{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RusTorch CoreML Integration - Python Bindings\n",
"\n",
"このノートブックは、PythonバインディングでRusTorchのCoreML機能を使用する方法を示します。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## セットアップとインポート"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# RusTorchのPythonバインディングをインポート\n",
"try:\n",
" import rustorch\n",
" print(f\"✅ RusTorch version: {rustorch.__version__}\")\n",
" print(f\"📝 Description: {rustorch.__description__}\")\n",
" print(f\"👥 Author: {rustorch.__author__}\")\nexcept ImportError as e:\n",
" print(f\"❌ RusTorchのインポートに失敗: {e}\")\n",
" print(\"maturin develop でビルドしてください\")\n",
" exit(1)\n",
"\n",
"import numpy as np\n",
"import platform\n",
"\n",
"print(f\"🖥️ プラットフォーム: {platform.system()} {platform.release()}\")\n",
"print(f\"🐍 Python version: {platform.python_version()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CoreMLの可用性をチェック"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# CoreML機能の確認\n",
"try:\n",
" # CoreMLが利用可能かチェック\n",
" coreml_available = rustorch.is_coreml_available()\n",
" print(f\"🍎 CoreML available: {coreml_available}\")\n",
" \n",
" if coreml_available:\n",
" print(\"🎉 CoreMLが利用可能です！\")\n",
" \n",
" # デバイス情報を取得\n",
" device_info = rustorch.get_coreml_device_info()\n",
" print(\"📱 CoreMLデバイス情報:\")\n",
" print(device_info)\n",
" else:\n",
" print(\"⚠️ CoreMLは利用できません\")\n",
" if platform.system() != \"Darwin\":\n",
" print(\"CoreMLはmacOSでのみ利用可能です\")\n",
" else:\n",
" print(\"CoreMLフィーチャーが有効になっていない可能性があります\")\n",
" \nexcept AttributeError:\n",
" print(\"❌ CoreML関数が見つかりません\")\n",
" print(\"CoreMLフィーチャーでビルドされていない可能性があります\")\n",
" coreml_available = False\nexcept Exception as e:\n",
" print(f\"❌ CoreMLチェック中にエラー: {e}\")\n",
" coreml_available = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CoreMLデバイスの作成と操作"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if coreml_available:\n",
" try:\n",
" # CoreMLデバイスを作成\n",
" device = rustorch.CoreMLDevice(device_id=0)\n",
" print(f\"🖥️ CoreMLデバイス作成: {device}\")\n",
" \n",
" # デバイス情報を取得\n",
" print(f\"🆔 Device ID: {device.device_id()}\")\n",
" print(f\"✅ Available: {device.is_available()}\")\n",
" print(f\"💾 Memory limit: {device.memory_limit()} bytes\")\n",
" print(f\"🧮 Compute units limit: {device.compute_units_limit()}\")\n",
" print(f\"📚 Model cache size: {device.model_cache_size()}\")\n",
" \n",
" # キャッシュのクリーンアップ\n",
" device.cleanup_cache()\n",
" print(\"🧹 Cache cleaned up\")\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ CoreMLデバイス操作エラー: {e}\")\nelse:\n",
" print(\"⚠️ CoreMLが利用できないため、デバイス操作をスキップ\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CoreMLバックエンドの設定"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if coreml_available:\n",
" try:\n",
" # CoreMLバックエンド設定を作成\n",
" config = rustorch.CoreMLBackendConfig(\n",
" enable_caching=True,\n",
" max_cache_size=200,\n",
" enable_profiling=True,\n",
" auto_fallback=True\n",
" )\n",
" print(f\"⚙️ Backend config: {config}\")\n",
" \n",
" # 設定値を確認・変更\n",
" print(f\"📊 Enable caching: {config.enable_caching}\")\n",
" print(f\"🗂️ Max cache size: {config.max_cache_size}\")\n",
" print(f\"📈 Enable profiling: {config.enable_profiling}\")\n",
" print(f\"🔄 Auto fallback: {config.auto_fallback}\")\n",
" \n",
" # 設定を変更\n",
" config.enable_profiling = False\n",
" config.max_cache_size = 150\n",
" print(f\"\\n🔧 Updated config: {config}\")\n",
" \n",
" # CoreMLバックエンドを作成\n",
" backend = rustorch.CoreMLBackend(config)\n",
" print(f\"🚀 CoreML backend: {backend}\")\n",
" print(f\"✅ Backend available: {backend.is_available()}\")\n",
" \n",
" # バックエンド統計を取得\n",
" stats = backend.get_stats()\n",
" print(f\"📊 Backend stats: {stats}\")\n",
" print(f\" Total operations: {stats.total_operations}\")\n",
" print(f\" Cache hits: {stats.cache_hits}\")\n",
" print(f\" Cache misses: {stats.cache_misses}\")\n",
" print(f\" Fallback operations: {stats.fallback_operations}\")\n",
" print(f\" Cache hit rate: {stats.cache_hit_rate():.2%}\")\n",
" print(f\" Fallback rate: {stats.fallback_rate():.2%}\")\n",
" print(f\" Avg execution time: {stats.average_execution_time_ms:.2f}ms\")\n",
" \n",
" # キャッシュクリーンアップ\n",
" backend.cleanup_cache()\n",
" print(\"\\n🧹 Backend cache cleaned\")\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ CoreMLバックエンド操作エラー: {e}\")\nelse:\n",
" print(\"⚠️ CoreMLが利用できないため、バックエンド操作をスキップ\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 基本的なテンソル操作（CPU）\n",
"\n",
"CoreMLとの比較のために、まずCPUでの基本操作を実行します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" # 基本的なテンソル作成と操作\n",
" print(\"🧮 基本的なテンソル操作（CPU）\")\n",
" \n",
" # NumPy配列からテンソルを作成（簡略化されたインターフェース）\n",
" data_a = np.random.randn(2, 3).astype(np.float32)\n",
" data_b = np.random.randn(3, 2).astype(np.float32)\n",
" \n",
" print(f\"📐 Matrix A shape: {data_a.shape}\")\n",
" print(f\"📐 Matrix B shape: {data_b.shape}\")\n",
" \n",
" # NumPyで行列乗算（比較用）\n",
" numpy_result = np.matmul(data_a, data_b)\n",
" print(f\"✅ NumPy matmul result shape: {numpy_result.shape}\")\n",
" print(f\"📊 Result (first few elements): {numpy_result.flatten()[:4]}\")\n",
" \n",
" print(\"\\n🚀 CPU演算完了\")\n",
" \nexcept Exception as e:\n",
" print(f\"❌ テンソル操作エラー: {e}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## パフォーマンス比較のシミュレーション"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"def benchmark_matrix_operations():\n",
" \"\"\"異なるサイズの行列でパフォーマンスを比較\"\"\"\n",
" \n",
" sizes = [(64, 64), (128, 128), (256, 256), (512, 512)]\n",
" \n",
" print(\"🏁 パフォーマンス比較:\")\n",
" print(\"Size\\t\\tCPU Time (ms)\\tExpected CoreML (ms)\")\n",
" print(\"-\" * 50)\n",
" \n",
" for size in sizes:\n",
" # CPU実行時間を測定\n",
" a = np.random.randn(*size).astype(np.float32)\n",
" b = np.random.randn(size[1], size[0]).astype(np.float32)\n",
" \n",
" start_time = time.time()\n",
" result = np.matmul(a, b)\n",
" cpu_time = (time.time() - start_time) * 1000\n",
" \n",
" # CoreMLの予想時間（仮定的）\n",
" # 実際の実装では、CoreMLバックエンドからの実測値を使用\n",
" expected_coreml_time = cpu_time * 0.6 # 仮定: CoreMLは40%高速\n",
" \n",
" print(f\"{size[0]}x{size[1]}\\t\\t{cpu_time:.2f}\\t\\t{expected_coreml_time:.2f}\")\n",
"\n",
"benchmark_matrix_operations()\n",
"\n",
"print(\"\\n📝 注意: CoreML時間は仮定値です。実際の値は具体的な実装と依存します。\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## デバイス選択のシミュレーション"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulate_device_selection():\n",
" \"\"\"スマートデバイス選択をシミュレート\"\"\"\n",
" \n",
" operations = [\n",
" (\"小さい行列乗算\", (16, 16), \"CPU\"),\n",
" (\"中程度行列乗算\", (128, 128), \"Metal GPU\"),\n",
" (\"大きい行列乗算\", (512, 512), \"CoreML\" if coreml_available else \"Metal GPU\"),\n",
" (\"活性化関数\", (32, 64, 128, 128), \"Metal GPU\"),\n",
" (\"畳み込み（小）\", (1, 3, 32, 32), \"CPU\"),\n",
" (\"畳み込み（大）\", (16, 64, 224, 224), \"CoreML\" if coreml_available else \"Metal GPU\"),\n",
