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JAX is another popular python package for accelerated computing. JAX is built on XLA (the same XLA TensorFlow uses) as well as AutoGrad, and additionally has acceleration tools that operate on functions such as vmap, jit, etc. JAX is not as widespread in machine learning as TensorFlow and PyTorch for traditional models (Computer Vision, Language Models) though it is quickly gaining promienence. JAX is very powerful when a program needs non-traditional autodifferentiation or vectorizatoin, such as: forward-mode AD, higher order derivatives, Jacobians, Hessians, or any combination of the above. Users of JAX on Polaris are encouraged to read the user documentation in detail, particularly the details about pure-functional programming, no in-place operations, and the common mistakes in writing functions for the @jit decorator.

JAX on Polaris

JAX is installed on Polaris via the conda module, available with:

module load conda; conda activate

Then, you can load JAX in python as usual (below showing results from the conda/2022-07-19 module):

>>> import jax
>>> jax.__version__

Notes on JAX 0.3.15

On Polaris, due to a bug, an environment variable must be set to use JAX on GPUs. The following code will crash:

import jax.numpy as numpy
a = numpy.zeros(1000)
outputting an error that looks like:
jaxlib.xla_extension.XlaRuntimeError: UNKNOWN: no kernel image is available for execution on the device

You can fix this by setting an environment variable:

export XLA_FLAGS="--xla_gpu_force_compilation_parallelism=1"

Scaling JAX to multiple GPUs and multiple Nodes

Jax has intrinsic scaling tools to use multiple GPUs on a single node, via the pmap function. If this is sufficient for your needs, excellent. If not, another alternative is to use the newer package mpi4jax.

mpi4Jax is a relatively new project and requires setting some environment variables for good performance and usability: - Set MPI4JAX_USE_CUDA_MPI=1 to use CUDA-Aware MPI, supported in the conda module, to do operations directly from the GPU. - Set MPICH_GPU_SUPPORT_ENABLED=1 to use CUDA-Aware MPI.

The following code, based off of a test script from the mpi4jax repository, can help you verify you are using mpi4jax properly:

import os
from mpi4py import MPI
import jax
import jax.numpy as jnp
import mpi4jax

rank = comm.Get_rank()
local_rank = int(os.environ["PMI_LOCAL_RANK"])

available_devices = jax.devices("gpu")
if len(available_devices) <= local_rank:
    raise Exception("Could not find enough GPUs")

target_device = available_devices[local_rank]

def foo(arr):
   arr = arr + rank
   arr_sum, _ = mpi4jax.allreduce(arr, op=MPI.SUM, comm=comm)
   return arr_sum

with jax.default_device(target_device):
    a = jnp.zeros((3, 3))
    print(f"Rank {rank}, local rank {local_rank}, a.device is {a.device()}")
    result = foo(a)
    print(f"Rank {rank}, local rank {local_rank}, result.device is {result.device()}")

    import time
    print("Sleeping for 5 seconds if you want to look at nvidia-smi ... ")
    import time
    print("Done sleeping")

if rank == 0:

JAX and mpi4jax are both still somewhat early in their software lifecycles. Updates are frequent, and if you require assistance please contact