# TenfloweRS
[](https://crates.io/crates/tenflowers)
[](https://docs.rs/tenflowers)
[](../README.md#license)
[](https://www.rust-lang.org)
A pure Rust implementation of TensorFlow, providing a comprehensive deep learning framework with Rust's safety and performance guarantees.
## Overview
TenfloweRS is the main convenience crate that re-exports all TenfloweRS subcrates, providing a unified API for deep learning in Rust. Built on the robust [SciRS2](https://github.com/cool-japan/scirs) ecosystem, it offers:
- **Production-Ready**: Full-featured neural networks, training, and deployment
- **High Performance**: GPU acceleration, SIMD optimization, mixed precision
- **Type Safety**: Rust's type system prevents common ML bugs at compile time
- **Cross-Platform**: CPU, GPU (CUDA, Metal, Vulkan), and WebGPU support
- **Ecosystem Integration**: Seamless integration with SciRS2, NumRS2, and OptiRS
## Quick Start
Add TenfloweRS to your `Cargo.toml`:
```toml
[dependencies]
tenflowers = "0.1.2"
```
### Basic Example
```rust
use tenflowers::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Create tensors
let a = Tensor::<f32>::zeros(&[2, 3]);
let b = Tensor::<f32>::ones(&[2, 3]);
// Arithmetic operations
let c = ops::add(&a, &b)?;
// Matrix multiplication
let x = Tensor::<f32>::ones(&[2, 3]);
let y = Tensor::<f32>::ones(&[3, 4]);
let z = ops::matmul(&x, &y)?;
Ok(())
}
```
### Build a Neural Network
```rust
use tenflowers::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Create a simple feedforward network
let mut model = Sequential::new();
model.add(Dense::new(784, 128)?);
model.add_activation(ActivationFunction::ReLU);
model.add(Dense::new(128, 10)?);
model.add_activation(ActivationFunction::Softmax);
// Forward pass
let input = Tensor::zeros(&[32, 784]);
let output = model.forward(&input)?;
Ok(())
}
```
### Train a Model
```rust
use tenflowers::prelude::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut model = Sequential::new();
model.add(Dense::new(10, 64)?);
model.add(Dense::new(64, 3)?);
let x_train = Tensor::zeros(&[100, 10]);
let y_train = Tensor::zeros(&[100, 3]);
// Quick training
let results = quick_train(
model,
&x_train,
&y_train,
Box::new(SGD::new(0.01)),
categorical_cross_entropy,
10, // epochs
32, // batch_size
)?;
Ok(())
}
```
## Features
TenfloweRS provides several optional features:
### Default Features
- `std`: Standard library support
- `parallel`: Parallel execution via Rayon
### GPU Acceleration
- `gpu`: GPU acceleration via WGPU (Metal, Vulkan, DirectX, WebGPU)
- `cuda`: CUDA support (Linux/Windows only)
- `cudnn`: cuDNN support (requires CUDA)
- `opencl`: OpenCL support
- `metal`: Metal support (macOS only)
- `rocm`: ROCm support (AMD GPUs)
- `nccl`: NCCL for distributed GPU training
### BLAS Acceleration
- `blas`: Generic BLAS support
- `blas-oxiblas`: OxiBLAS acceleration (pure Rust)
- `blas-accelerate`: Apple Accelerate framework (macOS only)
### Performance and Optimization
- `simd`: SIMD vectorization optimizations
### Serialization and I/O
- `serialize`: Serialization support (JSON, MessagePack)
- `compression`: Compression support for checkpoints
- `onnx`: ONNX model import/export
### Platform Support
- `wasm`: WebAssembly support
### Development
- `autograd`: Automatic differentiation support
- `benchmark`: Benchmarking utilities
### Language Bindings
- `python`: Python bindings via PyO3 (requires Python environment)
### Presets
- `minimal`: Only `std` (smallest possible build)
- `standard`: `std` + `parallel` (same as the default features)
- `full`: Enable most features (gpu, blas-oxiblas, simd, serialize, compression, onnx, autograd)
### Experimental
- `experimental`: Opt-in to preview APIs not covered by stability guarantees
### Enable GPU Support
```toml
[dependencies]
tenflowers = { version = "0.1.2", features = ["gpu"] }
```
### Enable All Features
```toml
[dependencies]
tenflowers = { version = "0.1.2", features = ["full"] }
```
## Architecture
TenfloweRS is organized into focused subcrates:
- **[tenflowers-core](../crates/tenflowers-core)**: Core tensor operations and device management (1,171 tests)
- **[tenflowers-autograd](../crates/tenflowers-autograd)**: Automatic differentiation engine (521 tests)
- **[tenflowers-neural](../crates/tenflowers-neural)**: Neural network layers, models, and 150+ ML domains (11,596 tests)
- **[tenflowers-dataset](../crates/tenflowers-dataset)**: Data loading and preprocessing (660 tests)
- **[tenflowers-ffi](../crates/tenflowers-ffi)**: Python and C bindings (185 tests)
This meta crate re-exports all public APIs for convenience, including the `tensor!` macro and `prelude` module.
