update samples from Release-108 as a part of SDK release

Author: amlrelsa-ms

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb

@@ -47,8 +47,9 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Dataset\n",
+        "from azureml.core import Workspace, Experiment, Dataset, RunConfiguration\n",
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.environment import CondaDependencies\n",
         "from azureml.data.dataset_consumption_config import DatasetConsumptionConfig\n",
         "from azureml.widgets import RunDetails\n",
         "\n",
@@ -223,6 +224,18 @@
         "Note that the ```file_ds_consumption``` and ```tabular_ds_consumption``` are specified as both arguments and inputs to create a step."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "conda_dep = CondaDependencies()\n",
+        "conda_dep.add_pip_package(\"pandas\")\n",
+        "\n",
+        "run_config = RunConfiguration(conda_dependencies=conda_dep)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -235,7 +248,8 @@
         "    arguments=[\"--param1\", file_ds_consumption, \"--param2\", tabular_ds_consumption],\n",
         "    inputs=[file_ds_consumption, tabular_ds_consumption],\n",
         "    compute_target=compute_target,\n",
-        "    source_directory=source_directory)\n",
+        "    source_directory=source_directory,\n",
+        "    runconfig=run_config)\n",
         "\n",
         "print(\"train_step created\")\n",
         "\n",
@@ -498,7 +512,7 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.7"
     },
-    "order_index": 13,
+    "order_index": 13.0,
     "star_tag": [
       "featured"
     ],