Getting started¶

This guide shows the fastest path from install to a working online deep learning model.

deep-river follows the river streaming API while letting you use PyTorch modules for representation learning.

The core workflow is a stream loop: make a prediction for the next item, update a metric, and then call learn_one so the model adapts before the next item arrives.

Install¶

pip install deep-river

or:

pip install "river[deep]"

To use the latest development branch:

pip install https://github.com/online-ml/deep-river/archive/refs/heads/main.zip

Build your first online classifier¶

The example below defines a small PyTorch module, wraps it in a deep-river Classifier, and evaluates it online on river's phishing dataset.

import random

import numpy as np
from river import compose, datasets, metrics, preprocessing
from torch import manual_seed, nn

from deep_river.classification import Classifier

_ = manual_seed(42)
random.seed(42)
np.random.seed(42)

first_x, _ = next(iter(datasets.Phishing()))
n_features = len(first_x)


class MLP(nn.Module):
    def __init__(self, n_features: int):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_features, 16),
            nn.ReLU(),
            nn.Linear(16, 2),
        )

    def forward(self, x):
        return self.net(x)


model = compose.Pipeline(
    preprocessing.StandardScaler(),
    Classifier(
        module=MLP(n_features),
        loss_fn="cross_entropy",
        optimizer_fn="adam",
        lr=1e-3,
        is_class_incremental=True,
    ),
)

metric = metrics.Accuracy()

for i, (x, y) in enumerate(datasets.Phishing().take(200)):
    if i > 0:
        y_pred = model.predict_one(x)
        metric.update(y, y_pred)
    model.learn_one(x, y)

print(f"Accuracy: {metric.get():.4f}")

Core concepts¶

• learn_one updates the model online: each sample is used exactly once in sequence.
• Pipelines are composable: combine river transformers and deep-river estimators.
• PyTorch modules stay flexible: define any architecture you need and pass it to deep-river.
• Metrics update continuously: evaluate stream performance during training.

Where to go next¶

• Use Examples for end-to-end notebooks across tasks.
• Inspect Benchmarks to compare model families.
• Use API Reference for parameter-level documentation.

If something is unclear or broken, please open an issue.