ExBurn

Status: Early development. Not yet ready for production use.

ExBurn is a middle layer between Nx and Burn that enables GPU-accelerated ML/DL on mobile and desktop devices.

Architecture

Axon model
   ↓
Nx.Defn graph
   ↓
ExBurn.Defn.Compiler (Nx.Defn.Compiler behaviour)
   ↓
ExBurn.Backend (Nx.Backend behaviour)
   ↓
ExBurn.Nif (Rustler NIF) ←→ ExCubecl (GPU buffers, kernels, pipelines)
   ↓
Burn Autodiff<CubeCL> (Rust)
   ↓
CubeCL kernels
   ↓
Metal (iOS) / Vulkan (Android) / CUDA → GPU

Status

Version 0.6.0 — Early Alpha

⚠️ Note: This library is in early development. The API may change between minor versions. Not yet recommended for production use.

Known limitations

• Dtype support: The NIF stores tensors as f32 only. Other dtypes are value-converted to f32 at the backend boundary (the direct tensor API raises instead). Full dtype preservation is planned for a future release.
• Gradient computation: The default gradient method is :numerical (finite differences). Autodiff gradients are planned but not yet implemented.
• Global backend mutation: By default, Training.fit/3 no longer changes the global Nx backend. Pass set_default_backend?: true to restore the previous behavior.
• Evaluation loss: Loss is now correctly weighted by sample count when batches have different sizes.

Direct NIF operation constraints

When calling ExBurn.BurnBridge / ExBurn.Nifdirectly (bypassing the Nx backend, which normalizes these cases), the Rust layer currently imposes:

The Nx.Backend layer (ExBurn.Backend) lifts several of these by computing general cases exactly via Nx — prefer going through Nx unless you need the raw single-NIF-call path.

Features

• Nx Backend: Nx.Backend behaviour implementation — partial drop-in replacement for Nx.BinaryBackend with some dtype limitations
• Nx Defn Compiler: Custom Nx.Defn.Compiler that executes defn expressions on the Burn GPU backend
• GPU Acceleration: Burn's CubeCL backend with CUDA (NVIDIA), Metal (Apple), Vulkan (Android)
• ExCubecl Integration: GPU buffer management, kernel execution, async commands, and pipeline orchestration via ExCubecl
• Autodiff: Planned automatic differentiation via Burn's Autodiff backend decorator; currently uses numerical gradients
• Training Loop: Complete training with Adam, SGD, RMSprop optimizers, LR scheduling, gradient clipping, callbacks
• Model Management: Save/load, serialize, quantize (f16), benchmark
• Structured Errors: ExBurn.Error exception type with operation context

Quick Start

1. Install

Add ex_burn to your mix.exs:

def deps do
  [
    {:ex_burn, "~> 0.5"},
    {:nx, ">= 0.12.0 and < 2.0.0"},
    {:axon, "~> 0.8"}
  ]
end

mix deps.get
mix compile   # first build compiles the Rust NIF — expect a few minutes

The NIF builds CPU-only by default. For GPU acceleration see GPU Backends below.

2. Verify the installation

# In iex -S mix:
ExBurn.smoke_test()
#=> :ok                       ← Nx → Backend → NIF → Burn pipeline works

ExBurn.summary()
#=>
# ExBurn v0.5.0
# ──────────────────────────────
# Device: Metal (Apple M2 Pro)  ← or "NdArray (CPU)" without GPU features
# GPU: available
# Backends: metal

3. Tensor operations through Burn

# Set ExBurn as the default Nx backend
Nx.default_backend(ExBurn.Backend)
# …or equivalently: ExBurn.configure!()

t = Nx.tensor([1.0, 2.0, 3.0])
Nx.add(t, t) |> Nx.to_list()
#=> [2.0, 4.0, 6.0]

4. GPU-accelerated functions with defn

# Set ExBurn as both backend and compiler
Nx.default_backend(ExBurn.Backend)
Nx.Defn.global_default_options(compiler: ExBurn.Defn.Compiler)

defmodule MyMath do
  import Nx.Defn

  defn add_and_scale(x, y, scale) do
    x
    |> Nx.add(y)
    |> Nx.multiply(scale)
  end
end

result = MyMath.add_and_scale(Nx.tensor([1.0, 2.0]), Nx.tensor([3.0, 4.0]), Nx.tensor(2.0))
Nx.to_list(result)
#=> [8.0, 12.0]

