synor/sdk/rust/README.md

Synor Compute SDK for Rust

Access distributed heterogeneous compute at 90% cost reduction.

Installation

Add to Cargo.toml:

[dependencies]
synor-compute = "0.1"
tokio = { version = "1", features = ["full"] }

Quick Start

use synor_compute::{SynorCompute, Tensor, Precision, ProcessorType};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = SynorCompute::new("your-api-key");

    // Matrix multiplication on GPU
    let a = Tensor::rand(&[512, 512]);
    let b = Tensor::rand(&[512, 512]);

    let result = client.matmul(&a, &b)
        .precision(Precision::FP16)
        .processor(ProcessorType::Gpu)
        .send()
        .await?;

    if result.is_success() {
        println!("Time: {}ms", result.execution_time_ms.unwrap_or(0));
        println!("Cost: ${}", result.cost.unwrap_or(0.0));
    }

    Ok(())
}

Tensor Operations

// Create tensors
let zeros = Tensor::zeros(&[3, 3]);
let ones = Tensor::ones(&[2, 2]);
let random = Tensor::rand(&[10, 10]);    // Uniform [0, 1)
let randn = Tensor::randn(&[100]);       // Normal distribution
let eye = Tensor::eye(3);                // Identity matrix

// From data
let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
let tensor = Tensor::new(&[2, 3], data);

// Ranges
let range = Tensor::arange(0.0, 10.0, 1.0);
let linspace = Tensor::linspace(0.0, 1.0, 100);

// Operations
let reshaped = tensor.reshape(&[3, 2]);
let transposed = tensor.transpose();

// Math
let mean = tensor.mean();
let sum = tensor.sum();
let std = tensor.std();

// Activations
let relu = tensor.relu();
let sigmoid = tensor.sigmoid();
let softmax = tensor.softmax();

Builder Pattern API

// Matrix multiplication with options
let result = client.matmul(&a, &b)
    .precision(Precision::FP16)
    .processor(ProcessorType::Gpu)
    .priority(Priority::High)
    .send()
    .await?;

// 2D Convolution
let result = client.conv2d(&input, &kernel)
    .stride((1, 1))
    .padding((1, 1))
    .precision(Precision::FP32)
    .send()
    .await?;

// Attention
let result = client.attention(&query, &key, &value)
    .num_heads(8)
    .flash(true)
    .precision(Precision::FP16)
    .send()
    .await?;

LLM Inference

// Single response
let response = client.inference("llama-3-70b", "Explain quantum computing")
    .send()
    .await?;
println!("{}", response.result.unwrap_or_default());

// Streaming with futures
use futures::StreamExt;

let mut stream = client.inference_stream("llama-3-70b", "Write a poem").await?;
while let Some(token) = stream.next().await {
    print!("{}", token?);
}

Configuration

use synor_compute::Config;

let config = Config::new("your-api-key")
    .base_url("https://api.synor.io/compute/v1")
    .default_processor(ProcessorType::Gpu)
    .default_precision(Precision::FP16)
    .timeout_secs(30)
    .debug(true);

let client = SynorCompute::with_config(config);

Error Handling

use synor_compute::{Error, Result};

async fn compute() -> Result<()> {
    let result = client.matmul(&a, &b).send().await?;

    match result.status {
        JobStatus::Completed => println!("Success!"),
        JobStatus::Failed => {
            if let Some(err) = result.error {
                eprintln!("Job failed: {}", err);
            }
        }
        _ => {}
    }
    Ok(())
}

// Pattern matching on errors
match client.matmul(&a, &b).send().await {
    Ok(result) => println!("Result: {:?}", result),
    Err(Error::Api { status_code, message }) => {
        eprintln!("API error {}: {}", status_code, message);
    }
    Err(Error::InvalidArgument(msg)) => {
        eprintln!("Invalid argument: {}", msg);
    }
    Err(e) => eprintln!("Other error: {}", e),
}

Types

// Processor types
ProcessorType::Cpu
ProcessorType::Gpu
ProcessorType::Tpu
ProcessorType::Npu
ProcessorType::Lpu
ProcessorType::Fpga
ProcessorType::Auto  // Automatic selection

// Precision levels
Precision::FP64
Precision::FP32
Precision::FP16
Precision::BF16
Precision::INT8
Precision::INT4

// Job status
JobStatus::Pending
JobStatus::Running
JobStatus::Completed
JobStatus::Failed
JobStatus::Cancelled

// Priority
Priority::Low
Priority::Normal
Priority::High
Priority::Critical

Features

Enable optional features in Cargo.toml:

[dependencies]
synor-compute = { version = "0.1", features = ["serde", "rayon"] }

• serde - Serialization support (enabled by default)
• rayon - Parallel tensor operations

Testing

cargo test

License

MIT