AI News, Provision a Linux CentOS Data Science Virtual Machine on Azure

Provision a Linux CentOS Data Science Virtual Machine on Azure

The Linux Data Science Virtual Machine is a CentOS-based Azure virtual machine that comes with a collection of pre-installed tools.

The key software components included are: Doing data science involves iterating on a sequence of tasks: Data scientists use various tools to complete these tasks.

It can be quite time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions.

You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision with the VM image.

To connect to the Linux VM graphical desktop, do the following on your client: After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2Go client, you are ready to start using the tools that are installed and configured on the VM.

If you are using the Emacs editor, note that the Emacs package ESS (Emacs Speaks Statistics), which simplifies working with R files within the Emacs editor, has been pre-installed.

This distribution contains the base Python along with about 300 of the most popular math, engineering, and data analytics packages.

Since we have both Python 2.7 and 3.5, you need to specifically activate the desired Python version (conda environment) you want to work on in the current session.

To activate the Python 2.7 conda environment, run the following command from the shell: Python 2.7 is installed at /anaconda/bin.

To install additional Python libraries, you need to run conda or pip command under sudo and provide full path of the Python package manager (conda or pip) to install to the correct Python environment.

You can see the link to the samples on the notebook home page after you authenticate to the Jupyter notebook by using your local Linux user name and password.

standalone instance of Apache Spark is preinstalled on the Linux DSVM to help you develop Spark applications locally first before testing and deploying on large clusters.

Before running in Spark context in Microsoft R Server, you need to do a one time setup step to enable a local single node Hadoop HDFS and Yarn instance.

In order to enable it, you need to run the following commands as root the first time: You can stop the Hadoop related services when you dont need them by running systemctl stop hadoop-namenode hadoop-datanode hadoop-yarn A

sample demonstrating how to develop and test MRS in remote Spark context (which is the standalone Spark instance on the DSVM) is provided and available in the /dsvm/samples/MRS directory.

It allows you to create, develop, test, and deploy Azure applications using the Eclipse development environment that supports languages like Java.

The open source database Postgres is available on the VM, with the services running and initdb already completed.

The ODBC driver package for SQL Server also comes with two command-line tools: bcp: The bcp utility bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format.

The bcp utility can be used to import large numbers of new rows into SQL Server tables, or to export data out of tables into data files.

To import data into a table, you must either use a format file created for that table, or understand the structure of the table and the types of data that are valid for its columns.

sqlcmd: You can enter Transact-SQL statements with the sqlcmd utility, as well as system procedures, and script files at the command prompt.

Azure Machine Learning is a fully managed cloud service that enables you to build, deploy, and share predictive analytics solutions.

Vowpal Wabbit is a machine learning system that uses techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

The objective of this library is to push the computation limits of machines to the extremes needed to provide large-scale tree boosting that is scalable, portable, and accurate.

Here is a simple example you can run in R prompt: To run the xgboost command line, here are the commands to execute in the shell: A

It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new data sets.

It also generates R code, replicating the operations in the UI that can be run directly in R or used as a starting point for further analysis.

In some of the steps below, you are prompted to automatically install and load some required R packages that are not already on the system.

Especially for beginners in R, this is an easy way to quickly do analysis and machine learning in a simple graphical interface, while automatically generating code in R to modify and/or learn.

What's new in SQL Server Machine Learning Services

Machine learning capabilities are added to SQL Server in each release as we continue to expand, extend, and deepen the integration between the data platform and the data science, analytics, and supervised learning you want to implement over your data.

New capabilities for R include package management, with the following highlights: T-SQL and Python integration is now supported through the sp_execute_external_script system stored procedure.

Code runs in a secure, dual architecture that enables enterprise-grade deployment of Python models and scripts, callable from an application using a simple stored procedure.

You can use the T-SQL PREDICT function to perform native scoring on a pre-trained model that has been previously saved in the required binary format.

This release also adds SQL Server Machine Learning Server (Standalone), a fully independent data science server, supporting statistical and predictive analytics in R and Python.

This release introduced machine learning capabilities into SQL Server through SQL Server 2016 R Services, an in-database analytics engine for processing R script on resident data within a database engine instance.

How to Execute R and Python in SQL Server with Machine Learning Services

By Kyle Weller, Microsoft Azure Machine Learning Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement.

Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database.

If you are excited to try out SQL Server Machine Learning Services, check out the hands on tutorial below. If you do not have Machine Learning Services installed in SQL Server,you will first want to follow the getting started tutorial I published here: In this tutorial, I will cover the basics of how to Execute R and Python in T-SQL statements.

Open a new query and paste this basic example: (While I use Python in these samples, you can do everything with R as well) EXEC sp_execute_external_script @language = N'Python',@script = N'print(3+4)' Sp_execute_external_script is a special system stored procedure that enables R and Python execution in SQL Server.

If you need to convert scalar values into a dataframe here is an example: EXEC sp_execute_external_script  @language =N'Python',@script=N'import pandas as pdc = 1/2d = 1*2s = pd.Series([c,d])df = pd.DataFrame(s)OutputDataSet = df' Variables c and d are both scalar values, which you can add to a pandas Series if you like, and then convert them to a pandas dataframe.

This one shows a little bit more complicated example, go read up on the python pandas package documentation for more details and examples: EXEC sp_execute_external_script  @language =N'Python',@script=N'import pandas as pds = {'col1': [1, 2], 'col2': [3, 4]}df = pd.DataFrame(s)OutputDataSet = df' You now know the basics to execute Python in T-SQL!

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