sng4onnx
A simple tool that automatically generates and assigns an OP name to each OP in an old format ONNX file.
Decision gist · record as of 2026-08-14
Yes. Install if you work with ONNX models and need to normalize operation names in older or unconventionally formatted files. The tool is lightweight, actively maintained, has no complex dependencies, carries a permissive MIT license, and solves a real ONNX workflow problem. No security vulnerabilities are known.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- Actively maintained as of 2026-02-24 with recent updates.
License · maintenance · safety
MIT License (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions.
last release 2026-02-24 (171 days) · last repo commit 2026-02-24 · 2 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 307,157 downloads/mo, #7,779 on PyPI
Alternatives
Verify before relying
pip install sng4onnx
from sng4onnx import generate
onnx_graph = generate(
input_onnx_file_path="model.onnx",
output_onnx_file_path="model_renamed.onnx"
)- Whether the tool handles all ONNX operator types or only a subset
- Performance characteristics on large models with thousands of operations
- Compatibility with ONNX versions beyond the current release
What it is and what it does
sng4onnx is a lightweight command-line and Python utility that solves a specific ONNX workflow problem: older ONNX model files sometimes lack proper operation names, making them harder to inspect, debug, or process. This tool automatically traverses an ONNX graph and generates systematic names for any unnamed operators, then writes the corrected model back to disk or returns it as a Python object.
The package works both as a CLI tool (via `sng4onnx` command) and as an importable Python function. It requires only Python 3.6 or later and has no external runtime dependencies beyond what ONNX itself needs. The tool is part of a broader collection of simple ONNX processing utilities and is maintained actively.
Use it for
- Prepare legacy ONNX models for inspection or visualization by ensuring all operators have readable names
- Preprocess ONNX files before feeding them into model analysis or debugging tools that expect named operations
- Batch-convert a collection of old-format ONNX models to have consistent operation naming conventions
- Integrate into an ONNX model pipeline to normalize graph structure before downstream processing
- Verify and repair ONNX files exported from older frameworks or converters that may not name all operations
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Install if you work with ONNX models and need to normalize operation names in older or unconventionally formatted files. The tool is lightweight, actively maintained, has no complex dependencies, carries a permissive MIT license, and solves a real ONNX workflow problem. No security vulnerabilities are known.
Install
sng4onnx on PyPI
Before you install
Low install friction with no runtime dependencies. Actively maintained as of 2026-02-24 with recent updates.
License in practice
MIT License permits unrestricted use, modification, and distribution with minimal restrictions.
Quickstart
pip install sng4onnx
from sng4onnx import generate
onnx_graph = generate(
input_onnx_file_path="model.onnx",
output_onnx_file_path="model_renamed.onnx"
)Verify before relying
- Whether the tool handles all ONNX operator types or only a subset
- Performance characteristics on large models with thousands of operations
- Compatibility with ONNX versions beyond the current release
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 171 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 307,157 / month, #7,779 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: sng4onnx-2.0.1-py3-none-any.whl
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See also sam4onnx · scc4onnx · sna4onnx · snc4onnx · sog4onnx · sor4onnx · sod4onnx · snd4onnx · svs4onnx · soc4onnx