Public API: jax package

Subpackages

• jax.numpy module
• jax.scipy module
• jax.lax module
• jax.random module
• jax.sharding module
• jax.debug module
• jax.dlpack module
• jax.distributed module
• jax.dtypes module
• jax.flatten_util module
• jax.image module
• jax.nn module
• jax.ops module
• jax.profiler module
• jax.stages module
• jax.tree_util module
• jax.typing module
• jax.example_libraries module
• jax.experimental module

Configuration

config
check_tracer_leaks	Context manager for jax_check_tracer_leaks config option.
checking_leaks	Context manager for jax_check_tracer_leaks config option.
debug_nans	Context manager for jax_debug_nans config option.
debug_infs	Context manager for jax_debug_infs config option.
default_device	Context manager for jax_default_device config option.
default_matmul_precision	Context manager for jax_default_matmul_precision config option.
default_prng_impl	Context manager for jax_default_prng_impl config option.
enable_checks	Context manager for jax_enable_checks config option.
enable_custom_prng	Context manager for jax_enable_custom_prng config option (transient).
enable_custom_vjp_by_custom_transpose	Context manager for jax_enable_custom_vjp_by_custom_transpose config option (transient).
log_compiles	Context manager for jax_log_checkpoint_residuals config option.
numpy_rank_promotion	Context manager for jax_numpy_rank_promotion config option.
transfer_guard(new_val)	A contextmanager to control the transfer guard level for all transfers.

Just-in-time compilation (jit)

jit(fun[, in_shardings, out_shardings, ...])	Sets up fun for just-in-time compilation with XLA.
disable_jit([disable])	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.
ShapeDtypeStruct(shape, dtype[, ...])	A container for the shape, dtype, and other static attributes of an array.
device_put(x[, device, src])	Transfers x to device.
device_put_replicated(x, devices)	Transfer array(s) to each specified device and form Array(s).
device_put_sharded(shards, devices)	Transfer array shards to specified devices and form Array(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.
named_scope(name)	A context manager that adds a user specified name to the JAX name stack.
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(fun, *primals[, has_aux, reduce_axes])	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, *[, prevent_cse, policy, ...])	Make fun recompute internal linearization points when differentiated.

jax.Array (jax.Array)

Array()	Array base class for JAX
make_array_from_callback(shape, sharding, ...)	Returns a jax.Array via data fetched from data_callback.
make_array_from_single_device_arrays(shape, ...)	Returns a jax.Array from a sequence of jax.Arrays on a single device.

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.

Callbacks

pure_callback(callback, result_shape_dtypes, ...)	Applies a functionally pure Python callable.
debug.callback(callback, *args[, ordered])	Calls a stageable Python callback.

Miscellaneous

Device	A descriptor of an available device.
print_environment_info([return_string])	Returns a string containing local environment & JAX installation information.