orbax-checkpoint
Orbax Checkpoint
Decision gist · record as of 2026-08-14
Yes. Orbax Checkpoint is actively maintained (release 2 days old), has no known vulnerabilities, and solves a real problem for JAX practitioners: efficient, non-blocking checkpoint management. The permissive license and low install friction make it a straightforward choice. Install it if you're running JAX training jobs that need reliable state persistence.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later and JAX installed; asynchronous features depend on uvloop and aiofiles being available.
- Low friction install with a pure-Python wheel.
- Active maintenance with a release 2 days old and recent commits.
License · maintenance · safety
permissive license (permissive) — Permissive Apache license allows free use, modification, and distribution in both open and closed projects with minimal restrictions.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 528 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,731,181 downloads/mo, #2,045 on PyPI
Alternatives
Verify before relying
pip install orbax-checkpoint
import orbax.checkpoint
# Basic checkpoint save/restore with JAX arrays
checkpointer = orbax.checkpoint.PyTreeCheckpointer()
checkpointer.save(path, pytree_state)- Whether the package's 'highly customizable and composable API' covers specific storage backends beyond what tensorstore provides.
- Performance characteristics of asynchronous checkpointing relative to synchronous alternatives in typical training loops.
- Compatibility guarantees with different JAX versions and distributed training frameworks.
What it is and what it does
Orbax Checkpoint is a checkpointing library built for JAX machine learning workflows, designed to handle the state-saving needs of training pipelines. It wraps JAX's pytree structures and provides asynchronous I/O capabilities to avoid blocking training loops while persisting model weights and optimizer state to disk or cloud storage. The library sits on top of tensorstore for flexible storage backends and uses aiofiles and uvloop to manage non-blocking I/O operations.
The package targets researchers and practitioners who need reliable, efficient checkpointing during long-running JAX training jobs. It abstracts away the complexity of coordinating I/O with training steps, supporting various serialization formats and storage systems. With multiple runtime dependencies including protobuf, msgpack, and pyyaml, it provides a complete ecosystem for checkpoint management rather than a minimal utility.
Use it for
- Save JAX model state asynchronously during training without pausing gradient computation.
- Restore model weights and optimizer state from checkpoints to resume interrupted training runs.
- Manage multiple checkpoint versions and storage backends through a unified API.
- Coordinate checkpointing across distributed JAX training setups with tensorstore integration.
- Serialize complex nested pytree structures with custom types using pluggable handlers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Orbax Checkpoint is actively maintained (release 2 days old), has no known vulnerabilities, and solves a real problem for JAX practitioners: efficient, non-blocking checkpoint management. The permissive license and low install friction make it a straightforward choice. Install it if you're running JAX training jobs that need reliable state persistence.
Install
orbax-checkpoint on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance with a release 2 days old and recent commits. Depends on JAX and many other runtime packages including tensorstore, aiofiles, and uvloop for async I/O support.
Requires Python 3.11 or later and JAX installed; asynchronous features depend on uvloop and aiofiles being available.
License in practice
Permissive Apache license allows free use, modification, and distribution in both open and closed projects with minimal restrictions.
Quickstart
pip install orbax-checkpoint
import orbax.checkpoint
# Basic checkpoint save/restore with JAX arrays
checkpointer = orbax.checkpoint.PyTreeCheckpointer()
checkpointer.save(path, pytree_state)Verify before relying
- Whether the package's 'highly customizable and composable API' covers specific storage backends beyond what tensorstore provides.
- Performance characteristics of asynchronous checkpointing relative to synchronous alternatives in typical training loops.
- Compatibility guarantees with different JAX versions and distributed training frameworks.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagesabsl-pyetilstyping_extensionsmsgpackjaxnumpyprometheus-clientpyyamltensorstoreaiofilesprotobufhumanizesimplejsonpsutiluvloopnest_asyncio |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,731,181 / month, #2,045 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_checkpoint-0.12.4-py3-none-any.whl
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