sne4onnx

library2.0.1pypypiunverified

A lightweight tool for extracting subgraphs (model slicing) from ONNX models. Useful when onnx-simplifier exceeds the 2GB Protocol Buffers file size limit, or to split models into arbitrary sizes. Current version 2.0.1, released June 2024. Development is active.

ai-ml

pip install sne4onnx

github ↗package ↗

INSTALL

IMPORT

SIG · SNE4ONNX

sne4onnx

ai-mlpythonv2.0.1

Install

1.5s avg

Import

—

Disk

16MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 17.9MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 1.5s · import 0.000s · 18MB

16MB installed

● package 16MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

sne4onnx

✓ from sne4onnx import sne4onnx

✗ import sne4onnx

Extract a subgraph from an ONNX model by specifying input and output operation names.

import onnx from sne4onnx import extraction # Load an ONNX model model = onnx.load('model.onnx') # Define input and output operation names for the subgraph input_names = ['input'] # name of the input tensor or node output_names = ['output'] # name of the output tensor or node # Extract the subgraph new_model = extraction(model, input_names, output_names) # Save the extracted model onnx.save(new_model, 'extracted.onnx')

sne4onnx --version

Debug

Known issues

breakingIn version 2.0.0, dependency on `onnx_graphsurgeon` was removed. Code that relied on internals of `onnx_graphsurgeon` or used sne4onnx together with it may break.

fix

Update to v2.0.0+ and ensure no usage of `onnx_graphsurgeon` inside sne4onnx (it is no longer a dependency).

affects: <2.0.0

gotchaThe extraction function expects operation (node) names, not tensor names. Providing tensor names may silently fail or produce unexpected subgraphs.

fix

Use node names (e.g., from model.graph.node[i].name) as input/output_op_names. Inspect the model's graph nodes to find correct names.

affects: all

gotchaWhen using external data (large models with external data files), the library may corrupt the model if shape inference is run. Version 1.0.15 fixed this, but if you manually call `onnx.shape_inference.infer_shapes()` before extraction, you may encounter issues.

fix

Avoid calling `onnx.shape_inference.infer_shapes()` on models with external data before extraction. Let sne4onnx handle it.

affects: >=1.0.15

gotchaThe CLI `sne4onnx` arguments changed between versions. Short form parameters were added in v1.0.10 but long form (`--input_onnx_file_path`) may still be used. In v2.0.0, the CLI remains similar but ensure you use the correct syntax.

fix

Use `sne4onnx -h` to see current CLI arguments. Prefer long-form options for scripts to avoid ambiguity.

affects: all

Upgrade

Version history

2.0.1latest on PyPI · released Feb 24, 2026

Audit

Dependencies

onnxrequiredCore dependency for loading, manipulating, and saving ONNX models

Agent activity

16 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/PINTO0309/sne4onnx ↗

packagehttps://pypi.org/project/sne4onnx/ ↗