born

Born - Production-Ready ML for Go

"Models are born production-ready"

Born is a modern deep learning framework for Go, inspired by Burn (Rust). Build ML models in pure Go and deploy as single binaries - no Python runtime, no complex dependencies.

Project Status: 🎉 v0.1.0 Initial Release! (MNIST: 97.44% MLP, 98.18% CNN - production-ready)

Celebrating 16 years of Go (2009-2025) with production-ready ML 🎂

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Why Born?

The Problem

Deploying ML models is hard:

• Python runtime required
• Complex dependency management
• Large Docker images
• Slow startup times
• Integration friction with Go backends

The Born Solution

import "github.com/born-ml/born"

// Models "born" ready for production
model := born.Load("resnet50.born")
prediction := model.Predict(image)

// That's it. No Python. No containers. Just Go.

Benefits:

• Single binary deployment
• Fast startup (< 100ms)
• Small memory footprint
• Native Go integration
• Cross-platform out of the box

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Features

• Pure Go - No CGO dependencies, trivial cross-compilation
• Type Safe - Generics-powered API for compile-time guarantees
• Multiple Backends - CPU (SIMD), CUDA, Vulkan, Metal, WebGPU
• Autodiff - Automatic differentiation via decorators
• Production Ready - ONNX support, quantization, serving
• WebAssembly - Run inference in browsers natively
• Single Binary - Models embedded in executables

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Quick Start

Installation

# Clone repository
git clone https://github.com/born-ml/born.git
cd born

# Build
make build

# Or install CLI
make install

Development Setup

Requirements:

• Go 1.25+
• Make (optional, but recommended)
• golangci-lint (for linting)

Build:

make build          # Build all binaries
make test           # Run tests
make lint           # Run linter
make bench          # Run benchmarks

Example: MNIST Classification

Working example included! See examples/mnist/ for complete implementation.

package main

import (
    "github.com/born-ml/born/internal/autodiff"
    "github.com/born-ml/born/internal/backend/cpu"
    "github.com/born-ml/born/internal/nn"
)

func main() {
    // Create backend with autodiff
    backend := autodiff.New(cpu.New())

    // Define model (784 → 128 → 10)
    model := NewMNISTNet(backend)

    // Create loss and optimizer
    criterion := nn.NewCrossEntropyLoss(backend)
    optimizer := optim.NewAdam(model.Parameters(), optim.AdamConfig{
        LR:    0.001,
        Betas: [2]float32{0.9, 0.999},
    }, backend)

    // Training loop
    for epoch := range 10 {
        // Forward pass
        logits := model.Forward(batch.ImagesTensor)
        loss := criterion.Forward(logits, batch.LabelsTensor)

        // Backward pass
        optimizer.ZeroGrad()
        grads := backend.Backward(loss.Raw())
        optimizer.Step(grads)

        // Log progress
        acc := nn.Accuracy(logits, batch.LabelsTensor)
        fmt.Printf("Epoch %d: Loss=%.4f, Accuracy=%.2f%%\n",
            epoch, loss.Raw().AsFloat32()[0], acc*100)
    }
}

Run it: cd examples/mnist && go run .

Phase 1 includes:

• ✅ Tensor operations (Add, MatMul, Reshape, etc.)
• ✅ Automatic differentiation with gradient tape
• ✅ Neural network modules (Linear, ReLU, activations)
• ✅ Optimizers (SGD with momentum, Adam with bias correction)
• ✅ CrossEntropyLoss with numerical stability (log-sum-exp trick)
• ✅ Full MNIST training example

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Architecture

Backend Abstraction

Born uses a backend interface for device independence:

type Backend interface {
    Add(a, b *RawTensor) *RawTensor
    MatMul(a, b *RawTensor) *RawTensor
    // ... other operations
}

Available Backends:

Backend	Status	Description
CPU	Planned	Pure Go with SIMD optimizations
CUDA	Planned	NVIDIA GPU via direct driver calls
Vulkan	Planned	Cross-platform GPU compute
Metal	Planned	Apple GPU (macOS/iOS)
WebGPU	Planned	Modern browser GPU

Decorator Pattern

Functionality composed via decorators (inspired by Burn):

// Basic backend
base := cpu.New()

// Add autodiff
withAutodiff := autodiff.New(base)

// Add kernel fusion
optimized := fusion.New(withAutodiff)

// Your code works with any backend!
model := createModel(optimized)

Type Safety with Generics

type Tensor[T DType, B Backend] struct {
    raw     *RawTensor
    backend B
}

// Compile-time type checking
func (t *Tensor[float32, B]) MatMul(other *Tensor[float32, B]) *Tensor[float32, B]

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Roadmap

Phase 1: Core (v0.1) - ✅ COMPLETE (Nov 2025)

• Tensor API with generics
• CPU backend (pure Go)
• Autodiff decorator with gradient tape
• NN modules (Linear, ReLU, Sigmoid, Tanh, Sequential)
• SGD/Adam optimizers with momentum/bias correction
• CrossEntropyLoss with numerical stability
• MNIST classification example

Status: All 7 core tasks complete. 132 unit tests, 83.8% average coverage, 0 linter issues.

Phase 2: GPU (v0.2) - Q2 2025

• Vulkan backend
• CUDA backend
• Kernel fusion
• Memory optimization

Phase 3: Production (v0.3) - Q3 2025

• ONNX import/export
• Model quantization
• Model serving
• Distributed training

Phase 4: Advanced (v1.0) - Q4 2025

• Metal backend
• WebGPU backend
• Advanced optimizations
• Model zoo

Full roadmap: See project milestones

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Documentation

• Getting Started - Installation and first steps (coming soon)
• API Reference - Complete API documentation (coming soon)
• Examples - Sample code and tutorials (coming soon)

For Contributors

• Contributing - How to contribute (coming soon)

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Philosophy

"Born Ready"

Models trained anywhere (PyTorch, TensorFlow) are imported and born production-ready:

Training → Birth → Production
 (Burn)    (Born)    (Run)

PyTorch trains  →  Born imports  →  Born deploys
TensorFlow trains → Born imports → Born deploys
Born trains    →  Born ready   →  Born serves

Production First

• Single Binary: Entire model in one executable
• No Runtime: No Python, no dependencies
• Fast Startup: < 100ms cold start
• Small Memory: Minimal footprint
• Cloud Native: Natural fit for Go services

Developer Experience

• Type Safe: Catch errors at compile time
• Clean API: Intuitive and ergonomic
• Great Docs: Comprehensive documentation
• Easy Deploy: go build and you're done

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Performance

Benchmarks coming with Phase 1 implementation

Targets (Intel i9-13900K, NVIDIA RTX 4090):

Operation	CPU Target	GPU Target	Speedup
MatMul 1024x1024	< 20ms	< 1ms	20x
Conv2d 224x224	< 100ms	< 3ms	30x
Transformer Block	< 150ms	< 5ms	30x

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Inspiration

Born is inspired by and learns from:

• Burn - Architecture patterns, decorator design
• PyTorch - API ergonomics
• TinyGrad - Simplicity principles
• Gonum - Go numerical computing
• HDF5 for Go - Model serialization, dataset storage (planned)

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Community

Project is in early development. Star the repo to follow progress!

• GitHub Org: github.com/born-ml
• Main Repo: github.com/born-ml/born (repository creation in progress)
• Discussions: GitHub Discussions (coming soon)
• Issues: Report bugs or request features (coming soon)

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License

Licensed under the Apache License, Version 2.0.

Why Apache 2.0?

• ✅ Patent protection - Critical for ML algorithms and production use
• ✅ Enterprise-friendly - Clear legal framework for commercial adoption
• ✅ Industry standard - Same as TensorFlow, battle-tested in ML ecosystem
• ✅ Contributor protection - Explicit patent grant and termination clauses

See LICENSE file for full terms.

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FAQ

Q: Why not use Gorgonia? A: Gorgonia is great but uses a different approach. Born focuses on modern Go (generics), pure Go (no CGO), and production-first design inspired by Burn.

Q: When will it be ready? A: Phase 1 (CPU, basic training) targeted for Q1 2026. Follow development on GitHub.

Q: Can I use PyTorch models? A: Yes! Via ONNX import (Phase 3). Train in PyTorch, deploy with Born.

Q: WebAssembly support? A: Yes! Pure Go compiles to WASM natively. Inference in browsers out of the box.

Q: How can I help? A: Watch this space! Contributing guide coming soon.

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