# JAX v0.10.0 — JAX v0.10.0

- Product: JAX (https://whatsnew.fyi/product/jax)
- Vendor: Google
- Date: 2026-04-16
- Version: v0.10.0
- Original notes: https://github.com/jax-ml/jax/releases/tag/jax-v0.10.0
- Permalink: https://whatsnew.fyi/product/jax/releases/v0.10.0

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

---

- **added** — Add ResizeMethod.CUBIC_PYTORCH to jax.image.resize to match PyTorch's bicubic resize
- **added** — Support differentiation of jax.lax.linalg.qr for wide matrices and when full_matrices is True
- **added** — Parallelize LAPACK operations along the batch dimension on CPU
- **added** — Add perturb_singular argument to jax.lax.linalg.tridiagonal_solve to handle singular matrices by perturbing near-zero pivots in the LU decomposition
- **added** — Support computing eigenvectors on CPU and GPU in jax.scipy.linalg.eigh_tridiagonal
- **added** — Add jax.numpy.ndarray.byteswap method
- **removed** — Remove PartitionSpec equality with tuples
- **removed** — Remove .vma property from jax.core.ShapedArray in favor of .manual_axis_type.varying
- **changed** — JAX CPU devices now report their names as cpu:0, cpu:1, etc. instead of TFRT_CPU_0, TFRT_CPU_1
- **removed** — Remove config state jax_pmap_shmap_merge; jax.pmap now always uses the new implementation that wraps jax.jit(jax.shard_map)
- **removed** — Remove jax.device_put_sharded and jax.device_put_replicated from the public API
- **removed** — Remove C++ pmap infrastructure including jax.sharding.PmapSharding and related APIs from jaxlib.xla_extension and jax.interpreters.pxla
- **removed** — Remove deprecated keyword arguments a, a_min, and a_max from jax.numpy.clip
- **removed** — Remove support for non-ArrayLike inputs to jax.numpy.hstack, jax.numpy.vstack, jax.numpy.dstack, jax.numpy.column_stack, jax.numpy.atleast_1d, jax.numpy.atleast_2d, and jax.numpy.atleast_3d
- **changed** — jax.scipy.stats.rankdata now returns floating point values in all cases, following SciPy 1.18
- **changed** — Increase minimum supported SciPy version to 1.14
- **changed** — Replace vma parameter of jax.ShapeDtypeStruct with manual_axis_type: jax.sharding.ManualAxisType
- **removed** — Remove experimental jax.experimental.custom_dce.custom_dce
- **fixed** — Fix a bug that led to differing output between CPU and GPU for non-symmetric multidimensional IRFFTs
- **fixed** — Fix an error when tiny matrices were passed to jax.lax.linalg.tridiagonal_solve on GPU
- **fixed** — Fix a bug in jax.scipy.fft.dctn and idctn where axes=None incorrectly defaulted to all axes when s was specified
- **fixed** — Fix jax.distributed.initialize() on a GCE TPU Managed Instance Group raising an IndexError

* New features:
  * Added `ResizeMethod.CUBIC_PYTORCH` to jax.image.resize to match
    PyTorch's bicubic resize (#15768).
  * We now support differentiation of jax.lax.linalg.qr for wide
    matrices and when `full_matrices` is `True`.
  * LAPACK operations are now parallelized along the batch dimension on CPU.
  * Added `perturb_singular` argument to
    jax.lax.linalg.tridiagonal_solve to handle singular matrices by
    perturbing near-zero pivots in the LU decomposition. This is useful for
    solving numerically singular systems when computing eigenvectors by inverse
    iteration.
  * jax.scipy.linalg.eigh_tridiagonal now supports computing
    eigenvectors on CPU and GPU.
  * Added the jax.numpy.ndarray.byteswap method.

