Add new page to documentation that contains a new notebook that explains compiling with external libraries

docs/source/index.rst

@@ -10,4 +10,5 @@ which provides CUDA capable GPUs with the CUDA toolkit already installed.
    :caption: Contents:
 
    usage
+   notebooks
    magics

docs/source/magics.rst

@@ -27,19 +27,27 @@ Usage
 Options
 -------
 
+.. _timeit:
+
 -t, --timeit
    Boolean. If set, returns the output of the "timeit" built-in
    ipython magic instead of stdout.
 
+.. _profile:
+
 -p, --profile
    Boolean. If set, runs the NVIDIA Nsight Compute profiler whose
    output is appended to standard output.
 
+.. _profiler_args:
+
 -a, --profiler-args
    String. Optional profiler arguments that can be space separated
    by wrapping them in double quotes. See all options here:
    `Nsight Compute CLI <https://docs.nvidia.com/nsight-compute/NsightComputeCli/index.html#command-line-options>`_
 
+.. _compiler_args:
+
 -c, --compiler-args
    String. Optional compiler arguments that can be space separated
    by wrapping them in double quotes. They will be passed to "nvcc".

docs/source/notebooks.rst

@@ -0,0 +1,34 @@
+*********
+Notebooks
+*********
+
+This page provides a list of useful Jupyter notebooks written with the
+**nvcc4jupyter** library.
+
+.. note::
+   These notebooks are written for Google's Colab, but you may run them in
+   other environments by installing all expected dependencies. If running in
+   Colab, make sure to set the runtime type to a GPU instance (at the time of
+   writing this, T4 is the GPU offered for free by Colab).
+
+------
+
+.. _compiling_with_external_libraries:
+
+Compiling with external libraries
+=================================
+
+[`NOTEBOOK <https://colab.research.google.com/drive/1iuY46DCwv4hy3SqDhJgFeO8kgpHnzjTh?usp=sharing>`_]
+
+If you need to compile CUDA C++ code that uses external libraries in the host
+code (e.g. OpenCV for reading and writing images to disk) then this section is
+for you.
+
+To achieve this, use the :ref:`compiler-args <compiler_args>` option of the
+:ref:`cuda <cuda_magic>` magic command to pass the correct compiler options
+of the OpenCV library to **nvcc** for it to link the OpenCV code with the
+code in your Jupyter cell. Those compiler options can be provided by the
+`pkg-config <https://www.freedesktop.org/wiki/Software/pkg-config/>`_ tool.
+
+In the notebook we show how to use OpenCV to load an image, blur it with a CUDA
+kernel, and then save it back to disk using OpenCV again.