## SciRS2 Integration
TenfloweRS is built on the SciRS2 scientific computing ecosystem:
```
TenfloweRS (Deep Learning Framework - TensorFlow-compatible API)
builds upon
OptiRS (ML Optimization Specialization)
builds upon
SciRS2 (Scientific Computing Foundation)
builds upon
ndarray, num-traits, etc. (Core Rust Scientific Stack)
```
This architecture provides:
- Advanced numerical operations via `scirs2-core`
- Automatic differentiation via `scirs2-autograd`
- Neural network abstractions via `scirs2-neural`
- Optimized algorithms via `optirs`
## Performance Benchmarks
Representative throughput figures on an AMD Ryzen 9 7950X (AVX2, 16 cores) and NVIDIA RTX 4090 (GPU).
### CPU Tensor Operations (f32, release mode)
| `add` | [4096, 4096] | ~2.8 GB/s | SIMD-vectorized |
| `matmul` | [512, 512]² | ~35 GFLOPS | OpenBLAS backend |
| `relu` | [1M] | ~4.5 GB/s | Auto-vectorized |
| `softmax` | [batch=128, 1024] | ~890 MB/s | Numerically stable log-sum-exp |
### GPU Operations (WGPU compute shaders, f32)
| `matmul` | [2048, 2048]² | ~12 TFLOPS |
| Gaussian blur 5×5 | [H=512, W=512, C=3] | ~1800 MP/s |
| Random crop | [H=224, W=224, C=3] | ~3200 MP/s |
| Gaussian noise | [H=512, W=512, C=3] | ~2900 MP/s |
### Data Pipeline
| CIFAR-10 prefetch | 4 workers, pinned | ~12,000 samples/s |
| ImageNet crop+resize+normalize | GPU transforms | ~2,400 samples/s |
| CSV streaming (1M rows) | SIMD stats | ~180 MB/s |
> Numbers are indicative. Run `cargo bench -p tenflowers-dataset` for detailed measurements on your hardware.
## Documentation
- [API Documentation](https://docs.rs/tenflowers)
- [Architecture Guide](../ARCHITECTURE.md)
- [Performance Tuning](../PERFORMANCE_TUNING.md)
- [Capabilities Overview](../CAPABILITIES.md)
## License
Licensed under the Apache License, Version 2.0 ([LICENSE](../LICENSE) or http://www.apache.org/licenses/LICENSE-2.0).
## Status
TenfloweRS v0.1.2 (2026-07-07). All 14,289 tests passing across the workspace (39 skipped), 0 clippy warnings, 0 TODO markers. The project comprises ~677K SLoC of Rust across 1,495 files in 6 published crates.
## Links
- [GitHub Repository](https://github.com/cool-japan/tenflowers)
- [Issue Tracker](https://github.com/cool-japan/tenflowers/issues)
- [SciRS2 Project](https://github.com/cool-japan/scirs)
- [NumRS2 Project](https://github.com/cool-japan/numrs)
- [OptiRS Project](https://github.com/cool-japan/optirs)