SPARKMLLIB
- Spark Mllib
About
MLlib is Spark’s machine learning library, focusing on learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
We are launching a product which will configure and publish Spark MLlib, an open source software solution which is embedded pre-configured tool with Ubuntu OS and ready-to-launch VM on Azure that contains Spark MBlib, Hadoop 2.7, Scala, Linux, PHP (LAMP).
MLlib fits into Spark’s APIs and interoperates with Scala. You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.
Why MLlib? It is built on Apache Spark, which is a fast and general engine for large scale processing. Supposedly, running times or up to 100x faster than Hadoop MapReduce, or 10x faster on disk. Supports writing applications in Java, Scala, or Python.
MLlib contains many algorithms and utilities
Classification: logistic regression, naive Bayes
Regression: generalized linear regression, survival regression
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs)
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
MLlib will still support the RDD-based API in spark.mllib with bug fixes.
MLlib will not add new features to the RDD-based API.
In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
The RDD-based API is expected to be removed in Spark 3.0.
DataFrames provide a more user-friendly API than RDDs. The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.
The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the Pipelines guide for details.
Data types
Classification and regression
Collaborative filtering
Clustering
Dimensionality reduction
Feature extraction and transformation
- Type virtual machines in the search.
- Under Services, select Virtual machines.
- In the Virtual machines page, select Add. The Create a virtual machine page opens.
- In the Basics tab, under Project details, make sure the correct subscription is selected and then choose to Create new resource group. Type myResourceGroup for the name.*.
- Under Instance details, type myVM for the Virtual machine name, choose East US for your Region, and choose Ubuntu 18.04 LTS for your Image. Leave the other defaults.
- Under Administrator account, select SSH public key, type your user name, then paste in your public key. Remove any leading or trailing white space in your public key.
- Under Inbound port rules > Public inbound ports, choose Allow selected ports and then select SSH (22) and HTTP (80) from the drop-down.
- Leave the remaining defaults and then select the Review + create button at the bottom of the page.
- On the Create a virtual machine page, you can see the details about the VM you are about to create. When you are ready, select Create.
It will take a few minutes for your VM to be deployed. When the deployment is finished, move on to the next section.
Connect to virtual machine
Create an SSH connection with the VM.
- Select the Connect button on the overview page for your VM.
- In the Connect to virtual machine page, keep the default options to connect by IP address over port 22. In Login using VM local account a connection command is shown. Select the button to copy the command. The following example shows what the SSH connection command looks like:
bashCopy
ssh azureuser@10.111.12.123
- Using the same bash shell you used to create your SSH key pair (you can reopen the Cloud Shell by selecting >_ again or going to https://shell.azure.com/bash), paste the SSH connection command into the shell to create an SSH session.
Usage / Deployment Instruction
Step 1: Access the Spark in Azure Marketplace and click ON Get it now button.
Click on Get it now and then on create;
Click on Create;
Step 2: In the Create a virtual machine, enter or select appropriate values for zone, machine type, resource group and so on as per your choice.
Click on review+create then on create;
Once Deployment is complete, follow the Below Steps;
Step 1: Open Putty for SSH
Step 2: Open Putty and Type <instanceID>> at “Host Name”
Step 3: Open Conncetion->SSH->Auth tab from Left Side Area
Step 4: Click on browse button and select ppk file for Instance and then click on Open
Step 5: Type “ubuntu” as user name Password auto taken from PPK file
Step 6: Use following Linux command to start Hadoop
Step 6.1: sudo vi /etc/hosts
Take the Private Ip address from your machine as per the below screenshot and then replace the second line of your command screen with that Private ip address
Step 6.2: ssh-keygen -t rsa -P “”
This command is used to generate the ssh key.
Step 6.3: cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys
This command is used to move the generated ssh key to the desired location
Step 6.4: ssh localhost
Step 6.5: hdfs namenode –format
You have to write “yes” when it prompts you – Are you sure you want to continue?
Step 6.6: start-all.sh
Step 6.7: After the above command executes successfully, you should check the below urls in the browser –
http://<instanceId>:8088
http://<instanceId>:50070
http://<instanceId>:50090
Step 7: Use following Linux command to start Scala and Spark
Step 7.1: cd spark-2.1.0/
Step 7.2: ./bin/spark-shell
Step 7.3: You can check the spark by going on to the following url in your browser –
http://<instanceId>:4040
Step 7.4: Now you can execute your scala programs as below –
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The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.
