{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Extracts subgraphs from ONNX model files by specifying input and output operation names, generating smaller ONNX files without the overhead of standard extraction methods.","skillfed_tags":["onnx-tools","model-optimization"],"use_cases":["Extract a specific layer or stage from a large ONNX model to debug or profile that portion independently.","Split an oversized ONNX model into smaller subgraphs to stay under the 2GB Protocol Buffers limit.","Isolate a trained backbone or encoder from a full model for transfer learning or fine-tuning.","Partition a multi-stage inference pipeline (e.g., preprocessing, main model, postprocessing) into separate deployable units.","Reduce model file size for embedded or edge deployment by keeping only the necessary computation graph."],"what_it_does":"sne4onnx is a lightweight utility for extracting subgraphs from ONNX neural network models. It takes an ONNX file and a pair of input and output operation names, then isolates the computation graph between those points and writes a new, smaller ONNX file. The tool was built to address a specific problem: when onnx-simplifier optimization would push a model past the Protocol Buffers 2GB file size limit, or when you simply want to partition a large model into smaller pieces for deployment or analysis.\n\nThe package provides both a command-line interface and a Python API. It implements its own graph extraction logic rather than relying on the slower onnx.utils.extractor.extract_model function. With no runtime dependencies and support for Python versions >=3.6, it integrates easily into existing ONNX workflows. The tool is commonly used in model optimization pipelines, particularly in computer vision and deep learning contexts where model size and inference latency matter.","worth_installing":"Yes. The package is lightweight, actively maintained, has no runtime dependencies, carries a permissive MIT license, and solves a real problem in ONNX model workflows\u2014particularly when dealing with large models or size constraints. Install it if you work with ONNX models and need to extract subgraphs or partition files by operation boundaries."},"id":"sne4onnx","links":{"html":"https://skillfed.io/packages/sne4onnx","md":"https://skillfed.io/packages/sne4onnx.md","pypi":"https://pypi.org/project/sne4onnx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-24","license_spdx":null,"license_treatment":"permissive","name":"sne4onnx","python_support":"supports_current","summary":"A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want. Simple Network Extraction for ONNX."},"popularity":{"monthly_downloads":127775,"position":11731,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.1"}