Using JAX in multi-host and multi-process environments#
This guide explains how to use JAX in environments such as GPU clusters and Cloud TPU pods where accelerators are spread across multiple CPU hosts or JAX processes. We’ll refer to these as “multi-process” environments.
This guide specifically focuses on how to use collective communication
jax.lax.psum() ) in multi-process settings, although
other communication methods may be useful too depending on your use case (e.g.
RPC, mpi4jax). If you’re not already
familiar with JAX’s collective operations, we recommend starting with the
Parallel Evaluation in JAX notebook. An important requirement of
multi-process environments in JAX is direct communication links between
accelerators, e.g. the high-speed interconnects for Cloud TPUs or
NCCL for GPUs. These links allow
collective operations to run across multiple processes’ worth of accelerators
with high performance.
Multi-process programming model#
You must run at least one JAX process per host.
You should initialize the cluster with
Each process has a distinct set of local devices it can address. The global devices are the set of all devices across all processes.
Make sure all processes run the same parallel computations in the same order.
Launching JAX processes#
Unlike other distributed systems where a single controller node manages many worker nodes, JAX uses a “multi-controller” programming model where each JAX Python process runs independently, sometimes referred to as a Single Program, Multiple Data (SPMD) model. Generally, the same JAX Python program is run in each process, with only slight differences between each process’s execution (e.g. different processes will load different input data). Furthermore, you must manually run your JAX program on each host! JAX doesn’t automatically start multiple processes from a single program invocation.
(The requirement for multiple processes is why this guide isn’t offered as a notebook – we don’t currently have a good way to manage multiple Python processes from a single notebook.)
Initializing the cluster#
To initialize the cluster, you should call
the start of each process.
jax.distributed.initialize() must be called
early in the program, before any JAX computations are executed.
jax.distributed.initialize() takes several arguments, namely:
coordinator_address: the IP address of process 0 in your cluster, together with a port available on that process. Process 0 will start a JAX service exposed via that IP address and port, to which the other processes in the cluster will connect.
num_processes: the number of processes in the cluster
process_id: the ID number of this process, in the range
[0 .. num_processes).
local_device_ids: Restricts the visible devices of the current process to
For example on GPU, a typical usage is:
On Cloud TPU, Slurm and Open MPI environments, you can simply call
jax.distributed.initialize() with no
arguments. Default values for the arguments will be chosen automatically.
When running on GPUs with Slurm and Open MPI, it is assumed that one process is started per GPU, i.e. each process will
be assigned only one visible local device. Otherwise it is assumed that one process is started per host,
i.e. each process will be assigned all local devices.
The Open MPI auto-initialization is only used when the JAX processes are launched via
On TPU at present calling
jax.distributed.initialize() is optional, but
recommended since it enables additional checkpointing and health checking features.
Local vs. global devices#
Before we get to running multi-process computations from your program, it’s important to understand the distinction between local and global devices.
A process’s local devices are those that it can directly address and launch
computations on. For example, on a GPU cluster, each host can only launch
computations on the directly attached GPUs. On a Cloud TPU pod, each host can
only launch computations on the 8 TPU cores attached directly to that host (see
Cloud TPU System Architecture
documentation for more details). You can see a process’s local devices via
The global devices are the devices across all processes. A computation can
span devices across processes and perform collective operations via the direct
communication links between devices, as long as each process launches the
computation on its local devices. You can see all available global devices via
jax.devices() . A process’s local devices are always a subset of the
Running multi-process computations#
So how do you actually run a computation involving cross-process communication? Use the same parallel evaluation APIs that you would in a single process!
pmap() can be used to run a parallel computation across
multiple processes. (If you’re not already familiar with how to use
pmap() to run across multiple devices within a single process, check
out the Parallel Evaluation in JAX notebook.) Each process should call the
same pmapped function and pass in arguments to be mapped across its local
devices (i.e., the pmapped axis size is equal to the number of local devices).
Similarly, the function will return outputs sharded across local devices only.
Inside the function, however, collective communication operations are run across
all global devices, across all processes. Conceptually, this can be thought of
as running a pmap over a single array sharded across hosts, where each host
“sees” only its local shard of the input and output.
Here’s an example of multi-process pmap in action:
# The following is run in parallel on each host on a GPU cluster or TPU pod slice.
>>> import jax
>>> jax.distributed.initialize() # On GPU, see above for the necessary arguments.
>>> jax.device_count() # total number of accelerator devices in the cluster
>>> jax.local_device_count() # number of accelerator devices attached to this host
# The psum is performed over all mapped devices across the pod slice
>>> xs = jax.numpy.ones(jax.local_device_count())
>>> jax.pmap(lambda x: jax.lax.psum(x, 'i'), axis_name='i')(xs)
ShardedDeviceArray([32., 32., 32., 32., 32., 32., 32., 32.], dtype=float32)
xmap() works similarly when using a physical
hardware mesh (see the xmap tutorial if you’re
not familiar with the single-process version). Like
pmap() , the
inputs and outputs are local and any parallel communication inside the xmapped
function is global. The mesh is also global.
It’s very important that all processes run the same cross-process computations in the same order. Running the same JAX Python program in each process is usually sufficient. Some common pitfalls to look out for that may cause differently-ordered computations despite running the same program:
Processes passing differently-shaped inputs to the same parallel function can cause hangs or incorrect return values. Differently-shaped inputs are safe so long as they result in identically-shaped per-device data shards across processes; e.g. passing in different leading batch sizes in order to run on different numbers of local devices per process is ok, but having each process pad its batch to a different max example length is not.
“Last batch” issues where a parallel function is called in a (training) loop, and one or more processes exit the loop earlier than the rest. This will cause the rest to hang waiting for the already-finished processes to start the computation.
Conditions based on non-deterministic ordering of collections can cause code processes to hang. For example, iterating over
seton current Python versions or
dictbefore Python 3.7 may result in a different ordering on different processes, even with the same insertion order.