{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"A collection of command-line tools for transforming ONNX models: merging, splitting, deleting nodes, compressing size, modifying attributes and constants, changing opsets, converting channel formats, adjusting batch sizes, and converting to/from JSON.","skillfed_tags":["onnx-tools","model-optimization","ml-infrastructure"],"use_cases":["Merge multiple trained ONNX models into a single graph for ensemble inference.","Split a large ONNX model to stay under the 2GB Protocol Buffers file size limit.","Remove unused nodes and operations to reduce model file size and inference latency.","Convert model channel layout from RGB to BGR or NCHW to NHWC for different inference frameworks.","Adjust batch size dimensions in pre-trained models to match deployment requirements.","Export ONNX graphs to JSON for inspection, debugging, or programmatic analysis."],"what_it_does":"simple-onnx-processing-tools is a metapackage that bundles 25 specialized command-line utilities for ONNX model manipulation. Each tool addresses a specific transformation task: merging multiple models, extracting subgraphs, deleting unused nodes, shrinking file size by deduplicating constants, modifying operation attributes, changing opsets, converting between NCHW and NHWC layouts, adjusting batch dimensions, renaming operations, and bidirectional JSON serialization.\n\nThe package is designed for model engineers and researchers who need to preprocess or postprocess ONNX graphs before deployment or inference. Rather than a single monolithic API, it provides a suite of focused CLI tools that can be chained or used independently. Installation includes optional dependencies (via the [full] extra) for heavier operations like model simplification and runtime testing. The tools assume familiarity with ONNX graph structure and are most useful in workflows where model optimization, format conversion, or structural modification is required.","worth_installing":"Yes, if you work with ONNX models and need CLI-based transformation tools. The package is stable (low install friction, no known vulnerabilities, permissive license) but dormant\u2014last updated 844 days ago. Install it for specific model preprocessing tasks, but do not expect active maintenance or new features. Verify tool compatibility with your ONNX opset version before relying on it in production pipelines."},"id":"simple-onnx-processing-tools","links":{"html":"https://skillfed.io/packages/simple-onnx-processing-tools","md":"https://skillfed.io/packages/simple-onnx-processing-tools.md","pypi":"https://pypi.org/project/simple-onnx-processing-tools/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-04-22","license_spdx":null,"license_treatment":"permissive","name":"simple-onnx-processing-tools","python_support":"supports_current","summary":"A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, change opset, change to the specified input order, addition of OP, RGB to BGR conversion, change batch size, batch rename of OP, and JSON convertion for ONNX models."},"popularity":{"monthly_downloads":153004,"position":10889,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.32"}