Provision a Linux Cent.OS Data Science Virtual Machine on Azure.Microsoft Active Directory Hashing Algorithm Example' title='Microsoft Active Directory Hashing Algorithm Example' />The Linux Data Science Virtual Machine is a Cent.OS based Azure virtual machine that comes with a collection of pre installed tools.These tools are commonly used for doing data analytics and machine learning.The key software components included are Operating System Linux Cent.OS distribution. Microsoft R Server Developer Edition.Microsoft Active Directory Hashing Algorithm Example' title='Microsoft Active Directory Hashing Algorithm Example' />Anaconda Python distribution versions 2.Julia. Pro a curated distribution of Julia language with popular scientific and data analytics libraries.The General Hash Function Algorithm library contains implementations for a series of commonly used additive and rotative string hashing algorithm in the Object Pascal.Microsoft Active Directory Hashing Algorithm Example' title='Microsoft Active Directory Hashing Algorithm Example' />Standalone Spark instance and single node Hadoop HDFS, YarnJupyter.Hub a multiuser Jupyter notebook server supporting R, Python, Py.Spark, Julia kernels.Azure Storage Explorer.Azure command line interface CLI for managing Azure resources.Postgres. SQL Database.Machine learning tools.Cognitive Toolkit A deep learning software toolkit from Microsoft Research.Vowpal Wabbit A fast machine learning system supporting techniques such as online, hashing, allreduce, reductions, learning.XGBoost A tool providing fast and accurate boosted tree implementation.Rattle the R Analytical Tool To Learn Easily A tool that makes getting started with data analytics and machine learning in R easy, with GUI based data exploration, and modeling with automatic R code generation.Azure SDK in Java, Python, node.Ruby, PHPLibraries in R and Python for use in Azure Machine Learning and other Azure services.Development tools and editors RStudio, Py.Charm, Intelli. J, Emacs, gedit, viDoing data science involves iterating on a sequence of tasks Finding, loading, and pre processing data.Building and testing models.Deploying the models for consumption in intelligent applications.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.The Linux Data Science Virtual Machine can ease this burden substantially.Use it to jump start your analytics project.It enables you to work on tasks in various languages, including R, Python, SQL, Java, and C.Eclipse provides an IDE to develop and test your code that is easy to use.The Azure SDK included in the VM allows you to build your applications by using various services on Linux for the Microsoft cloud platform.In addition, you have access to other languages like Ruby, Perl, PHP, and node.There are no software charges for this data science VM image.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.More details on the compute fees can be found on the VM listing page on the Azure Marketplace.Other Versions of the Data Science Virtual Machine.An Ubuntu image is also available, with many of the same tools as the Cent.OS image plus deep learning frameworks.A Windows image is available as well.Prerequisites. Before you can create a Linux Data Science Virtual Machine, you must have the following An Azure subscription To obtain one, see Get Azure free trial.An Azure storage account To create one, see Create an Azure storage account.Alternatively, if you do not want to use an existing account, the storage account can be created as part of the process of creating the VM.Create your Linux Data Science Virtual Machine.Here are the steps to create an instance of the Linux Data Science Virtual Machine Navigate to the virtual machine listing on the Azure portal.Click Create at the bottom to bring up the wizard.The following sections provide the inputs for each of the steps in the wizard enumerated on the right of the preceding figure used to create the Microsoft Data Science Virtual Machine.Here are the inputs needed to configure each of these steps a.Basics Name Name of your data science server you are creating.User Name First account sign in ID.Password First account password you can use SSH public key instead of password.Subscription If you have more than one subscription, select the one on which the machine is to be created and billed.You must have resource creation privileges for this subscription.Resource Group You can create a new one or use an existing group.Location Select the data center that is most appropriate.Usually it is the data center that has most of your data, or is closest to your physical location for fastest network access.Size Select one of the server types that meets your functional requirement and cost constraints.Select View All to see more choices of VM sizes.Settings Disk Type Choose Premium if you prefer a solid state drive SSD.Otherwise, choose Standard.Storage Account You can create a new Azure storage account in your subscription, or use an existing one in the same location that was chosen on the Basics step of the wizard.Other parameters In most cases, you just use the default values.To consider non default values, hover over the informational link for help on the specific fields.Summary Verify that all information you entered is correct.Buy To start the provisioning, click Buy.A link is provided to the terms of the transaction.The VM does not have any additional charges beyond the compute for the server size you chose in the Size step.The provisioning should