rocm_jax/docs/installation.md

Installing JAX

JAX is written in pure Python, but it depends on XLA, which needs to be installed as the jaxlib package. Use the following instructions to install a binary package with pip or conda, to use a Docker container, or to build JAX from source.

Supported platforms

	Linux x86_64	Linux aarch64	Mac x86_64	Mac ARM	Windows x86_64	Windows WSL2 x86_64
CPU	yes	yes	yes	yes	yes	yes
NVIDIA GPU	yes	yes	no	n/a	no	experimental
Google 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

We support installing or building jaxlib on Linux (Ubuntu 20.04 or later) and macOS (10.12 or later) platforms. There is also experimental native Windows support.

Windows users can use JAX on CPU and GPU via the Windows Subsystem for Linux, or alternatively they can use the native Windows CPU-only support.

CPU

pip installation: CPU

We currently release jaxlib wheels for the following operating systems and architectures:

• Linux, x86-64
• Mac, Intel
• Mac, ARM
• 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. We recommend installing the newest driver available from NVIDIA, but the driver must be version >= 525.60.13 for CUDA 12 and >= 450.80.02 for CUDA 11 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: GPU (CUDA, installed via pip, easier)

There are two ways to install JAX with NVIDIA GPU support: using CUDA and CUDNN installed from pip wheels, and using a self-installed CUDA/CUDNN. We strongly recommend 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

# CUDA 12 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# CUDA 11 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

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

• make sure that LD_LIBRARY_PATH is not set, since LD_LIBRARY_PATH can override the CUDA libraries.
• make sure that the CUDA libraries installed are those requested by JAX. Rerunning the installation command above should work.

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

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

You should use an NVIDIA driver version that is at least as new as your 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 two CUDA wheel variants:

• CUDA 12.2, cuDNN 8.9, NCCL 2.16
• CUDA 11.8, cuDNN 8.6, NCCL 2.16

You may use a JAX wheel provided the major version of your CUDA, cuDNN, and NCCL installations match, and the minor versions are the same or newer. JAX checks the versions of your libraries, and will report an error if they are not sufficiently new.

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 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

# Installs the wheel compatible with CUDA 11 and cuDNN 8.6 or newer.
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_local]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

These pip installations do not work with Windows, and may fail silently; see 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 us know on the issue tracker if you run into any errors or problems with the prebuilt wheels.

Docker containers: NVIDIA GPU

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

Nightly installation

Nightly releases reflect the state of the main 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 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 GPU (Cuda 12):

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

• Jaxlib GPU (Cuda 11):

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

Google 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 interactive notebook users: Colab TPUs no longer support JAX as of JAX version 0.4. However, for an interactive TPU notebook in the cloud, you can use Kaggle TPU notebooks, which fully support JAX.

Apple GPU

pip installation: Apple GPUs

Apple provides an experimental Metal plugin for Apple GPU hardware. For details, see Apple's JAX on Metal documentation.

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.

Conda

Conda installation

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

conda install jax -c conda-forge

To install 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.

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

Building JAX from source

See Building JAX from source.

Installing older jaxlib wheels

Due to storage limitations on the Python package index, we periodically remove 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