* Breaking changes:
  * `PartitionSpec` objects no longer report themselves to be equal to tuples.
    Convert tuples to `PartitionSpec` objects before testing equality.
  * The `.vma` property has been removed from `jax.core.ShapedArray`. Use
    `.manual_axis_type.varying` instead.
  * JAX CPU devices now report their names as `cpu:0`, `cpu:1`, etc. instead of
    `TFRT_CPU_0`, `TFRT_CPU_1`.
  * The config state `jax_pmap_shmap_merge` has been removed. `jax.pmap`
    will now always use the new implementation that wraps
    `jax.jit(jax.shard_map)`. Please see
    https://docs.jax.dev/en/latest/migrate_pmap.html for more information.
  * `jax.device_put_sharded` and `jax.device_put_replicated` have been removed
    from the public API and now raise an `AttributeError` when accessed.
    Please see
    https://docs.jax.dev/en/latest/migrate_pmap.html#drop-in-replacements for
    drop-in replacements.
  * The C++ pmap infrastructure has been removed. The following public APIs
    are no longer available:
    * `jax.sharding.PmapSharding`
    * From `jaxlib.xla_extension`: `PmapFunction`, `pmap`,
      `NoSharding`, `Chunked`, `Unstacked`, `ShardedAxis`, `Replicated`,
      `ShardingSpec`.
    * From `jax.interpreters.pxla`: `MapTracer`, `PmapExecutable`,
      `parallel_callable`, `shard_args`, `xla_pmap_p`, `Chunked`,
      `NoSharding`, `Replicated`, `ShardedAxis`, `ShardingSpec`,
      `Unstacked`, `spec_to_indices`.
  * The deprecated keyword arguments `a`, `a_min`, and `a_max` to
    `jax.numpy.clip` have been removed.
  * Functions `jax.numpy.hstack`, `jax.numpy.vstack`, `jax.numpy.dstack`,
    `jax.numpy.column_stack`, `jax.numpy.atleast_1d`, `jax.numpy.atleast_2d`,
    and `jax.numpy.atleast_3d` no longer accept non-`ArrayLike` inputs.
    Doing so previously issued a `DeprecationWarning`.
  * jax.scipy.stats.rankdata now returns floating point values in
    all cases, following a similar change in the SciPy 1.18 release.

* Deprecations:
  * A number of internal APIs in `jax.core` have been newly deprecated and
    some have been moved to `jax.extend.core`. These include `CallPrimitive`,
    `DebugInfo`, `DropVar`, `Effect`, `Effects`, `InconclusiveDimensionOperation`,
    `JaxprTypeError`, `check_jaxpr`, `concrete_or_error`, `find_top_trace`,
    `gensym`, `get_opaque_trace_state`, `jaxprs_in_params`, `new_jaxpr_eqn`,
    `no_effects`, `nonempty_axis_env_DO_NOT_USE`, `primal_dtype_to_tangent_dtype`,
    `unsafe_am_i_under_a_jit_DO_NOT_USE`, `unsafe_am_i_under_a_vmap_DO_NOT_USE`,
    `unsafe_get_axis_names_DO_NOT_USE`, `valid_jaxtype`, `JaxprPpContext`,
    `JaxprPpSettings`, `OutputType`, `abstract_token`, `aval_mapping_handlers`,
    `call`, `concretization_function_error`, `custom_typechecks`, `is_concrete`,
    `is_constant_dim`, `is_constant_shape`, `literalable_types`, `no_axis_name`,
    `pytype_aval_mappings`, and `trace_ctx`.

* Changes:
  * The minimum supported SciPy version is now 1.14.
  * `vma` parameter of `jax.ShapeDtypeStruct` has been replaced with
    `manual_axis_type: jax.sharding.ManualAxisType`. The `.vma` property has
    been replaced with `.manual_axis_type.varying`.
  * Removed experimental jax.exper

_[Truncated at 4000 characters — full notes: https://github.com/jax-ml/jax/releases/tag/jax-v0.10.0]_