Public API: jax package

Subpackages

• jax.numpy package
• jax.scipy package
• JAX configuration
• jax.dlpack module
• jax.distributed module
• jax.example_libraries package
• jax.experimental package
• jax.flatten_util package
• jax.image package
• jax.lax package
• jax.nn package
• jax.ops package
• jax.profiler module
• jax.random package
• jax.tree_util package

Just-in-time compilation (jit)

jit(fun, *[, static_argnums, ...])	Sets up fun for just-in-time compilation with XLA.
disable_jit()	Context manager that disables jit() behavior under its dynamic context.
ensure_compile_time_eval()	Context manager to ensure evaluation at trace/compile time (or error).
xla_computation(fun[, static_argnums, ...])	Creates a function that produces its XLA computation given example args.
make_jaxpr(fun[, static_argnums, axis_env, ...])	Creates a function that produces its jaxpr given example args.
eval_shape(fun, *args, **kwargs)	Compute the shape/dtype of fun without any FLOPs.
device_put(x[, device])	Transfers x to device.
device_put_replicated(x, devices)	Transfer array(s) to each specified device and form ShardedDeviceArray(s).
device_put_sharded(shards, devices)	Transfer array shards to specified devices and form ShardedDeviceArray(s).
device_get(x)	Transfer x to host.
default_backend()	Returns the platform name of the default XLA backend.
named_call(fun, *[, name])	Adds a user specified name to a function when staging out JAX computations.
block_until_ready(x)	Tries to call a block_until_ready method on pytree leaves.

Automatic differentiation

grad(fun[, argnums, has_aux, holomorphic, ...])	Creates a function that evaluates the gradient of fun.
value_and_grad(fun[, argnums, has_aux, ...])	Create a function that evaluates both fun and the gradient of fun.
jacfwd(fun[, argnums, has_aux, holomorphic])	Jacobian of fun evaluated column-by-column using forward-mode AD.
jacrev(fun[, argnums, has_aux, holomorphic, ...])	Jacobian of fun evaluated row-by-row using reverse-mode AD.
hessian(fun[, argnums, has_aux, holomorphic])	Hessian of fun as a dense array.
jvp(fun, primals, tangents[, has_aux])	Computes a (forward-mode) Jacobian-vector product of fun.
linearize(fun, *primals)	Produces a linear approximation to fun using jvp() and partial eval.
linear_transpose(fun, *primals[, reduce_axes])	Transpose a function that is promised to be linear.
vjp()	Compute a (reverse-mode) vector-Jacobian product of fun.
custom_jvp(fun[, nondiff_argnums])	Set up a JAX-transformable function for a custom JVP rule definition.
custom_vjp(fun[, nondiff_argnums])	Set up a JAX-transformable function for a custom VJP rule definition.
closure_convert(fun, *example_args)	Closure conversion utility, for use with higher-order custom derivatives.
checkpoint(fun[, concrete, prevent_cse, policy])	Make fun recompute internal linearization points when differentiated.

Vectorization (vmap)

vmap(fun[, in_axes, out_axes, axis_name, ...])	Vectorizing map.
numpy.vectorize(pyfunc, *[, excluded, signature])	Define a vectorized function with broadcasting.

Parallelization (pmap)

pmap(fun[, axis_name, in_axes, out_axes, ...])	Parallel map with support for collective operations.
devices([backend])	Returns a list of all devices for a given backend.
local_devices([process_index, backend, host_id])	Like jax.devices(), but only returns devices local to a given process.
process_index([backend])	Returns the integer process index of this process.
device_count([backend])	Returns the total number of devices.
local_device_count([backend])	Returns the number of devices addressable by this process.
process_count([backend])	Returns the number of JAX processes associated with the backend.