rocm_jax/CHANGELOG.md

Changelog

These are the release notes for JAX.

jax 0.1.59 (unreleased)

Breaking changes

• The minimum jaxlib version is now 0.1.38.

New features

• Reverse-mode automatic differentiation (e.g. grad) of lax.cond, making it now differentiable in both modes (https://github.com/google/jax/pull/2091)
• JAX now supports DLPack, which allows sharing CPU and GPU arrays in a zero-copy way with other libraries, such as PyTorch.
• JAX GPU DeviceArrays now support __cuda_array_interface__, which is another zero-copy protocol for sharing GPU arrays with other libraries such as CuPy and Numba.
• JAX CPU device buffers now implement the Python buffer protocol, which allows zero-copy buffer sharing between JAX and NumPy.

jaxlib 0.1.38 (January 29, 2020)

• CUDA 9.0 is no longer supported.
• CUDA 10.2 wheels are now built by default.

jax 0.1.58 (January 28, 2020)

Breaking changes

• JAX has dropped Python 2 support, because Python 2 reached its end of life on January 1, 2020. Please update to Python 3.5 or newer.

New features

• Forward-mode automatic differentiation (jvp) of while loop (https://github.com/google/jax/pull/1980)
• New NumPy and SciPy functions:
  • jax.numpy.fft.fft2
  • jax.numpy.fft.ifft2
  • jax.numpy.fft.rfft
  • jax.numpy.fft.irfft
  • jax.numpy.fft.rfft2
  • jax.numpy.fft.irfft2
  • jax.numpy.fft.rfftn
  • jax.numpy.fft.irfftn
  • jax.numpy.fft.fftfreq
  • jax.numpy.fft.rfftfreq
  • jax.numpy.linalg.matrix_rank
  • jax.numpy.linalg.matrix_power
  • jax.scipy.special.betainc
• Batched Cholesky decomposition on GPU now uses a more efficient batched kernel.

Notable bug fixes

• With the Python 3 upgrade, JAX no longer depends on fastcache, which should help with installation.