take about 1.The status of the provisioning is displayed on the Azure portal.How to access the Linux Data Science Virtual Machine.After the VM is created, you can sign in to it by using SSH.Use the account credentials that you created in the Basics section of step 3 for the text shell interface.On Windows, you can download an SSH client tool like Putty.If you prefer a graphical desktop X Windows System, you can use X1.Putty or install the X2.Go client. Note. The X2.Go client performed significantly better than X1.We recommend using the X2.Go client for a graphical desktop interface.Installing and configuring X2.Go client. The Linux VM is already provisioned with X2.Go server and ready to accept client connections.To connect to the Linux VM graphical desktop, do the following on your client Download and install the X2.Go client for your client platform from X2.Go. Run the X2. Go client, and select New Session.It opens a configuration window with multiple tabs.Enter the following configuration parameters Session tab Host The host name or IP address of your Linux Data Science VM.Login User name on the Linux VM.SSH Port Leave it at 2.Session Type Change the value to XFCE.Currently the Linux VM only supports XFCE desktop.Media tab If you dont need to use sound support and client printing, you can turn them off.Shared folders If you want directories from your client machines mounted on the Linux VM, add the client machine directories that you want to share with the VM on this tab.After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2.Go client, you are ready to start using the tools that are installed and configured on the VM.On XFCE, you can see applications menu shortcuts and desktop icons for many of the tools.Microsoft R Server.R is one of the most popular languages for data analysis and machine learning.If you want to use R for your analytics, the VM has Microsoft R Server MRS with the Microsoft R Open MRO and Math Kernel Library MKL.The MKL optimizes math operations common in analytical algorithms.MRO is 1. 00 percent compatible with CRAN R, and any of the R libraries published in CRAN can be installed on the MRO.MRS gives you scaling and operationalization of R models into web services.You can edit your R programs in one of the default editors, like RStudio, vi, Emacs, or gedit.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.To launch R console, you just type R in the shell.This takes you to an interactive environment.To develop your R program, you typically use an editor like Emacs or vi or gedit, and then run the scripts within R.With RStudio, you have a full graphical IDE environment to develop your R program.There is also an R script for you to install the Top 2.R packages if you want.This script can be run after you are in the R interactive interface, which can be entered as mentioned by typing R in the shell.Python. For development using Python, Anaconda Python distribution 2.This distribution contains the base Python along with about 3.XML Signature Syntax and Processing Version 1.The general structure of an XML signature is described in.Signature Overview and Examples.This section provides detailed syntax of the core signature.Features described in this section are mandatory to.The syntax is defined via an.XMLSCHEMA 1XMLSCHEMA 2 with the following XML.Additional markup defined in version 1.The syntax is defined in an XML schema with the.The Signed. Info Element.The structure of Signed.Info includes the canonicalization.The. Signed. Info element may contain an optional ID attribute that will allow.Signed. Info does not include explicit signature or digest.If an application needs to associate properties with the signature or.Signature. Properties.Object element. Schema.Definition lt elementnameSigned.Infotypeds Signed.Info. Type lt complex.TypenameSigned. Info.Type lt sequence lt elementrefds Canonicalization.Method lt elementrefds Signature.Method lt elementrefds Referencemax.Occursunbounded lt sequence lt attributenameIdtypeIDuseoptional lt complex.Type 4. 4. 1 The Canonicalization.Method Element. Canonicalization.Method is a required element that specifies.Signed. Info element prior to performing signature calculations.This element uses the general structure for algorithms described in.Algorithm Identifiers and Implementation Requirements.Implementations MUST support the REQUIREDcanonicalization algorithms.Alternatives to the REQUIREDcanonicalization algorithms section 6.Canonical XML with Comments section.CRLF and charset.NOT REQUIRED. Consequently, their use may.XML Canonicalization and Syntax Constraint Considerations.Security issues may also arise in the treatment of entity.XML aware canonicalization algorithms are not.Only. What is Seen Should be Signed.The way in which the Signed.Info element is presented to the.The following applies to.XML as nodes or characters XML based canonicalization implementations MUST be provided.XPATH. node set originally formed from the document containing the.Signed. Info and currently indicating the.Signed. Info, its descendants, and the attribute and namespace.Signed. Info and its descendant elements.Text based canonicalization algorithms such as CRLF and charset.UTF 8 octets that represent the.Signed. Info element, from the first.XML representation, inclusive.This includes the entire.Signed. Info. element as well as all.Use of text based canonicalization of.Signed. Info is NOT RECOMMENDED.We recommend applications that implement a text based instead of XML