{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RusTorch CoreML Integration - Python Bindings\n",
"\n",
"This notebook demonstrates how to use RusTorch's CoreML functionality through Python bindings."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup and Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import RusTorch Python bindings\n",
"try:\n",
" import rustorch\n",
" print(f\"ā
RusTorch version: {rustorch.__version__}\")\n",
" print(f\"š Description: {rustorch.__description__}\")\n",
" print(f\"š„ Author: {rustorch.__author__}\")\n",
"except ImportError as e:\n",
" print(f\"ā Failed to import RusTorch: {e}\")\n",
" print(\"Please build with maturin develop\")\n",
" exit(1)\n",
"\n",
"import numpy as np\n",
"import platform\n",
"\n",
"print(f\"š„ļø Platform: {platform.system()} {platform.release()}\")\n",
"print(f\"š Python version: {platform.python_version()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check CoreML Availability"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check CoreML functionality\n",
"try:\n",
" # Check if CoreML is available\n",
" coreml_available = rustorch.is_coreml_available()\n",
" print(f\"š CoreML available: {coreml_available}\")\n",
" \n",
" if coreml_available:\n",
" print(\"š CoreML is available!\")\n",
" \n",
" # Get device information\n",
" device_info = rustorch.get_coreml_device_info()\n",
" print(\"š± CoreML device information:\")\n",
" print(device_info)\n",
" else:\n",
" print(\"ā ļø CoreML is not available\")\n",
" if platform.system() != \"Darwin\":\n",
" print(\"CoreML is only available on macOS\")\n",
" else:\n",
" print(\"CoreML features may not be enabled\")\n",
" \n",
"except AttributeError:\n",
" print(\"ā CoreML functions not found\")\n",
" print(\"May not be built with CoreML features\")\n",
" coreml_available = False\n",
"except Exception as e:\n",
" print(f\"ā Error checking CoreML: {e}\")\n",
" coreml_available = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CoreML Device Creation and Operations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if coreml_available:\n",
" try:\n",
" # Create CoreML device\n",
" device = rustorch.CoreMLDevice(device_id=0)\n",
" print(f\"š„ļø CoreML device created: {device}\")\n",
" \n",
" # Get device information\n",
" print(f\"š Device ID: {device.device_id()}\")\n",
" print(f\"ā
Available: {device.is_available()}\")\n",
" print(f\"š¾ Memory limit: {device.memory_limit()} bytes\")\n",
" print(f\"š§® Compute units limit: {device.compute_units_limit()}\")\n",
" print(f\"š Model cache size: {device.model_cache_size()}\")\n",
" \n",
" # Cache cleanup\n",
" device.cleanup_cache()\n",
" print(\"š§¹ Cache cleaned up\")\n",
" \n",
" except Exception as e:\n",
" print(f\"ā CoreML device operation error: {e}\")\n",
"else:\n",
" print(\"ā ļø Skipping device operations as CoreML is not available\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CoreML Backend Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if coreml_available:\n",
" try:\n",
" # Create CoreML backend configuration\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\"āļø Backend config: {config}\")\n",
" \n",
" # Check and modify configuration values\n",
" print(f\"š Enable caching: {config.enable_caching}\")\n",
" print(f\"šļø Max cache size: {config.max_cache_size}\")\n",
" print(f\"š Enable profiling: {config.enable_profiling}\")\n",
" print(f\"š Auto fallback: {config.auto_fallback}\")\n",
" \n",
" # Modify configuration\n",
" config.enable_profiling = False\n",
" config.max_cache_size = 150\n",
" print(f\"\\nš§ Updated config: {config}\")\n",
" \n",
" # Create CoreML backend\n",
" backend = rustorch.CoreMLBackend(config)\n",
" print(f\"š CoreML backend: {backend}\")\n",
" print(f\"ā
Backend available: {backend.is_available()}\")\n",
" \n",
" # Get backend statistics\n",
" stats = backend.get_stats()\n",
" print(f\"š 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",
" # Cache cleanup\n",
" backend.cleanup_cache()\n",
" print(\"\\nš§¹ Backend cache cleaned\")\n",
" \n",
" except Exception as e:\n",
" print(f\"ā CoreML backend operation error: {e}\")\n",
"else:\n",
" print(\"ā ļø Skipping backend operations as CoreML is not available\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Tensor Operations (CPU)\n",
"\n",
"For comparison with CoreML, let's first perform basic operations on CPU."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" # Basic tensor creation and operations\n",
" print(\"š§® Basic tensor operations (CPU)\")\n",
" \n",
" # Create tensors from NumPy arrays (simplified interface)\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\"š Matrix A shape: {data_a.shape}\")\n",
" print(f\"š Matrix B shape: {data_b.shape}\")\n",
" \n",
" # Matrix multiplication with NumPy (for comparison)\n",
" numpy_result = np.matmul(data_a, data_b)\n",
" print(f\"ā
NumPy matmul result shape: {numpy_result.shape}\")\n",
" print(f\"š Result (first few elements): {numpy_result.flatten()[:4]}\")\n",
" \n",
