EMLX iOS Integration Summary

This document summarizes the EMLX integration for iOS in the Dala framework.

What Was Added

1. Core Modules (lib/dala/ml/)

2. Documentation

• guides/ios_ml_support.md - Simplified guide covering:

  • Quick start instructions
  • EMLX configuration for iOS devices vs simulator
  • Build instructions
  • Limitations and troubleshooting

• AGENTS.md - Updated with simplified iOS ML Support section

Key Features

Platform Detection

Dala.Ml.Emlx.ios_device?()    # true for real iOS device
Dala.Ml.Emlx.ios_simulator?() # true for simulator
Dala.Ml.Emlx.platform_config() # returns appropriate config

Automatic Backend Selection

Dala.Ml.Nx.init_for_ios()
# Automatically selects EMLX (if available) or falls back to Nx.BinaryBackend

Verification

Dala.Ml.Emlx.available?()           # check if EMLX is working
Dala.Ml.Emlx.verify_installation()  # test with a simple tensor operation
Dala.Ml.Emlx.benchmark()           # run a simple performance test

Usage in a Dala iOS App

Step 1: Add Dependencies

In your app's mix.exs:

def deps do
  [
    {:nx, github: "elixir-nx/nx", sparse: "nx"},
    {:axon, "~> 0.6"},
    {:emlx, github: "elixir-nx/emlx", branch: "main"}
  ]
end

Step 2: Configure

In config/config.exs:

# Disable JIT for iOS devices
config :emlx, jit_enabled: false

# Use Metal GPU (recommended for Apple Silicon)
config :nx, :default_backend, {EMLX.Backend, device: :gpu}

Step 3: Initialize

In your app's startup:

defmodule MyApp.App do
  use Dala.App

  def start(_type, _args) do
    Dala.Ml.Nx.init_for_ios()
    # ... rest of app
  end
end

Step 4: Use ML

# Create tensors
tensor = Nx.tensor([1.0, 2.0, 3.0])

# Matrix operations
a = Nx.tensor([[1.0, 2.0], [3.0, 4.0]])
b = Nx.tensor([[5.0], [6.0]])
result = Nx.dot(a, b)  # Runs on GPU via EMLX

Important Constraints

1. No JIT on iOS devices - W^X policy blocks JIT. Use LIBMLX_ENABLE_JIT=false.
2. Metal GPU available - EMLX uses MLX with Metal on iOS devices and simulator.
3. Unified memory - Apple Silicon's shared CPU/GPU memory makes EMLX efficient.
4. No 64-bit floats - Metal doesn't support them. Use 32-bit floats. result = Nx.dot(a, b) # Runs on GPU via EMLX

   
   ## Repository Analysis Summary
   
   | Repository | iOS Support | Notes |
   |------------|--------------|-------|
   | **Nx** | ✅ Ready | Pure Elixir, works on any platform |
   | **Axon** | ✅ Ready | Neural networks, pure Elixir |
   | **EMLX** | ⚠️ Setup needed | **Recommended for iOS** |
   
   **Not supported on iOS:**
   - Emily (macOS-only)
   - NxIREE (IREE runtime doesn't target iOS)
   - EXLA/XLA (XLA doesn't target iOS)
   - Torchx (requires LibTorch cross-compile)
   
   ## Files Modified/Created
   
   ### New Files
   - `lib/dala/ml/emlx.ex` - EMLX integration module
   - `lib/dala/ml/nx.ex` - Nx helpers
   - `lib/dala/ml/example.ex` - Usage examples
   - `lib/dala/ml/config_helper.ex` - Configuration helper
   - `guides/ios_ml_support.md` - Complete iOS ML guide
   - `guides/emlx_ios_summary.md` - This summary
   
   ### Modified Files
   - `AGENTS.md` - Added iOS ML Support section
   
   ## Next Steps
   
   1. **Test in iOS Simulator** - Verify EMLX works in iOS simulator
   2. **Test on iOS Device** - Cross-compile MLX for iOS arm64 and test
   3. **Add Precompiled Binaries** - Consider providing precompiled MLX iOS binaries
   4. **Integration Tests** - Add tests for the Dala.ML modules
   5. **Update dala_new Templates** - Add EMLX configuration to project templates