# 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](#docker-containers-nvidia-gpu), or to [build JAX from
source](developer.md#building-from-source).

## Supported platforms

|            | Linux x86_64 | Linux aarch64           | Mac x86_64   | Mac ARM        | Windows x86_64 | Windows WSL2 x86_64 |
|------------|--------------|-------------------------|--------------|----------------|----------------|---------------------|
| CPU        | [yes](#cpu)         | [yes](#cpu) | [yes](#cpu)          | [yes](#cpu)            | [yes](#cpu)            | [yes](#cpu)                 |
| NVIDIA GPU | [yes](#nvidia-gpu)               | [yes](#nvidia-gpu) | no           | n/a            | no             | [experimental](#nvidia-gpu)        |
| Google TPU | [yes](#google-tpu)  | n/a                     | n/a          | n/a            | n/a            | n/a                 |
| AMD GPU    | [experimental](#amd-gpu) | no                      | no           | n/a                 | no             | no                  |
| Apple GPU  | n/a                 | no                      | [experimental](#apple-gpu) | [experimental](#apple-gpu)   | 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](https://docs.microsoft.com/en-us/windows/wsl/about), 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

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

On Windows, you may also need to install the
[Microsoft Visual Studio 2019 Redistributable](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170#visual-studio-2015-2017-2019-and-2022)
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](https://docs.nvidia.com/deploy/cuda-compatibility/)
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.

```bash
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](https://developer.nvidia.com/cuda-downloads) and
[CuDNN](https://developer.nvidia.com/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](developer.md#building-from-source).

You should use an NVIDIA driver version that is at least as new as your
[CUDA toolkit's corresponding driver version](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions).
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](https://docs.nvidia.com/deploy/cuda-compatibility/)
that NVIDIA provides for this purpose.

JAX currently ships two CUDA wheel variants:
* CUDA 12.3, 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

```bash
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](#installing-jax).**

You can find your CUDA version with the command:

```bash
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](https://github.com/google/jax/issues)
if you run into any errors or problems with the prebuilt wheels.

### Docker containers: NVIDIA GPU

NVIDIA provides the [JAX
Toolbox](https://github.com/NVIDIA/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:
```bash
pip install -U --pre jax -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
```

* Jaxlib CPU:
```bash
pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
```

* Jaxlib TPU:
```bash
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):
```bash
pip install -U --pre jaxlib -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_cuda12_releases.html
```

* Jaxlib GPU (Cuda 11):
```bash
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](https://cloud.google.com/tpu/docs/users-guide-tpu-vm).
To install JAX along with appropriate versions of `jaxlib` and `libtpu`, you can run
the following in your cloud TPU VM:
```bash
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](https://www.kaggle.com/docs/tpu), 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](https://developer.apple.com/metal/jax/).

There are several caveats with the Metal plugin:
* the Metal plugin is new and experimental and has a number of
  [known issues](https://github.com/google/jax/issues?q=is%3Aissue+is%3Aopen+label%3A%22Apple+GPU+%28Metal%29+plugin%22).
  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](https://hub.docker.com/r/rocm/jax), or
* [build from source](developer.md#additional-notes-for-building-a-rocm-jaxlib-for-amd-gpus).

## Conda

### Conda installation

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

```bash
conda install jax -c conda-forge
```

To install on a machine with an NVIDIA GPU, run
```bash
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](https://conda-forge.org/docs/user/tipsandtricks.html#installing-cuda-enabled-packages-like-tensorflow-and-pytorch)
section of the `conda-forge` website.

See the `conda-forge`
[jaxlib](https://github.com/conda-forge/jaxlib-feedstock#installing-jaxlib) and
[jax](https://github.com/conda-forge/jax-feedstock#installing-jax) repositories
for more details.

## Building JAX from source
See [Building JAX from source](developer.md#building-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
```