---
description: The example models, and how to compile one.
---

# Examples

The examples are in https://github.com/belfortlabs/eml-examples. Each `vX.Y.Z` tag of the
repository pins them to that Belfort release. The paths below are relative to
its root:

```bash
git clone https://github.com/belfortlabs/eml-examples
cd eml-examples
```

`quickstart/` holds the [Quickstart](quickstart.md)'s files as a standalone uv
project; its README has the steps.

The directories below hold trained models with their definitions and 2000
samples of test data each.

| Example | What it is | Input shape | Activations |
|---|---|---|---|
| `mlp` | A three-layer MLP classifying MNIST digits | `(1, 1, 28, 28)` | squares |
| `lola` | The LoLA CNN on MNIST: one convolution block, then two linear layers | `(1, 1, 28, 28)` | squares |

Each has `<name>.py` (the `nn.Module`), the trained model (`<name>.pth`), and
`test_data.npz` with the inputs under `X` and the labels under `y`.

## Compiling one

The `.pth` files hold whole pickled modules, so the example's own module has to
be importable when you load one. From the examples' root:

```python title="compile_mlp.py"
import sys

import numpy as np
import torch

import belfort_ml

sys.path.insert(0, "mlp")
net = torch.load("mlp/mlp.pth", weights_only=False).eval()

samples = np.load("mlp/test_data.npz")["X"][:512]
calibration = [torch.from_numpy(x).float().unsqueeze(0) for x in samples]

artifacts = belfort_ml.compile(
    net, calibration[0], "mlp", "./build", calibration_inputs=calibration
)
```

From there, upload it with
`provider.models.get(name="mlp", create_from=artifacts)` as `provider.py` does
in step 4 of the [Quickstart](quickstart.md). The end client passes each input as
a flat list: `client.infer([samples[0].flatten().tolist()])` for the first test
image.