paddle-inference-rs 0.1.0

# paddle-inference-rs


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Rust bindings for PaddlePaddle inference library, providing safe and ergonomic access to PaddlePaddle's C API for deep learning inference.

## Features


- **Safe Rust API**: Type-safe wrappers around PaddlePaddle's C inference API
- **Cross-platform**: Supports Windows, Linux, and macOS
- **Async support**: Optional async/await support for inference operations
- **Memory safe**: Proper resource management with RAII patterns
- **Zero-cost abstractions**: Minimal overhead compared to direct C API usage

## Installation


Add this to your `Cargo.toml`:

```toml
[dependencies]
paddle-inference-rs = "0.1.0"
```

## Prerequisites


You need to have the PaddlePaddle inference library installed. The library expects the following structure:

```
paddle/
├── include/
│   ├── pd_common.h
│   ├── pd_config.h
│   ├── pd_inference_api.h
│   ├── pd_predictor.h
│   ├── pd_tensor.h
│   ├── pd_types.h
│   └── pd_utils.h
└── lib/
    ├── paddle_inference_c.dll (Windows)
    ├── paddle_inference_c.so (Linux)
    └── paddle_inference_c.dylib (macOS)
```

## Usage


### Basic Example


```rust
use paddle_inference_rs::{Config, Predictor, PrecisionType, PlaceType};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create configuration
    let config = Config::new()?
        .set_model("model_dir", "model_file", "params_file")?
        .enable_gpu(0)?
        .set_precision(PrecisionType::Float32)?
        .enable_memory_optim()?;

    // Create predictor
    let predictor = Predictor::create(config)?;

    // Get input and output names
    let input_names = predictor.get_input_names();
    let output_names = predictor.get_output_names();

    // Prepare input data
    let mut input_tensor = predictor.get_input_handle(&input_names[0])?;
    input_tensor.reshape(&[1, 3, 224, 224])?;
    
    // Copy data to tensor (example with random data)
    let input_data = vec![0.0f32; 1 * 3 * 224 * 224];
    input_tensor.copy_from_cpu(&input_data)?;

    // Run inference
    predictor.run()?;

    // Get output
    let output_tensor = predictor.get_output_handle(&output_names[0])?;
    let output_shape = output_tensor.get_shape()?;
    let mut output_data = vec![0.0f32; output_shape.iter().product()];
    output_tensor.copy_to_cpu(&mut output_data)?;

    println!("Inference completed successfully!");
    println!("Output shape: {:?}", output_shape);
    println!("Output data: {:?}", &output_data[0..5]);

    Ok(())
}
```

### Advanced Example with Async


```rust
use paddle_inference_rs::{Config, Predictor};
use tokio::task;

async fn async_inference() -> Result<(), Box<dyn std::error::Error>> {
    let config = Config::new()?
        .set_model("model_dir", "model_file", "params_file")?
        .enable_gpu(0)?;

    // Run inference in a separate thread
    let result = task::spawn_blocking(move || {
        let predictor = Predictor::create(config)?;
        predictor.run()?;
        Ok(())
    }).await??;

    Ok(())
}
```

## API Overview


### Config

- Model configuration and optimization settings
- Hardware backend selection (CPU/GPU/XPU)
- Precision settings (FP32/FP16/INT8)
- Memory optimization options

### Predictor

- Main inference interface
- Input/output tensor management
- Batch inference support
- Thread-safe operations

### Tensor

- Multi-dimensional data container
- Data type support (Float32, Int32, Int64, UInt8, Int8)
- Shape manipulation and data copying
- Lod (Level of Detail) support for variable-length sequences

## Building from Source


1. Clone the repository:
```bash
git clone https://github.com/your-username/paddle-inference-rs.git

cd paddle-inference-rs

```

2. Build with cargo:
```bash
cargo build --release

```

3. For binding generation (requires bindgen):
```bash
cargo build --features gen

```

## Platform Support


- **Windows**: Requires Visual Studio build tools and PaddlePaddle Windows binaries
- **Linux**: Requires gcc/clang and PaddlePaddle Linux binaries
- **macOS**: Requires Xcode command line tools and PaddlePaddle macOS binaries

## Performance


The library provides near-native performance with minimal overhead:
- <1% overhead compared to direct C API calls
- Zero-copy data transfer when possible
- Efficient memory management with RAII
- Thread-safe operations for concurrent inference

## Contributing


Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License


This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments


- PaddlePaddle team for the excellent inference library
- Rust community for amazing tools and libraries
- Contributors and users of this crate

## Support


If you encounter any issues or have questions:

1. Check the [documentation](https://docs.rs/paddle-inference-rs)
2. Search existing [issues](https://github.com/your-username/paddle-inference-rs/issues)
3. Create a new issue with detailed information

## Version Compatibility


| paddle-inference-rs | PaddlePaddle | Rust |
|---------------------|--------------|------|
| 0.1.x               | 2.4+         | 1.65+|

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