[DOCS] Added .ipynb tutorials in docs

python/tutorials/01-vector-add.ipynb

@@ -2,10 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "induced-zoning",
+   "id": "acute-possession",
    "metadata": {},
    "source": [
-    "# Getting Started"
+    "# Vector Addition"
    ]
   },
   {
@@ -24,7 +24,7 @@
    "id": "identical-conditions",
    "metadata": {},
    "source": [
-    "# Writing the Compute Kernel"
+    "## Writing the Compute Kernel"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "id": "forbidden-wednesday",
    "metadata": {},
    "source": [
-    "# Writing the Torch bindings"
+    "## Writing the Torch bindings"
    ]
   },
   {
@@ -174,7 +174,7 @@
    "id": "exclusive-salvation",
    "metadata": {},
    "source": [
-    "# Writing a Unit Test"
+    "## Writing a Unit Test"
    ]
   },
   {
@@ -218,7 +218,7 @@
    "id": "polished-australia",
    "metadata": {},
    "source": [
-    "# Writing a Benchmark"
+    "## Writing a Benchmark"
    ]
   },
   {
@@ -266,7 +266,9 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "pleasant-valley",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -301,14 +303,6 @@
    "source": [
     "Our op is on-par with Torch's vectorized element-wise kernel when the vectors are large enough. One caveat is that the latency of PyTorch is much smaller for small vectors (3us vs 18-20us). This is something we are actively working on to reduce."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "agreed-backing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {