diff --git a/docs/source/advanced/profiler.rst b/docs/source/advanced/profiler.rst
index eccba79978..53171d3695 100644
--- a/docs/source/advanced/profiler.rst
+++ b/docs/source/advanced/profiler.rst
@@ -208,6 +208,50 @@ To visualize the profiled operation, you can either:
     python -c 'import torch; print(torch.autograd.profiler.load_nvprof("trace_name.prof"))'
 
 
+XLA Profiler
+============
+
+:class:`~pytorch_lightning.profiler.xla.XLAProfiler` will help you debug and optimize training
+workload performance for your models using Cloud TPU performance tools.
+
+.. code-block:: python
+
+    # by passing the `XLAProfiler` alias
+    trainer = Trainer(..., profiler="xla")
+
+    # or by passing an instance
+    from pytorch_lightning.profiler import XLAProfiler
+
+    profiler = XLAProfiler(port=9001)
+    trainer = Trainer(..., profiler=profiler)
+
+
+Manual Capture via TensorBoard
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The following instructions are for capturing traces from a running program:
+
+0. This `guide <https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm#tpu-vm>`_ will
+help you with the Cloud TPU setup with the required installations.
+
+1. Start a `TensorBoard <https://www.tensorflow.org/tensorboard>`_ server. You could view the TensorBoard output at ``http://localhost:9001`` on your local machine, and then open the
+``PROFILE`` plugin from the top right dropdown or open ``http://localhost:9001/#profile``
+
+.. code-block:: bash
+
+    tensorboard --logdir ./tensorboard --port 9001
+
+2. Once the code you'd like to profile is running, click on the ``CAPTURE PROFILE`` button. Enter
+``localhost:9001`` (default port for XLA Profiler) as the Profile Service URL. Then, enter
+the number of milliseconds for the profiling duration, and click ``CAPTURE``
+
+3. Make sure the code is running while you are trying to capture the traces. Also, it would lead to better
+performance insights if the profiling duration is longer than the step time.
+
+4. Once the capture is finished, the page will refresh and you can browse through the insights using the
+``Tools`` dropdown at the top left
+
+
 ----------------
 
 ****************
diff --git a/pytorch_lightning/profiler/xla.py b/pytorch_lightning/profiler/xla.py
index be158f7be4..120b858206 100644
--- a/pytorch_lightning/profiler/xla.py
+++ b/pytorch_lightning/profiler/xla.py
@@ -11,33 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""XLA Profiler will help you debug and optimize training workload performance for your models using Cloud TPU
-performance tools.
-
-Manual capture via TensorBoard
-
-The following instructions are for capturing trace from a running program
-
-0. This [guide](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm#tpu-vm) will
-help you with the Cloud TPU setup with the required installations
-
-1. Start a TensorBoard Server
-
->> tensorboard --logdir ./tensorboard --port 9001
-
-You could view the TensorBoard output at http://localhost:9001 on your local machine, and then open the
-``PROFILE`` plugin from the top right dropdown or open http://localhost:9001/#profile
-
-2. Once the code you'd like to profile is running, click on ``CAPTURE PROFILE`` button. You could enter
-``localhost:9012`` (default port for XLA Profiler) as the Profile Service URL. Then, you could enter
-the number of milliseconds for the profiling duration, and click ``CAPTURE``
-
-3. Make sure the code is running, while you are trying to capture the traces. Also, it would lead to better
-performance insights if the profiling duration is longer than the step time
-
-4. Once the capture is finished, the page will refresh and you could browse through the insights using the
-``Tools`` dropdown at the top left
-"""
 import logging
 from typing import Dict
 
@@ -63,8 +36,13 @@ class XLAProfiler(BaseProfiler):
     }
 
     def __init__(self, port: int = 9012) -> None:
-        """This Profiler will help you debug and optimize training workload performance for your models using Cloud
-        TPU performance tools."""
+        """XLA Profiler will help you debug and optimize training workload performance for your models using Cloud
+        TPU performance tools.
+
+        Args:
+            port: the port to start the profiler server on. An exception is
+                raised if the provided port is invalid or busy.
+        """
         if not _TPU_AVAILABLE:
             raise MisconfigurationException("`XLAProfiler` is only supported on TPUs")
         if not _TORCH_GREATER_EQUAL_1_8: