Device Memory Profiling#


May 2023 update: we recommend using Tensorboard profiling for device memory analysis. After taking a profile, open the memory_viewer tab of the Tensorboard profiler for more detailed and understandable device memory usage.

The JAX Device Memory Profiler allows us to explore how and why JAX programs are using GPU or TPU memory. For example, it can be used to:

  • Figure out which arrays and executables are in GPU memory at a given time, or

  • Track down memory leaks.


The JAX device memory profiler emits output that can be interpreted using pprof (google/pprof). Start by installing pprof, by following its installation instructions. At the time of writing, installing pprof requires first installing Go of version 1.16+, Graphviz, and then running

go install

which installs pprof as $GOPATH/bin/pprof, where GOPATH defaults to ~/go.


The version of pprof from google/pprof is not the same as the older tool of the same name distributed as part of the gperftools package. The gperftools version of pprof will not work with JAX.

Understanding how a JAX program is using GPU or TPU memory#

A common use of the device memory profiler is to figure out why a JAX program is using a large amount of GPU or TPU memory, for example if trying to debug an out-of-memory problem.

To capture a device memory profile to disk, use jax.profiler.save_device_memory_profile(). For example, consider the following Python program:

import jax
import jax.numpy as jnp
import jax.profiler

def func1(x):
  return jnp.tile(x, 10) * 0.5

def func2(x):
  y = func1(x)
  return y, jnp.tile(x, 10) + 1

x = jax.random.normal(jax.random.key(42), (1000, 1000))
y, z = func2(x)



If we first run the program above and then execute

pprof --web

pprof opens a web browser containing the following visualization of the device memory profile in callgraph format:

Device memory profiling example

The callgraph is a visualization of the Python stack at the point the allocation of each live buffer was made. For example, in this specific case, the visualization shows that func2 and its callees were responsible for allocating 76.30MB, of which 38.15MB was allocated inside the call from func1 to func2. For more information about how to interpret callgraph visualizations, see the pprof documentation.

Functions compiled with jax.jit() are opaque to the device memory profiler. That is, any memory allocated inside a jit-compiled function will be attributed to the function as a whole.

In the example, the call to block_until_ready() is to ensure that func2 completes before the device memory profile is collected. See Asynchronous dispatch for more details.

Debugging memory leaks#

We can also use the JAX device memory profiler to track down memory leaks by using pprof to visualize the change in memory usage between two device memory profiles taken at different times. For example, consider the following program which accumulates JAX arrays into a constantly-growing Python list.

import jax
import jax.numpy as jnp
import jax.profiler

def afunction():
  return jax.random.normal(jax.random.key(77), (1000000,))

z = afunction()

def anotherfunc():
  arrays = []
  for i in range(1, 10):
    x = jax.random.normal(jax.random.key(42), (i, 10000))


If we simply visualize the device memory profile at the end of execution (, it may not be obvious that each iteration of the loop in anotherfunc accumulates more device memory allocations:

pprof --web

Device memory profile at end of execution

The large but fixed allocation inside afunction dominates the profile but does not grow over time.

By using pprof’s --diff_base feature to visualize the change in memory usage across loop iterations, we can identify why the memory usage of the program increases over time:

pprof --web --diff_base

Device memory profile at end of execution

The visualization shows that the memory growth can be attributed to the call to normal inside anotherfunc.