test dummy ipynb

learn_the_basics/03_compile_code.ipynb

@@ -32,10 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install ivy\n",
-    "!pip install torch\n",
-    "!pip install jax\n",
-    "!pip intall jaxlib"
+    "!pip install ivy"
    ]
   },
   {

learn_the_basics/_compile_code_test.ipynb

@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Turn your Ivy code into an efficient fully-functional graph, removing wrappers and unused parts of the code."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "⚠️ If you are running this notebook in Colab, you will have to install `Ivy` and some dependencies manually. You can do so by running the cell below ⬇️\n",
+    "\n",
+    "If you want to run the notebook locally but don't have Ivy installed just yet, you can check out the [Get Started section of the docs.](https://unify.ai/docs/ivy/overview/get_started.html)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "!pip install ivy"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Firstly, let's pick up where we left off in the [last notebook](), with our unified `normalize` function:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import ivy\n",
+    "\n",
+    "def normalize(x):\n",
+    "    mean = torch.mean(x)\n",
+    "    std = torch.std(x)\n",
+    "    return torch.div(torch.sub(x, mean), std)\n",
+    "\n",
+    "normalize = ivy.unify(normalize, source=\"torch\")\n",
+    "\n",
+    "# set ivy's backend to jax\n",
+    "ivy.set_backend(\"jax\")\n",
+    "\n",
+    "# Import jax\n",
+    "import jax\n",
+    "\n",
+    "# create random jax arrays for testing\n",
+    "key = jax.random.PRNGKey(42)\n",
+    "x = jax.random.uniform(key, shape=(10,))\n",
+    "\n",
+    "normalize(x)\n",
+    "\n",
+    "ivy.set_backend(\"jax\")\n",
+    "comp = ivy.compile(normalize)  # compiles to jax, due to ivy.set_backend\n",
+    "\n",
+    "comp(x)\n",
+    "\n",
+    "%%timeit\n",
+    "normalize(x)\n",
+    "\n",
+    "%%timeit\n",
+    "comp(x)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

tests/notebook_testing.py

@@ -227,7 +227,7 @@ def test_notebook(nb):
 
 
 if __name__ == "__main__":
-    for ipynb in ["learn_the_basics/03_compile_code.ipynb"]:  # sys.argv[1:]:
+    for ipynb in ["learn_the_basics/_compile_code_test.ipynb"]:  # sys.argv[1:]:
         print("testing %s" % ipynb)
         with open(ipynb) as f:
             nb = nbformat.reads(f.read(), nbformat.current_nbformat)