tf2onnx
Tensorflow to ONNX converter
Decision gist · record as of 2026-08-14
Yes, if you need to convert TensorFlow models to ONNX. The package is production-stable, actively maintained, has no known security issues, and installs with low friction. The main caveat is that not all TensorFlow operations map cleanly to ONNX, so conversion success depends on your specific model architecture. The active search for a new maintainer is a yellow flag for long-term support, but current maintenance is solid.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow 2.13 or later and Python 3.10-3.12; TensorFlow must be installed separately before conversion.
- Low friction install with stable maintenance.
- The package is actively maintained, has no known vulnerabilities, and depends on standard libraries (numpy, onnx, protobuf, flatbuffers, requests).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is a permissive license allowing commercial use, modification, and distribution with minimal restrictions, making this package suitable for most production and research contexts.
last release 2026-03-04 (163 days) · last repo commit 2026-08-03 · 2,551 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 395,329 downloads/mo, #6,981 on PyPI
Alternatives
Verify before relying
pip install tf2onnx
python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx- Extent of tensorflow.js model support coverage beyond the noted experimental status and tested tfhub models
- Performance characteristics and conversion success rates for complex or custom TensorFlow operations
- Compatibility guarantees with TensorFlow versions outside the tested 2.13-2.15 range
What it is and what it does
tf2onnx is a model converter that takes trained TensorFlow models in various formats (saved_model, checkpoint, graphdef, tflite, tensorflow.js) and translates them into ONNX format. This enables models trained in TensorFlow to run on any ONNX-compatible runtime, improving portability and deployment flexibility. The package works via a command-line interface or Python API, with support for opset versions 14-18 (default 15). It requires TensorFlow 2.13 or later and Python 3.10-3.12.
The converter handles the fundamental challenge that TensorFlow has more operations than ONNX, so not all models convert cleanly. The project documents supported operations and provides troubleshooting guidance. TensorFlow.js support is experimental. The package is actively maintained but currently seeking a new maintainer, which is worth noting when evaluating long-term reliability.
Use it for
- Deploy TensorFlow models on inference engines that don't natively support TensorFlow format
- Convert Keras models to ONNX for cross-platform inference on mobile, edge, or cloud deployments
- Migrate TFLite models to ONNX when targeting different hardware accelerators or inference frameworks
- Enable model interoperability in ML pipelines that mix models from different training frameworks
- Export trained models for production serving on systems where TensorFlow runtime is unavailable
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to convert TensorFlow models to ONNX.
The package is production-stable, actively maintained, has no known security issues, and installs with low friction. The main caveat is that not all TensorFlow operations map cleanly to ONNX, so conversion success depends on your specific model architecture. The active search for a new maintainer is a yellow flag for long-term support, but current maintenance is solid.
Install
tf2onnx on PyPI
Before you install
Low friction install with stable maintenance. The package is actively maintained, has no known vulnerabilities, and depends on standard libraries (numpy, onnx, protobuf, flatbuffers, requests). However, the project is actively seeking a new maintainer, which may signal future maintenance uncertainty.
Requires TensorFlow 2.13 or later and Python 3.10-3.12; TensorFlow must be installed separately before conversion.
License in practice
Apache-2.0 is a permissive license allowing commercial use, modification, and distribution with minimal restrictions, making this package suitable for most production and research contexts.
Quickstart
pip install tf2onnx
python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnxVerify before relying
- Extent of tensorflow.js model support coverage beyond the noted experimental status and tested tfhub models
- Performance characteristics and conversion success rates for complex or custom TensorFlow operations
- Compatibility guarantees with TensorFlow versions outside the tested 2.13-2.15 range
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyonnxrequestsflatbuffersprotobuf |
| Maintenance | Actively maintained 163 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 395,329 / month, #6,981 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tf2onnx-1.17.0-py3-none-any.whl
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See also onnx2tf · onnxmltools · tensorflowjs · onnx2torch · tflite · onnxsim · onnx-graphsurgeon · onnxconverter-common · scc4onnx · soc4onnx