update samples from Release-101 as a part of SDK release

update samples from Release-98 as a part of  SDK release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -254,6 +254,8 @@
         "\n",
         "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
         "\n",
         "The cluster parameters are:\n",

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -36,9 +36,9 @@
         "\n",
         "<a id=\"Introduction\"></a>\n",
         "## Introduction\n",
-        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.github.io) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.github.io/).\n",
+        "This notebook shows how to use [Fairlearn (an open source fairness assessment and unfairness mitigation package)](http://fairlearn.org) and Azure Machine Learning Studio for a binary classification problem. This example uses the well-known adult census dataset. For the purposes of this notebook, we shall treat this as a loan decision problem. We will pretend that the label indicates whether or not each individual repaid a loan in the past. We will use the data to train a predictor to predict whether previously unseen individuals will repay a loan or not. The assumption is that the model predictions are used to decide whether an individual should be offered a loan. Its purpose is purely illustrative of a workflow including a fairness dashboard - in particular, we do **not** include a full discussion of the detailed issues which arise when considering fairness in machine learning. For such discussions, please [refer to the Fairlearn website](http://fairlearn.org/).\n",
         "\n",
-        "We will apply the [grid search algorithm](https://fairlearn.github.io/master/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
+        "We will apply the [grid search algorithm](https://fairlearn.org/v0.4.6/api_reference/fairlearn.reductions.html#fairlearn.reductions.GridSearch) from the Fairlearn package using a specific notion of fairness called Demographic Parity. This produces a set of models, and we will view these in a dashboard both locally and in the Azure Machine Learning Studio.\n",
         "\n",
         "### Setup\n",
         "\n",
@@ -48,7 +48,7 @@
         "* `azureml-contrib-fairness`\n",
         "* `fairlearn==0.4.6` (v0.5.0 will work with minor modifications)\n",
         "* `joblib`\n",
-        "* `shap`\n",
+        "* `liac-arff`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]
@@ -88,7 +88,6 @@
         "from fairlearn.widget import FairlearnDashboard\n",
         "\n",
         "from sklearn.compose import ColumnTransformer\n",
-        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -112,9 +111,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "from fairness_nb_utils import fetch_census_dataset\n",
         "\n",
-        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "data = fetch_census_dataset()\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",
@@ -584,7 +583,7 @@
         "<a id=\"Conclusion\"></a>\n",
         "## Conclusion\n",
         "\n",
-        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.github.io/) provides that discussion"
+        "In this notebook we have demonstrated how to use the `GridSearch` algorithm from Fairlearn to generate a collection of models, and then present them in the fairness dashboard in Azure Machine Learning Studio. Please remember that this notebook has not attempted to discuss the many considerations which should be part of any approach to unfairness mitigation. The [Fairlearn website](http://fairlearn.org/) provides that discussion"
       ]
     },
     {

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -5,3 +5,4 @@ dependencies:
   - azureml-contrib-fairness
   - fairlearn==0.4.6
   - joblib
+  - liac-arff

contrib/fairness/fairness_nb_utils.py

@@ -4,7 +4,13 @@
 
 """Utilities for azureml-contrib-fairness notebooks."""
 
+import arff
+from collections import OrderedDict
+from contextlib import closing
+import gzip
+import pandas as pd
 from sklearn.datasets import fetch_openml
+from sklearn.utils import Bunch
 import time
 
 
@@ -15,7 +21,7 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
             print("Download attempt {0} of {1}".format(i + 1, max_retries))
             data = fetch_openml(data_id=data_id, as_frame=True)
             break
-        except Exception as e:
+        except Exception as e:  # noqa: B902
             print("Download attempt failed with exception:")
             print(e)
             if i + 1 != max_retries:
@@ -26,3 +32,80 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
         raise RuntimeError("Unable to download dataset from OpenML")
 
     return data
+
+
+_categorical_columns = [
+    'workclass',
+    'education',
+    'marital-status',
+    'occupation',
+    'relationship',
+    'race',
+    'sex',
+    'native-country'
+]
+
+
+def fetch_census_dataset():
+    """Fetch the Adult Census Dataset.
+
+    This uses a particular URL for the Adult Census dataset. The code
+    is a simplified version of fetch_openml() in sklearn.
+
+    The data are copied from:
+    https://openml.org/data/v1/download/1595261.gz
+    (as of 2021-03-31)
+    """
+    try:
+        from urllib import urlretrieve
+    except ImportError:
+        from urllib.request import urlretrieve
+
+    filename = "1595261.gz"
+    data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
+
+    remaining_attempts = 5
+    sleep_duration = 10
+    while remaining_attempts > 0:
+        try:
+            urlretrieve(data_url + filename, filename)
+
+            http_stream = gzip.GzipFile(filename=filename, mode='rb')
+
+            with closing(http_stream):
+                def _stream_generator(response):
+                    for line in response:
+                        yield line.decode('utf-8')
+
+                stream = _stream_generator(http_stream)
+                data = arff.load(stream)
+        except Exception as exc:  # noqa: B902
+            remaining_attempts -= 1
+            print("Error downloading dataset from {} ({} attempt(s) remaining)"
+                  .format(data_url, remaining_attempts))
+            print(exc)
+            time.sleep(sleep_duration)
+            sleep_duration *= 2
+            continue
+        else:
+            # dataset successfully downloaded
+            break
+    else:
+        raise Exception("Could not retrieve dataset from {}.".format(data_url))
+
+    attributes = OrderedDict(data['attributes'])
+    arff_columns = list(attributes)
+
+    raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
+
+    target_column_name = 'class'
+    target = raw_df.pop(target_column_name)
+    for col_name in _categorical_columns:
+        dtype = pd.api.types.CategoricalDtype(attributes[col_name])
+        raw_df[col_name] = raw_df[col_name].astype(dtype, copy=False)
+
+    result = Bunch()
+    result.data = raw_df
+    result.target = target
+
+    return result

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -50,7 +50,7 @@
         "* `azureml-contrib-fairness`\n",
         "* `fairlearn==0.4.6` (should also work with v0.5.0)\n",
         "* `joblib`\n",
-        "* `shap`\n",
+        "* `liac-arff`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]
@@ -88,7 +88,6 @@
       "source": [
         "from sklearn import svm\n",
         "from sklearn.compose import ColumnTransformer\n",
-        "from sklearn.datasets import fetch_openml\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.model_selection import train_test_split\n",
@@ -110,9 +109,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from fairness_nb_utils import fetch_openml_with_retries\n",
+        "from fairness_nb_utils import fetch_census_dataset\n",
         "\n",
-        "data = fetch_openml_with_retries(data_id=1590)\n",
+        "data = fetch_census_dataset()\n",
         "    \n",
         "# Extract the items we want\n",
         "X_raw = data.data\n",

contrib/fairness/upload-fairness-dashboard.yml

@@ -5,3 +5,4 @@ dependencies:
   - azureml-contrib-fairness
   - fairlearn==0.4.6
   - joblib
+  - liac-arff

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -21,9 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.23.0
+  - azureml-widgets~=1.29.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_win32_requirements.txt [--no-deps]
-  - PyJWT < 2.0.0

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -21,10 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.23.0
+  - azureml-widgets~=1.29.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_linux_requirements.txt [--no-deps]
-  - PyJWT < 2.0.0
-

