Samples on Azure Data Science Virtual Machines

Azure Data Science Virtual Machines (DSVMs) include a comprehensive set of sample code. These samples include Jupyter notebooks and scripts in languages like Python and R.

Note

For more information about how to run Jupyter notebooks on your data science virtual machines, see the Access Jupyter section.

Prerequisites

In order to run these samples, you must have provisioned a Data Science Virtual Machine. See the quickstarts for Windows and Ubuntu.

Available samples

Samples category Description Locations
R language Samples illustrate scenarios such as how to connect with Azure-based cloud data stores and how to compare open-source R and Microsoft Machine Learning Server. They also explain how to operationalize models on Microsoft Machine Learning Server and SQL Server.
R language

~notebooks

~samples/MicrosoftR

~samples/RSqlDemo

~samples/SQLRServices

Python language Samples explain scenarios like how to connect with Azure-based cloud data stores and how to work with Azure Machine Learning.
Python language

~notebooks

Julia language Provides a detailed description of plotting and deep learning in Julia. Also explains how to call C and Python from Julia.
Julia language

Windows:
~notebooks/Julia_notebooks

Linux:
~notebooks/julia

Azure Machine Learning Illustrates how to build machine-learning and deep-learning models with Machine Learning. Deploy models anywhere. Use automated machine learning and intelligent hyperparameter tuning. Also use model management and distributed training.
Machine Learning

~notebooks/AzureML

PyTorch notebooks Deep-learning samples that use PyTorch-based neural networks. Notebooks range from beginner to advanced scenarios.
PyTorch notebooks

~notebooks/Deep_learning_frameworks/pytorch

TensorFlow A variety of neural network samples and techniques implemented by using the TensorFlow framework.
TensorFlow

~notebooks/Deep_learning_frameworks/tensorflow

Microsoft Cognitive Toolkit
Deep-learning samples published by the Cognitive Toolkit team at Microsoft.
Cognitive Toolkit

~notebooks/DeepLearningTools/CNTK/Tutorials

Linux:
~notebooks/CNTK

Caffe2 Deep-learning samples that use Caffe2-based neural networks. Several notebooks familiarize users with Caffe2 and how to use it effectively. Examples include image preprocessing and dataset creation. They also include regression and how to use pretrained models.
Caffe2

~notebooks/Deep_learning_frameworks/caffe2

H2O Python-based samples that use H2O for real-world problem scenarios.
H2O

~notebooks/h2o

SparkML language Samples that use features of the Apache Spark MLLib toolkit through pySpark and MMLSpark: Microsoft Machine Learning for Apache Spark on Apache Spark 2.x.
SparkML language

~notebooks/SparkML/pySpark
~notebooks/MMLSpark

XGBoost Standard machine-learning samples in XGBoost for scenarios like classification and regression.
XGBoost

Windows:
\dsvm\samples\xgboost\demo


Access Jupyter

To access Jupyter, select the Jupyter icon on the desktop or application menu. You also can access Jupyter on a Linux edition of a DSVM. To access remotely from a web browser, go to https://<Full Domain Name or IP Address of the DSVM>:8000 on Ubuntu.

To add exceptions and make Jupyter access available over a browser, use the following guidance:

Enable Jupyter exception

Sign in with the same password that you use to log in to the Data Science Virtual Machine.

Jupyter home
Jupyter home

R language


R samples

Python language


Python samples

Julia language


Julia samples

Azure Machine Learning


Azure Machine Learning samples

PyTorch


PyTorch samples

TensorFlow


TensorFlow samples

CNTK


CNTK samples

Caffe2


caffe2 samples

H2O


H2O samples

SparkML


SparkML samples

XGBoost


XGBoost samples