fix parameters in export training data (Esri#1118)

Co-authored-by: priyankatuteja <priyanka.datascience@gmail.com>

Author: priyankatuteja

guide/14-deep-learning/object-detection.ipynb

@@ -164,8 +164,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chips = export_training_data(planet_mosaic_data, well_pads, \"PNG\", {\"x\":448,\"y\":448}, {\"x\":224,\"y\":224}, \n",
-    "                             \"PASCAL_VOC_rectangles\", 75, \"planetdemo\")"
+    "chips = export_training_data(input_raster=planet_mosaic_data,\n",
+    "                             input_class_data=well_pads,\n",
+    "                             chip_format=\"PNG\",\n",
+    "                             tile_size={\"x\":448,\"y\":448},\n",
+    "                             stride_size={\"x\":224,\"y\":224},\n",
+    "                             metadata_format=\"PASCAL_VOC_rectangles\",\n",
+    "                             classvalue_field='pad_type',\n",
+    "                             buffer_radius=75,\n",
+    "                             output_location=\"/rasterStores/rasterstorename/wellpaddata\")"
    ]
   },
   {
@@ -187,7 +194,7 @@
    "source": [
     "from arcgis.learn import prepare_data\n",
     "\n",
-    "data = prepare_data('/arcgis/directories/rasterstore/planetdemo', {1: '  Pad'})"
+    "data = prepare_data('/rasterStores/rasterstorename/wellpaddata', {1: '  Pad'})"
    ]
   },
   {
@@ -453,44 +460,21 @@
    "outputs": [],
    "source": [
     "# save the trained model\n",
-    "ssd.save('wellpad_planet_model')"
+    "ssd.save('wellpad_planet_model', publish=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Deploy trained model"
+    "Once a model has been trained, it can be added to ArcGIS Enterprise as a deep learning package by passing ``publish=True`` parameter."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once a model has been trained, it can be added to ArcGIS Enterprise as a deep learning package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Upload as first class item on agol or portal as a deep learning package \n",
-    "trained_model = '/arcgis/directories/rasterstore/planetdemo/models/wellpad_model_planet_2501/wellpad_model_planet_2501.zip'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_package = gis.content.add(item_properties={\"type\":\"Deep Learning Package\",\"typeKeywords\":\"Deep Learning, Raster\",\n",
-    "                                                 \"title\":\"Well Pad Detection Model Planet 2501\",\n",
-    "                                                 \"tags\":\"deeplearning\", 'overwrite':'True'}, data=trained_model)\n",
-    "# Or publish using:\n",
-    "# ssd.save('Well Pad Detection Model Planet 2501', publish=True, gis=gis)"
+    "## Model management"
    ]
   },
   {
@@ -528,16 +512,10 @@
     }
    ],
    "source": [
+    "model_package = gis.content.get('id=32cb1df2834943f7a6ff3ab461aa9352')\n",
     "model_package"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model management"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -732,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.11"
   },
   "toc": {
    "base_numbering": 1,