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -22,9 +22,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.23.0
+  - azureml-widgets~=1.29.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_darwin_requirements.txt [--no-deps]
-  - PyJWT < 2.0.0

how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

@@ -32,6 +32,7 @@ if [ $? -ne 0 ]; then
 fi
 
 sed -i '' 's/AZUREML-SDK-VERSION/latest/' $AUTOML_ENV_FILE
+brew install libomp
 
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -165,6 +165,9 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -374,15 +377,6 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

@@ -0,0 +1,4 @@
+name: auto-ml-classification-bank-marketing-all-features
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -127,6 +127,9 @@
       "source": [
         "## Create or Attach existing AmlCompute\n",
         "A compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -255,15 +258,6 @@
         "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

@@ -0,0 +1,4 @@
+name: auto-ml-classification-credit-card-fraud
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -138,6 +138,8 @@
         "## Set up a compute cluster\n",
         "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively.  If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
       ]
     },
@@ -281,7 +283,7 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"experiment_timeout_minutes\": 20,\n",
+        "    \"experiment_timeout_minutes\": 30,\n",
         "    \"primary_metric\": 'accuracy',\n",
         "    \"max_concurrent_iterations\": num_nodes, \n",
         "    \"max_cores_per_iteration\": -1,\n",
@@ -319,15 +321,6 @@
         "automl_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -494,7 +487,7 @@
       "outputs": [],
       "source": [
         "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
-        "                         train_dataset, test_dataset, target_column_name, model_name)"
+        "                         test_dataset, target_column_name, model_name)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

@@ -0,0 +1,4 @@
+name: auto-ml-classification-text-dnn
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -5,7 +5,7 @@ from azureml.core.run import Run
 
 
 def run_inference(test_experiment, compute_target, script_folder, train_run,
-                  train_dataset, test_dataset, target_column_name, model_name):
+                  test_dataset, target_column_name, model_name):
 
     inference_env = train_run.get_environment()
 
@@ -16,7 +16,6 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--model_name': model_name
                     },
                     inputs=[
-                        train_dataset.as_named_input('train_data'),
                         test_dataset.as_named_input('test_data')
                     ],
                     compute_target=compute_target,

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -1,5 +1,6 @@
 import argparse
 
+import pandas as pd
 import numpy as np
 
 from sklearn.externals import joblib
@@ -32,22 +33,21 @@ model = joblib.load(model_path)
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
-train_dataset = run.input_datasets['train_data']
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
                         .to_pandas_dataframe()
 y_test_df = test_dataset.with_timestamp_columns(None) \
                         .keep_columns(columns=[target_column_name]) \
                         .to_pandas_dataframe()
-y_train_df = test_dataset.with_timestamp_columns(None) \
-                         .keep_columns(columns=[target_column_name]) \
-                         .to_pandas_dataframe()
 
 predicted = model.predict_proba(X_test_df)
 
+if isinstance(predicted, pd.DataFrame):
+    predicted = predicted.values
+
 # Use the AutoML scoring module
-class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
 train_labels = model.classes_
+class_labels = np.unique(np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1)))))
 classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
 scores = scoring.score_classification(y_test_df.values, predicted,
                                       classification_metrics,

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -141,6 +141,9 @@
         "#### Create or Attach existing AmlCompute\n",
         "\n",
         "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

@@ -0,0 +1,4 @@
+name: auto-ml-continuous-retraining
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -54,17 +54,17 @@ try:
     end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
     print("Dataset {0} last updated on {1}".format(args.ds_name,
                                                    end_time_last_slice))
-except Exception as e:
+except Exception:
     print(traceback.format_exc())
     print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
     register_dataset = True
-    end_time_last_slice = datetime.today() - relativedelta(weeks=2)
+    end_time_last_slice = datetime.today() - relativedelta(weeks=4)
 
 end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
 
 if train_df.size > 0:
-    print("Received {0} rows of new data after {0}.".format(
+    print("Received {0} rows of new data after {1}.".format(
         train_df.shape[0], end_time_last_slice))
     folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
                                                                         end_time.month, end_time.day,

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client.cmd

@@ -5,7 +5,7 @@ set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl_experimental"
-IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_thin_client_env.yml"
 
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_linux.sh

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_env.yml"
+  AUTOML_ENV_FILE="automl_thin_client_env.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then

how-to-use-azureml/automated-machine-learning/experimental/automl_setup_thin_client_mac.sh

@@ -12,7 +12,7 @@ fi
 
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
-  AUTOML_ENV_FILE="automl_env.yml"
+  AUTOML_ENV_FILE="automl_thin_client_env_mac.yml"
 fi
 
 if [ ! -f $AUTOML_ENV_FILE ]; then

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml

@@ -7,6 +7,8 @@ dependencies:
 - nb_conda
 - cython
 - urllib3<1.24
+- PyJWT < 2.0.0
+- numpy==1.18.5
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
@@ -14,4 +16,3 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - pandas
-  - PyJWT < 2.0.0

how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env_mac.yml

@@ -8,6 +8,8 @@ dependencies:
 - nb_conda
 - cython
 - urllib3<1.24
+- PyJWT < 2.0.0
+- numpy==1.18.5
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
@@ -15,4 +17,3 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - pandas
-  - PyJWT < 2.0.0

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

@@ -39,6 +39,7 @@
       "source": [
         "## Introduction\n",
         "In this example we use an experimental feature, Model Proxy, to do a predict on the best generated model without downloading the model locally. The prediction will happen on same compute and environment that was used to train the model. This feature is currently in the experimental state, which means that the API is prone to changing, please make sure to run on the latest version of this notebook if you face any issues.\n",
+        "This notebook will also leverage MLFlow for saving models, allowing for more portability of the resulting models. See https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow for more details around MLFlow is AzureML.\n",
         "\n",
         "If you are using an Azure Machine Learning Compute Instance, you are all set.  Otherwise, go through the [configuration](../../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
         "\n",
@@ -90,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -194,7 +195,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**scenario**|We need to set this parameter to 'Latest' to enable some experimental features. This parameter should not be set outside of this experimental notebook.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -213,17 +213,17 @@
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
+        "    \"experiment_timeout_hours\": 0.3, #for real scenarios we recommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",
+        "    \"save_mlflow\": True,\n",
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             compute_target = compute_target,\n",
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
-        "                             scenario='Latest',\n",
         "                             **automl_settings\n",
         "                            )"
       ]

