras (CLIP / DINOv2 / Torch / Transformers):

WIREML_EXTRAS=ml curl -fsSL https://raw.githubusercontent.com/tejasnaladala/wireml/main/install.sh | sh
# or
uv tool install "git+https://github.com/tejasnaladala/wireml" --with "wireml[ml]"

Run

wireml                       # launch the TUI
wireml device                # show the detected compute device
wireml templates             # list built-in templates
wireml run demo-synthetic    # headless run — no downloads needed

What you get

A dark, tight, single-binary-feel terminal workbench:

┌─ WireML · node-graph ML on foundation models — terminal edition ──────────────┐
│                                                                               │
│ ┌ TEMPLATES ─────────────────┐   ┌ DEVICE ────────────────────┐               │
│ │ ▸ Synthetic demo           │   │ CUDA     RTX 4090          │               │
│ │   Image classifier         │   │          24.0 GB VRAM      │               │
│ │   k-NN (no training)       │   │          sm_89             │               │
│ └────────────────────────────┘   │                             │               │
│                                   │ SHORTCUTS                   │               │
│                                   │   ↑↓    move selection      │               │
│                                   │   Enter open template       │               │
│                                   │   r     run synthetic demo  │               │
│                                   │   q     quit                │               │
│                                   └─────────────────────────────┘               │
│                                                                               │
└─ wireml 0.2.0 ───────────────────────────────────────────────────────────────┘

Every template opens a pipeline view with a run log and a results table.

How it works

WireML is a linear-pipeline executor on top of a small node catalog. Templates are pre-wired pipelines of stages; each stage delegates to a Python runner:

data.synthetic  →  backbone.identity  →  head.linear  →  eval.accuracy

The engine walks stages in order, routes outputs by port name, and reports progress via a callback the TUI renders live.

Node catalog (v1):

Category	Nodes
Data	data.synthetic · data.upload
Backbone	backbone.clip.vit-b-32 · backbone.clip.vit-l-14 · backbone.dinov2.small · backbone.identity
Head	head.linear · head.knn
Eval	eval.accuracy · eval.confusion
Deploy	deploy.export-onnx

wireml[ml] extras add the CLIP / DINOv2 backbones via PyTorch + Transformers. The synthetic + k-NN templates run without them.

Device support

wireml device reports the best backend the current machine exposes. Detection priority:

1. CUDA (NVIDIA)
2. MLX (Apple Silicon) — fastest path on M-series Macs (wireml[mlx])
3. MPS (Apple Silicon fallback) — PyTorch native Metal
4. ROCm (AMD) — auto-detected when torch has version.hip set
5. DirectML (Windows) — via onnxruntime-directml (wireml[directml])
6. XPU (Intel) — via torch.xpu
7. CPU (always available)

Development

git clone https://github.com/tejasnaladala/wireml
cd wireml
uv sync --extra dev
uv run pytest              # tests (no model downloads)
uv run ruff check wireml tests
uv run wireml              # launch TUI against your checkout

Repo layout

wireml/
├── wireml/                     the Python package
│   ├── cli.py                  typer entry (launches TUI by default)
│   ├── device.py               CUDA / MPS / MLX / ROCm / DirectML autodetect
│   ├── engine.py               linear pipeline executor + runner registry
│   ├── registry.py             NodeSchema catalog
│   ├── templates.py            canonical pre-wired pipelines
│   ├── schema.py               data types (DeviceInfo, NodeSchema, Pipeline…)
│   ├── nodes/                  runner implementations (data, backbones, heads, eval)
│   └── tui/                    Textual app, screens, and theme
├── tests/                      pytest suite (no model downloads in CI)
├── docs/                       specs, audit, and historical prompt packs
├── install.sh                  one-line installer
├── pyproject.toml              hatchling package
└── README.md

License

MIT — see LICENSE.