To use the compiler for a single call only (no global setting):

Nx.Defn.jit_apply(&MyMath.add_and_scale/3, [x, y, scale],
  compiler: ExBurn.Defn.Compiler
)

5. Train a model with Axon

model =
  Axon.input("input", shape: {nil, 784})
  |> Axon.dense(256, activation: :relu)
  |> Axon.dropout(rate: 0.2)
  |> Axon.dense(10)

compiled =
  ExBurn.Model.compile(model,
    loss: :cross_entropy,
    optimizer: :adam,
    learning_rate: 0.001
  )

trained =
  ExBurn.Training.fit(compiled, {train_x, train_y},
    epochs: 10,
    batch_size: 32,
    validation_data: {val_x, val_y},
    callbacks: [&ExBurn.Training.LoggingCallback.log/1]
  )

{:ok, output} = ExBurn.Model.forward(trained, input_tensor)

6. Batched inference with Nx.Serving

serving =
  ExBurn.Serving.new(trained, batch_size: 8, batch_timeout: 10)

%Nx.Serving{} = result = Nx.Serving.run(serving, Nx.Batch.stack([input1, input2]))
output = result.output

Prerequisites

• Elixir ~> 1.18 and OTP 27+
• Rust stable toolchain (required for NIF compilation)

  curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
  

• For iOS development: Xcode + aarch64-apple-ios target

  rustup target add aarch64-apple-ios
  

• For Android development: Android NDK + aarch64-linux-android target

  rustup target add aarch64-linux-android
  

Note: Precompiled NIF binaries are planned. Until then, a Rust toolchain is required to build the NIF from source.

Training on Mobile — Caveats

Burn's Autodiff backend is memory-intensive. On iOS/Android with limited RAM, training even small models may cause out-of-memory errors. Realistic expectations:

• Fine-tuning small models (< 10M parameters) is feasible on modern devices
• Full training of large models is not recommended on mobile
• Inference is the primary use case for mobile deployment
• Minimum recommended: 4GB RAM, A12+ chip (iOS) / Snapdragon 700+ (Android)

The training loop in ExBurn currently uses numerical gradients (finite differences). Two methods are available: :numerical (central differences, more accurate) and :numerical_batch (one-sided, ~2x faster). Burn's autodiff integration is planned for v0.3.0 and will replace numerical gradients entirely.

Examples

# Linear regression (simplest possible ML workflow)
mix run examples/linear_regression.exs

# MNIST-like classifier (full deep learning pipeline)
mix run examples/mnist_simple.exs

# XOR classifier (non-linear problem, early stopping, model summary)
mix run examples/xor_classifier.exs

# Dataset utilities (split, normalize, one-hot, data loaders)
mix run examples/dataset_utils.exs

# BurnBridge direct tensor operations (arithmetic, math, linear algebra)
mix run examples/burn_bridge_ops.exs

# Model management (save/load, quantize, freeze, benchmark, export)
mix run examples/model_management.exs

# Training callbacks (logging, early stopping, checkpoint, LR scheduling)
mix run examples/training_callbacks.exs

Benchmarks

Benchmark scripts in bench/ compare ExBurn (Burn GPU backend) against plain Nx (BinaryBackend) across tensor sizes from 10×10 to 2000×2000.

# Tensor creation (zeros, ones, rand)
mix run bench/tensor_creation_bench.exs

# Element-wise arithmetic (add, mul, exp)
mix run bench/arithmetic_bench.exs

# Linear algebra (matmul, transpose)
mix run bench/linear_algebra_bench.exs

# Nx <-> Burn tensor conversion overhead
mix run bench/conversion_bench.exs

# End-to-end training (small/medium MLPs, optimizer comparison)
mix run bench/training_bench.exs

# Inference latency and throughput (single + batched + Nx.Serving)
mix run bench/serving_bench.exs

Each script prints a formatted table with timing results. All benchmarks include warmup runs and report averaged measurements.

Testing

mix test                          # full suite (~600 tests, <30s)
mix test test/backend_test.exs    # single file
mix test --only nif               # only NIF-backed tests
mix test --cover                  # suite + coverage report (threshold enforced)

Test tags (:nif, :cuda, :metal, :vulkan) are excluded automatically when the NIF or matching GPU hardware isn't available — see test/test_helper.exs.

