Merge pull request #267 from microsoft/hlu/Add_reference_running_time…

…_to_more_notebooks Hlu/add reference running time to more notebooks
microsoft · Aug 13, 2019 · d7bc4e8 · d7bc4e8
2 parents 0642773 + 4dcd11b
commit d7bc4e8
Show file tree

Hide file tree

Showing 5 changed files with 425 additions and 154 deletions.
diff --git a/scenarios/entailment/entailment_multinli_bert.ipynb b/scenarios/entailment/entailment_multinli_bert.ipynb
@@ -22,7 +22,9 @@
    "source": [
     "# Before You Start\n",
     "\n",
-    "The running time shown in this notebook is on a Standard_NC24s_v3 Azure Deep Learning Virtual Machine with 4 NVIDIA Tesla V100 GPUs. If you want to run through the notebook quickly, you can set the **`QUICK_RUN`** flag in the cell below to **`True`** to run the notebook on a small subset of the data and a smaller number of epochs. \n",
+    "The running time shown in this notebook is on a Standard_NC24s_v3 Azure Deep Learning Virtual Machine with 4 NVIDIA Tesla V100 GPUs. \n",
+    "> **Tip:** If you want to run through the notebook quickly, you can set the **`QUICK_RUN`** flag in the cell below to **`True`** to run the notebook on a small subset of the data and a smaller number of epochs. \n",
+    "\n",
     "The table below provides some reference running time on different machine configurations.  \n",
     "\n",
     "|QUICK_RUN|Machine Configurations|Running time|\n",
@@ -38,37 +40,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "tags": [
-     "parameters"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "## Set QUICK_RUN = True to run the notebook on a small subset of data and a smaller number of epochs.\n",
-    "QUICK_RUN = False\n",
-    "\n",
-    "TRAIN_DATA_USED_PERCENT = 1\n",
-    "DEV_DATA_USED_PERCENT = 1\n",
-    "NUM_EPOCHS = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "if QUICK_RUN:\n",
-    "    TRAIN_DATA_USED_PERCENT = 0.001\n",
-    "    DEV_DATA_USED_PERCENT = 0.01\n",
-    "    NUM_EPOCHS = 1\n",
-    "\n",
-    "import torch\n",
-    "if torch.cuda.is_available():\n",
-    "    BATCH_SIZE = 32\n",
-    "else:\n",
-    "    BATCH_SIZE = 16"
+    "## Set QUICK_RUN = True to run the notebook on a small subset of data and a smaller number of epochs.\n",
+    "QUICK_RUN = False"
    ]
   },
   {
@@ -96,6 +72,8 @@
     "from sklearn.metrics import classification_report\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "\n",
+    "import torch\n",
+    "\n",
     "nlp_path = os.path.abspath('../../')\n",
     "if nlp_path not in sys.path:\n",
     "    sys.path.insert(0, nlp_path)\n",
@@ -116,9 +94,27 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
    "outputs": [],
    "source": [
+    "TRAIN_DATA_USED_PERCENT = 1\n",
+    "DEV_DATA_USED_PERCENT = 1\n",
+    "NUM_EPOCHS = 2\n",
+    "\n",
+    "if QUICK_RUN:\n",
+    "    TRAIN_DATA_USED_PERCENT = 0.001\n",
+    "    DEV_DATA_USED_PERCENT = 0.01\n",
+    "    NUM_EPOCHS = 1\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    BATCH_SIZE = 32\n",
+    "else:\n",
+    "    BATCH_SIZE = 16\n",
+    "\n",
     "# set random seeds\n",
     "RANDOM_SEED = 42\n",
     "random.seed(RANDOM_SEED)\n",