Show HN: CLI for working with Apple Core ML models

Summary

A native command-line interface for working with Apple Core ML models on macOS. Inspect, run inference, benchmark, and manage Core ML models without Xcode or Python. Features Inspect - View model structure, inputs/outputs, and metadata - View model structure, inputs/outputs, and metadata Predict - Run inference on images, text, or JSON data - Run inference on images, text, or JSON data Batch - Process multiple files with concurrent execution - Process multiple files with concurrent execution Benchmark - Measure inference latency and throughput - Measure inference latency and throughput Compile - Convert .mlmodel to optimized .mlmodelc format - Convert to optimized format Metadata - View and manage model metadata Installation Homebrew (Recommended) brew tap schappim/coreml-cli brew install coreml-cli Download Binary Download the latest release from GitHub Releases: curl -L https://github.com/schappim/coreml-cli/releases/download/v1.0.0/coreml-1.0.0-macos.tar.gz -o coreml.tar.gz tar -xzf coreml.tar.gz sudo mv coreml /usr/local/bin/ Build from Source Requires macOS 13+ and Swift 5.9+ git clone https://github.com/schappim/coreml-cli.git cd coreml-cli swift build -c release sudo cp .build/release/coreml /usr/local/bin/ Verify Installation coreml --version # coreml 1.0.0 Usage Inspect a Model View model structure, inputs, outputs, and metadata: coreml inspect MobileNetV2.mlmodel Output: Model: MobileNetV2 Size: 24.7 MB Compiled: No Inputs: image: image 224x224 BGRA32 Outputs: classLabel: string classLabelProbs: dictionary Metadata: Author: Original Paper: Mark Sandler, Andrew Howard... Description: Detects the dominant objects present in an image... JSON output for scripting: coreml inspect MobileNetV2.mlmodel --json Run Inference Classify an image: coreml predict MobileNetV2.mlmodel --input photo.jpg Output: Input: photo.jpg Inference time: 1.66 ms Outputs: classLabel: golden retriever classLabelProbs: golden retriever: 0.8721, Labrador retriever: 0.0543... Save results ...