{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/8"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/12"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"ExecuTorch exports and runs PyTorch models on mobile, embedded, and edge devices with support for hardware acceleration backends like XNNPACK, Core ML, and QNN.","skillfed_tags":["mobile-deployment","edge-inference","model-export"],"use_cases":["Export a trained PyTorch model and deploy it to iOS or Android apps without requiring the full PyTorch runtime on the device.","Accelerate inference on edge devices using hardware-specific backends like Core ML on Apple Silicon or QNN on Qualcomm processors.","Run large language models locally on mobile devices by exporting and quantizing them with ExecuTorch's optimization pipeline.","Build AR/VR applications that execute neural networks directly on wearable hardware with minimal latency and power consumption.","Create a minimal export-only environment for model conversion without bundling runtime dependencies like pandas or hydra-core."],"what_it_does":"ExecuTorch is a PyTorch deployment platform that bridges the gap between model development and on-device execution. It provides tools to export trained PyTorch models into a portable `.pte` format optimized for mobile, embedded, and edge hardware\u2014including AR/VR wearables, iOS, and Android devices. The package includes both an export path (EXIR) for converting models and a prebuilt runtime module for executing those models with hardware acceleration.\n\nThe runtime is built around a restricted but efficient operator set and pluggable backend delegates. The prebuilt wheel includes XNNPACK for general acceleration, with platform-specific backends: Core ML and MPS on macOS, QNN on Linux x86_64, and OpenVINO on Linux. This architecture trades some model flexibility for predictable performance and small binary size on constrained devices. A minimal build mode is available for export-only workflows, reducing dependencies significantly.","worth_installing":"Yes, if you need to deploy PyTorch models to mobile or edge devices. The active maintenance, permissive license, and platform-specific prebuilt backends make it a practical choice for on-device AI. Install with conditions: verify that your model uses only core ATen operators, confirm backend availability for your target platform, and plan for the 21 runtime dependencies unless you use the minimal build mode."},"id":"executorch","links":{"html":"https://skillfed.io/packages/executorch","md":"https://skillfed.io/packages/executorch.md","pypi":"https://pypi.org/project/executorch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"executorch","python_support":"supports_current","summary":"On-device AI across mobile, embedded and edge for PyTorch"},"popularity":{"monthly_downloads":448392,"position":6601,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.1"}