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.yml

@@ -0,0 +1,4 @@
+name: auto-ml-regression-model-proxy
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -162,7 +162,9 @@
       },
       "source": [
         "### Using AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {
@@ -365,7 +367,9 @@
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "forecasting_parameters = ForecastingParameters(\n",
-        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    freq='MS' # Set the forecast frequency to be monthly (start of the month)\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -401,8 +405,7 @@
       },
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output= False)\n",
+        "remote_run = experiment.submit(automl_config, show_output= True)"
-        "remote_run"
       ]
     },
     {
@@ -419,15 +422,6 @@
         "# remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run.wait_for_completion()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-beer-remote
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -129,6 +129,9 @@
       "source": [
         "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -318,7 +321,8 @@
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=forecast_horizon,\n",
         "    country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
-        "    target_lags='auto' # use heuristic based lag setting    \n",
+        "    target_lags='auto', # use heuristic based lag setting\n",
+        "    freq='D' # Set the forecast frequency to be daily\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -349,8 +353,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run = experiment.submit(automl_config, show_output=False)"
-        "remote_run"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-bike-share
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -342,7 +342,9 @@
       "source": [
         "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n",
         "forecasting_parameters = ForecastingParameters(\n",
-        "    time_column_name=time_column_name, forecast_horizon=forecast_horizon\n",
+        "    time_column_name=time_column_name,\n",
+        "    forecast_horizon=forecast_horizon,\n",
+        "    freq='H' # Set the forecast frequency to be hourly\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
@@ -375,15 +377,6 @@
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-energy-demand
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -263,7 +263,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {
@@ -319,7 +321,8 @@
         "    time_column_name=TIME_COLUMN_NAME,\n",
         "    forecast_horizon=forecast_horizon,\n",
         "    time_series_id_column_names=[ TIME_SERIES_ID_COLUMN_NAME ],\n",
-        "    target_lags=lags\n",
+        "    target_lags=lags,\n",
+        "    freq='H' # Set the forecast frequency to be hourly\n",
         ")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-function
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -124,6 +124,9 @@
       "source": [
         "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -423,7 +426,8 @@
         "forecasting_parameters = ForecastingParameters(\n",
         "    time_column_name=time_column_name,\n",
         "    forecast_horizon=n_test_periods,\n",
-        "    time_series_id_column_names=time_series_id_column_names\n",
+        "    time_series_id_column_names=time_series_id_column_names,\n",
+        "    freq='W-THU' # Set the forecast frequency to be weekly (start on each Thursday)\n",
         ")\n",
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',\n",
@@ -455,8 +459,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)\n",
+        "remote_run = experiment.submit(automl_config, show_output=False)"
-        "remote_run"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

@@ -0,0 +1,4 @@
+name: auto-ml-forecasting-orange-juice-sales
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -215,15 +215,6 @@
         "#local_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml

@@ -0,0 +1,4 @@
+name: auto-ml-classification-credit-card-fraud-local
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -130,6 +130,8 @@
         "### Create or Attach existing AmlCompute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
@@ -305,15 +307,6 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -448,7 +441,7 @@
         "\n",
         "### Retrieve any AutoML Model for explanations\n",
         "\n",
-        "Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "Below we select an AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for any logged `metric` or for a particular `iteration`."
       ]
     },
     {
@@ -457,7 +450,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_run, fitted_model = remote_run.get_output(metric='r2_score')"
+        "#automl_run, fitted_model = remote_run.get_output(metric='r2_score')\n",
+        "automl_run, fitted_model = remote_run.get_output(iteration=2)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.yml

@@ -0,0 +1,4 @@
+name: auto-ml-regression-explanation-featurization
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -27,7 +27,7 @@ automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable
 if not automl_check_model_if_explainable(automl_run):
-    raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
+    raise Exception("Model explanations are currently not supported for " + automl_run.get_properties().get(
         'run_algorithm'))
 
 # Download the best model from the artifact store
@@ -38,16 +38,16 @@ fitted_model = joblib.load('model.pkl')
 
 # Get the train dataset from the workspace
 train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
-# Drop the lablled column to get the training set.
+# Drop the labeled column to get the training set.
 X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
 y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
 
-# Get the train dataset from the workspace
+# Get the test dataset from the workspace
 test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
-# Drop the lablled column to get the testing set.
+# Drop the labeled column to get the testing set.
 X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
 
-# Setup the class for explaining the AtuoML models
+# Setup the class for explaining the AutoML models
 automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
                                                              X=X_train, X_test=X_test,
                                                              y=y_train)

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -256,15 +256,6 @@
         "#remote_run = AutoMLRun(experiment = experiment, run_id = '<replace with your run id>')"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml

@@ -0,0 +1,4 @@
+name: auto-ml-regression
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/azure-synapse/README.md

@@ -0,0 +1,84 @@
+Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs. A core offering within Azure Synapse Analytics are serverless Apache Spark pools enhanced for big data workloads.  
+
+Synapse in Aml integration is for customers who want to use Apache Spark in Azure Synapse Analytics to prepare data at scale in Azure ML before training their ML model. This will allow customers to work on their end-to-end ML lifecycle including large-scale data preparation, model training and deployment within Azure ML workspace without having to use suboptimal tools for machine learning or switch between multiple tools for data preparation and model training. The ability to perform all ML tasks within Azure ML will reduce time required for customers to iterate on a machine learning project which typically includes multiple rounds of data preparation and training.
+
+In the public preview, the capabilities are provided:
+
+- Link Azure Synapse Analytics workspace to Azure Machine Learning workspace (via ARM, UI or SDK) 
+- Attach Apache Spark pools powered by Azure Synapse Analytics as Azure Machine Learning compute targets (via ARM, UI or SDK) 
+- Launch Apache Spark sessions in notebooks and perform interactive data exploration and preparation.  This interactive experience leverages Apache Spark magic and customers will have session-level Conda support to install packages. 
+- Productionize ML pipelines by leveraging Apache Spark pools to pre-process big data 
+
+# Using Synapse in Azure machine learning
+
+## Create synapse resources
+
+Follow up the documents to create Synapse workspace and resource-setup.sh is available for you to create the resources.
+
+- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace)
+- Create from [Cli](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace-cli)
+
+Follow up the documents to create Synapse spark pool
+
+- Create from [Portal](https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal)
+- Create from [Cli](https://docs.microsoft.com/en-us/cli/azure/ext/synapse/synapse/spark/pool?view=azure-cli-latest)
+
+## Link Synapse Workspace
+
+Make sure you are the owner of synapse workspace so that you can link synapse workspace into AML.
+You can run resource-setup.py to link the synapse workspace and attach compute
+
+```python
+from azureml.core import Workspace
+ws = Workspace.from_config()
+
+from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration
+synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration(
+    subscription_id="<subscription id>",
+    resource_group="<resource group",
+    name="<synapse workspace name>"
+)
+
+linked_service = LinkedService.register(
+    workspace=ws,
+    name='<link name>',
+    linked_service_config=synapse_link_config)
+
+```
+
+## Attach synapse spark pool as AzureML compute
+
+```python
+
+from azureml.core.compute import SynapseCompute, ComputeTarget
+spark_pool_name = "<spark pool name>"
+attached_synapse_name = "<attached compute name>"
+
+attach_config = SynapseCompute.attach_configuration(
+        linked_service,
+        type="SynapseSpark",
+        pool_name=spark_pool_name)
+
+synapse_compute=ComputeTarget.attach(
+        workspace=ws,
+        name=attached_synapse_name,
+        attach_configuration=attach_config)
+
+synapse_compute.wait_for_completion()
+```
+
+## Set up permission
+
+Grant Spark admin role to system assigned identity of the linked service so that the user can submit experiment run or pipeline run from AML workspace to synapse spark pool.
+
+Grant Spark admin role to the specific user so that the user can start spark session to synapse spark pool.
+
+You can get the system assigned identity information by running
+
+```python
+print(linked_service.system_assigned_identity_principal_id)
+```
+
+- Launch synapse studio of the synapse workspace and grant linked service MSI "Synapse Apache Spark administrator" role.
+
+- In azure portal grant linked service MSI "Storage Blob Data Contributor" role of the primary adlsgen2 account of synapse workspace to use the library management feature.

