orbax-export
Orbax Export
Decision gist · record as of 2026-08-14
Yes, if you train models in JAX and need to deploy them in TensorFlow environments or share them across frameworks. The package is actively maintained, has no known vulnerabilities, and uses a permissive license. The main consideration is ensuring TensorFlow is installed separately and that your models fit the supported export patterns—check the documentation before committing to a production workflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- TensorFlow must be installed separately; use `pip install orbax-export[all]` for standard TensorFlow, or install your preferred TensorFlow version independently.
- Low install friction with a pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
last release 2025-09-17 (331 days) · last repo commit 2026-08-14 · 528 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 503,647 downloads/mo, #6,302 on PyPI
Alternatives
Verify before relying
pip install orbax-export
import orbax.export
# Export a JAX model to SavedModel format
# (See documentation for specific export API usage)- Specific export API surface and supported JAX model architectures beyond SavedModel capability
- Performance characteristics or limitations when exporting large or complex JAX models
- Compatibility matrix with specific JAX and TensorFlow versions
What it is and what it does
Orbax Export is a serialization library that bridges JAX and TensorFlow by converting JAX models into TensorFlow's SavedModel format. This enables JAX users to export trained models for deployment in TensorFlow-based production systems or to share models across frameworks. The package is part of the larger Orbax ecosystem and integrates with orbax-checkpoint for model persistence.
The package requires nine runtime dependencies centered on jax, jaxlib, jaxtyping, numpy, and serialization tools like protobuf and dataclasses-json. TensorFlow is not bundled by default to allow flexibility in version selection—users must either install the optional [all] extra or manage their own TensorFlow installation. The library targets Python 3.10 or later.
Use it for
- Export a trained JAX model to SavedModel format for deployment in TensorFlow-based inference systems.
- Share JAX models with teams or collaborators who work primarily in TensorFlow environments.
- Convert research models from JAX to a standardized format for production deployment pipelines.
- Integrate JAX training workflows with existing TensorFlow-based model serving infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you train models in JAX and need to deploy them in TensorFlow environments or share them across frameworks.
The package is actively maintained, has no known vulnerabilities, and uses a permissive license. The main consideration is ensuring TensorFlow is installed separately and that your models fit the supported export patterns—check the documentation before committing to a production workflow.
Install
orbax-export on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance with recent commits and steady releases since first release. Requires Python 3.10 or later and nine runtime dependencies including jax, jaxlib, and orbax-checkpoint.
Requires Python 3.10 or later. TensorFlow must be installed separately; use `pip install orbax-export[all]` for standard TensorFlow, or install your preferred TensorFlow version independently.
License in practice
Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install orbax-export
import orbax.export
# Export a JAX model to SavedModel format
# (See documentation for specific export API usage)Verify before relying
- Specific export API surface and supported JAX model architectures beyond SavedModel capability
- Performance characteristics or limitations when exporting large or complex JAX models
- Compatibility matrix with specific JAX and TensorFlow versions
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesabsl-pydataclasses-jsonetilsjaxjaxlibjaxtypingnumpyprotobuforbax-checkpoint |
| Maintenance | Actively maintained 331 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 503,647 / month, #6,302 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: orbax_export-0.0.8-py3-none-any.whl
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