snd4onnx
Simple node deletion tool for onnx.
Decision gist · record as of 2026-08-14
Yes, if you need to surgically remove nodes from ONNX graphs and accept the trade-off of limited test coverage. Install friction is minimal and the MIT license is unrestrictive. The aging maintenance status and author's caveat about bugs mean it is best for ad-hoc model editing rather than critical production pipelines; for complex graph surgery, verify behavior on your specific models first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing ONNX model file or in-memory onnx.ModelProto object; node names must be known in advance.
- Low friction install with no runtime dependencies.
- Last commit was 2025-10-04 and the package is marked aging, indicating infrequent updates but not abandoned.
License · maintenance · safety
MIT License (permissive) — MIT License permits use, modification, and distribution with minimal restrictions, making it safe for both open and closed projects.
last release 2025-10-04 (314 days) · last repo commit 2025-10-04 · 7 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,427 downloads/mo, #14,076 on PyPI
Alternatives
Verify before relying
pip install snd4onnx
from snd4onnx import remove
onnx_graph = remove(
remove_node_names=['node_name_a', 'node_name_b'],
input_onnx_file_path='input.onnx'
)- Whether the tool correctly handles edge cases like nodes with multiple downstream consumers or complex graph topologies beyond the sample patterns shown.
- Performance characteristics when removing nodes from large models.
- Compatibility with all ONNX opset versions and operator types.
What it is and what it does
snd4onnx is a lightweight utility for removing named nodes from ONNX model graphs. It works both as a command-line tool and as a Python library, accepting a list of node names to delete and either an input file path or an in-memory graph object, then outputting the modified model. The package has no runtime dependencies beyond Python itself.
The tool is designed for model optimization and experimentation workflows where you need to strip out specific operations from a trained neural network—for instance, removing debug layers, unused branches, or operations incompatible with a target inference engine. The author notes it is a hobby project with limited test coverage and likely bugs, so it is best suited for exploratory work rather than production pipelines.
Use it for
- Remove debug or instrumentation nodes from a trained ONNX model before deployment.
- Strip out unsupported operations to make a model compatible with a specific inference runtime.
- Simplify a model graph by deleting unused branches or redundant layers during optimization.
- Experiment with model architecture by removing operations to test inference behavior.
- Preprocess models downloaded from model zoos before fine-tuning or conversion.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to surgically remove nodes from ONNX graphs and accept the trade-off of limited test coverage.
Install friction is minimal and the MIT license is unrestrictive. The aging maintenance status and author's caveat about bugs mean it is best for ad-hoc model editing rather than critical production pipelines; for complex graph surgery, verify behavior on your specific models first.
Install
snd4onnx on PyPI
Before you install
Low friction install with no runtime dependencies. Last commit was 2025-10-04 and the package is marked aging, indicating infrequent updates but not abandoned.
Requires an existing ONNX model file or in-memory onnx.ModelProto object; node names must be known in advance.
License in practice
MIT License permits use, modification, and distribution with minimal restrictions, making it safe for both open and closed projects.
Quickstart
pip install snd4onnx
from snd4onnx import remove
onnx_graph = remove(
remove_node_names=['node_name_a', 'node_name_b'],
input_onnx_file_path='input.onnx'
)Verify before relying
- Whether the tool correctly handles edge cases like nodes with multiple downstream consumers or complex graph topologies beyond the sample patterns shown.
- Performance characteristics when removing nodes from large models.
- Compatibility with all ONNX opset versions and operator types.
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 | Aging 314 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,427 / month, #14,076 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: snd4onnx-1.1.7-py3-none-any.whl
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See also sde4onnx · simple-onnx-processing-tools · ssc4onnx · sng4onnx · sod4onnx · ssi4onnx · snc4onnx · sne4onnx · onnx-graphsurgeon · svs4onnx