# Welcome to Blackjax!#

Warning

The documentation corresponds to the current state of the `main`

branch. There may be differences with the latest released version.

Blackjax is a library of samplers for JAX that works on CPU as well as GPU. It is designed with two categories of users in mind:

People who just need state-of-the-art samplers that are fast, robust and well tested;

Researchers who can use the library’s building blocks to design new algorithms.

It integrates really well with PPLs as long as they can provide a (potentially unnormalized) log-probability density function compatible with JAX.

# Hello World#

```
import jax
import jax.numpy as jnp
import jax.scipy.stats as stats
import numpy as np
import blackjax
observed = np.random.normal(10, 20, size=1_000)
def logdensity_fn(x):
logpdf = stats.norm.logpdf(observed, x["loc"], x["scale"])
return jnp.sum(logpdf)
# Build the kernel
step_size = 1e-3
inverse_mass_matrix = jnp.array([1., 1.])
nuts = blackjax.nuts(logdensity_fn, step_size, inverse_mass_matrix)
# Initialize the state
initial_position = {"loc": 1., "scale": 2.}
state = nuts.init(initial_position)
# Iterate
rng_key = jax.random.key(0)
step = jax.jit(nuts.step)
for i in range(1_000):
nuts_key = jax.random.fold_in(rng_key, i)
state, _ = nuts.step(nuts_key, state)
```

Note

If you want to use Blackjax with a model implemented with a PPL, go to the related tutorials in the left menu.

# Installation#

```
pip install blackjax
```

Conda

```
conda install blackjax -c conda-forge
```

GPU instructions

BlackJAX is written in pure Python but depends on XLA via JAX. By default, the
version of JAX that will be installed along with BlackJAX will make your code
run on CPU only. **If you want to use BlackJAX on GPU/TPU** we recommend you follow
these instructions to install JAX
with the relevant hardware acceleration support.