how-to-use-azureml/azure-synapse/Titanic.csv

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+848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
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+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
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+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
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+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
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+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
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+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
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+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
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how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb

@@ -0,0 +1,507 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_job_on_synapse_spark_pool.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Using Synapse Spark Pool as a Compute Target from Azure Machine Learning Remote Run\n",
+        "1. To use Synapse Spark Pool as a compute target from Experiment Run, [ScriptRunConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) is used, the same as other Experiment Runs. This notebook demonstrates how to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster.\n",
+        "2. To use Synapse Spark Pool as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [SynapseSparkStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.synapse_spark_step.synapsesparkstep?view=azure-ml-py) is used. This notebook demonstrates how to leverage SynapseSparkStep in Azure Machine Learning Pipeline.\n",
+        "\n",
+        "## Before you begin:\n",
+        "1. **Create an Azure Synapse workspace**, check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace) for more information.\n",
+        "2. **Create Spark Pool in Synapse workspace**: check [this] (https://docs.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-apache-spark-pool-portal) for more information."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Azure Machine Learning and Pipeline SDK-specific imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute, SynapseCompute\n",
+        "from azureml.exceptions import ComputeTargetException\n",
+        "from azureml.data import HDFSOutputDatasetConfig\n",
+        "from azureml.core.datastore import Datastore\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import PythonScriptStep, SynapseSparkStep\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Link Synapse workspace to AML \n",
+        "You have to be an \"Owner\" of Synapse workspace resource to perform linking. You can check your role in the Azure resource management portal, if you don't have an \"Owner\" role, you can contact an \"Owner\" to link the workspaces for you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Replace with your resource info before running.\n",
+        "\n",
+        "synapse_subscription_id=os.getenv(\"SYNAPSE_SUBSCRIPTION_ID\", \"<my-synapse-subscription-id>\")\n",
+        "synapse_resource_group=os.getenv(\"SYNAPSE_RESOURCE_GROUP\", \"<my-synapse-resource-group>\")\n",
+        "synapse_workspace_name=os.getenv(\"SYNAPSE_WORKSPACE_NAME\", \"<my-synapse-workspace-name>\")\n",
+        "synapse_linked_service_name=os.getenv(\"SYNAPSE_LINKED_SERVICE_NAME\", \"<my-synapse-linked-service-name>\")\n",
+        "\n",
+        "synapse_link_config = SynapseWorkspaceLinkedServiceConfiguration(\n",
+        "    subscription_id=synapse_subscription_id,\n",
+        "    resource_group=synapse_resource_group,\n",
+        "    name=synapse_workspace_name\n",
+        ")\n",
+        "\n",
+        "linked_service = LinkedService.register(\n",
+        "    workspace=ws,\n",
+        "    name=synapse_linked_service_name,\n",
+        "    linked_service_config=synapse_link_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Linked service property\n",
+        "\n",
+        "A MSI (system_assigned_identity_principal_id) will be generated for each linked service, for example:\n",
+        "\n",
+        "name=synapselink,</p>\n",
+        "type=Synapse, </p>\n",
+        "linked_service_resource_id=/subscriptions/4faaaf21-663f-4391-96fd-47197c630979/resourceGroups/static_resources_synapse_test/providers/Microsoft.Synapse/workspaces/synapsetest2, </p>\n",
+        "system_assigned_identity_principal_id=eb355d52-3806-4c5a-aec9-91447e8cfc2e </p>\n",
+        "\n",
+        "#### Make sure you grant \"Synapse Apache Spark Administrator\" role of the synapse workspace to the generated workspace linking MSI in Synapse studio portal before you submit job."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "linked_service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "LinkedService.list(ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Attach Synapse spark pool as AML compute target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "synapse_spark_pool_name=os.getenv(\"SYNAPSE_SPARK_POOL_NAME\", \"<my-synapse-spark-pool-name>\")\n",
+        "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"<my-synapse-compute-name>\")\n",
+        "\n",
+        "attach_config = SynapseCompute.attach_configuration(\n",
+        "        linked_service,\n",
+        "        type=\"SynapseSpark\",\n",
+        "        pool_name=synapse_spark_pool_name)\n",
+        "\n",
+        "synapse_compute=ComputeTarget.attach(\n",
+        "        workspace=ws,\n",
+        "        name=synapse_compute_name,\n",
+        "        attach_configuration=attach_config)\n",
+        "\n",
+        "synapse_compute.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Start an experiment run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prepare data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Use the default blob storage\n",
+        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+        "print('Datastore {} will be used'.format(def_blob_store.name))\n",
+        "\n",
+        "# We are uploading a sample file in the local directory to be used as a datasource\n",
+        "file_name = \"Titanic.csv\"\n",
+        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
+        " "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Tabular dataset as input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n",
+        "input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## File dataset as input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n",
+        "input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Output config: the output will be registered as a File dataset\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.data import HDFSOutputDatasetConfig\n",
+        "output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Dataprep script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "os.makedirs(\"code\", exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile code/dataprep.py\n",
+        "import os\n",
+        "import sys\n",
+        "import azureml.core\n",
+        "from pyspark.sql import SparkSession\n",
+        "from azureml.core import Run, Dataset\n",
+        "\n",
+        "print(azureml.core.VERSION)\n",
+        "print(os.environ)\n",
+        "\n",
+        "import argparse\n",
+        "parser = argparse.ArgumentParser()\n",
+        "parser.add_argument(\"--tabular_input\")\n",
+        "parser.add_argument(\"--file_input\")\n",
+        "parser.add_argument(\"--output_dir\")\n",
+        "args = parser.parse_args()\n",
+        "\n",
+        "# use dataset sdk to read tabular dataset\n",
+        "run_context = Run.get_context()\n",
+        "dataset = Dataset.get_by_id(run_context.experiment.workspace,id=args.tabular_input)\n",
+        "sdf = dataset.to_spark_dataframe()\n",
+        "sdf.show()\n",
+        "\n",
+        "# use hdfs path to read file dataset\n",
+        "spark= SparkSession.builder.getOrCreate()\n",
+        "sdf = spark.read.option(\"header\", \"true\").csv(args.file_input)\n",
+        "sdf.show()\n",
+        "\n",
+        "sdf.coalesce(1).write\\\n",
+        ".option(\"header\", \"true\")\\\n",
+        ".mode(\"append\")\\\n",
+        ".csv(args.output_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up Conda dependency for the following Script Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.environment import CondaDependencies\n",
+        "conda_dep = CondaDependencies()\n",
+        "conda_dep.add_pip_package(\"azureml-core==1.20.0\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to leverage ScriptRunConfig to submit an experiment run to an attached Synapse Spark cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import RunConfiguration\n",
+        "from azureml.core import ScriptRunConfig \n",
+        "from azureml.core import Experiment\n",
+        "\n",
+        "run_config = RunConfiguration(framework=\"pyspark\")\n",
+        "run_config.target = synapse_compute_name\n",
+        "\n",
+        "run_config.spark.configuration[\"spark.driver.memory\"] = \"1g\" \n",
+        "run_config.spark.configuration[\"spark.driver.cores\"] = 2 \n",
+        "run_config.spark.configuration[\"spark.executor.memory\"] = \"1g\" \n",
+        "run_config.spark.configuration[\"spark.executor.cores\"] = 1 \n",
+        "run_config.spark.configuration[\"spark.executor.instances\"] = 1 \n",
+        "\n",
+        "run_config.environment.python.conda_dependencies = conda_dep\n",
+        "\n",
+        "script_run_config = ScriptRunConfig(source_directory = './code',\n",
+        "                                    script= 'dataprep.py',\n",
+        "                                    arguments = [\"--tabular_input\", input1, \n",
+        "                                                 \"--file_input\", input2,\n",
+        "                                                 \"--output_dir\", output],\n",
+        "                                    run_config = run_config) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment \n",
+        "exp = Experiment(workspace=ws, name=\"synapse-spark\") \n",
+        "run = exp.submit(config=script_run_config) \n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to leverage SynapseSparkStep in an AML pipeline to orchestrate data prep step on Synapse Spark and training step on AzureML compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpucluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=1)\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "cpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile code/train.py\n",
+        "import glob\n",
+        "import os\n",
+        "import sys\n",
+        "from os import listdir\n",
+        "from os.path import isfile, join\n",
+        "\n",
+        "mypath = os.environ[\"step2_input\"]\n",
+        "files = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
+        "for file in files:\n",
+        "    with open(join(mypath,file)) as f:\n",
+        "        print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "titanic_tabular_dataset = Dataset.Tabular.from_delimited_files(path=[(def_blob_store, file_name)])\n",
+        "titanic_file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])\n",
+        "\n",
+        "step1_input1 = titanic_tabular_dataset.as_named_input(\"tabular_input\")\n",
+        "step1_input2 = titanic_file_dataset.as_named_input(\"file_input\").as_hdfs()\n",
+        "step1_output = HDFSOutputDatasetConfig(destination=(def_blob_store,\"test\")).register_on_complete(name=\"registered_dataset\")\n",
+        "\n",
+        "step2_input = step1_output.as_input(\"step2_input\").as_download()\n",
+        "\n",
+        "\n",
+        "from azureml.core.environment import Environment\n",
+        "env = Environment(name=\"myenv\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"azureml-core==1.20.0\")\n",
+        "\n",
+        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
+        "                          file = 'dataprep.py',\n",
+        "                          source_directory=\"./code\", \n",
+        "                          inputs=[step1_input1, step1_input2],\n",
+        "                          outputs=[step1_output],\n",
+        "                          arguments = [\"--tabular_input\", step1_input1, \n",
+        "                                       \"--file_input\", step1_input2,\n",
+        "                                       \"--output_dir\", step1_output],\n",
+        "                          compute_target = synapse_compute_name,\n",
+        "                          driver_memory = \"7g\",\n",
+        "                          driver_cores = 4,\n",
+        "                          executor_memory = \"7g\",\n",
+        "                          executor_cores = 2,\n",
+        "                          num_executors = 1,\n",
+        "                          environment = env)\n",
+        "\n",
+        "step_2 = PythonScriptStep(script_name=\"train.py\",\n",
+        "                          arguments=[step2_input],\n",
+        "                          inputs=[step2_input],\n",
+        "                          compute_target=cpu_cluster_name,\n",
+        "                          source_directory=\"./code\",\n",
+        "                          allow_reuse=False)\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[step_1, step_2])\n",
+        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "yunzhan"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb

@@ -0,0 +1,327 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/spark_session_on_synapse_spark_pool.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Interactive Spark Session on Synapse Spark Pool"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Install package"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install -U \"azureml-synapse\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For JupyterLab, please additionally run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!jupyter lab build --minimize=False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## PLEASE restart kernel and then refresh web page before starting spark session."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 0. How to leverage Spark Magic for interactive Spark experience"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.busy": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.idle": "2020-06-05T03:22:14.970398Z",
+          "shell.execute_reply": "2020-06-05T03:22:14.969397Z",
+          "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# show help\n",
+        "%synapse ?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Start Synapse Session"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "synapse_compute_name=os.getenv(\"SYNAPSE_COMPUTE_NAME\", \"<my-synapse-compute-name>\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use Synapse compute linked to the Compute Instance's workspace with an aml envrionment.\n",
+        "# conda dependencies specified in the environment will be installed before the spark session started.\n",
+        "\n",
+        "%synapse start -c $synapse_compute_name -e AzureML-Minimal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use Synapse compute from anther workspace via its config file\n",
+        "\n",
+        "# %synapse start -c <compute-name> -f config.json"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use Synapse compute from anther workspace via subscription_id, resource_group and workspace_name\n",
+        "\n",
+        "# %synapse start -c <compute-name> -s <subscription-id> -r <resource group> -w <workspace-name>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# start a spark session with an AML environment, \n",
+        "# %synapse start -c <compute-name> -s <subscription-id> -r <resource group> -w <workspace-name> -e AzureML-Minimal"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Data prepration\n",
+        "\n",
+        "Three types of datastore are supported in synapse spark, and you have two ways to load the data.\n",
+        "\n",
+        "\n",
+        "| Datastore Type     | Data Acess                    |\n",
+        "|--------------------|-------------------------------|\n",
+        "| Blob               | Credential                    |\n",
+        "| Adlsgen1           | Credential & Credential-less  |\n",
+        "| Adlsgen2           | Credential & Credential-less  |"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Example 1: Data loading by HDFS path"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Read data from Blob**\n",
+        "\n",
+        "```python\n",
+        "# setup access key or sas token\n",
+        "\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.key.<storage account name>.blob.core.windows.net\", \"<acess key>\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.sas.<container name>.<storage account name>.blob.core.windows.net\", \"sas token\")\n",
+        "\n",
+        "df = spark.read.parquet(\"wasbs://<container name>@<storage account name>.blob.core.windows.net/<path>\")\n",
+        "```\n",
+        "\n",
+        "**Read data from Adlsgen1**\n",
+        "\n",
+        "```python\n",
+        "# setup service pricinpal which has access of the data\n",
+        "# If no data Credential is setup, the user identity will be used to do access control\n",
+        "\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.access.token.provider.type\",\"ClientCredential\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.client.id\", \"<client id>\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.credential\", \"<client secret>\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.adl.account.<storage account name>.oauth2.refresh.url\", \"https://login.microsoftonline.com/<tenant id>/oauth2/token\")\n",
+        "\n",
+        "df = spark.read.csv(\"adl://<storage account name>.azuredatalakestore.net/<path>\")\n",
+        "```\n",
+        "\n",
+        "**Read data from Adlsgen2**\n",
+        "\n",
+        "```python\n",
+        "# setup service pricinpal which has access of the data\n",
+        "# If no data Credential is setup, the user identity will be used to do access control\n",
+        "\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.auth.type.<storage account name>.dfs.core.windows.net\",\"OAuth\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth.provider.type.<storage account name>.dfs.core.windows.net\", \"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.id.<storage account name>.dfs.core.windows.net\", \"<client id>\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.secret.<storage account name>.dfs.core.windows.net\", \"<client secret>\")\n",
+        "sc._jsc.hadoopConfiguration().set(\"fs.azure.account.oauth2.client.endpoint.<storage account name>.dfs.core.windows.net\", \"https://login.microsoftonline.com/<tenant id>/oauth2/token\")\n",
+        "\n",
+        "df = spark.read.csv(\"abfss://<container name>@<storage account>.dfs.core.windows.net/<path>\")\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-06-04T08:11:18.812276Z",
+          "iopub.status.busy": "2020-06-04T08:11:18.812276Z",
+          "iopub.status.idle": "2020-06-04T08:11:23.854526Z",
+          "shell.execute_reply": "2020-06-04T08:11:23.853525Z",
+          "shell.execute_reply.started": "2020-06-04T08:11:18.812276Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%%synapse\n",
+        "\n",
+        "from pyspark.sql.functions import col, desc\n",
+        "\n",
+        "df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n",
+        "df.filter(col('Survived') == 1).groupBy('Age').count().orderBy(desc('count')).show(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Example 2: Data loading by AML Dataset\n",
+        "\n",
+        "You can create tabular data by following the [guidance](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets) and use to_spark_dataframe() to load the data.\n",
+        "\n",
+        "```text\n",
+        "%%synapse\n",
+        "\n",
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)\n",
+        "\n",
+        "from azureml.core import Workspace, Dataset\n",
+        "ws = Workspace.get(name='<workspace name>', subscription_id='<subscription id>', resource_group='<resource group>')\n",
+        "ds = Dataset.get_by_name(ws, \"<tabular dataset name>\")\n",
+        "df = ds.to_spark_dataframe()\n",
+        "\n",
+        "# You can do more data transformation on spark dataframe\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Session Metadata\n",
+        "After session started, you can check the session's metadata, find the links to Synapse portal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%synapse meta"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Stop Session\n",
+        "When current session reach the status timeout, dead or any failure, you must explicitly stop it before start new one. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%synapse stop"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "yunzhan"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.yml

