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:

Terminal

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

quickstart/ holds the Quickstart'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:

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. The end client passes each input as a flat list: client.infer([samples[0].flatten().tolist()]) for the first test image.