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.. _profiler_advanced:

######################################## Find bottlenecks in your code (advanced) ######################################## Audience: Users who want to profile their TPU models to find bottlenecks and improve performance.

Profile cloud TPU models

To profile TPU models use the :class:~lightning.pytorch.profilers.xla.XLAProfiler

.. code-block:: python

from lightning.pytorch.profilers import XLAProfiler

profiler = XLAProfiler(port=9001)
trainer = Trainer(profiler=profiler)

Capture profiling logs in Tensorboard

To capture profile logs in Tensorboard, follow these instructions:

0: Setup the required installs

Use this guide <https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm#tpu-vm>_ to help you with the Cloud TPU required installations.

1: Start Tensorboard

Start the TensorBoard <https://www.tensorflow.org/tensorboard>_ server:

.. code-block:: bash

tensorboard --logdir ./tensorboard --port 9001

Now open the following url on your browser

.. code-block:: bash

http://localhost:9001/#profile

2: Capture the profile

Once the code you want to profile is running:

1. click on the CAPTURE PROFILE button.
2. Enter localhost:9001 (default port for XLA Profiler) as the Profile Service URL.
3. Enter the number of milliseconds for the profiling duration
4. Click CAPTURE

3: Don't stop your code

Make sure the code is running while you are trying to capture the traces. It will lead to better performance insights if the profiling duration is longer than the step time.

4: View the profiling logs

Once the capture is finished, the page will refresh and you can browse through the insights using the Tools dropdown at the top left