@@ -0,0 +1,6 @@
+name: multi-model-register-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml

@@ -0,0 +1,6 @@
+name: model-register-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -157,7 +157,9 @@
       "metadata": {},
       "source": [
         "## Provision the AKS Cluster\n",
-        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
+        "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.yml

@@ -0,0 +1,4 @@
+name: deploy-aks-with-controlled-rollout
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb

@@ -267,7 +267,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create AKS compute if you haven't done so."
+        "### Create AKS compute if you haven't done so.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.yml

@@ -0,0 +1,4 @@
+name: enable-app-insights-in-production-service
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/onnx/mnist.py

@@ -94,6 +94,17 @@ def main():
     os.makedirs(output_dir, exist_ok=True)
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+    # Use Azure Open Datasets for MNIST dataset
+    datasets.MNIST.resources = [
+        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+         "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
+        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+         "d53e105ee54ea40749a09fcbcd1e9432"),
+        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+         "9fb629c4189551a2d022fa330f9573f3"),
+        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+         "ec29112dd5afa0611ce80d1b7f02629c")
+    ]
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST('data', train=True, download=True,
                        transform=transforms.Compose([transforms.ToTensor(),

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml

@@ -0,0 +1,8 @@
+name: onnx-convert-aml-deploy-tinyyolo
+dependencies:
+- pip:
+  - azureml-sdk
+  - numpy
+  - git+https://github.com/apple/coremltools@v2.1
+  - onnx<1.7.0
+  - onnxmltools

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.yml

@@ -0,0 +1,9 @@
+name: onnx-inference-facial-expression-recognition-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - numpy
+  - onnx<1.7.0
+  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.yml

@@ -0,0 +1,9 @@
+name: onnx-inference-mnist-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - numpy
+  - onnx<1.7.0
+  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.yml

@@ -0,0 +1,4 @@
+name: onnx-model-register-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.yml

@@ -0,0 +1,4 @@
+name: onnx-modelzoo-aml-deploy-resnet50
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.yml

@@ -1,7 +1,5 @@
-name: day1-part4-data
+name: onnx-train-pytorch-aml-deploy-mnist
 dependencies:
 - pip:
   - azureml-sdk
   - azureml-widgets
-  - pytorch
-  - torchvision

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml

@@ -0,0 +1,5 @@
+name: production-deploy-to-aks-gpu
+dependencies:
+- pip:
+  - azureml-sdk
+  - tensorflow

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb

@@ -211,6 +211,8 @@
         "# Provision the AKS Cluster with SSL\n",
         "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
       ]
     },

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.yml

@@ -0,0 +1,8 @@
+name: production-deploy-to-aks-ssl
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb

@@ -325,7 +325,9 @@
       "metadata": {},
       "source": [
         "# Provision the AKS Cluster\n",
-        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.yml

@@ -0,0 +1,8 @@
+name: production-deploy-to-aks
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.yml

@@ -0,0 +1,4 @@
+name: model-register-and-deploy-spark
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb

@@ -203,6 +203,8 @@
       "source": [
         "### Provision a compute target\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
         "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
@@ -255,9 +257,6 @@
         "# Set compute target to AmlCompute target created in previous step\n",
         "run_config.target = cpu_cluster.name\n",
         "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
         "azureml_pip_packages = [\n",
         "    'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
         "]\n",

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -0,0 +1,13 @@
+name: explain-model-on-amlcompute
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - azureml-dataset-runtime
+  - ipywidgets

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -226,36 +226,6 @@
         "                      ('classifier', SVC(C=1.0, probability=True))])"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "\n",
-        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
-        "\n",
-        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
-        "\n",
-        "transformations = numeric_transformations + categorical_transformations\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', SVC(C=1.0, probability=True))]) \n",
-        "\n",
-        "\n",
-        "\n",
-        "'''"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -0,0 +1,12 @@
+name: save-retrieve-explanations-run-history
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -166,12 +166,12 @@
       "source": [
         "from sklearn.model_selection import train_test_split\n",
         "import joblib\n",
+        "from sklearn.compose import ColumnTransformer\n",
         "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
         "from sklearn.impute import SimpleImputer\n",
         "from sklearn.pipeline import Pipeline\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn_pandas import DataFrameMapper\n",
         "\n",
         "from interpret.ext.blackbox import TabularExplainer\n",
         "\n",
@@ -201,17 +201,23 @@
         "# Store the numerical columns in a list numerical\n",
         "numerical = attritionXData.columns.difference(categorical)\n",
         "\n",
-        "numeric_transformations = [([f], Pipeline(steps=[\n",
+        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
+        "numeric_transformer = Pipeline(steps=[\n",
         "    ('imputer', SimpleImputer(strategy='median')),\n",
-        "    ('scaler', StandardScaler())])) for f in numerical]\n",
+        "    ('scaler', StandardScaler())])\n",
         "\n",
-        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
         "\n",
-        "transformations = numeric_transformations + categorical_transformations\n",
+        "transformations = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numeric_transformer, numerical),\n",
+        "        ('cat', categorical_transformer, categorical)])\n",
         "\n",
         "# Append classifier to preprocessing pipeline.\n",
         "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
+        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
         "                      ('classifier', RandomForestClassifier())])\n",
         "\n",
         "# Split data into train and test\n",
@@ -350,7 +356,7 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "myenv = CondaDependencies.create(pip_packages=['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
+        "myenv = CondaDependencies.create(pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages,\n",
         "                                 pin_sdk_version=False)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
@@ -382,6 +388,7 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
+        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -395,7 +402,12 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-deploy-local', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -0,0 +1,12 @@
+name: train-explain-model-locally-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb

@@ -204,6 +204,8 @@
       "source": [
         "### Provision a compute target\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
         "\n",
         "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
@@ -257,9 +259,6 @@
         "# Set compute target to AmlCompute target created in previous step\n",
         "run_config.target = cpu_cluster.name\n",
         "\n",
-        "# Enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
         "# Set Docker base image to the default CPU-based image\n",
         "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
         "\n",
@@ -294,7 +293,7 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
+        "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
         "run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "# Now submit a run on AmlCompute\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
@@ -458,7 +457,7 @@
         "# the submitted job is run in.  Note the remote environment(s) needs to be similar to the local\n",
         "# environment, otherwise if a model is trained or deployed in a different environment this can\n",
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
-        "azureml_pip_packages.extend(['sklearn-pandas', 'pyyaml', sklearn_dep, pandas_dep])\n",
+        "azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
         "myenv = CondaDependencies.create(pip_packages=azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
@@ -489,6 +488,7 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
         "from azureml.core.environment import Environment\n",
+        "from azureml.exceptions import WebserviceException\n",
         "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
@@ -502,7 +502,12 @@
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
+        "try:\n",
+        "    service.wait_for_deployment(show_output=True)\n",
+        "except WebserviceException as e:\n",
+        "    print(e.message)\n",
+        "    print(service.get_logs())\n",
+        "    raise"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -0,0 +1,14 @@
+name: train-explain-model-on-amlcompute-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-interpret
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
+  - matplotlib
+  - azureml-dataset-runtime
+  - azureml-core
+  - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py

@@ -5,13 +5,13 @@
 import os
 import pandas as pd
 import zipfile
-from sklearn.model_selection import train_test_split
 import joblib
+from sklearn.compose import ColumnTransformer
+from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.impute import SimpleImputer
 from sklearn.pipeline import Pipeline
 from sklearn.linear_model import LogisticRegression
-from sklearn_pandas import DataFrameMapper
 
 from azureml.core.run import Run
 from interpret.ext.blackbox import TabularExplainer
@@ -57,16 +57,22 @@ for col, value in attritionXData.iteritems():
 # store the numerical columns
 numerical = attritionXData.columns.difference(categorical)
 
-numeric_transformations = [([f], Pipeline(steps=[
+# We create the preprocessing pipelines for both numeric and categorical data.
+numeric_transformer = Pipeline(steps=[
     ('imputer', SimpleImputer(strategy='median')),
-    ('scaler', StandardScaler())])) for f in numerical]
+    ('scaler', StandardScaler())])
 
-categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
+categorical_transformer = Pipeline(steps=[
+    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
+    ('onehot', OneHotEncoder(handle_unknown='ignore'))])
 
-transformations = numeric_transformations + categorical_transformations
+transformations = ColumnTransformer(
+    transformers=[
+        ('num', numeric_transformer, numerical),
+        ('cat', categorical_transformer, categorical)])
 
 # append classifier to preprocessing pipeline
-clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
+clf = Pipeline(steps=[('preprocessor', transformations),
                       ('classifier', LogisticRegression(solver='lbfgs'))])
 
 # get the run this was submitted from to interact with run history

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.yml

@@ -1,7 +1,5 @@
-name: day1-part3-train-model
+name: aml-pipelines-data-transfer
 dependencies:
 - pip:
   - azureml-sdk
   - azureml-widgets
-  - pytorch
-  - torchvision

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

@@ -209,6 +209,8 @@
         "#### Retrieve or create a Azure Machine Learning compute\n",
         "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
         "\n",
         "1. Create the configuration\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-getting-started
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-how-to-use-modulestep
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb

@@ -55,7 +55,9 @@
       "metadata": {},
       "source": [
         "### Compute Target\n",
-        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline."
+        "Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-how-to-use-pipeline-drafts
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb

@@ -42,15 +42,13 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-        "from azureml.core import Workspace, Experiment, Datastore, Dataset\n",
+        "from azureml.core import Workspace, Environment, Experiment, Datastore, Dataset, ScriptRunConfig\n",
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.exceptions import ComputeTargetException\n",
         "from azureml.pipeline.steps import HyperDriveStep, HyperDriveStepRun, PythonScriptStep\n",
         "from azureml.pipeline.core import Pipeline, PipelineData, TrainingOutput\n",
-        "from azureml.train.dnn import TensorFlow\n",
-        "# from azureml.train.hyperdrive import *\n",
         "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveConfig, PrimaryMetricGoal\n",
         "from azureml.train.hyperdrive import choice, loguniform\n",
         "\n",
@@ -121,12 +119,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "os.makedirs('./data/mnist', exist_ok=True)\n",
+        "data_folder = os.path.join(os.getcwd(), 'data/mnist')\n",
+        "os.makedirs(data_folder, exist_ok=True)\n",
         "\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n",
+        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-images-idx3-ubyte.gz',\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n",
+        "                           filename=os.path.join(data_folder, 'train-images.gz'))\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n",
+        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/train-labels-idx1-ubyte.gz',\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')"
+        "                           filename=os.path.join(data_folder, 'train-labels.gz'))\n",
+        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz',\n",
+        "                           filename=os.path.join(data_folder, 'test-images.gz'))\n",
+        "urllib.request.urlretrieve('https://azureopendatastorage.blob.core.windows.net/mnist/t10k-labels-idx1-ubyte.gz',\n",
+        "                           filename=os.path.join(data_folder, 'test-labels.gz'))"
       ]
     },
     {
@@ -146,11 +149,11 @@
         "from utils import load_data\n",
         "\n",
         "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n",
-        "X_train = load_data('./data/mnist/train-images.gz', False) / 255.0\n",
+        "X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / np.float32(255.0)\n",
-        "y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)\n",
+        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / np.float32(255.0)\n",
+        "y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)\n",
+        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)\n",
         "\n",
-        "X_test = load_data('./data/mnist/test-images.gz', False) / 255.0\n",
-        "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n",
         "\n",
         "count = 0\n",
         "sample_size = 30\n",
@@ -207,6 +210,8 @@
         "## Retrieve or create a Azure Machine Learning compute\n",
         "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
         "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+        "\n",
         "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n",
         "\n",
         "1. Create the configuration\n",
@@ -277,13 +282,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create TensorFlow estimator\n",
+        "## Retrieve an Environment\n",
-        "Next, we construct an [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) estimator object.\n",
+        "In this tutorial, we will use one of Azure ML's curated TensorFlow environments for training. Curated environments are available in your workspace by default. Specifically, we will use the TensorFlow 2.0 GPU curated environment."
-        "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker.\n",
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release.\n",
-        "\n",
-        "The TensorFlow estimator also takes a `framework_version` parameter -- if no version is provided, the estimator will default to the latest version supported by AzureML. Use `TensorFlow.get_supported_versions()` to get a list of all versions supported by your current SDK version or see the [SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn?view=azure-ml-py) for the versions supported in the most current release."
       ]
     },
     {
@@ -292,12 +292,45 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "est = TensorFlow(source_directory=script_folder,                 \n",
+        "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Setup an input for the ScriptRunConfig step\n",
+        "You can mount dataset to remote compute."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_folder = dataset.as_mount()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure the training job\n",
+        "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "src = ScriptRunConfig(source_directory=script_folder,\n",
+        "                      script='tf_mnist.py',\n",
+        "                      arguments=['--data-folder', data_folder],\n",
         "                      compute_target=compute_target,\n",
-        "                 entry_script='tf_mnist.py', \n",
+        "                      environment=tf_env)"
-        "                 use_gpu=True,\n",
-        "                 framework_version='2.0',\n",
-        "                 pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
       ]
     },
     {
@@ -361,7 +394,7 @@
       },
       "outputs": [],
       "source": [
-        "hd_config = HyperDriveConfig(estimator=est, \n",
+        "hd_config = HyperDriveConfig(run_config=src, \n",
         "                             hyperparameter_sampling=ps,\n",
         "                             policy=early_termination_policy,\n",
         "                             primary_metric_name='validation_acc', \n",
@@ -370,25 +403,6 @@
         "                             max_concurrent_runs=4)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Add HyperDrive as a step of pipeline\n",
-        "\n",
-        "### Setup an input for the hypderdrive step\n",
-        "You can mount dataset to remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "data_folder = dataset.as_mount()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -397,7 +411,6 @@
         "HyperDriveStep can be used to run HyperDrive job as a step in pipeline.\n",
         "- **name:** Name of the step\n",
         "- **hyperdrive_config:** A HyperDriveConfig that defines the configuration for this HyperDrive run\n",
-        "- **estimator_entry_script_arguments:** List of command-line arguments for estimator entry script\n",
         "- **inputs:** List of input port bindings\n",
         "- **outputs:** List of output port bindings\n",
         "- **metrics_output:** Optional value specifying the location to store HyperDrive run metrics as a JSON file\n",
@@ -432,7 +445,6 @@
         "hd_step = HyperDriveStep(\n",
         "    name=hd_step_name,\n",
         "    hyperdrive_config=hd_config,\n",
-        "    estimator_entry_script_arguments=['--data-folder', data_folder],\n",
         "    inputs=[data_folder],\n",
         "    outputs=[metrics_data, saved_model])"
       ]