Troubleshooting

Contributing

See CONTRIBUTING.md for the full development workflow: environment setup, running tests and coverage, linting, and a step-by-step walkthrough for adding new operations.

Guides

• Getting Started — Installation, basic ops, GPU check
• Training Models — Models, training, callbacks, save/load
• Mobile Deployment — iOS/Android compilation, optimization
• Architecture — Deep-dive into the pipeline
• Training Optimization — Best practices for fast, stable training
• Deep Learning Guide — Step-by-step lessons for learning DL with ExBurn
• Benchmarks — Performance benchmarks and how to run them
• Contributing — Development workflow, testing, adding operations

Project Structure

lib/ex_burn/
  ex_burn.ex          — Main API (version, configure!, default_device)
  defn_compiler.ex    — Nx.Defn.Compiler for GPU-accelerated defn
  backend.ex          — Nx.Backend implementation (delegates to Burn via NIF)
  nif.ex              — Rustler NIF stubs (40+ functions)
  tensor.ex           — Nx ↔ Burn tensor conversion utilities
  error.ex            — Structured error type (ExBurn.Error)
  burn_bridge.ex      — High-level Burn API (direct tensor ops)
  cubecl_bridge.ex    — GPU compute via ExCubecl (buffers, kernels, pipelines)
  model.ex            — Model definition, compilation, save/load
  training.ex         — Training loop (optimizers, LR schedules, callbacks)

native/ex_burn_nif/
  src/lib.rs          — Rust NIF with real Burn Autodiff<CubeCL> operations
  Cargo.toml          — Burn 0.21 + CubeCL + Autodiff dependencies

examples/
  linear_regression.exs  — Simplest ML workflow
  mnist_simple.exs        — Full deep learning pipeline
  xor_classifier.exs      — Non-linear classification with early stopping
  dataset_utils.exs       — Data preprocessing utilities
  burn_bridge_ops.exs     — Direct Burn tensor operations
  model_management.exs    — Save/load, quantize, freeze, benchmark
  training_callbacks.exs  — All callback types + custom callbacks

bench/
  tensor_creation_bench.exs   — zeros/ones/rand: Nx vs Burn
  arithmetic_bench.exs       — add/mul/exp: Nx vs Burn
  linear_algebra_bench.exs   — matmul/transpose: Nx vs Burn
  conversion_bench.exs       — Nx<->Burn conversion overhead
  training_bench.exs         — End-to-end training performance
  serving_bench.exs          — Inference latency & throughput

guides/
  01_getting_started.md   — Installation, basic ops, GPU check
  02_training.md          — Models, training, callbacks, save/load
  03_mobile_deployment.md — iOS/Android compilation, optimization
  04_architecture.md      — Deep-dive into the pipeline

GPU Backends

CUDA Support

The NIF compiles without a GPU backend by default (CPU-only NdArray). To build with GPU acceleration, use the bundled helper script, which auto-detects the best backend for your platform:

./build.sh              # auto-detect: cuda / metal / vulkan / cpu
./build.sh metal        # force a specific backend

Or set the Cargo feature manually before compiling:

RUSTLER_NIF_CARGO_FEATURES=cuda mix compile

On systems without the requested GPU hardware, the NIF automatically falls back to the NdArray (CPU) backend at runtime.

Check CUDA availability from Elixir:

ExBurn.cuda_available?()   # true if NVIDIA GPU detected
ExBurn.device_name()       # "CUDA (NVIDIA GPU)" or "NdArray (CPU)"
ExBurn.device_info()       # full device info map

Error Handling

All operations raise ExBurn.Error with structured context:

raise ExBurn.Error,
  op: :matmul,
  reason: "shape mismatch",
  details: %{lhs: [3, 4], rhs: [5, 6]}

Dependencies

• Burn — Deep learning framework (Rust)
• Nx — Numerical Elixir
• Axon — Neural network library
• CubeCL — GPU compute language
• ExCubecl v0.5+ — GPU compute runtime for Elixir (buffers, kernels, pipelines, media)

Topics: elixir · machine-learning · burn · ios · android · nx · rustler · gpu · deep-learning

License

Apache 2.0