diff --git a/mmap_utils.py b/mmap_utils.py
index 8a6e5a9..c3a4cf7 100644
--- a/mmap_utils.py
+++ b/mmap_utils.py
@@ -1,5 +1,5 @@
 import os
-from IPython.parallel import interactive
+from ipyparallel import interactive
 
 
 @interactive
diff --git a/model_selection.py b/model_selection.py
index e56a04c..417667a 100644
--- a/model_selection.py
+++ b/model_selection.py
@@ -6,8 +6,8 @@
 from collections import namedtuple
 import os
 
-from IPython.parallel import interactive
-from IPython.parallel import TaskAborted
+from ipyparallel import interactive
+from ipyparallel import TaskAborted
 from scipy.stats import sem
 import numpy as np
 
@@ -203,7 +203,7 @@ def __repr__(self):
 
     def boxplot_parameters(self, display_train=False):
         """Plot boxplot for each parameters independently"""
-        import pylab as pl
+        import matplotlib.pyplot as pl
         results = [Evaluation(*task.get())
                    for task_group in self.task_groups
                    for task in task_group
diff --git a/notebooks/00 - Tutorial Setup .ipynb b/notebooks/00 - Tutorial Setup .ipynb
index 2746f71..07c6936 100644
--- a/notebooks/00 - Tutorial Setup .ipynb	
+++ b/notebooks/00 - Tutorial Setup .ipynb	
@@ -1,779 +1,830 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorial Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check your install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import psutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import ipyparallel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finding the location of an installed package and its version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "numpy.__path__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "numpy.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check that you have the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%run ../fetch_data.py\n",
+    "# %run ../fetch_data.py twenty_newsgroups sentiment140 covertype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "for fname in os.listdir('../datasets/'):\n",
+    "    print(fname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A NumPy primer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NumPy array dtypes and shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = np.array([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b = np.array([[0, 2, 4], [1, 3, 5]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.zeros(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.ones(shape=(3, 4), dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Common array operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c = b * 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = a + c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d.sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reshaping and inplace update"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e = np.arange(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = e.reshape(3, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e[5:] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Combining arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.concatenate([a, a, a])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.vstack([a, b, d])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.hstack([b, d])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Matplotlib primer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 2, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x, 'o-');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x, x, 'o-', label='linear')\n",
+    "plt.plot(x, x ** 2, 'x-', label='quadratic')\n",
+    "\n",
+    "plt.legend(loc='best')\n",
+    "plt.title('Linear vs Quadratic progression')\n",
+    "plt.xlabel('Input')\n",
+    "plt.ylabel('Output');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples = np.random.normal(loc=1.0, scale=0.5, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples[:30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(samples, bins=50);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "samples_1 = np.random.normal(loc=1, scale=.5, size=10000)\n",
+    "samples_2 = np.random.standard_t(df=10, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bins = np.linspace(-3, 3, 50)\n",
+    "_ = plt.hist(samples_1, bins=bins, alpha=0.5, label='samples 1')\n",
+    "_ = plt.hist(samples_2, bins=bins, alpha=0.5, label='samples 2')\n",
+    "plt.legend(loc='upper left');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(samples_1, samples_2, alpha=0.1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.0"
-  },
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorial Setup"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Check your install"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import scipy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sklearn"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import psutil"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pandas"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import IPython.parallel"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Finding the location of an installed package and its version:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "numpy.__path__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "numpy.__version__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Check that you have the datasets"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../fetch_data.py\n",
-      "# %run ../fetch_data.py twenty_newsgroups sentiment140 covertype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os\n",
-      "for fname in os.listdir('../datasets/'):\n",
