update samples from Release-60 as a part of SDK release

tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb

@@ -413,7 +413,7 @@
         "\n",
         "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset.\n",
         "\n",
-        "First, create the environment that contains: the scikit-learn library, azureml-dataprep required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
+        "First, create the environment that contains: the scikit-learn library, azureml-dataset-runtime required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
         "\n",
         "Once the environment is defined, register it with the Workspace to re-use it in part 2 of the tutorial."
       ]
@@ -429,7 +429,7 @@
         "\n",
         "# to install required packages\n",
         "env = Environment('tutorial-env')\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
         "\n",
         "env.python.conda_dependencies = cd\n",
         "\n",

tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb

@@ -65,7 +65,7 @@
         "\n",
         "# to install required packages\n",
         "env = Environment('tutorial-env')\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
         "\n",
         "env.python.conda_dependencies = cd\n",
         "\n",

tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb

@@ -81,8 +81,8 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "# to install required packages\n",
-        "env = Environment('tutorial-env')\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults', 'azure-storage-blob', 'encrypted-inference==0.9'], conda_packages = ['scikit-learn==0.22.1'])\n",
+        "env = Environment('tutorial-encryption-env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults', 'azure-storage-blob', 'encrypted-inference==0.9'], conda_packages = ['scikit-learn==0.22.1'])\n",
         "\n",
         "env.python.conda_dependencies = cd\n",
         "\n",
@@ -282,7 +282,7 @@
         "ws = Workspace.from_config()\n",
         "model = Model(ws, 'sklearn_mnist')\n",
         "\n",
-        "myenv = Environment.get(workspace=ws, name=\"tutorial-env\")\n",
+        "myenv = Environment.get(workspace=ws, name=\"tutorial-encryption-env\")\n",
         "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
         "\n",
         "service_name = 'sklearn-mnist-svc-' + str(uuid.uuid4())[:4]\n",

tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb

@@ -124,9 +124,7 @@
         "\n",
         "input_images = Dataset.File.from_files((batchscore_blob, \"batchscoring/images/\"))\n",
         "label_ds = Dataset.File.from_files((batchscore_blob, \"batchscoring/labels/\"))\n",
-        "output_dir = PipelineData(name=\"scores\", \n",
-        "                          datastore=def_data_store, \n",
-        "                          output_path_on_compute=\"batchscoring/results\")"
+        "output_dir = PipelineData(name=\"scores\", datastore=def_data_store)"
       ]
     },
     {
@@ -142,15 +140,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "input_images = input_images.register(workspace = ws, name = \"input_images\")\n",
-        "label_ds = label_ds.register(workspace = ws, name = \"label_ds\", create_new_version=True)"
+        "input_images = input_images.register(workspace=ws, name=\"input_images\")\n",
+        "label_ds = label_ds.register(workspace=ws, name=\"label_ds\", create_new_version=True)"
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
+      "cell_type": "markdown",
       "metadata": {},
-      "outputs": [],
       "source": [
         "## Download and register the model"
       ]
@@ -277,7 +273,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-defaults` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support."
+        "Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-core` and `azureml-dataset-runtime[fuse]` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support."
       ]
     },
     {
@@ -291,7 +287,7 @@
         "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
         "\n",
         "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.15.2\",\n",
-        "                                            \"azureml-core\", \"azureml-dataprep[fuse]\"])\n",
+        "                                            \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
         "\n",
         "env = Environment(name=\"parallelenv\")\n",
         "env.python.conda_dependencies=cd\n",
@@ -319,6 +315,7 @@
         "    entry_script=\"batch_scoring.py\",\n",
         "    source_directory=\"scripts\",\n",
         "    output_action=\"append_row\",\n",
+        "    append_row_file_name=\"parallel_run_step.txt\",\n",
         "    mini_batch_size=\"20\",\n",
         "    error_threshold=1,\n",
         "    compute_target=compute_target,\n",
@@ -424,15 +421,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "batch_run = next(pipeline_run.get_children())\n",
-        "batch_output = batch_run.get_output_data(\"scores\")\n",
-        "batch_output.download(local_path=\"inception_results\")\n",
-        "\n",
         "import pandas as pd\n",
-        "for root, dirs, files in os.walk(\"inception_results\"):\n",
-        "    for file in files:\n",
-        "        if file.endswith(\"parallel_run_step.txt\"):\n",
-        "            result_file = os.path.join(root,file)\n",
+        "import tempfile\n",
+        "\n",
+        "batch_run = pipeline_run.find_step_run(batch_score_step.name)[0]\n",
+        "batch_output = batch_run.get_output_data(output_dir.name)\n",
+        "\n",
+        "target_dir = tempfile.mkdtemp()\n",
+        "batch_output.download(local_path=target_dir)\n",
+        "result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
         "\n",
         "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
         "df.columns = [\"Filename\", \"Prediction\"]\n",