Installing JAX#

Using JAX requires installing two packages: jax, which is pure Python and cross-platform, and jaxlib which contains compiled binaries, and requires different builds for different operating systems and accelerators.

TL;DR For most users, a typical JAX installation may look something like this:

  • CPU-only (Linux/macOS/Windows)

    pip install -U "jax[cpu]"
    
  • GPU (NVIDIA, CUDA 12, x86_64)

    pip install -U "jax[cuda12]"
    
  • GPU (NVIDIA, CUDA 12, x86_64) legacy

You should prefer jax[cuda12], which uses the common CPU jaxlib and adds GPU support as a plugin. The monolithic jax[cuda12_pip] option will be removed in a future JAX release.

pip install -U "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Supported platforms#

The table below shows all supported platforms and installation options. Check if your setup is supported; and if it says “yes” or “experimental”, then click on the corresponding link to learn how to install JAX in greater detail.

Linux, x86_64

Linux, aarch64

macOS, Intel x86_64, AMD GPU

macOS, Apple Silicon, ARM-based

Windows, x86_64

Windows WSL2, x86_64

CPU

yes

yes

yes

yes

yes

yes

NVIDIA GPU

yes

yes

no

n/a

no

experimental

Google Cloud TPU

yes

n/a

n/a

n/a

n/a

n/a

AMD GPU

experimental

no

no

n/a

no

no

Apple GPU

n/a

no

experimental

experimental

n/a

n/a

CPU#

pip installation: CPU#

Currently, the JAX team releases jaxlib wheels for the following operating systems and architectures:

  • Linux, x86_64

  • macOS, Intel

  • macOS, Apple ARM-based

  • Windows, x86_64 (experimental)

To install a CPU-only version of JAX, which might be useful for doing local development on a laptop, you can run:

pip install --upgrade pip
pip install --upgrade "jax[cpu]"

On Windows, you may also need to install the Microsoft Visual Studio 2019 Redistributable if it is not already installed on your machine.

Other operating systems and architectures require building from source. Trying to pip install on other operating systems and architectures may lead to jaxlib not being installed alongside jax, although jax may successfully install (but fail at runtime).

NVIDIA GPU#

JAX supports NVIDIA GPUs that have SM version 5.2 (Maxwell) or newer. Note that Kepler-series GPUs are no longer supported by JAX since NVIDIA has dropped support for Kepler GPUs in its software.

You must first install the NVIDIA driver. You’re recommended to install the newest driver available from NVIDIA, but the driver version must be >= 525.60.13 for CUDA 12 on Linux.

If you need to use a newer CUDA toolkit with an older driver, for example on a cluster where you cannot update the NVIDIA driver easily, you may be able to use the CUDA forward compatibility packages that NVIDIA provides for this purpose.

pip installation: NVIDIA GPU (CUDA, installed via pip, easier)#

There are two ways to install JAX with NVIDIA GPU support:

  • Using NVIDIA CUDA and cuDNN installed from pip wheels

  • Using a self-installed CUDA/cuDNN

The JAX team strongly recommends installing CUDA and cuDNN using the pip wheels, since it is much easier!

This method is only supported on x86_64, because NVIDIA has not released aarch64 CUDA pip packages.

pip install --upgrade pip

# NVIDIA CUDA 12 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12]"

# Legacy way of NVIDIA CUDA 12 installation. You should prefer `jax[cuda12]`,
# which uses the common CPU jaxlib and adds GPU support as a plugin. The
# monolithic `jax[cuda12_pip]` option will be removed in a future JAX release.
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

If JAX detects the wrong version of the NVIDIA CUDA libraries, there are several things you need to check:

  • Make sure that LD_LIBRARY_PATH is not set, since LD_LIBRARY_PATH can override the NVIDIA CUDA libraries.

  • Make sure that the NVIDIA CUDA libraries installed are those requested by JAX. Rerunning the installation command above should work.

pip installation: NVIDIA GPU (CUDA, installed locally, harder)#

If you prefer to use a preinstalled copy of NVIDIA CUDA, you must first install NVIDIA CUDA and cuDNN.

JAX provides pre-built CUDA-compatible wheels for Linux x86_64 only. Other combinations of operating system and architecture are possible, but require building from source (refer to Building from source to learn more}.

You should use an NVIDIA driver version that is at least as new as your NVIDIA CUDA toolkit’s corresponding driver version. If you need to use a newer CUDA toolkit with an older driver, for example on a cluster where you cannot update the NVIDIA driver easily, you may be able to use the CUDA forward compatibility packages that NVIDIA provides for this purpose.

JAX currently ships one CUDA wheel variant:

Built with

Compatible with

CUDA 12.3

CUDA >=12.1

CUDNN 8.9

CUDNN >=8.9, <9.0

NCCL 2.19

NCCL >=2.18

JAX checks the versions of your libraries, and will report an error if they are not sufficiently new. Setting the JAX_SKIP_CUDA_CONSTRAINTS_CHECK environment variable will disable the check, but using older versions of CUDA may lead to errors, or incorrect results.

NCCL is an optional dependency, required only if you are performing multi-GPU computations.

To install, run:

pip install --upgrade pip

# Installs the wheel compatible with NVIDIA CUDA 12 and cuDNN 8.9 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_local]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

These pip installations do not work with Windows, and may fail silently; refer to the table above.

You can find your CUDA version with the command:

nvcc --version

JAX uses LD_LIBRARY_PATH to find CUDA libraries and PATH to find binaries (ptxas, nvlink). Please make sure that these paths point to the correct CUDA installation.

Please let the JAX team know on the GitHub issue tracker if you run into any errors or problems with the pre-built wheels.

NVIDIA GPU Docker containers#

NVIDIA provides the JAX Toolbox containers, which are bleeding edge containers containing nightly releases of jax and some models/frameworks.

JAX nightly installation#

Nightly releases reflect the state of the main JAX repository at the time they are built, and may not pass the full test suite.

  • jax:

pip install -U --pre jax -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
  • jaxlib CPU:

pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
  • jaxlib Google Cloud TPU:

pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
pip install -U libtpu-nightly -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
  • jaxlib NVIDIA GPU (CUDA 12):

pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
pip install -U --pre jax-cuda12-pjrt jax-cuda12-plugin -f https://storage.googleapis.com/jax-releases/jax_cuda_plugin_nightly_releases.html
  • jaxlib NVIDIA GPU (CUDA 12) legacy:

pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_cuda12_releases.html

Google Cloud TPU#

pip installation: Google Cloud TPU#

JAX provides pre-built wheels for Google Cloud TPU. To install JAX along with appropriate versions of jaxlib and libtpu, you can run the following in your cloud TPU VM:

pip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

For users of Colab (https://colab.research.google.com/), be sure you are using TPU v2 and not the older, deprecated TPU runtime.

Apple Silicon GPU (ARM-based)#

pip installation: Apple ARM-based Silicon GPUs#

Apple provides an experimental Metal plugin for Apple ARM-based GPU hardware. For details, refer to Apple’s JAX on Metal documentation.

Note: There are several caveats with the Metal plugin:

  • The Metal plugin is new and experimental and has a number of known issues. Please report any issues on the JAX issue tracker.

  • The Metal plugin currently requires very specific versions of jax and jaxlib. This restriction will be relaxed over time as the plugin API matures.

AMD GPU#

JAX has experimental ROCm support. There are two ways to install JAX:

Conda (community-supported)#

Conda installation#

There is a community-supported Conda build of jax. To install it using conda, simply run:

conda install jax -c conda-forge

To install it on a machine with an NVIDIA GPU, run:

conda install jaxlib=*=*cuda* jax cuda-nvcc -c conda-forge -c nvidia

Note the cudatoolkit distributed by conda-forge is missing ptxas, which JAX requires. You must therefore either install the cuda-nvcc package from the nvidia channel, or install CUDA on your machine separately so that ptxas is in your path. The channel order above is important (conda-forge before nvidia).

If you would like to override which release of CUDA is used by JAX, or to install the CUDA build on a machine without GPUs, follow the instructions in the Tips & tricks section of the conda-forge website.

Go to the conda-forge jaxlib and jax repositories for more details.

Building JAX from source#

Refer to Building from source.

Installing older jaxlib wheels#

Due to storage limitations on the Python package index, the JAX team periodically removes older jaxlib wheels from the releases on http://pypi.org/project/jax. These can still be installed directly via the URLs here. For example:

# Install jaxlib on CPU via the wheel archive
pip install jax[cpu]==0.3.25 -f https://storage.googleapis.com/jax-releases/jax_releases.html

# Install the jaxlib 0.3.25 CPU wheel directly
pip install jaxlib==0.3.25 -f https://storage.googleapis.com/jax-releases/jax_releases.html

For specific older GPU wheels, be sure to use the jax_cuda_releases.html URL; for example

pip install jaxlib==0.3.25+cuda11.cudnn82 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html