This document discusses scaling machine learning using Apache Spark. It covers several key topics:
1) Parallelizing machine learning algorithms and neural networks to distribute computation across clusters. This includes data, model, and parameter server parallelism.
2) Apache Spark's Resilient Distributed Datasets (RDDs) programming model which allows distributing data and computation across a cluster in a fault-tolerant manner.
3) Examples of very large neural networks trained on clusters, such as a Google face detection model using 1,000 servers and a IBM brain-inspired chip model using 262,144 CPUs.
What is Distributed Computing, Why we use Apache SparkAndy Petrella
In this talk we introduce the notion of distributed computing then we tackle the Spark advantages.
The Spark core content is very tiny because the whole explanation has been done live using a Spark Notebook (https://github.com/andypetrella/spark-notebook/blob/geek/conf/notebooks/Geek.snb).
This talk has been given together by @xtordoir and myself at the University of Liège, Belgium.
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
In this second part, we'll continue the Spark's review and introducing SparkSQL which allows to use data frames in Python, Java, and Scala; read and write data in a variety of structured formats; and query Big Data with SQL.
What is Distributed Computing, Why we use Apache SparkAndy Petrella
In this talk we introduce the notion of distributed computing then we tackle the Spark advantages.
The Spark core content is very tiny because the whole explanation has been done live using a Spark Notebook (https://github.com/andypetrella/spark-notebook/blob/geek/conf/notebooks/Geek.snb).
This talk has been given together by @xtordoir and myself at the University of Liège, Belgium.
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
In this second part, we'll continue the Spark's review and introducing SparkSQL which allows to use data frames in Python, Java, and Scala; read and write data in a variety of structured formats; and query Big Data with SQL.
Spark Based Distributed Deep Learning Framework For Big Data Applications Humoyun Ahmedov
Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, such as fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing, which can utilize cluster computing to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
Video to talk: https://www.youtube.com/watch?v=gd4Jqtyo7mM
Apache Spark is a next generation engine for large scale data processing built with Scala. This talk will first show how Spark takes advantage of Scala's function idioms to produce an expressive and intuitive API. You will learn about the design of Spark RDDs and the abstraction enables the Spark execution engine to be extended to support a wide variety of use cases(Spark SQL, Spark Streaming, MLib and GraphX). The Spark source will be be referenced to illustrate how these concepts are implemented with Scala.
http://www.meetup.com/Scala-Bay/events/209740892/
These are the slides for the Productionizing your Streaming Jobs webinar on 5/26/2016.
Apache Spark Streaming is one of the most popular stream processing framework that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. In this talk, we will focus on the following aspects of Spark streaming:
- Motivation and most common use cases for Spark Streaming
- Common design patterns that emerge from these use cases and tips to avoid common pitfalls while implementing these design patterns
- Performance Optimization Techniques
Strava Labs: Exploring a Billion Activity Dataset from Athletes with Apache S...Databricks
At Strava we have extensively leveraged Apache Spark to explore our data of over a billion activities, from tens of millions of athletes. This talk will be a survey of the more unique and exciting applications: A Global Heatmap gives a ~2 meter resolution density map of one billion runs, rides, and other activities consisting of three trillion GPS points from 17 billion miles of exercise data. The heatmap was rewritten from a non-scalable system into a highly scalable Spark job enabling great gains in speed, cost, and quality. Locally sensitive hashing for GPS traces was used to efficiently cluster 1 billion activities. Additional processes categorize and extract data from each cluster, such as names and statistics. Clustering gives an automated process to extract worldwide geographical patterns of athletes.
Applications include route discovery, recommendation systems, and detection of events and races. A coarse spatiotemporal index of all activity data is stored in Apache Cassandra. Spark streaming jobs maintain this index and compute all space-time intersections (“flybys”) of activities in this index. Intersecting activity pairs are then checked for spatiotemporal correlation, indicated by connected components in the graph of highly correlated pairs form “Group Activities”, creating a social graph of shared activities and workout partners. Data from several hundred thousand runners was used to build an improved model of the relationship between running difficulty and elevation gradient (Grade Adjusted Pace).
