tinax 0.1.0

Reliable productivity primitives for the JAX ecosystem

Tinax

Tinax is a small, typed library of explicit productivity primitives for JAX, Flax NNX, Optax, Orbax, Grain, Chex, and Safetensors workflows.

It provides stable policies for array and RNG ownership, bounded diagnostics, NNX graph copies, explicit stdlib application boundaries, deterministic input pipelines, complete checkpoints, sharding, and weight interchange. Tested ecosystem recipes live under examples/ without stable API guarantees.

Requirements

• CPython 3.14 with the standard GIL-enabled build
• glibc Linux x86-64/AArch64, Windows x86-64, or Apple Silicon macOS
• The exact JAX ecosystem versions declared by Tinax

Grain 0.2.18 does not provide a cp314t wheel, so free-threaded CPython is not supported.

Install

pip install tinax

Install a JAX accelerator distribution when needed:

pip install "tinax[gpu]"
pip install "tinax[tpu]"

See the installation guide for platform and accelerator details.

Quick Start

import numpy as np

from tinax.arrays import from_numpy, inspect_array, to_numpy

host = np.arange(8, dtype=np.float32)
device = from_numpy(host, copy=True)
info = inspect_array(device)
round_trip = to_numpy(device, writable=False)

Importing tinax alone is inert. Import the domain that owns the behavior you need.

Documentation

• Documentation
• Changelog
• Contributing
• Security policy
• Release guide

License

Apache-2.0. See LICENSE.