" print(\"\\nš CPU operations completed\")\n",
" \n",
"except Exception as e:\n",
" print(f\"ā Tensor operation error: {e}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance Comparison Simulation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"def benchmark_matrix_operations():\n",
" \"\"\"Compare performance with different matrix sizes\"\"\"\n",
" \n",
" sizes = [(64, 64), (128, 128), (256, 256), (512, 512)]\n",
" \n",
" print(\"š Performance comparison:\")\n",
" print(\"Size\\t\\tCPU Time (ms)\\tExpected CoreML (ms)\")\n",
" print(\"-\" * 50)\n",
" \n",
" for size in sizes:\n",
" # Measure CPU execution time\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",
" # Expected CoreML time (hypothetical)\n",
" # In actual implementation, use real measurements from CoreML backend\n",
" expected_coreml_time = cpu_time * 0.6 # Assumption: CoreML is 40% faster\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(\"\\nš Note: CoreML times are hypothetical. Actual values depend on specific implementation.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Device Selection Simulation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulate_device_selection():\n",
" \"\"\"Simulate smart device selection\"\"\"\n",
" \n",
" operations = [\n",
" (\"Small matrix multiplication\", (16, 16), \"CPU\"),\n",
" (\"Medium matrix multiplication\", (128, 128), \"Metal GPU\"),\n",
" (\"Large matrix multiplication\", (512, 512), \"CoreML\" if coreml_available else \"Metal GPU\"),\n",
" (\"Activation function\", (32, 64, 128, 128), \"Metal GPU\"),\n",
" (\"Small convolution\", (1, 3, 32, 32), \"CPU\"),\n",
" (\"Large convolution\", (16, 64, 224, 224), \"CoreML\" if coreml_available else \"Metal GPU\"),\n",
" (\"Complex number operations\", (128, 128), \"Metal GPU\"), # CoreML not supported\n",
" (\"Statistical distribution\", (1000,), \"CPU\"), # CoreML not supported\n",
" ]\n",
" \n",
" print(\"šÆ Smart device selection simulation:\")\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(\"\\nš Selection logic:\")\n",
" print(\" ā¢ Small operations: CPU (avoid overhead)\")\n",
" print(\" ā¢ Medium operations: Metal GPU (balanced)\")\n",
" print(\" ā¢ Large operations: CoreML (optimized)\")\n",
" print(\" ā¢ Unsupported operations: GPU/CPU fallback\")\n",
"\n",
"simulate_device_selection()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Practical Example: Simple Neural Network Layer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulate_neural_network_layer():\n",
" \"\"\"Simulate neural network layer\"\"\"\n",
" \n",
" print(\"š§ Neural network layer simulation:\")\n",
" \n",
" # Batch size and layer configuration\n",
" batch_size = 32\n",
" input_features = 784 # 28x28 MNIST\n",
" hidden_features = 256\n",
" output_features = 10 # 10 classes\n",
" \n",
" print(f\"š Batch size: {batch_size}\")\n",
" print(f\"š¢ Input features: {input_features}\")\n",
" print(f\"š§® Hidden features: {hidden_features}\")\n",
" print(f\"šÆ Output features: {output_features}\")\n",
" \n",
" # Forward pass simulation\n",
" steps = [\n",
" (\"Input ā 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 ā 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(\"\\nš Forward pass simulation:\")\n",
" total_time = 0\n",
" \n",
" for step, shape, device in steps:\n",
" # Virtual execution time (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\"\\nā±ļø Total forward pass time: {total_time:.2f}ms\")\n",
" print(f\"š Estimated throughput: {1000/total_time:.0f} batches/second\")\n",
"\n",
"simulate_neural_network_layer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary and Next Steps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"š RusTorch CoreML Integration Summary:\")\n",
"print()\n",
"print(\"ā
Completed items:\")\n",
"print(\" ā¢ Jupyter environment setup\")\n",
"print(\" ā¢ Rust kernel and Python bindings creation\")\n",
"print(\" ā¢ CoreML availability check\")\n",
"print(\" ā¢ Device management and configuration\")\n",
"print(\" ā¢ Backend statistics and profiling\")\n",
"print(\" ā¢ Smart device selection\")\n",
"print()\n",
"print(\"š§ Future development:\")\n",
"print(\" ā¢ Actual CoreML operation implementation\")\n",
"print(\" ā¢ Performance benchmarking\")\n",
"print(\" ā¢ More activation functions and layer types\")\n",
"print(\" ā¢ Error handling improvements\")\n",
"print(\" ā¢ Memory optimization\")\n",
"print()\n",
"print(\"šÆ Recommended next steps:\")\n",
"print(\" 1. Load and test actual CoreML models\")\n",
"print(\" 2. Compare Metal and CoreML performance\")\n",
"print(\" 3. Test with real deep learning workflows\")\n",
"print(\" 4. Evaluate in production environment\")\n",
"\n",
"if coreml_available:\n",
" print(\"\\nš Congratulations! CoreML is available and all features can be tested.\")\n",
"else:\n",
" print(\"\\nā ļø CoreML is not available, but basic features are working.\")\n",
" print(\" We recommend building with CoreML features enabled on macOS.\")"
]
}
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