Merge remote-tracking branch 'upstream/master'

configuration.ipynb

@@ -96,7 +96,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.6 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -373,4 +373,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

@@ -20,7 +20,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c2\u00a0in Azure.\n",
+        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model in Azure.\n",
         " \n",
         "In this notebook, we will do the following:\n",
         " \n",
@@ -406,4 +406,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb

@@ -81,7 +81,7 @@
         "from azureml.core import Experiment, Workspace\n",
         "\n",
         "# Check core SDK version number\n",
-        "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")\n",
         "print(\"\")\n",
         "\n",
@@ -703,4 +703,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}