-      "    print(fname)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "A NumPy primer"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "NumPy array dtypes and shapes"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.array([1, 2, 3])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = np.array([[0, 2, 4], [1, 3, 5]])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b.dtype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.dtype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.zeros(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.ones(shape=(3, 4), dtype=np.int32)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Common array operations"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = b * 0.5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c.dtype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = a + c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[0, 0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[:, 0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.sum()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.mean()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.mean(axis=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Reshaping and inplace update"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e = np.arange(12)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f = e.reshape(3, 4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e[5:] = 0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Combining arrays"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.concatenate([a, a, a])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.vstack([a, b, d])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.hstack([b, d])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "A Matplotlib primer"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = np.linspace(0, 2, 10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.plot(x, 'o-');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.plot(x, x, 'o-', label='linear')\n",
-      "plt.plot(x, x ** 2, 'x-', label='quadratic')\n",
-      "\n",
-      "plt.legend(loc='best')\n",
-      "plt.title('Linear vs Quadratic progression')\n",
-      "plt.xlabel('Input')\n",
-      "plt.ylabel('Output');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "samples = np.random.normal(loc=1.0, scale=0.5, size=1000)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "samples.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "samples.dtype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "samples[:30]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.hist(samples, bins=50);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "samples_1 = np.random.normal(loc=1, scale=.5, size=10000)\n",
-      "samples_2 = np.random.standard_t(df=10, size=10000)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "bins = np.linspace(-3, 3, 50)\n",
-      "_ = plt.hist(samples_1, bins=bins, alpha=0.5, label='samples 1')\n",
-      "_ = plt.hist(samples_2, bins=bins, alpha=0.5, label='samples 2')\n",
-      "plt.legend(loc='upper left');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.scatter(samples_1, samples_2, alpha=0.1);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
   }
- ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/notebooks/06 - Distributed Model Selection and Assessment.ipynb b/notebooks/06 - Distributed Model Selection and Assessment.ipynb
index 4565d48..e5ad0cc 100644
--- a/notebooks/06 - Distributed Model Selection and Assessment.ipynb	
+++ b/notebooks/06 - Distributed Model Selection and Assessment.ipynb	
@@ -38,7 +38,7 @@
      "source": [
       "Outline of the session:\n",
       "\n",
-      "- Introduction to **IPython.parallel**\n",
+      "- Introduction to **ipyparallel**\n",
       "- Sharing Data Between Processes with **Memory Mapping**\n",
       "- **Parallel Grid Search** and Model Selection\n",
       "- **Parallel** Computation of **Learning Curves** (TODO)\n",
@@ -70,7 +70,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "IPython.parallel, a Primer"
+      "ipyparallel, a Primer"
      ]
     },
     {
@@ -79,11 +79,11 @@
      "source": [
       "This section gives a primer on some tools best utilizing computational resources when doing predictive modeling in the Python / NumPy ecosystem namely:\n",
       "\n",
-      "- optimal usage of available CPUs and cluster nodes with **`IPython.parallel`**\n",
+      "- optimal usage of available CPUs and cluster nodes with **`ipyparallel`**\n",
       "\n",
       "- optimal memory re-use using shared memory between Python processes using **`numpy.memmap`** and **`joblib`**\n",
       "\n",
-      "### What is so great about `IPython.parallel`:\n",
+      "### What is so great about `ipyparallel`:\n",
       "\n",
       "- Single node multi-CPUs\n",
       "- Multiple node multi-CPUs\n",
@@ -127,7 +127,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "client = Client()"
      ],
      "language": "python",
@@ -771,7 +771,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "client = Client()\n",
       "len(client)"
      ],
@@ -1406,7 +1406,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Let's leverage IPython.parallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.\n",
+      "Let's leverage ipyparallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.\n",
       "\n",
       "Assume that we want to reproduce the grid search from the previous session:"
      ]
@@ -1512,7 +1512,7 @@
      "collapsed": false,
      "input": [
       "from sklearn.svm import SVC\n",
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "\n",
       "client = Client()\n",
       "lb_view = client.load_balanced_view()\n",
@@ -1844,7 +1844,7 @@
       "    setup_class = starcluster.plugins.pypkginstaller.PyPkgInstaller\n",
       "    packages = scikit-learn, psutil\n",
       "    \n",
-      "    # Base configuration for IPython.parallel cluster\n",
+      "    # Base configuration for ipyparallel cluster\n",
       "    [cluster iptemplate]\n",
       "    KEYNAME = mykey\n",
       "    CLUSTER_SIZE = 1\n",
@@ -1917,7 +1917,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "When using the `LoadBalancedView` API of `IPython.parallel.Client` is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.\n",
+      "When using the `LoadBalancedView` API of `ipyparallel.Client` is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.\n",
       "\n",
       "This can be achieved using the `addnode` command, for instance to add 3 more nodes using $0.50 bid price on the Spot Instance market:\n",
       "    \n",
@@ -1925,7 +1925,7 @@
       "    \n",
       "Each node will automatically run the `IPCluster` plugin and register new `IPEngine` processes to the existing `IPController` process running on master.\n",
       "\n",
-      "It is also possible to terminate individual running nodes of the cluster with `removenode` command but this will kill any task running on that node and IPython.parallel will **not** restart the failed task automatically."
+      "It is also possible to terminate individual running nodes of the cluster with `removenode` command but this will kill any task running on that node and ipyparallel will **not** restart the failed task automatically."
