Scrub output and metadata of ipynb files for git commits (pyro-ppl#400)

Author: neerajprad

.gitattributes

@@ -0,0 +1,2 @@
+*.ipynb filter=nbstripout
+*.ipynb diff=ipynb

.gitconfig

@@ -0,0 +1,6 @@
+[filter "nbstripout"]
+    clean = "python -m nbstripout"
+    smudge = cat
+    required = true
+[diff "ipynb"]
+    textconv = nbstripout -t

CONTRIBUTING.md

@@ -1,6 +1,20 @@
 # Development
 For consistency, please adhere to the established coding style including variable names, module imports, and function definitions.  The Pyro codebase uses the PEP8 style guide.
 
+# Setup
+
+Install all the dev dependencies for Pyro.
+
+```sh
+pip install -e .[dev]
+```
+
+Add the repository's git configuration to your local project `.gitconfig` file.
+
+```sh
+git config --local include.path ../.gitconfig
+```
+
 # Testing
 
 Before submitting a pull request, please ensure that linting and unit tests pass locally

setup.py

@@ -30,6 +30,7 @@
             'pytest-xdist',
             'nbval',
             'yapf',
+            'nbstripout',
             'sphinx',
             'sphinx_rtd_theme',
         ],

tutorial/bayesian_regression.ipynb

@@ -23,10 +23,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -51,10 +49,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "N = 100  # size of toy data\n",
@@ -78,10 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class RegressionModel(nn.Module):\n",
@@ -105,33 +99,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "105.713066101\n",
-      "102.354705811\n",
-      "102.354682922\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "102.354660034\n",
-      "Parameters: [('linear.weight', Parameter containing:\n",
-      " 2.9949\n",
-      "[torch.FloatTensor of size 1x1]\n",
-      "), ('linear.bias', Parameter containing:\n",
-      " 1.0565\n",
-      "[torch.FloatTensor of size 1]\n",
-      ")]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loss_fn = torch.nn.MSELoss(size_average=False)\n",
     "optim = torch.optim.Adam(regression_model.parameters(), lr=0.01)\n",
@@ -175,10 +145,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "mu = Variable(torch.zeros(1, 1))\n",
@@ -203,10 +171,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def model(data):\n",
@@ -256,26 +222,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch avg loss 1.53375732422\n",
-      "epoch avg loss 1.55264312744\n",
-      "epoch avg loss 1.54637115479\n",
-      "epoch avg loss 1.56145004272\n",
-      "epoch avg loss 1.55594955444\n",
-      "epoch avg loss 1.55865844727\n",
-      "epoch avg loss 1.55234939575\n",
-      "epoch avg loss 1.55432601929\n",
-      "epoch avg loss 1.55954589844\n",
-      "epoch avg loss 1.55867294312\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "optim = Adam({\"lr\": 0.01})\n",
     "svi = SVI(model, guide, optim, loss=\"ELBO\")\n",
@@ -303,29 +252,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'guide_log_sigma_bias': Variable containing:\n",
-      "-2.2687\n",
-      "[torch.FloatTensor of size 1]\n",
-      ", 'guide_log_sigma_weight': Variable containing:\n",
-      "-3.5816\n",
-      "[torch.FloatTensor of size 1x1]\n",
-      ", 'guide_mean_weight': Variable containing:\n",
-      " 2.9820\n",
-      "[torch.FloatTensor of size 1x1]\n",
-      ", 'guide_mean_bias': Variable containing:\n",
-      " 1.2036\n",
-      "[torch.FloatTensor of size 1]\n",
-      "}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print pyro.get_param_store()._params"
    ]
@@ -346,21 +275,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Variable containing:\n",
-      "1.00000e-04 *\n",
-      "  5.8031\n",
-      "[torch.FloatTensor of size 1]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "X = np.linspace(8, 12, num=20)\n",
     "y = 3 * X + 1\n",