#sparkML

[govindhtech]

Micron DDR5 128GB Memory And AMD EPYC 128-Core CPUs

Important lessons learned

A potent solution for AI and database infrastructures is provided by Micron DDR5 128GB memory and 5th Gen AMD EPYC CPUs with 128 cores. The computational complexity of huge model sizes and extensive datasets, which are features of AI, machine learning, data analytics, and IMDB applications, is successfully managed by this technique.SVM 1.3x Improves AI/ML support vector machine (SVM) performance by 1.3x.1.3x higher bandwidth use due to higher memory clock speeds on Micron 128GB RDIMMs, along with increased core counts on 5th Gen AMD EPYC processors.IMDB REDIS 1.2x Improves IMDB Redis performance by 1.2x for a set:get ratio of 1:10.30% Delivers 30% better average latency and 60% better p99 latency.SAP SD 201k users Improves SAP Sales and Distribution (SAP SD) benchmark. With 30% higher memory capacity and 30% higher clock, SAP SD scores higher than previous six-socket performance score.

Pairing Micron 128GB DDR5 RDIMMs with 5th Gen AMD EPYC processors (codenamed Turin).

As compared to Micron 96GB DDR5 RDIMMs and 4th Gen AMD EPYC processors with 96 cores (codenamed Genoa).

High-capacity memory and substantial processing power are necessary for modern data centers to support enterprise machine learning (ML) and artificial intelligence (AI) programs by running a range of workloads. Micron DDR5 128GB RDIMMs and 5th Gen AMD EPYC processors work together to provide exceptional performance and capabilities for a variety of server workloads that data centers handle, such as hosting demanding corporate applications and powering expansive cloud-based infrastructures.

In this blog, Micron present the outcomes of benchmark tests for Redis in-memory database (IMDB), SAP SD, and AI/ML support vector machine (SVM), where Its hardware configuration contrasted the following:

5th Gen AMD EPYC processors (codenamed Turin) with Micron DDR5 128GB DIMMs

With 4th Gen AMD EPYC processors (codenamed Genoa), Micron DDR5 96GB DIMMs

Its testing demonstrates that Micron DDR5 RDIMMs with increased capacity (128GB) and bandwidth (capable up to 8000 MT/s) improve performance for SVM, SAP SD, and Redis IMDB.

Hardware and system setup

The table below displays the specifics of the system architecture. Two systems, designated A and B in this blog, were compared. System A has Micron 96GB DDR5 DIMMs with 4th Gen AMD EPYC CPUs (96 cores), while System B had 128GB and 5th Gen. Both systems support a 12GB/core configuration; the 96-core CPU has 96GB across 12 memory channels, while the 128-core CPU has 128GB. System ASystem BHardware4th Gen AMD EPYC processors (codenamed Genoa)5th Gen AMD EPYC processors (codenamed Turin)MemoryMicron 96GB DDR5 4800 MT/sDual rank, 12 channelsMicron 128GB DDR5 6400 MT/sDual rank, 12 channelsCPUDual-socket AMD EPYC 9654 (96-core)Dual-socket AMD EPYC (128-core)Storage (for SVM)Micron 9400 8TB (3)Micron 9400 8TB (3)

Support Vector Machine in AI/ML

SVM is a kind of machine learning technique that is frequently used to prepare datasets for a variety of cloud-based data science applications. Its processed a 2TB dataset using the SparkML engine and the Intel Hi-Bench architecture in tests.

Faster execution time

System B outperformed system A by 30% in terms of SVM execution time. This is mostly because system B’s processor has more cores, the 128GB memory modules offer more capacity and bandwidth, and the bandwidth is used efficiently.

Higher bandwidth use

Because of the faster memory rate (6400 MT/s vs. 4800 MT/s) and the extra Zen5 cores made possible by the 5th Gen AMD EPYC processors with 128 cores, data indicate that SVM on system B uses 1.3 times more bandwidth than system A.

The SVM can keep more data in memory because to system B’s larger capacity (128GB vs. 96GB), which reduces storage input/output. For both setups, maintained a consistent memory capacity of 12GB per core. Compared to the baseline configuration (system A), this method allowed us to separate the effects of more computation capacity and faster clock speed (memory).

Redis

Applications that need minimal latency can store and retrieve data using Redis, a fast in-memory database (IMBD). Memtier simulates a multithreaded and multiclient execution model by benchmarking Redis with multiple set:get operations.

Running Redis on system B (128GB and 128 cores) results in a 1.2x speedup. Additionally, the identical combination reduces p99 latency by 60% and average latency by 30%. Higher core counts, such as the 5th Gen’s 128 cores, can better utilize the Micron DDR5 128GB DIMMs’ greater capacity and bandwidth than earlier AMD EPYC CPU generations. Enterprise data centers can easily serve more users with the increased throughput made possible by the additional cores.

SAP Sales and Distribution (SAP SD)

An enterprise resource planning (ERP) software suite that is frequently used is called Systems Application and Products (SAP). As a component of the SAP ecosystem, it is composed of several smaller parts. Setting a new performance world record of 201,000 users for the SAP SD benchmark on a two-socket system, the component that includes all of the operations and processes for SAP Sales and Distribution (SAP SD) was benchmarked against the Dell PowerEdge R6725 server and outfitted with Micron DDR5 128GB RDIMMs and 5th Gen AMD EPYC processors.

That surpasses the top six-socket score. The increased number of benchmark users suggests that using Micron memory in conjunction with 5th Gen AMD EPYC CPUs on Dell PowerEdge servers for database use cases offers a performance advantage.

AI in data centers

For data center infrastructures to efficiently manage the computational complexity, massive model sizes, and extensive datasets typical of AI, machine learning, data analytics, and IMDB applications, high-capacity memory, high memory bandwidth, and low latency are essential. Micron workload findings demonstrate that Micron DDR5 128GB memory modules combined with 5th Gen AMD EPYC processors provide a potent solution for these situations.

Read more on govindhtech.com

[codefiy]

WEEK 2:  SparkML

1) Select the best definition of a machine learning system. -> A machine learning system applies a specific machine learning algorithm to train data models. After training the model, the system infers or “predicts” results on previously unseen data. 2) Which of the following options are true about Spark ML inbuilt utilities? -> Spark ML inbuilt utilities includes a linear algebra package. ->…

[biblempreg]

went insane and bought a pink sparkly dress for my sister's wedding

[edgartechtumbsup]

How to train, deploy and develop TensorFlow AI Models, SparkML from Jupyter Notebook to production

How to train, deploy and develop TensorFlow AI Models, SparkML from Jupyter Notebook to production

  Today I would like to post a more technical and pure engineering topic. The heart of the matter in Artificial Intelligence(AI) is more practical/empirical based than theoretical. Even though the conceptual framework is undoubtedly important. But to get a good grasp of the real work involved in setting up all the apparatus for a machine learning/deep learning and AI model or project we need to…

[blogdasuma-blog]

What to Expect for Big Data and Apache Spark in 2017

New Post has been published on http://dasuma.es/es/expect-big-data-apache-spark-2017/

What to Expect for Big Data and Apache Spark in 2017

Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.

This talk was originally presented at Spark Summit East 2017.

You can view the slides on Slideshare: http://www.slideshare.net/databricks/…

[spaceepigeon]

call put post for @rhovers , won’t let me conduct important scientific research

[excelrtuhin]

Knowledge Science Course

Within the current day quick paced world, a lot development has been made within the subject of technology. The data scientists are involved within the root degree the place they work on the database to obtain data and contribute in developing the product. IT Professionals willing to make a shift in their profession path can opt for Knowledge Science since it promises a better future with its phenomenal progress within the Indian IT Sector. Majority of the people in India wish to pursue knowledge science certification course attributable to it is gaining recognition.

It took me a lot of time to zero in on one particular organization for data science coaching. This course is meant for graduates who've information of math, statistics, and programming information. The Laqshya Institute's Information Science classroom program is a 3-month program for fresher and professionals as nicely. This system appears for college kids who have a clear historical past of superior lecturers (GPA, class standing, courses taken), in addition to a distinguished historical past of exhibiting leadership and community involvement.

You will have all of the information at your disposal to method and deal with various kinds of data science problems. ninety six% of Dataquest college students say they'd recommend Dataquest for career advancement. Be taught the concepts of information science from trade consultants and give a tempo to your career. Professional information scientists are adept at presenting their findings to the rest of their groups.

If you are new within the area of data science, this is one of the best course to start out with. The institute provides high quality and project-primarily based coaching for hundreds of scholars and help them get their dream job with their placement program. The award-winning openSAP platform supplies massive open on-line courses (MOOCs) to anybody interested by studying about main applied sciences, the most recent improvements and the digital financial system.

Now, we can discuss some vital causes that spotlight the benefits of learning Python. Multisoft Digital Academy one of many world's main Coaching and Certification Organization handled On-line, Classroom, Company and Bootcamp Training Applications. With the assistance of Information Science, companies can recommend those products to their clients that they might have some curiosity in. As a result, it could help them make better choices.

Trade Knowledge - Last, but not least, this is maybe some of the vital expertise. Koenig answer provide Data Science Profession Enabler, Information Science Expert Boot Camp, Microsoft Data Science, Knowledge Science with R, Information Science with SparkML and more. On the platform, additionally, you will discover programs on machine learning or massive studying , large data, apache spark and the different programming languages.

There are many explanation why taking the blockchain training will assist the scholars to get excessive wage jobs, for data safety, digital identity, and various other reasons. Huge variety of profession alternatives are available for Information Science professionals since they're required in several areas comparable to Data Analytics, Information Research, Large Knowledge Management and lots of more.

click here to know more details about data science course in mumbai 

[sigmaratings]

A Practical Intro to using Spark-NLP BERT word embeddings

Leveraging Google’s Bert via Pyspark

The seemingly endless possibilities of Natural Language Processing are limited only by your imagination... and compute power.  What good are ground breaking word vectors if it takes days to preprocess your data and train a model?  Or maybe you already know PySpark, but don't know Scala. Well let’s explore combining PySpark, Spark ML, and Spark-NLP. The assumption throughout the rest of this post is that you have some familiarity with Spark and SparkML. 

What are Word vectors and why is BERT a big deal? 

Word vectors (or embeddings) are words that are mapped to a vector of numbers. There are several approaches to representing words as vectors. Good vectorization approaches create vectors where similar words have similar vectors. This allows for nearest neighbor searches as well as something known as word vector algebra. The classic word vector algebra example is that in many embeddings vector(“King”) - vector(“man”) + vector(“woman") = vector(“queen”). Pre-trained vectors can also be used as features in machine learning models, transferring the learning to another domain.

Bidirectional Encoder Representations from Transformer (BERT) are state of the art word vectors developed and researched by Google in 2019. 

Bidirectional → left to right and right to left contexts of words

Encoder → encodes data to a vector

Representations → a vector of real numbers from

Transformer → novel model architecture

One of the main advantages of techniques such as BERT, or an earlier similar technique ELMo, is that the vector of a word changes depending on how it is used in a sentence. This allows for much richer meanings of embedded words. 

Using Spark-NLP With Pyspark

Check you dependencies and make sure distributions match.  For this tutorial, we're using spark-nlp:2.2.1.  The PyPi distribution should be 2.2.1 and the maven distribution should be 2.2.1. If they don't match, you'll get unexpected behavior. 

Pretrained pipelines offer general nlp functionality out of box. 

https://gist.github.com/cd48ff2b261c8889f88f2467569debbb

You can also create your own pipelines using both SparkML and Spark-NLP transformers. 

https://gist.github.com/476242b479eb3bd70f6f3d44b2349322

DocumentAssembler → A transformer to get raw data, text, to an annotator for processing

Tokenizer → An Annotator that identifies tokens

BertEmbeddings → An annotator that outputs BERT word embeddings

Spark nlp supports a lot of annotators. Check them out here. There's also a number of different models you can use if you want to use GloVe vectors, models trained in other languages or even a multi-language BERT model.

Combining Spark-NLP with SparkML

Let’s demonstrate a simple example of using document vectors from a piece of text to write a classifier. Our process will be to:

Average all the vectors in a text into one document vector

Convert that vector to a DenseVector that SparkML can train against

Train a Logistic regression model

https://gist.github.com/12b457f3d941a8f5e9923d0ac5917366

So in just a few steps we’ve managed to write a classifier using state of the art word embeddings. We’ve also managed to do it in a way that can scale to billions of data points, because we’ve used Spark. At Sigma, we’re using the power of word embeddings and spark to build advance topic models used to extract incredible insights from news and social media.

[craigbrownphd-blog-blog]

Dear Spark developers: Welcome to Azure Cognitive Services

#ICYDK: Today at Spark AI Summit 2019, we're excited to introduce a new set of models in the SparkML ecosystem that make it easy to leverage the Azure Cognitive Services at terabyte scales. http://bit.ly/2ZoO8KA

[freetutorialstack-blog]

Apache Spark for Java Developers

Description

Get began with the superb Apache Spark parallel computing framework - this course is designed particularly for Java Builders. In the event you're new to Knowledge Science and need to discover out about how large datasets are processed in parallel, then the Java API for spark is a good way to get began, quick. The entire fundamentals it's good to perceive the primary operations you may carry out in Spark Core, SparkSQL and Data Frames are coated intimately, with simple to observe examples. You can observe together with all the examples, and run them by yourself native improvement laptop. Included with the course is a module masking SparkML, an thrilling addition to Spark that lets you apply Machine Studying fashions to your Huge Knowledge! No mathematical expertise is important! And at last, there is a full three hour module masking Spark Streaming, the place you're going to get hands-on expertise of integrating Spark with Apache Kafka to deal with real-time massive information streams. We use each the DStream and the Structured Streaming APIs. Optionally, if in case you have an AWS account, you will see easy methods to deploy your work to a dwell EMR (Elastic Map Scale back) cluster. In the event you're not aware of AWS you may skip this video, nevertheless it's nonetheless worthwhile to observe fairly than following together with the coding. You will be going deep into the internals of Spark and you will learn the way it optimizes your execution plans. We'll be evaluating the efficiency of RDDs vs SparkSQL, and you will study in regards to the main efficiency pitfalls which may save some huge cash for dwell tasks. All through the course, you will be getting some nice observe with Java eight Lambdas - a good way to study functional-style Java in the event you're new to it. NOTE: Java eight is required for the course. Spark doesn't at present help Java9+ (we are going to replace when this modifications) and Java eight is required for the lambda syntax. Read the full article

[un-enfant-immature]

IBM launches cloud tool to detect AI bias and explain automated decisions

IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.

The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.

It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.

The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.

It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.

Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.

IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.

For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.

The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.

However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.

Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.

So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.

And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)

In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.

“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”

[myfreecourses]

Apache Spark For Java Developers

Apache Spark For Java Developers

Get processing Big Data using RDDs, DataFrames, SparkSQL and Machine Learning – and real time streaming with Kafka!

What you’ll learn

Use functional style Java to define complex data processing jobs

Learn the differences between the RDD and DataFrame APIs

Use an SQL style syntax to produce reports against Big Data sets

Use Machine Learning Algorithms with Big Data and SparkML

Connect…

[omarkhanca]

machine learning > DataHack Summit 2018: Workshop on Machine Learning using SparkML for Big Data | 2018-11-08T07:25:11.000Z

[thegloober]

IBM launches IBM AI OpenScale to combat AI sprawl as part of broad open strategy

IBM launched IBM AI OpenScale, a platform designed to help enterprises build, run, manage and operate artificial intelligence applications.

The launch of AI OpenScale is part of IBM’s ongoing effort to become a management plane for AI and add transparency to so-called black box approaches. If AI sprawl isn’t here today it soon will be and enterprises are likely to have a management headache ahead. IBM has been pushing for more AI transparency and tools that allow data scientists as well as business executives find flaws in models.

For IBM, AI OpenScale is one front of a multi-pronged strategy to position its wares as being more open and serve as an integrator for data, multiple clouds and security analysis. IBM’s big message: Serving as an agnostic technology provider can better help enterprises.

Beth Smith, general manager of Watson Data and AI at IBM, said AI today is a mesh of tools, models and frameworks. “People use a variety of tools. Some are roll your own,” said Smith, who added that IBM AI OpenScale is an interoperable system that can support AI implementations.

The AI, machine learning, and data science conundrum: Who will manage the algorithms? | IBM launches tools to detect AI fairness, bias and open sources some code

IBM’s AI OpenScale platform, which will be available later this year on IBM Cloud and IBM Cloud Private, will operate AI applications and debug them for things like bias wherever they were built or currently run. The platform will support frameworks such as Watson, Tensorflow, Keras, SparkML, AWS SageMaker, Azure ML and others.

Part of the AI OpenScale launch includes NeuNetS, which is a system that can automate and build AI. Smith noted that NeuNetS can save data scientists time and can narrow an enterprise skills gap.

IBM AI OpenScale is designed to explain how AI applications reach decisions, provide an audit trail and ensure AI models are fair at runtime.

Here are some points and examples of how AI OpenScale would work in practice:

AI OpenScale manages and optimizes AI applications, but data scientists would build models in the framework of their choice.

However, IBM AI OpenScale would automate many items in the AI development process. For instance, de-biasing would be automated. “AI OpenScale would bring fairness to an attribute in a model and does it in a way that doesn’t alter the base model,” said Smith.

AI OpenScale would leave the original model alone, but de-bias it with a new auto-generated model.

NeuNetS would be used to fine tune AI and models and could speed up development time by months. NeuNetS has been in use at IBM for “several months,” said Smith, who said the service would start out as a beta within the platform.

For IBM AI OpenScale to work within an enterprise the client would have to be able to point Big Blue to a direct end point where the AI black box resides. IBM wouldn’t be able to manage embedded AI in another application. For instance, Salesforce’s Einstein couldn’t be accessed from AI OpenScale, but the CRM giant could use IBM’s platform to manage its models that it embeds into applications.

AI: The view from the Chief Data Science Office | Primers: What is AI? Everything you need to know about Artificial Intelligence | Machine learning? | Deep learning? |Artificial general intelligence?

Separately, IBM launched Multi-cloud Manager, an operations platform based on Kubernetes containers to manage public and hybrid cloud deployments.

The console from IBM is optimized on IBM Cloud, but can integrate and manage workloads on clouds from the likes of Amazon Web Services, Red Hat and Microsoft. The Multi-cloud Manager runs on IBM Cloud Private.

According to Big Blue, the differentiator for its cloud management console is that it’s based on open standards to manage data and apps across clouds.

Multi-cloud Manager, available this month, includes:

The ability to interconnect different clouds, unify systems and automate operations.

A dashboard to manage thousands of Kubernetes applications and data where its located.

An integrated compliance and rules engine for enterprise policies and security standards.

Automation to define how, where and when Kubernetes applications are deployed and how they are backed up.

In addition, IBM outlined IBM Security Connect, an open platform that aims to aggregate and analyze federated security data across multiple systems and environments.

IBM Security Connect is based on machine learning and AI.

According to IBM, Security Connect will open its framework, micro services, software developer kits and application programming interfaces for integration and development. IBM Security Connect will house the company’s current Security App Exchange and all of its security applications.

The company also committed to using existing open security and protocol standards. IBM Security Connect will be available in the first quarter of 2019.

Source: https://bloghyped.com/ibm-launches-ibm-ai-openscale-to-combat-ai-sprawl-as-part-of-broad-open-strategy/

[-h]

IBM推出了一项软件服务，可以扫描人工智能系统的工作情况，以便检测偏见并为正在做出的自动决策提供解释。 一定程度的透明度可能是合规目的所必需的，而不仅仅是公司自己的尽职调查。 新的信任和透明度系统运行在IBM云上，并与从IBM作为各种流行的机器学习框架和AI构建环境所构建的模型一兼容，包括自己的Watson技术，以及Tensorflow、SparkML、AWS SageMaker和AzureML。 它表示，可以通过编程来定制服务以满足特定的组织需求，以考虑“任何业务工作流程的独特决策因素”。 完全自动化的SaaS解释了决策并在运行时检测AI模型中的偏见，这意味着它正在捕获“发生时可能产生的不公平结果”。 它还会自动建议添加到模型中的数据，以帮助减少已检测到的任何偏见。 人工智能决策的解释包括显示哪些因素加重了某个方向的决策、对建议的信心和信心背后的因素。 IBM还表示该软件会记录AI模型的准确性、性能和公平性，以及人工智能系统的系谱，这意味着它们可以“以客户服务、监管或合规原因轻松跟踪和召回”。

[craigbrownphd-blog-blog]

Dear Spark developers: Welcome to Azure Cognitive Services

#ICYMI: Today at Spark AI Summit 2019, we're excited to introduce a new set of models in the SparkML ecosystem that make it easy to leverage the Azure Cognitive Services at terabyte scales. http://bit.ly/2HerMFw