update samples from Release-42 as a part of SDK release

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

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -2,6 +2,7 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -701,7 +701,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright, nirovins"
+        "name": "erwright"
       }
     ],
     "category": "tutorial",

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

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets

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

@@ -30,7 +30,9 @@
         "\n",
         "## Prerequisites\n",
         "\n",
-        "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites)."
+        "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites).\n",
+        "\n",
+        "On the computer running this notebook, conda install matplotlib, numpy, scikit-learn=0.22.1"
       ]
     },
     {
@@ -309,7 +311,7 @@
         "import glob\n",
         "\n",
         "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.externals import joblib\n",
+        "import joblib\n",
         "\n",
         "from azureml.core import Run\n",
         "from utils import load_data\n",
@@ -397,15 +399,20 @@
       "source": [
         "### Create an estimator\n",
         "\n",
-        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create SKLearn estimator for scikit-learn model, by specifying\n",
+        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create an estimator by specifying\n",
         "\n",
         "* The name of the estimator object, `est`\n",
         "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
         "* The compute target.  In this case you will use the AmlCompute you created\n",
         "* The training script name, train.py\n",
-        "* Parameters required from the training script \n",
+        "* An environment that contains the libraries needed to run the script\n",
+        "* Parameters required from the training script. \n",
+        "\n",
+        "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset.\n",
+        "\n",
+        "First, create the environment that contains: the scikit-learn library, azureml-dataprep required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
         "\n",
-        "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset."
+        "Once the environment is defined, register it with the Workspace to re-use it in part 2 of the tutorial."
       ]
     },
     {
@@ -418,10 +425,20 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "# to install required packages\n",
-        "env = Environment('my_env')\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk','scikit-learn==0.22.1','azureml-dataprep[pandas,fuse]>=1.1.14'])\n",
+        "env = Environment('tutorial-env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
         "\n",
-        "env.python.conda_dependencies = cd"
+        "env.python.conda_dependencies = cd\n",
+        "\n",
+        "# Register environment to re-use later\n",
+        "env.register(workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, create the estimator by specifying the training script, compute target and environment."
       ]
     },
     {
@@ -434,15 +451,15 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.train.sklearn import SKLearn\n",
+        "from azureml.train.estimator import Estimator\n",
         "\n",
         "script_params = {\n",
         "    # to mount files referenced by mnist dataset\n",
         "    '--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),\n",
         "    '--regularization': 0.5\n",
         "}\n",
         "\n",
-        "est = SKLearn(source_directory=script_folder,\n",
+        "est = Estimator(source_directory=script_folder,\n",
         "              script_params=script_params,\n",
         "              compute_target=compute_target,\n",
         "              environment_definition=env,\n",
@@ -667,7 +684,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.7.6"
     },
     "msauthor": "roastala"
   },