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.yml

@@ -0,0 +1,9 @@
+name: aml-pipelines-parameter-tuning-with-hyperdrive
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - numpy
+  - pandas_ml
+  - azureml-dataset-runtime[pandas,fuse]

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb

@@ -68,7 +68,9 @@
       "metadata": {},
       "source": [
         "### Compute Targets\n",
-        "#### Retrieve an already attached  Azure Machine Learning Compute"
+        "#### Retrieve an already attached  Azure Machine Learning Compute\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.yml

@@ -0,0 +1,6 @@
+name: aml-pipelines-publish-and-run-using-rest-endpoint
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - requests

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -54,7 +54,9 @@
       "metadata": {},
       "source": [
         "### Compute Targets\n",
-        "#### Retrieve an already attached Azure Machine Learning Compute"
+        "#### Retrieve an already attached Azure Machine Learning Compute\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-setup-schedule-for-a-published-pipeline
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb

@@ -78,7 +78,9 @@
       "source": [
         "####  Initialization, Steps to create a Pipeline\n",
         "\n",
-        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
+        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.yml

@@ -0,0 +1,6 @@
+name: aml-pipelines-setup-versioned-pipeline-endpoints
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - requests

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

@@ -109,7 +109,9 @@
       "metadata": {},
       "source": [
         "## Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-showcasing-datapath-and-pipelineparameter
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

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

@@ -111,7 +111,9 @@
       "metadata": {},
       "source": [
         "## Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

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

@@ -0,0 +1,5 @@
+name: aml-pipelines-showcasing-dataset-and-pipelineparameter
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -699,12 +699,162 @@
       ]
     },
     {
+      "source": [
+        "### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n",
+        "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
+        "\n",
+        "Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"."
+      ],
       "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    dbfs_ds = Datastore.get(workspace=ws, datastore_name='dbfs_datastore')\n",
+        "    print('DBFS Datastore already exists')\n",
+        "except Exception as ex:\n",
+        "    dbfs_ds = Datastore.register_dbfs(ws, datastore_name='dbfs_datastore')\n",
+        "\n",
+        "step_1_input = DataReference(datastore=dbfs_ds, path_on_datastore=\"FileStore\", data_reference_name=\"input\")\n",
+        "step_1_output = PipelineData(\"output\", datastore=dbfs_ds)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dbNbWithExistingClusterStep = DatabricksStep(\n",
+        "    name=\"DBFSReferenceWithExisting\",\n",
+        "    inputs=[step_1_input],\n",
+        "    outputs=[step_1_output],\n",
+        "    notebook_path=notebook_path,\n",
+        "    notebook_params={'myparam': 'testparam', \n",
+        "        'myparam2': pipeline_param},\n",
+        "    run_name='DBFS_Reference_With_Existing',\n",
+        "    compute_target=databricks_compute,\n",
+        "    existing_cluster_id=\"your existing cluster id\",\n",
+        "    allow_reuse=True\n",
+        ")"
+      ]
+    },
+    {
+      "source": [
+        "#### Build and submit the Experiment"
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "steps = [dbNbWithExistingClusterStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, 'DBFS_Reference_With_Existing').submit(pipeline)\n",
+        "pipeline_run.wait_for_completion()"
+      ]
+    },
+    {
+      "source": [
+        "#### View Run Details"
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
+      ]
+    },
+    {
+      "source": [
+        "### 6. Running a Python script in Databricks that currenlty is in local computer with existing cluster\n",
+        "When you access azure blob or data lake storage from an existing (interactive) cluster, you need to ensure the Spark configuration is set up correctly to access this storage and this set up may require the cluster to be restarted.\n",
+        "\n",
+        "If you set permit_cluster_restart to True, AML will check if the spark configuration needs to be updated and restart the cluster for you if required. This will ensure that the storage can be correctly accessed from the Databricks cluster."
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
+        "                                     data_reference_name=\"input\")\n",
+        "\n",
+        "dbPythonInLocalWithExistingStep = DatabricksStep(\n",
+        "    name=\"DBPythonInLocalMachineWithExisting\",\n",
+        "    inputs=[step_1_input],\n",
+        "    python_script_name=python_script_name,\n",
+        "    source_directory=source_directory,\n",
+        "    run_name='DB_Python_Local_existing_demo',\n",
+        "    compute_target=databricks_compute,\n",
+        "    existing_cluster_id=\"your existing cluster id\",\n",
+        "    allow_reuse=False,\n",
+        "    permit_cluster_restart=True\n",
+        ")"
+      ]
+    },
+    {
+      "source": [
+        "#### Build and submit the Experiment"
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "steps = [dbPythonInLocalWithExistingStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, 'DB_Python_Local_existing_demo').submit(pipeline)\n",
+        "pipeline_run.wait_for_completion()"
+      ]
+    },
+    {
+      "source": [
+        "#### View Run Details"
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
+      ]
+    },
+    {
       "source": [
         "# Next: ADLA as a Compute Target\n",
         "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
-      ]
+      ],
+      "cell_type": "markdown",
+      "metadata": {}
     }
   ],
   "metadata": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -125,7 +125,9 @@
       "metadata": {},
       "source": [
         "### Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.yml

@@ -0,0 +1,4 @@
+name: aml-pipelines-with-automated-machine-learning-step
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb

@@ -79,7 +79,9 @@
       "metadata": {},
       "source": [
         "## Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
+        "\n",
+        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.yml

@@ -0,0 +1,5 @@
+name: aml-pipelines-with-commandstep-r
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets