Profiling JAX computations with XProf

XProf is a great way to acquire and visualize performance traces and profiles of your program, including activity on GPU and TPU. The end result looks something like this:

XProf example

Programmatic capture

You can instrument your code to capture a profiler trace for JAX code via the jax.profiler.start_trace and jax.profiler.stop_trace methods. Call jax.profiler.start_trace with the directory to write trace files to. This should be the same --logdir directory used to start XProf. Then, you can XProf to view the traces.

For example, to take a profiler trace:

import jax

jax.profiler.start_trace("/tmp/profile-data")

# Run the operations to be profiled
key = jax.random.key(0)
x = jax.random.normal(key, (5000, 5000))
y = x @ x
y.block_until_ready()

jax.profiler.stop_trace()

Note the jax.block_until_ready call. We use this to make sure on-device execution is captured by the trace. See Asynchronous dispatch for details on why this is necessary.

You can also use the jax.profiler.trace context manager as an alternative to start_trace and stop_trace:

import jax

with jax.profiler.trace("/tmp/profile-data"):
  key = jax.random.key(0)
  x = jax.random.normal(key, (5000, 5000))
  y = x @ x
  y.block_until_ready()

Viewing the trace

After capturing a trace, you can view it using the XProf UI.

You can launch the profiler UI directly using the standalone XProf command by pointing it to your log directory:

$ xprof --port=8791 /tmp/profile-data
Attempting to start XProf server:
  Log Directory: /tmp/profile-data
  Port: 8791
  Worker Service Address: 0.0.0.0:50051
  Hide Capture Button: False
XProf at http://localhost:8791/ (Press CTRL+C to quit)

Navigate to the provided URL (e.g., http://localhost:8791/) in your browser to view the profile.

Available traces appear in the "Sessions" dropdown menu on the left. Select the session you're interested in, and then under the "Tools" dropdown, select "Trace Viewer". You should now see a timeline of the execution. You can use the WASD keys to navigate the trace, and click or drag to select events for more details. See the Trace Viewer Tool documentation for more details on using the trace viewer.

Manual capture via XProf

The following are instructions for capturing a manually-triggered N-second trace from a running program.

  1. Start an XProf server:

    xprof --logdir /tmp/profile-data/

    You should be able to load XProf at <http://localhost:8791/>. You can specify a different port with the --port flag.

  2. In the Python program or process you'd like to profile, add the following somewhere near the beginning:

    import jax.profiler
jax.profiler.start_server(9999)

    This starts the profiler server that XProf connects to. The profiler server must be running before you move on to the next step. When you're done using the server, you can call jax.profiler.stop_server() to shut it down.

    If you'd like to profile a snippet of a long-running program (e.g. a long training loop), you can put this at the beginning of the program and start your program as usual. If you'd like to profile a short program (e.g. a microbenchmark), one option is to start the profiler server in an IPython shell, and run the short program with %run after starting the capture in the next step. Another option is to start the profiler server at the beginning of the program and use time.sleep() to give you enough time to start the capture.

  3. Open <http://localhost:8791/>, and click the "CAPTURE PROFILE" button in the upper left. Enter "localhost:9999" as the profile service URL (this is the address of the profiler server you started in the previous step). Enter the number of milliseconds you'd like to profile for, and click "CAPTURE".

  4. If the code you'd like to profile isn't already running (e.g. if you started the profiler server in a Python shell), run it while the capture is running.

  5. After the capture finishes, XProf should automatically refresh. (Not all of the XProf profiling features are hooked up with JAX, so it may initially look like nothing was captured.) On the left under "Tools", select "Trace Viewer".

You should now see a timeline of the execution. You can use the WASD keys to navigate the trace, and click or drag to select events to see more details at the bottom. See the Trace Viewer Tool documentation for more details on using the trace viewer.

XProf and Tensorboard

XProf is the underlying tool that powers the profiling and trace capturing functionality in Tensorboard. As long as xprof is installed, a "Profile" tab will be present within Tensorboard. Using this is identical to launching XProf independently, as long as it is launched pointing to the same log directory. This includes profile capture, analysis, and viewing functionality. XProf supplants the tensorboard_plugin_profile functionality that was previously recommended.

$ tensorboard --logdir=/tmp/profile-data
[...]
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.19.0 at http://localhost:6006/ (Press CTRL+C to quit)

Adding custom trace events

By default, the events in the trace viewer are mostly low-level internal JAX functions. You can add your own events and functions by using jax.profiler.TraceAnnotation and jax.profiler.annotate_function in your code.

Configuring profiler options

The start_trace method accepts an optional profiler_options parameter, which allows for fine-grained control over the profiler's behavior. This parameter should be an instance of jax.profiler.ProfileOptions.

For example, to disable all python and host traces:

import jax

options = jax.profiler.ProfileOptions()
options.python_tracer_level = 0
options.host_tracer_level = 0
jax.profiler.start_trace("/tmp/profile-data", profiler_options=options)

# Run the operations to be profiled
key = jax.random.key(0)
x = jax.random.normal(key, (5000, 5000))
y = x @ x
y.block_until_ready()

jax.profiler.stop_trace()

General options

  1. host_tracer_level: Sets the trace level for host-side activities.

    Supported Values:

    • 0: Disables host (CPU) tracing entirely.
    • 1: Enables tracing of only user-instrumented TraceMe events.
    • 2: Includes level 1 traces plus high-level program execution details like expensive XLA operations (default).
    • 3: Includes level 2 traces plus more verbose, low-level program execution details such as cheap XLA operations.

  2. device_tracer_level: Controls whether device tracing is enabled.

    Supported Values:

    • 0: Disables device tracing.
    • 1: Enables device tracing (default).

  3. python_tracer_level: Controls whether Python tracing is enabled.

    Supported Values:

    • 0: Disables Python function call tracing (default).
    • 1: Enables Python tracing.

Advanced configuration options

TPU options

  1. tpu_trace_mode: Specifies the mode for TPU tracing.

    Supported Values:

    • TRACE_ONLY_HOST: This means only host-side (CPU) activities are traced, and no device (TPU/GPU) traces are collected.
    • TRACE_ONLY_XLA: This means only XLA-level operations on the device are traced.
    • TRACE_COMPUTE: This traces compute operations on the device.
    • TRACE_COMPUTE_AND_SYNC: This traces both compute operations and synchronization events on the device.

    If "tpu_trace_mode" is not provided the trace_mode defaults to TRACE_ONLY_XLA.

  2. tpu_num_sparse_cores_to_trace: Specifies the number of sparse cores to trace on the TPU.

  3. tpu_num_sparse_core_tiles_to_trace: Specifies the number of tiles within each sparse core to trace on the TPU.

  4. tpu_num_chips_to_profile_per_task: Specifies the number of TPU chips to profile per task.

GPU options

The following options are available for GPU profiling:

  • gpu_max_callback_api_events: Sets the maximum number of events collected by the CUPTI callback API. Defaults to 2*1024*1024.
  • gpu_max_activity_api_events: Sets the maximum number of events collected by the CUPTI activity API. Defaults to 2*1024*1024.
  • gpu_max_annotation_strings: Sets the maximum number of annotation strings that can be collected. Defaults to 1024*1024.
  • gpu_enable_nvtx_tracking: Enables NVTX tracking in CUPTI. Defaults to False.
  • gpu_enable_cupti_activity_graph_trace: Enables CUPTI activity graph tracing for CUDA graphs. Defaults to False.
  • gpu_pm_sample_counters: A comma-separated string of GPU Performance Monitoring metrics to collect using CUPTI's PM sampling feature (e.g. "sm__cycles_active.avg.pct_of_peak_sustained_elapsed"). PM sampling is disabled by default. For available metrics, see NVIDIA's CUPTI documentation.
  • gpu_pm_sample_interval_us: Sets the sampling interval in microseconds for CUPTI PM sampling. Defaults to 500.
  • gpu_pm_sample_buffer_size_per_gpu_mb: Sets the system memory buffer size per device in MB for CUPTI PM sampling. Defaults to 64MB. The maximum supported value is 4GB.
  • gpu_num_chips_to_profile_per_task: Specifies the number of GPU devices to profile per task. If not specified, set to 0, or set to an invalid value, all available GPUs will be profiled. This can be used to decrease the trace collection size.
  • gpu_dump_graph_node_mapping: If enabled, dumps CUDA graph node mapping information into the trace. Defaults to False.

For example:

options = ProfileOptions()
options.advanced_configuration = {"tpu_trace_mode" : "TRACE_ONLY_HOST", "tpu_num_sparse_cores_to_trace" : 2}

Returns InvalidArgumentError if any unrecognized keys or option values are found.