      ]
     },
     {
diff --git a/notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb b/notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb
index 38bb164..49a4cb3 100644
--- a/notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb	
+++ b/notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb	
@@ -847,14 +847,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Let's use IPython parallel to read partitions of the train CSV in different Python processes using the interactive IPython.parallel interface:"
+      "Let's use  ipyparallel to read partitions of the train CSV in different Python processes using the interactive ipyparallel interface:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "\n",
       "client = Client()\n",
       "len(client)"
@@ -958,7 +958,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We are now ready to read the data partition from the CSV file, vectorize it, and train an indepenent model on each IPython.parallel engine:"
+      "We are now ready to read the data partition from the CSV file, vectorize it, and train an indepenent model on each ipyparallel engine:"
      ]
     },
     {
diff --git a/rendered_notebooks/00 - Tutorial Setup .ipynb b/rendered_notebooks/00 - Tutorial Setup .ipynb
index f4d905e..57fcf26 100644
--- a/rendered_notebooks/00 - Tutorial Setup .ipynb	
+++ b/rendered_notebooks/00 - Tutorial Setup .ipynb	
@@ -110,7 +110,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import IPython.parallel"
+      "import ipyparallel"
      ],
      "language": "python",
      "metadata": {},
diff --git a/rendered_notebooks/06 - Distributed Model Selection and Assessment.ipynb b/rendered_notebooks/06 - Distributed Model Selection and Assessment.ipynb
index d81e454..69ba583 100644
--- a/rendered_notebooks/06 - Distributed Model Selection and Assessment.ipynb	
+++ b/rendered_notebooks/06 - Distributed Model Selection and Assessment.ipynb	
@@ -38,7 +38,7 @@
      "source": [
       "Outline of the session:\n",
       "\n",
-      "- Introduction to **IPython.parallel**\n",
+      "- Introduction to **ipyparallel**\n",
       "- Sharing Data Between Processes with **Memory Mapping**\n",
       "- **Parallel Grid Search** and Model Selection\n",
       "- **Parallel** Computation of **Learning Curves** (TODO)\n",
@@ -70,7 +70,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "IPython.parallel, a Primer"
+      "ipyparallel, a Primer"
      ]
     },
     {
@@ -79,11 +79,11 @@
      "source": [
       "This section gives a primer on some tools best utilizing computational resources when doing predictive modeling in the Python / NumPy ecosystem namely:\n",
       "\n",
-      "- optimal usage of available CPUs and cluster nodes with **`IPython.parallel`**\n",
+      "- optimal usage of available CPUs and cluster nodes with **`ipyparallel`**\n",
       "\n",
       "- optimal memory re-use using shared memory between Python processes using **`numpy.memmap`** and **`joblib`**\n",
       "\n",
-      "### What is so great about `IPython.parallel`:\n",
+      "### What is so great about `ipyparallel`:\n",
       "\n",
       "- Single node multi-CPUs\n",
       "- Multiple node multi-CPUs\n",
@@ -137,7 +137,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "client = Client()"
      ],
      "language": "python",
@@ -1091,7 +1091,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "client = Client()\n",
       "len(client)"
      ],
@@ -2105,7 +2105,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Let's leverage IPython.parallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.\n",
+      "Let's leverage ipyparallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.\n",
       "\n",
       "Assume that we want to reproduce the grid search from the previous session:"
      ]
@@ -2252,7 +2252,7 @@
      "collapsed": false,
      "input": [
       "from sklearn.svm import SVC\n",
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "\n",
       "client = Client()\n",
       "lb_view = client.load_balanced_view()\n",
@@ -2708,7 +2708,7 @@
       "    setup_class = starcluster.plugins.pypkginstaller.PyPkgInstaller\n",
       "    packages = scikit-learn, psutil\n",
       "    \n",
-      "    # Base configuration for IPython.parallel cluster\n",
+      "    # Base configuration for ipyparallel cluster\n",
       "    [cluster iptemplate]\n",
       "    KEYNAME = mykey\n",
       "    CLUSTER_SIZE = 1\n",
@@ -2781,7 +2781,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "When using the `LoadBalancedView` API of `IPython.parallel.Client` is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.\n",
+      "When using the `LoadBalancedView` API of `ipyparallel.Client` is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.\n",
       "\n",
       "This can be achieved using the `addnode` command, for instance to add 3 more nodes using $0.50 bid price on the Spot Instance market:\n",
       "    \n",
@@ -2789,7 +2789,7 @@
       "    \n",
       "Each node will automatically run the `IPCluster` plugin and register new `IPEngine` processes to the existing `IPController` process running on master.\n",
       "\n",
-      "It is also possible to terminate individual running nodes of the cluster with `removenode` command but this will kill any task running on that node and IPython.parallel will **not** restart the failed task automatically."
+      "It is also possible to terminate individual running nodes of the cluster with `removenode` command but this will kill any task running on that node and ipyparallel will **not** restart the failed task automatically."
      ]
     },
     {
diff --git a/rendered_notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb b/rendered_notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb
index 544c6d4..37581c9 100644
--- a/rendered_notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb	
+++ b/rendered_notebooks/08 - Large Scale Text Classification for Sentiment Analysis.ipynb	
@@ -1370,14 +1370,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Let's use IPython parallel to read partitions of the train CSV in different Python processes using the interactive IPython.parallel interface:"
+      "Let's use ipyparallel to read partitions of the train CSV in different Python processes using the interactive ipyparallel interface:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.parallel import Client\n",
+      "from ipyparallel import Client\n",
       "\n",
       "client = Client()\n",
       "len(client)"
@@ -1526,7 +1526,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We are now ready to read the data partition from the CSV file, vectorize it, and train an indepenent model on each IPython.parallel engine:"
+      "We are now ready to read the data partition from the CSV file, vectorize it, and train an indepenent model on each ipyparallel engine:"
      ]
     },
     {