Horizontally Scalable Relational Databases with Spark: Spark Summit East talk...Spark Summit
Scaling out doesn’t have to mean giving up transactions and efficient joins! Relational databases can scale horizontally, and using them as a store for Spark Streaming or batch computations can help cover areas in which Spark is typically weaker. Examples will be drawn from our experience using Citus (https://github.com/citusdata/citus), an open-source extension to Postgres, but lessons learned should be applicable to many databases.
A very short set of slides to describe an RDD data structure.
Extracted from my 3-day course: www.sparkInternals.com
There is also a video of this on YouTube: http://youtu.be/odcEg515Ne8
Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. We will cover approaches of processing Big Data on Spark cluster for real time analytic, machine learning and iterative BI and also discuss the pros and cons of using Spark in Azure cloud.
Unsupervised learning refers to a branch of algorithms that try to find structure in unlabeled data. Clustering algorithms, for example, try to partition elements of a dataset into related groups. Dimensionality reduction algorithms search for a simpler representation of a dataset. Spark's MLLib module contains implementations of several unsupervised learning algorithms that scale to huge datasets. In this talk, we'll dive into uses and implementations of Spark's K-means clustering and Singular Value Decomposition (SVD).
Bio:
Sandy Ryza is an engineer on the data science team at Cloudera. He is a committer on Apache Hadoop and recently led Cloudera's Apache Spark development.
What is Mesos? How does it works? In the following slides we make an interesting review of this open-source software project to manage computer clusters.
Cassandra Tools and Distributed Administration (Jeffrey Berger, Knewton) | C*...DataStax
At Knewton we operate across five different VPCs a total of 29 clusters, each ranging from 3 nodes to 24 nodes. For a team of three to maintain this is not herculean, however good tools to diagnose issues and gather information in a distributed manner are vital to moving quickly and minimizing engineering time spent.
The database team at Knewton has been successfully using a combination of Ansible and custom open sourced tools to maintain and improve the Cassandra deployment at Knewton. I will be talking about several of these tools and giving examples of how we are using them. Specifically I will discuss the cassandra-tracing tool, which analyzes the contents of the system_traces keyspace, and the cassandra-stat tool, which gives real-time output of the operations of a cassandra cluster. Distributed administration with ad-hoc Ansible will also be covered and I will walk through examples of using these commands to identify and remediate clusterwide issues.
About the Speaker
Jeffrey Berger Lead Database Engineer, Knewton
Dr. Jeffrey Berger is currently the lead database engineer at Knewton, an education tech startup in NYC. He joined the tech scene in NYC in 2013 and spent two years working with MongoDB, becoming a certified MongoDB administrator and a MongoDB Master. He received his Cassandra Administrator certification at Cassandra Summit 2015. He holds a Ph.D. in Theoretical Physics from Penn State and spent several years working on high energy nuclear interactions.
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...Chester Chen
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
Spark Based Distributed Deep Learning Framework For Big Data Applications Humoyun Ahmedov
Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, such as fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing, which can utilize cluster computing to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
Video to talk: https://www.youtube.com/watch?v=gd4Jqtyo7mM
Apache Spark is a next generation engine for large scale data processing built with Scala. This talk will first show how Spark takes advantage of Scala's function idioms to produce an expressive and intuitive API. You will learn about the design of Spark RDDs and the abstraction enables the Spark execution engine to be extended to support a wide variety of use cases(Spark SQL, Spark Streaming, MLib and GraphX). The Spark source will be be referenced to illustrate how these concepts are implemented with Scala.
http://www.meetup.com/Scala-Bay/events/209740892/
These are the slides for the Productionizing your Streaming Jobs webinar on 5/26/2016.
Apache Spark Streaming is one of the most popular stream processing framework that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. In this talk, we will focus on the following aspects of Spark streaming:
- Motivation and most common use cases for Spark Streaming
- Common design patterns that emerge from these use cases and tips to avoid common pitfalls while implementing these design patterns
- Performance Optimization Techniques
Strava Labs: Exploring a Billion Activity Dataset from Athletes with Apache S...Databricks
At Strava we have extensively leveraged Apache Spark to explore our data of over a billion activities, from tens of millions of athletes. This talk will be a survey of the more unique and exciting applications: A Global Heatmap gives a ~2 meter resolution density map of one billion runs, rides, and other activities consisting of three trillion GPS points from 17 billion miles of exercise data. The heatmap was rewritten from a non-scalable system into a highly scalable Spark job enabling great gains in speed, cost, and quality. Locally sensitive hashing for GPS traces was used to efficiently cluster 1 billion activities. Additional processes categorize and extract data from each cluster, such as names and statistics. Clustering gives an automated process to extract worldwide geographical patterns of athletes.
Applications include route discovery, recommendation systems, and detection of events and races. A coarse spatiotemporal index of all activity data is stored in Apache Cassandra. Spark streaming jobs maintain this index and compute all space-time intersections (“flybys”) of activities in this index. Intersecting activity pairs are then checked for spatiotemporal correlation, indicated by connected components in the graph of highly correlated pairs form “Group Activities”, creating a social graph of shared activities and workout partners. Data from several hundred thousand runners was used to build an improved model of the relationship between running difficulty and elevation gradient (Grade Adjusted Pace).
Horizontally Scalable Relational Databases with Spark: Spark Summit East talk...Spark Summit
Scaling out doesn’t have to mean giving up transactions and efficient joins! Relational databases can scale horizontally, and using them as a store for Spark Streaming or batch computations can help cover areas in which Spark is typically weaker. Examples will be drawn from our experience using Citus (https://github.com/citusdata/citus), an open-source extension to Postgres, but lessons learned should be applicable to many databases.
A very short set of slides to describe an RDD data structure.
Extracted from my 3-day course: www.sparkInternals.com
There is also a video of this on YouTube: http://youtu.be/odcEg515Ne8
Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. We will cover approaches of processing Big Data on Spark cluster for real time analytic, machine learning and iterative BI and also discuss the pros and cons of using Spark in Azure cloud.
Unsupervised learning refers to a branch of algorithms that try to find structure in unlabeled data. Clustering algorithms, for example, try to partition elements of a dataset into related groups. Dimensionality reduction algorithms search for a simpler representation of a dataset. Spark's MLLib module contains implementations of several unsupervised learning algorithms that scale to huge datasets. In this talk, we'll dive into uses and implementations of Spark's K-means clustering and Singular Value Decomposition (SVD).
Bio:
Sandy Ryza is an engineer on the data science team at Cloudera. He is a committer on Apache Hadoop and recently led Cloudera's Apache Spark development.
What is Mesos? How does it works? In the following slides we make an interesting review of this open-source software project to manage computer clusters.
Cassandra Tools and Distributed Administration (Jeffrey Berger, Knewton) | C*...DataStax
At Knewton we operate across five different VPCs a total of 29 clusters, each ranging from 3 nodes to 24 nodes. For a team of three to maintain this is not herculean, however good tools to diagnose issues and gather information in a distributed manner are vital to moving quickly and minimizing engineering time spent.
The database team at Knewton has been successfully using a combination of Ansible and custom open sourced tools to maintain and improve the Cassandra deployment at Knewton. I will be talking about several of these tools and giving examples of how we are using them. Specifically I will discuss the cassandra-tracing tool, which analyzes the contents of the system_traces keyspace, and the cassandra-stat tool, which gives real-time output of the operations of a cassandra cluster. Distributed administration with ad-hoc Ansible will also be covered and I will walk through examples of using these commands to identify and remediate clusterwide issues.
About the Speaker
Jeffrey Berger Lead Database Engineer, Knewton
Dr. Jeffrey Berger is currently the lead database engineer at Knewton, an education tech startup in NYC. He joined the tech scene in NYC in 2013 and spent two years working with MongoDB, becoming a certified MongoDB administrator and a MongoDB Master. He received his Cassandra Administrator certification at Cassandra Summit 2015. He holds a Ph.D. in Theoretical Physics from Penn State and spent several years working on high energy nuclear interactions.
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...Chester Chen
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
Even though there have been a large number of proposals to accelerate databases using specialized hardware, often the opinion of the community is pessimistic: the performance and energy efficiency benefits of specialization are seen to be outweighed by the limitations of the proposed solutions and the additional complexity of including specialized hardware, such as field programmable gate arrays (FPGAs), in servers. Recently, however, as an effect of stagnating CPU performance, server architectures started to incorporate various programmable hardware and the availability of such components brings opportunities to databases. In the light of a shifting hardware landscape and emerging analytics workloads, it is time to revisit our stance on hardware acceleration. In this talk we highlight several challenges that have traditionally hindered the deployment of hardware acceleration in databases and explain how they have been alleviated or removed altogether by recent research results and the changing hardware landscape. We also highlight a new set of questions that emerge around deep integration of heterogeneous programmable hardware in tomorrow’s databases.
Building and operating HPC-based AI computing environment inside Gwangju Institute of Science and Technology
For using the part of the slide, you need to cite "Narantuya Jargalsaikhan, GIST AI-X Computing Cluster, 2021".
Thank you!
Fast data in times of crisis with GPU accelerated database QikkDB | Business ...Matej Misik
Graphics cards (GPU) open up new ways of processing and analytics over big data, showing millisecond selections over billions of lines, as well as telling stories about data. #QikkDB
How to present data to be understood by everyone? Data analysis is for scientists, but data storytelling is for everyone. For managers, product owners, sales teams, the general public. #TellStory
Learn about high performance computing with GPU and how to present data with a rich Covid-19 data story example on the upcoming webinar.
A Dataflow Processing Chip for Training Deep Neural Networksinside-BigData.com
In this deck from the Hot Chips conference, Chris Nicol from Wave Computing presents: A Dataflow Processing Chip for Training Deep Neural Networks.
Watch the video: https://wp.me/p3RLHQ-k6W
Learn more: https://wavecomp.ai/
and
http://www.hotchips.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe several techniques available in R to speed up workloads like these, by running multiple iterations simultaneously, in parallel.
Many of these techniques require the use of a cluster of machines running R, and I'll provide examples of using cloud-based services to provision clusters for parallel computations. In particular, I will describe how you can use the SparklyR package to distribute data manipulations using the dplyr syntax, on a cluster of servers provisioned in the Azure cloud.
Presented by David Smith at Data Day Texas in Austin, January 27 2018.
Strata Singapore: GearpumpReal time DAG-Processing with Akka at ScaleSean Zhong
Gearpump is a Akka based realtime streaming engine, it use Actor to model everything. It has super performance and flexibility. It has performance of 18000000 messages/second and latency of 8ms on a cluster of 4 machines.
Similar to Machine learning at Scale with Apache Spark (20)
How Disney+ uses fast data ubiquity to improve the customer experience Martin Zapletal
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability.
Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY
Customer experience at disney+ through data perspectiveMartin Zapletal
Disney+ has rapidly scaled to provide a personalized and seamless experience to tens of millions of customers. This experience is powered by a robust data platform that ingests, processes and surfaces billions of events per hour using Delta lake, Databricks, and AWS technologies. The data produced by the platform is used by multitude of services including a recommendation engine for personalized experience, optimizing watch experience including group watch, and fraud and abuse prevention. In this session, you will learn how Disney+ built these capabilities, the architecture, technologies, design principles, and technical details that make it possible.
Using observability, logs, metrics and traces as a data source for supervised and reinforcement machine learning techniques with a goal to optimize large scale systems.
Intelligent Distributed Systems OptimizationsMartin Zapletal
This talk discusses techniques for achieving optimized performance, availability, cost or other attributes of a distributed system. Firstly, the presentation introduces and in depth explains optimization techniques used in state of the art large scale stream and fast data processing frameworks such as Akka Streams, Spark or Flink, including logical and physical optimizations or code generation. Consequently, powerful optimization concepts applicable to general distributed systems, including systems built using Akka, are explained on examples. Finally, the presentation highlights the role of machine learning and artificial intelligence in the area and explains how machine generated data such as logs and metrics can be used to model, minimize, maximize or find the perfect balance of selected attributes of the system, demonstrated on examples from practice. The attendees will gain an understanding of the available optimization approaches, tradeoffs and the value of machine learning and intelligence and ultimately will be able to apply some of the techniques to optimize general distributed systems as well as streaming data processing systems built using Spark, Flink or Akka Streams.
Data in Motion: Streaming Static Data Efficiently 2Martin Zapletal
Updated version for SD Berlin 2016. Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.
Data in Motion: Streaming Static Data EfficientlyMartin Zapletal
Distributed streaming performance, consistency, reliable delivery, durability, optimisations, event time processing and other concepts discussed and explained on Akka Persistence and other examples.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
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3. Scaling computation
● Analytics tools with poor scalability and integration
● Manual processes
● Slow iterations
● Not suitable for large amounts of data
● We want fast iteration, reliability, integration
● Serial implementation
● Parallel
● GPUs
● Distributed
6. Artificial neural network
● Network training
○ Many “optimal” solutions
○ Optimization and training techniques - LBFGS,
Backpropagation, batch and online gradient
descent, Downpour SGD, Sandblaster LBFGS, …
○ Vanishing gradient, amplifying parameters, ...
○ New methods for large networks - deep learning
8. Scaling computation
● Different programming models, Different languages,
Different levels
● Sequential
○ R, Matlab, Python, Scala
● Parallel
○ Theano, Torch, Caffe, Tensor Flow, Deeplearning4j
Elapsed times for 20 PageRank iterations
[3, 4]
9. Machine learning
● Linear algebra
● Vectors, matrices, vector spaces, matrix transformations,
eigenvectors/values
● Many machine learning algorithms are optimization problems
● Goal is to solve them in reasonable (bounded) time
● Goal not always to find the best possible model (data size, feature
engineering vs. algorithm/model complexity)
● Goal is to solve them reliably, at scale, support application needs
and improve
[5]
11. Consistency, time and order in DS
● Sequential program always one total order of
operations
● No order guarantees in distributed system
● At-most-once. Messages may be lost.
● At-least-once. Messages may be duplicated but not
lost.
● Exactly-once.
12. Failure in distributed system
● Node failures, network partitions, message loss, split brains,
inconsistencies
● Microsoft's data centers average failure rate is 5.2 devices per day
and 40.8 links per day, with a median time to repair of approximately
five minutes (and a maximum of one week).
● Google new cluster over one year. Five times rack issues 40-80
machines seeing 50 percent packet loss. Eight network maintenance
events (four of which might cause ~30-minute random connectivity
losses). Three router failures (resulting in the need to pull traffic
immediately for an hour).
● CENIC 500 isolating network partitions with median 2.7 and 32
minutes; 95th percentile of 19.9 minutes and 3.7 days, respectively
for software and hardware problems
[6]
13. Failure in distributed system
● MongoDB separated primary from its 2 secondaries. 2 hours later the old
primary rejoined and rolled back everything on the new primary
● A network partition isolated the Redis primary from all secondaries. Every API
call caused the billing system to recharge customer credit cards automatically,
resulting in 1.1 percent of customers being overbilled over a period of 40
minutes.
● The partition caused inconsistency in the MySQL database. Because foreign key
relationships were not consistent, Github showed private repositories to the
wrong users' dashboards and incorrectly routed some newly created
repositories.
● For several seconds, Elasticsearch is happy to believe two nodes in the same
cluster are both primaries, will accept writes on both of those nodes, and later
discard the writes to one side.
● RabbitMQ lost ~35% of acknowledged writes under those conditions.
● Redis threw away 56% of the writes it told us succeeded.
● In Riak, last-write-wins resulted in dropping 30-70% of writes, even with the
strongest consistency settings
● MongoDB “strictly consistent” reads see stale versions of documents, but they
can also return garbage data from writes that never should have occurred.
[6]
20. Parameter server
● Model and data parallelism
● Failures and slow machines
● Additional stochasticity due to asynchrony (relaxed
consistency, not up to data parameters, ordering not
guaranteed, …)
[11]
21. Examples
“Their network for face detection from youtube comprised millions of
neurons and 1 billion connection weights. They trained it on a dataset of 10
million 200x200 pixel RGB images to learn 20,000 object categories. The
training simulation ran for three days on a cluster of 1,000 servers totaling
16,000 CPU cores. Each instantiation of the network spanned 170 servers”
Google.
“We demonstrate near-perfect weak scaling on a 16 rack IBM Blue Gene/Q
(262144 CPUs, 256 TB memory), achieving an unprecedented scale of 256
million neurosynaptic cores containing 65 billion neurons and 16 trillion
synapses“
TrueNorth, part of project IBM SyNAPSE.
[11, 12]
25. Data processing pipeline
● Whole lifecycle of data
● Data processing
● Data stores
● Integration
● Distributed computing primitives
● Cluster managers and task schedulers
● Deployment, configuration management and DevOps
● Data analytics and machine learning
29. Apache Spark
● In memory dataflow distributed data processing
framework, streaming and batch
● Distributes computation using a higher level API
● Load balancing
● Moves computation to data
● Fault tolerant
47. Muvr
● Classify finished (in progress) exercises
● Gather data for improved classification
● Predict next exercises
● Predict weights, intensity
● Design a schedule of exercises and improvements
(personal trainer)
● Monitor exercise quality
48. Scaling model training
val sc = new SparkContext("local[4]", "NN")
val data = ...
val layers = Array[Int](inputSize, 250, 50, outputSize)
val trainer = new MultilayerPerceptronClassifier()
.setLayers(layers)
.setBlockSize(128)
.setSeed(1234L)
.setMaxIter(100)
val model = trainer.fit(data)
val result = model.transform(data)
println(result.select(result("prediction")).foreach(println))
val predictionAndLabels = result.select("prediction", "label")
val evaluator = new MulticlassClassificationEvaluator()
.setMetricName("precision")
println("Precision:" + evaluator.evaluate(predictionAndLabels))
49. Scaling model training
● Deeplearning4j, Neon, Tensor flow on Spark
Model 1 training
Model 2 training
Model 3 training
Best model
54. val events = sc.eventTable().cache().toDF()
val lr = new LinearRegression()
val pipeline = new Pipeline().setStages(Array(new UserFilter(), new ZScoreNormalizer(),
new IntensityFeatureExtractor(), lr))
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, Array(0.1, 0.01))
.addGrid(lr.fitIntercept, Array(true, false))
getEligibleUsers(events, sessionEndedBefore)
.map { user =>
val trainValidationSplit =
new TrainValidationSplit()
.setEstimator(pipeline)
.setEvaluator(new RegressionEvaluator)
.setEstimatorParamMaps(paramGrid)
val model = trainValidationSplit.fit(
events,
ParamMap(ParamPair(userIdParam, user)))
val testData = // Prepare test data.
val predictions = model.transform(testData)
submitResult(userId, predictions, config)
}
55. Queries and analytics
val events: RDD[(JournalKey, Any)] = sc.eventTable().cache().filterClass
[EntireResistanceExerciseSession].flatMap(_.deviations)
val deviationsFrequency = sqlContext.sql(
"""SELECT planned.exercise, hour(time), COUNT(1)
FROM exerciseDeviations
WHERE planned.exercise = 'bench press'
GROUP BY planned.exercise, hour(time)""")
val deviationsFrequency2 = exerciseDeviationsDF
.where(exerciseDeviationsDF("planned.exercise")
=== "bench press")
.groupBy(
exerciseDeviationsDF("planned.exercise"),
exerciseDeviationsDF("time”))
.count()
val deviationsFrequency3 = exerciseDeviations
.filter(_.planned.exercise == "bench press")
.groupBy(d => (d.planned.exercise, d.time.getHours))
.map(d => (d._1, d._2.size))
56. Clustering
def toVector(user: User): mllib.linalg.Vector =
Vectors.dense(
user.frequency,
user.performanceIndex,
user.improvementIndex)
val events: RDD[(JournalKey, Any)] =
sc.eventTable().cache()
val users: RDD[User] = events.filterClass[User]
val kmeans = new KMeans()
.setK(5)
.set...
val clusters = kmeans.run(users.map(_.toVector))
57. Recommendations
val weight: RDD[(JournalKey, Any)] = sc.eventTable().cache()
val exerciseDeviations = events
.filterClass[EntireResistanceExerciseSession]
.flatMap(session =>
session.sets.flatMap(set =>
set.sets.map(
exercise => (session.id.id, exercise.exercise))))
.groupBy(e => e)
.map(g =>
Rating(normalize(g._1._1), normalize(g._1._2),
normalize(g._2.size)))
val model = new ALS().run(ratings)
val predictions = model.predict(recommend)
bench
press
bicep
curl
dead
lift
user 1 5 2
user 2 4 3
user 3 5 2
user 4 3 1
58. Graph analysis
val events: RDD[(JournalKey, Any)] =
sc.eventTable().cache()
val connections = events.filterClass[Connections]
val vertices: RDD[(VertexId, Long)] =
connections.map(c => (c.id, 1l))
val edges: RDD[Edge[Long]] = connections
.flatMap(c => c.connections
.map(Edge(c.id, _, 1l)))
val graph = Graph(vertices, edges)
val ranks = graph.pageRank(0.0001).vertices