based.XML as their output serialization so as to mitigate.For instance, such an implementation.SHOULD at least generate.XML. instances XML1.Note The signature.Canonicalization.Method. For example, the canonicalization method could.URIs of the References being validated.Or, the. method could massively transform Signed.Info so that validation.Since. Canonicalization.Method is inside.Signed. Info, in the resulting canonical form it could erase itself.Signed. Info or modify the.Signed. Info element so that it appears that a different.Thus a. Signature which appears to authenticate the desired data with the.Digest. Method, and.Signature. Method, can be meaningless if a capricious.Canonicalization.Method is used. Schema.Definition lt elementnameCanonicalization.Methodtypeds Canonicalization.Method. Type lt complex.TypenameCanonicalization.Method. Typemixedtrue lt sequence lt anynamespaceanymin.Occurs0max. Occursunbounded lt 0,unbounded elements from 1,1 namespace lt sequence lt attributenameAlgorithmtypeany.URIuserequired lt complex.Type 4. 4. 2 The Signature.Method Element. Signature.Method is a required element that specifies the.This algorithm. identifies all cryptographic functions involved in the signature operation.MACs, padding, etc.This element uses.Algorithm Identifiers and Implementation Requirements.While there is a single identifier, that identifier may.Schema. Definition lt elementnameSignature.Methodtypeds Signature.Method. Type lt complex.TypenameSignature.Method. Typemixedtrue lt sequence lt elementnameHMACOutput.Lengthmin. Occurs0typeds HMACOutput.Length. Type lt anynamespaceothermin.Occurs0max. Occursunbounded lt 0,unbounded elements from 1,1 external namespace lt sequence lt attributenameAlgorithmtypeany.URIuserequired lt complex.Type The ds HMACOutput.Length parameter is used for HMAC HMAC algorithms.The. parameter specifies a truncation length in bits.If this parameter is trusted without further.CVE 2. 00. 9 0. Signatures MUST be deemed invalid if the truncation length is below.Note that some implementations are known to not.The Reference Element.Reference is an element that may occur one or more times.It. specifies a digest algorithm and digest value, and optionally an identifier of.The identification URI and transforms.The Type attribute facilitates the processing of.For example, while this specification makes no requirements.Manifest. An optional ID attribute permits a.Reference to be referenced from elsewhere.Schema. Definition lt elementnameReferencetypeds Reference.Type lt complex. TypenameReference.Type lt sequence lt elementrefds Transformsmin.Occurs0 lt elementrefds Digest.Method lt elementrefds Digest.Value lt sequence lt attributenameIdtypeIDuseoptional lt attributenameURItypeany.URIuseoptional lt attributenameTypetypeany.URIuseoptional lt complex.Type 4. 4. 3. The URI Attribute.The URI attribute identifies a data object using a.URI Reference URI.The mapping from this attributes value to a URI reference MUST be.XMLSCHEMA 2. Additionally Some existing implementations are known to verify the value of.URI attribute against the grammar in URI.It is therefore safest to perform any necessary escaping while generating the.URI attribute. We RECOMMEND XML Signature applications be able to dereference URIs in the.HTTP scheme. Dereferencing a URI in the HTTP scheme MUST comply with the Status Code Definitions of HTTP1.Applications should.HTTP cookies, HTML device profiles or content negotiation, may.URI. If a resource is identified by more than one URI, the most specific should.See. section 3. 2 Core Validation for further information on reference processing.If the URI attribute is omitted altogether, the receiving.For example, a. lightweight data protocol might omit this attribute given the identity of the.This attribute may be omitted from.Reference in any particular.Signed. Info, or Manifest.The optional Type attribute contains information about the type of object.Reference. transforms have been applied.This is represented as a URI.For example Typehttp www.ObjectTypehttp www.ManifestThe Type attribute applies to the item being pointed.For example, a reference that results in the digesting of an Object.Signature. Properties element is still of type.Object. The Type attribute is advisory.No validation of the.The Reference Processing Model.The data type of the result of URI dereferencing or subsequent Transforms.XPath node set. The Transforms specified in this document are defined with.The following is the default signature.If the data object is an octet stream and the next transform requires a.MUST attempt to parse the octets.XML1. 0. well formed processing.If the data object is a node set and the next transform requires octets.MUST attempt to convert the node set to an octet.Canonical XML XML C1.N. Users may specify alternative transforms that override these defaults in.The final octet. stream contains the data octets being secured.The digest algorithm specified.Digest. Method is then applied to these data octets, resulting in. Call Logging Software For Pbx Type . Digest. Value. Note The.Reference Generation.In this specification, a same document reference is defined as a.URI Reference that consists of a hash sign followed by a fragment or.URI URI. Unless the URI Reference is such a same document reference, the result.URI Reference MUST be an octet stream.In particular, an.XML document identified by URI is not parsed by the signature application.
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