{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Visualizes and debugs machine learning model graphs with hierarchical layer expansion, metadata overlay, and interactive exploration across TFLite, TensorFlow, TFJS, MLIR, and PyTorch formats.","skillfed_tags":["model-debugging","graph-visualization","ml-tools"],"use_cases":["Inspect and debug TFLite or PyTorch model architectures before deployment to understand layer structure and data flow.","Overlay metadata on model nodes to verify quantization, pruning, or other optimization parameters applied during conversion.","Search for and identify duplicate or redundant layers in a large model graph to spot optimization opportunities.","Interactively explore nested model hierarchies in a Jupyter notebook or Colab environment for rapid prototyping feedback.","Develop custom model format support by implementing an adapter extension for formats not yet built in."],"what_it_does":"Model Explorer is a visualization and debugging tool for machine learning model graphs. It presents model operations in a hierarchical, nested structure that you can expand or collapse interactively, making it easier to understand complex architectures. The tool supports multiple model formats\u2014TFLite, TensorFlow, TFJS, MLIR, and PyTorch Exported Programs\u2014and provides features like input/output highlighting, metadata overlay on nodes, interactive layer pop-ups, search, duplicate layer detection, and GPU-accelerated graph rendering.\n\nThe package depends on Flask for its web interface, IPython for interactive exploration, and a range of utilities including requests, numpy, watchdog, and termcolor. It includes an extension framework so you can add support for additional model formats. The package is in aging maintenance (last release 186 days ago) but carries no known security vulnerabilities and installs with low friction as a pure Python wheel.","worth_installing":"Yes, if you work with TFLite, PyTorch, or TensorFlow models and need interactive graph visualization for debugging. The low install friction, permissive license, and lack of known vulnerabilities make it a safe choice. The aging maintenance status means you should not expect rapid bug fixes, but the core visualization functionality is stable and the package remains actively supported enough for production use."},"id":"ai-edge-model-explorer","links":{"html":"https://skillfed.io/packages/ai-edge-model-explorer","md":"https://skillfed.io/packages/ai-edge-model-explorer.md","pypi":"https://pypi.org/project/ai-edge-model-explorer/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-02-09","license_spdx":null,"license_treatment":"permissive","name":"ai-edge-model-explorer","python_support":"supports_current","summary":"A modern model graph visualizer and debugger"},"popularity":{"monthly_downloads":176800,"position":10234,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.32"}