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Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays.
Type annotations and runtime type-checking for:
For example:
from jaxtyping import Array, Float, PyTree
# Accepts floating-point 2D arrays with matching axes
# You can replace `Array` with `torch.Tensor` etc.
def matrix_multiply(x: Float[Array, "dim1 dim2"],
y: Float[Array, "dim2 dim3"]
) -> Float[Array, "dim1 dim3"]:
...
def accepts_pytree_of_ints(x: PyTree[int]):
...
def accepts_pytree_of_arrays(x: PyTree[Float[Array, "batch c1 c2"]]):
...
pip install jaxtyping
Requires Python 3.10+.
JAX is an optional dependency, required for a few JAX-specific types. If JAX is not installed then these will not be available, but you may still use jaxtyping to provide shape/dtype annotations for PyTorch/NumPy/TensorFlow/etc.
The annotations provided by jaxtyping are compatible with runtime type-checking packages, so it is common to also install one of these. The two most popular are typeguard (which exhaustively checks every argument) and beartype (which checks random pieces of arguments).
Available at https://docs.kidger.site/jaxtyping.
Always useful
Equinox: neural networks and everything not already in core JAX!
Deep learning
Optax: first-order gradient (SGD, Adam, ...) optimisers.
Orbax: checkpointing (async/multi-host/multi-device).
Levanter: scalable+reliable training of foundation models (e.g. LLMs).
paramax: parameterizations and constraints for PyTrees.
Scientific computing
Diffrax: numerical differential equation solvers.
Optimistix: root finding, minimisation, fixed points, and least squares.
Lineax: linear solvers.
BlackJAX: probabilistic+Bayesian sampling.
sympy2jax: SymPy<->JAX conversion; train symbolic expressions via gradient descent.
PySR: symbolic regression. (Non-JAX honourable mention!)
Awesome JAX
Awesome JAX: a longer list of other JAX projects.
FAQs
Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays.
We found that jaxtyping demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Product
Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
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