DOC add running time warning in banded ridge example

Author: TomDLT

tutorials/movies_3T/04_plot_banded_ridge_model.py

@@ -6,12 +6,16 @@
 In this example, we model the fMRI responses with a `banded ridge regression`,
 with two different feature spaces: motion energy, and wordnet categories.
 
-Since the relative scaling of both feature spaces is unknown, we use two
-regularization hyperparameters (one per feature space) in a model called banded
-ridge regression [1]_. Just like with ridge regression, we optimize the
-hyperparameters over cross-validation. An efficient implementation of this
-model is available in the `himalaya <https://github.com/gallantlab/himalaya>`_
-package.
+*Banded ridge regression:* Since the relative scaling of both feature spaces is
+unknown, we use two regularization hyperparameters (one per feature space) in a
+model called banded ridge regression [1]_. Just like with ridge regression, we
+optimize the hyperparameters over cross-validation. An efficient implementation
+of this model is available in the `himalaya
+<https://github.com/gallantlab/himalaya>`_ package.
+
+*Running time:* This example is more computationally intensive than previous
+examples. With a GPU backend, the fitting of this model takes around 6 minutes.
+With a CPU backend, it can last 10 times more.
 """
 # sphinx_gallery_thumbnail_number = 2
 ###############################################################################

tutorials/notebooks/movies_3T/04_plot_banded_ridge_model.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Fit a banded ridge model with both wordnet and motion energy features\n\n\nIn this example, we model the fMRI responses with a `banded ridge regression`,\nwith two different feature spaces: motion energy, and wordnet categories.\n\nSince the relative scaling of both feature spaces is unknown, we use two\nregularization hyperparameters (one per feature space) in a model called banded\nridge regression [1]_. Just like with ridge regression, we optimize the\nhyperparameters over cross-validation. An efficient implementation of this\nmodel is available in the `himalaya <https://github.com/gallantlab/himalaya>`_\npackage.\n"
+        "\n# Fit a banded ridge model with both wordnet and motion energy features\n\n\nIn this example, we model the fMRI responses with a `banded ridge regression`,\nwith two different feature spaces: motion energy, and wordnet categories.\n\n*Banded ridge regression:* Since the relative scaling of both feature spaces is\nunknown, we use two regularization hyperparameters (one per feature space) in a\nmodel called banded ridge regression [1]_. Just like with ridge regression, we\noptimize the hyperparameters over cross-validation. An efficient implementation\nof this model is available in the `himalaya\n<https://github.com/gallantlab/himalaya>`_ package.\n\n*Running time:* This example is more computationally intensive than previous\nexamples. With a GPU backend, the fitting of this model takes around 6 minutes.\nWith a CPU backend, it can last 10 times more.\n"
       ]
     },
     {