svs4onnx
Simple model output OP additional tools.
Decision gist · record as of 2026-08-14
No, unless you have a specific legacy use case. The package is abandoned—last release 2022-09-17 with no commits since then—and has not been tested against current ONNX versions. If you need ONNX graph manipulation, consider maintained alternatives.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires onnx to be installed separately; not declared as a runtime dependency in the package metadata.
- Low install friction; pure Python wheel with no runtime dependencies.
- However, the package is abandoned—last release was 2022-09-17 and no commits since then.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use, modification, and distribution with minimal restrictions.
last release 2022-09-17 (1427 days) · last repo commit 2022-09-17 · 1 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,533 downloads/mo, #14,157 on PyPI
Alternatives
Verify before relying
pip install svs4onnx
from svs4onnx import variable_switch
onnx_graph = variable_switch(
from_output_variable_name="cast1_output",
to_input_variable_name="StatefulPartitionedCall/strided_slice_21",
input_onnx_file_path="model.onnx",
output_onnx_file_path="model_switched.onnx"
)- Whether onnx is a required peer dependency or optional; setup docs show it installed separately but package lists zero runtime dependencies.
- Compatibility with recent ONNX versions and whether the tool works with current model architectures.
What it is and what it does
svs4onnx is a lightweight utility for modifying ONNX model graphs by redirecting variable connections. It takes an ONNX model, identifies an output variable from one node, and rewires it to feed into a specified input variable of another node, then outputs the modified graph. This is useful when you need to restructure model topology—for example, bypassing intermediate operations or connecting different stages of a pipeline.
The tool offers both a command-line interface and a Python API. You can load an ONNX file or pass a graph object directly, specify the source output variable and target input variable, and get back a modified ModelProto. It supports Python 3.6 and later.
Use it for
- Redirect a model's intermediate output to skip or replace downstream operations during inference.
- Rewire multi-stage pipelines to connect outputs from one model directly to inputs of another.
- Modify model topology for debugging or testing by changing which nodes feed into which.
- Adapt pre-trained models to new input/output configurations by swapping variable connections.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you have a specific legacy use case.
The package is abandoned—last release 2022-09-17 with no commits since then—and has not been tested against current ONNX versions. If you need ONNX graph manipulation, consider maintained alternatives.
Install
svs4onnx on PyPI
Before you install
Low install friction; pure Python wheel with no runtime dependencies. However, the package is abandoned—last release was 2022-09-17 and no commits since then.
Requires onnx to be installed separately; not declared as a runtime dependency in the package metadata.
License in practice
MIT License permits commercial and private use, modification, and distribution with minimal restrictions.
Quickstart
pip install svs4onnx
from svs4onnx import variable_switch
onnx_graph = variable_switch(
from_output_variable_name="cast1_output",
to_input_variable_name="StatefulPartitionedCall/strided_slice_21",
input_onnx_file_path="model.onnx",
output_onnx_file_path="model_switched.onnx"
)Verify before relying
- Whether onnx is a required peer dependency or optional; setup docs show it installed separately but package lists zero runtime dependencies.
- Compatibility with recent ONNX versions and whether the tool works with current model architectures.
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 | Abandoned 1,427 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,533 / month, #14,157 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: svs4onnx-1.0.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “onnx graph variable rewiring”
- svs4onnxSwaps connections between output and input variables in ONNX model…
- onnx-graphsurgeonONNX GraphSurgeon lets you programmatically create and modify ONNX…
- soa4onnxAdds or modifies output nodes in ONNX model graphs, allowing you to…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also sod4onnx · soa4onnx · sng4onnx · soc4onnx · sor4onnx · snd4onnx · scc4onnx · sbi4onnx · sne4onnx · ssi4onnx