# 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/`)

| Module | Purpose |
|--------|---------|
| `Dala.Ml.Emlx` | iOS-specific EMLX configuration and helpers |
| `Dala.Ml.Nx` | Nx integration helpers and backend selection |
| `Dala.Ml.Example` | Practical examples of using EMLX on iOS |
| `Dala.Ml.ConfigHelper` | Configuration snippets for mix.exs |

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

### Verification
```elixir
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`:
```elixir
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`:
```elixir
# 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:
```elixir
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

```elixir
# 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