" (\"複素数演算\", (128, 128), \"Metal GPU\"), # CoreML非対応\n",
" (\"統計的分布\", (1000,), \"CPU\"), # CoreML非対応\n",
" ]\n",
" \n",
" print(\"🎯 スマートデバイス選択シミュレーション:\")\n",
" print(\"Operation\\t\\t\\tTensor Shape\\t\\tSelected Device\")\n",
" print(\"-\" * 70)\n",
" \n",
" for name, shape, device in operations:\n",
" shape_str = \"x\".join(map(str, shape))\n",
" print(f\"{name:<23}\\t{shape_str:<15}\\t{device}\")\n",
" \n",
" print(\"\\n📝 選択ロジック:\")\n",
" print(\" • 小さい操作: CPU（オーバーヘッド回避）\")\n",
" print(\" • 中程度操作: Metal GPU（バランス）\")\n",
" print(\" • 大きい操作: CoreML（最適化済み）\")\n",
" print(\" • 非対応操作: GPU/CPUフォールバック\")\n",
"\n",
"simulate_device_selection()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 実践的な使用例: 簡単なニューラルネットワーク層"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulate_neural_network_layer():\n",
" \"\"\"ニューラルネットワーク層のシミュレーション\"\"\"\n",
" \n",
" print(\"🧠 ニューラルネットワーク層シミュレーション:\")\n",
" \n",
" # バッチサイズとレイヤー設定\n",
" batch_size = 32\n",
" input_features = 784 # 28x28 MNIST\n",
" hidden_features = 256\n",
" output_features = 10 # 10クラス\n",
" \n",
" print(f\"📊 Batch size: {batch_size}\")\n",
" print(f\"🔢 Input features: {input_features}\")\n",
" print(f\"🧮 Hidden features: {hidden_features}\")\n",
" print(f\"🎯 Output features: {output_features}\")\n",
" \n",
" # 前向き伝播のシミュレーション\n",
" steps = [\n",
" (\"Input → Hidden\", f\"({batch_size}, {input_features}) @ ({input_features}, {hidden_features})\", \"CoreML\" if coreml_available else \"Metal\"),\n",
" (\"ReLU Activation\", f\"({batch_size}, {hidden_features})\", \"Metal\"),\n",
" (\"Hidden → Output\", f\"({batch_size}, {hidden_features}) @ ({hidden_features}, {output_features})\", \"CoreML\" if coreml_available else \"Metal\"),\n",
" (\"Softmax\", f\"({batch_size}, {output_features})\", \"CPU\"),\n",
" ]\n",
" \n",
" print(\"\\n🔄 Forward pass simulation:\")\n",
" total_time = 0\n",
" \n",
" for step, shape, device in steps:\n",
" # 仮想的な実行時間（ms）\n",
" if device == \"CoreML\":\n",
" time_ms = np.random.uniform(0.5, 2.0)\n",
" elif device == \"Metal\":\n",
" time_ms = np.random.uniform(1.0, 3.0)\n",
" else: # CPU\n",
" time_ms = np.random.uniform(0.2, 1.0)\n",
" \n",
" total_time += time_ms\n",
" print(f\" {step:<15} {shape:<30} {device:<8} {time_ms:.2f}ms\")\n",
" \n",
" print(f\"\\n⏱️ Total forward pass time: {total_time:.2f}ms\")\n",
" print(f\"🚀 Estimated throughput: {1000/total_time:.0f} batches/second\")\n",
"\n",
"simulate_neural_network_layer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## まとめと次のステップ"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"📋 RusTorch CoreML統合サマリー:\")\nprint()\nprint(\"✅ 完了項目:\")\nprint(\" • Jupyter環境の設定\")\nprint(\" • RustカーネルとPythonバインディングの作成\")\nprint(\" • CoreML可用性チェック\")\nprint(\" • デバイス管理と設定\")\nprint(\" • バックエンド統計とプロファイリング\")\nprint(\" • スマートデバイス選択\")\nprint()\nprint(\"🚧 今後の開発:\")\nprint(\" • 実際のCoreML演算の実装\")\nprint(\" • パフォーマンスベンチマーク\")\nprint(\" • より多くの活性化関数とレイヤータイプ\")\nprint(\" • エラーハンドリングの改善\")\nprint(\" • メモリ最適化\")\nprint()\nprint(\"🎯 推奨される次のステップ:\")\nprint(\" 1. 実際のCoreMLモデルのロードとテスト\")\nprint(\" 2. MetalとCoreMLのパフォーマンス比較\")\nprint(\" 3. 実際のディープラーニングワークフローでのテスト\")\nprint(\" 4. プロダクション環境での評価\")\n\nif coreml_available:\n print(\"\\n🎉 おめでとうございます！CoreMLが利用可能で、すべての機能をテストできます。\")\nelse:\n print(\"\\n⚠️ CoreMLが利用できませんが、基本的な機能は動作しています。\")\n print(\" macOSでCoreMLフィーチャーを有効にしてビルドすることをお勧めします。\")"
]
}
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