Burn Store

Advanced model storage and serialization for the Burn deep learning framework

A comprehensive storage library for Burn that enables efficient model serialization, cross-framework interoperability, and advanced tensor management.

Migrating from burn-import? See the Migration Guide for help moving from PyTorchFileRecorder/SafetensorsFileRecorder to the new Store API.

Features

• Burnpack Format - Native Burn format with CBOR metadata, memory-mapped loading, ParamId persistence for stateful training, and no-std support
• SafeTensors Format - Industry-standard format for secure and efficient tensor serialization
• PyTorch Support - Direct loading of PyTorch .pth/.pt files with automatic weight transformation
• Zero-Copy Loading - Memory-mapped files and lazy tensor materialization for optimal performance
• Flexible Filtering - Load/save specific model subsets with regex, exact paths, or custom predicates
• Tensor Remapping - Rename tensors during load/save for framework compatibility
• Half-Precision Storage - Automatic F32/F16 conversion with smart defaults for reduced model file size
• No-std Support - Burnpack and SafeTensors formats available in embedded and WASM environments

Quick Start

use burn_store::{ModuleSnapshot, PytorchStore, SafetensorsStore, BurnpackStore, HalfPrecisionAdapter};

// Load from PyTorch
let mut store = PytorchStore::from_file("model.pt");
model.load_from(&mut store)?;

// Load from SafeTensors (with PyTorch adapter)
let mut store = SafetensorsStore::from_file("model.safetensors")
    .with_from_adapter(PyTorchToBurnAdapter);
model.load_from(&mut store)?;

// Save to Burnpack
let mut store = BurnpackStore::from_file("model.bpk");
model.save_into(&mut store)?;

// Save with half-precision (F32 -> F16, ~50% smaller files)
let adapter = HalfPrecisionAdapter::new();
let mut store = BurnpackStore::from_file("model_f16.bpk")
    .with_to_adapter(adapter.clone());
model.save_into(&mut store)?;

// Load half-precision back (F16 -> F32, same adapter)
let mut store = BurnpackStore::from_file("model_f16.bpk")
    .with_from_adapter(adapter);
model.load_from(&mut store)?;

Documentation

For comprehensive documentation including:

• Exporting weights from PyTorch
• Loading weights into Burn models
• Saving models to various formats
• Advanced features (filtering, remapping, partial loading, zero-copy)
• API reference and troubleshooting

See the Burn Book - Saving and Loading chapter.

Running Benchmarks

# Generate model files (one-time setup)
uv run benches/generate_unified_models.py

# Run loading benchmarks
cargo bench --bench unified_loading

# Run saving benchmarks
cargo bench --bench unified_saving

# With specific backend
cargo bench --bench unified_loading --features metal

License

This project is dual-licensed under MIT and Apache-2.0.