modelbatch 0.1.0

Train many independent PyTorch models simultaneously on a single GPU using vectorized operations

ModelBatch

Train many independent PyTorch models simultaneously on a single GPU using vectorized operations.

⚠️ Current Status

ModelBatch is still in active development. Core functionality is tested and working, but the API may be subject to change.

🚀 Quick Start

Installation

From PyPI:

# recommended
uv add modelbatch

# alternative
pip install modelbatch

From source:

uv sync --dev
uv pip install -e ".[dev]"

Basic Example

import torch
from modelbatch import ModelBatch

# Create multiple models
num_models = 4  # choose the number of models to batch
models = [SimpleNet() for _ in range(num_models)]

# Wrap with ModelBatch - that's it!
mb = ModelBatch(models, lr_list=[0.001] * num_models, optimizer_cls=torch.optim.Adam)

# Train normally (but many times faster!), batched across models
for batch in dataloader:
    mb.zero_grad()
    outputs = mb(batch)
    loss = mb.compute_loss(outputs, targets)  
    loss.backward()
    mb.step()

See here for more examples.

📚 Documentation

See docs.

🛠️ Development

Environment Setup

uv sync --dev

Commands

# Tests (currently showing failures)
uv run -m pytest

# Linting  
uv run ruff check --fix . && uv run ruff format .

# Documentation
uv run mkdocs serve

📄 License

This project is licensed under the MIT License.