Merge pull request #801 from Azure/release_update/Release-39

update samples from Release-39 as a part of  SDK release

README.md

@@ -13,7 +13,7 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
 
 If you want to...
 

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.0rc1 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -4,6 +4,7 @@ dependencies:
   # Currently Azure ML only supports 3.5.2 and later.
 - pip<=19.3.1
 - python>=3.5.2,<3.6.8
+- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2

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

@@ -5,6 +5,7 @@ dependencies:
 - pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
+- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2

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

@@ -76,7 +76,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "from azureml.pipeline.steps import AutoMLStep\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
@@ -173,7 +173,12 @@
       "source": [
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'])\n",
+        "\n",
+        "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], \n",
+        "                              conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"

how-to-use-azureml/ml-frameworks/chainer/deployment/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb

@@ -673,13 +673,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "models = ws.models\n",
-        "for name, model in models.items():\n",
-        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "model = ws.models['chainer-dnn-mnist']\n",
+        "print(\"Model: {}, ID: {}\".format('chainer-dnn-mnist', model.id))\n",
         "       \n",
-        "webservices = ws.webservices\n",
-        "for name, webservice in webservices.items():\n",
-        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+        "webservice = ws.webservices['chainer-mnist-1']\n",
+        "print(\"Webservice: {}, scoring URI: {}\".format('chainer-mnist-1', webservice.scoring_uri))"
       ]
     },
     {

how-to-use-azureml/ml-frameworks/tensorflow/deployment/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -1115,13 +1115,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "models = ws.models\n",
-        "for name, model in models.items():\n",
-        "    print(\"Model: {}, ID: {}\".format(name, model.id))\n",
+        "model = ws.models['tf-dnn-mnist']\n",
+        "print(\"Model: {}, ID: {}\".format('tf-dnn-mnist', model.id))\n",
         "    \n",
-        "webservices = ws.webservices\n",
-        "for name, webservice in webservices.items():\n",
-        "    print(\"Webservice: {}, scoring URI: {}\".format(name, webservice.scoring_uri))"
+        "webservice = ws.webservices['tf-mnist-svc']\n",
+        "print(\"Webservice: {}, scoring URI: {}\".format('tf-mnist-svc', webservice.scoring_uri))"
       ]
     },
     {

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.1.0rc1, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.1.1rc0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.0rc1 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },