Installation

Installation#

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.

Summary: For most users, a typical JAX installation may look something like this:

CPU-only (Linux/macOS/Windows)
```
pip install -U jax
```
GPU (NVIDIA, CUDA 12)
```
pip install -U "jax[cuda12]"
```

TPU (Google Cloud TPU VM)

pip install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_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
Linux, aarch64
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

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!

NVIDIA has released CUDA pip packages only for x86_64 and aarch64; on other platforms you must use a local installation of CUDA.

pip install --upgrade pip

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

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 and Linux aarch64 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 9.1	CUDNN >=9.1, <10.0
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 9.0 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_local]"

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.

JAX requires libdevice10.bc, which typically comes from the cuda-nvvm package. Make sure that it is present in your 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.

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:

Use AMD’s Docker container; or
Build from source (refer to Building from source — a section called Additional notes for building a ROCM jaxlib for AMD GPUs).

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.

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.

Unlike the instructions for installing a JAX release, here we name all of JAX’s packages explicitly on the command line, so pip will upgrade them if a newer version is available.

CPU only:

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

Google Cloud TPU:

pip install -U --pre jax jaxlib libtpu-nightly requests -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

NVIDIA GPU (CUDA 12):

pip install -U --pre jax jaxlib jax-cuda12-plugin[with_cuda] jax-cuda12-pjrt -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html

NVIDIA GPU (CUDA 12) legacy:

Use the following for historical nightly releases of monolithic CUDA jaxlibs. You most likely do not want this; no further monolithic CUDA jaxlibs will be built and those that exist will expire by Sep 2024. Use the “CUDA 12” option above.

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

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