Hadoop, as a scalable system for parallel data processing, is useful for analyzing large data sets. Examples are search algorithms, market risk analysis, data mining on online retail data, and analytics on user behavior data.
Add the words “information security” (or “cybersecurity” if you like) before the term “data sets” in the definition above. Security and IT operations tools spit out an avalanche of data like logs, events, packets, flow data, asset data, configuration data, and assortment of other things on a daily basis. Security professionals need to be able to access and analyze this data in real-time in order to mitigate risk, detect incidents, and respond to breaches. These tasks have come to the point where they are “difficult to process using on-hand data management tools or traditional (security) data processing applications.”
Until now, small developers did not have the capital to acquire massive compute resources and ensure they had the capacity they needed to handle unexpected spikes in load. Amazon EC2 enables any developer to leverage Amazon’s own benefits of massive scale with no up-front investment or performance compromises. Developers are now free to innovate knowing that no matter how successful their businesses become, it will be inexpensive and simple to ensure they have the compute capacity they need to meet their business requirements.
The “Elastic” nature of the service allows developers to instantly scale to meet spikes in traffic or demand. When computing requirements unexpectedly change (up or down), Amazon EC2 can instantly respond, meaning that developers have the ability to control how many resources are in use at any given point in time. In contrast, traditional hosting services generally provide a fixed number of resources for a fixed amount of time, meaning that users have a limited ability to easily respond when their usage is rapidly changing, unpredictable, or is known to experience large peaks at various intervals.
Traditional hosting services generally provide a pre-configured resource for a fixed amount of time and at a predetermined cost. Amazon EC2 differs fundamentally in the flexibility, control and significant cost savings it offers developers, allowing them to treat Amazon EC2 as their own personal data center with the benefit of Amazon.com’s robust infrastructure.
When computing requirements unexpectedly change (up or down), Amazon EC2 can instantly respond, meaning that developers have the ability to control how many resources are in use at any given point in time. In contrast, traditional hosting services generally provide a fixed number of resources for a fixed amount of time, meaning that users have a limited ability to easily respond when their usage is rapidly changing, unpredictable, or is known to experience large peaks at various intervals.
Secondly, many hosting services don’t provide full control over the compute resources being provided. Using Amazon EC2, developers can choose not only to initiate or shut down instances at any time, they can completely customize the configuration of their instances to suit their needs – and change it at any time. Most hosting services cater more towards groups of users with similar system requirements, and so offer limited ability to change these.
Finally, with Amazon EC2 developers enjoy the benefit of paying only for their actual resource consumption – and at very low rates. Most hosting services require users to pay a fixed, up-front fee irrespective of their actual computing power used, and so users risk overbuying resources to compensate for the inability to quickly scale up resources within a short time frame.
No. You do not need an Elastic IP address for all your instances. By default, every instance comes with a private IP address and an internet routable public IP address. The private address is associated exclusively with the instance and is only returned to Amazon EC2 when the instance is stopped or terminated. The public address is associated exclusively with the instance until it is stopped, terminated or replaced with an Elastic IP address. These IP addresses should be adequate for many applications where you do not need a long lived internet routable end point. Compute clusters, web crawling, and backend services are all examples of applications that typically do not require Elastic IP addresses.
You have complete control over the visibility of your systems. The Amazon EC2 security systems allow you to place your running instances into arbitrary groups of your choice. Using the web services interface, you can then specify which groups may communicate with which other groups, and also which IP subnets on the Internet may talk to which groups. This allows you to control access to your instances in our highly dynamic environment. Of course, you should also secure your instance as you would any other server.
The Hadoop JDBC driver can be used to pull data out of Hadoop and then use the DataDirect JDBC Driver to bulk load the data into Oracle, DB2, SQL Server, Sybase, and other relational databases.
Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.
Highlights
- Data types, Classification, regression and Collaborative filtering
- It provides Clustering, Dimensionality reduction, Feature extraction and transformation
- It provides logistic regression, naive Bayes, Decision trees, random forests, and gradient-boosted trees
Application Installed
