Merge pull request #41 from MoseleyBioinformaticsLab/docs

Updates documentation

README.rst

@@ -3,12 +3,14 @@ gpu_tracker
 ###########
 Description
 -----------
-The ``gpu_tracker`` package provides a ``Tracker`` class and a commandline-interface that tracks (profiles) the usage of compute time, CPU usage, maximum RAM, and maximum GPU RAM.
+The ``gpu_tracker`` package provides a ``Tracker`` class and a commandline-interface that tracks (profiles) the usage of compute time, CPU utilization, maximum RAM, and maximum GPU RAM.
 The compute time is a measurement of the real time taken by the task as opposed to the CPU-utilization time.
-The GPU tracking is for Nvidia GPUs and uses the ``nvidia-smi`` command, assuming the Nvidia drivers have been installed.
+The GPU tracking is for Nvidia GPUs and uses the ``nvidia-smi`` command. If the Nvidia drivers have not been installed, then the max GPU RAM is not tracked and measurements are reported as 0.
 Computational resources are tracked throughout the duration of a context manager or the duration of explicit calls to the ``start()`` and ``stop()`` methods of the ``Tracker`` class.
 The ``gpu-tracker`` command-line interface alternatively tracks the computational-resource-usage of an arbitrary shell command.
 
+**NOTE: The tracking occurs in a separate process. To maximize the accuracy of the reported resource usage, you may want to have a core available solely for the tracking process e.g. if your job uses 3 workers, you may want to allocate 4 cores.**
+
 Documentation
 -------------
 The complete documentation for the ``gpu_tracker`` package, including tutorials, can be found `here <https://moseleybioinformaticslab.github.io/gpu_tracker/>`__.

docs/conf.py

@@ -66,3 +66,4 @@ def setup(app):
 # https://www.sphinx-doc.org/en/master/usage/extensions/todo.html#configuration
 
 todo_include_todos = True
+latex_elements = {'preamble': r'\usepackage{pmboxdraw}'}

docs/notebook/example_module.py

@@ -0,0 +1,6 @@
+import torch
+
+def example_function() -> torch.Tensor:
+    t1 = torch.tensor(list(range(10000000))).cuda()
+    t2 = torch.tensor(list(range(10000000))).cuda()
+    return t1 * t2