Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Future of Computing is Distributed
Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Modernizing Global Shared Data Analytics Platform and our Alluxio JourneyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Sandipan Chakraborty, Director of Engineering (Rakuten)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Unified Data Access with Gimel
Deepak Chandramouli, Engineering Lead
Anisha Nainani, Sr. Software Engineer
Dr. Vladimir Bacvanski, Principal Architect (Paypal)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
How to teach your data scientist to leverage an analytics cluster with Presto...Alluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
How to teach your data scientist to leverage an analytics cluster with Presto, Spark, and Alluxio
Katarzyna Orzechowska, Data Scientist (ING Tech)
Mariusz Derela, DevOps Engineer (ING Tech)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Alluxio Use Cases and Future DirectionsAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Data Orchestration for Analytics and AI in the Cloud Era
Calvin Jia, Founding Engineer (Alluxio)
Bin Fan, Founding Engineer, VP of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Realizing the promise of portable data processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Real-world Cloud HPC at Scale, for Production Workloads (BDT212) | AWS re:Inv...Amazon Web Services
"Running high-performance scientific and engineering applications is challenging no matter where you do it. Join IT executives from Hitachi Global Storage Technology, The Aerospace Corporation, Novartis, and Cycle Computing and learn how they have used the AWS cloud to deploy mission-critical HPC workloads.
Cycle Computing leads the session on how organizations of any scale can run HPC workloads on AWS. Hitachi Global Storage Technology discusses experiences using the cloud to create next-generation hard drives. The Aerospace Corporation provides perspectives on running MPI and other simulations, and offer insights into considerations like security while running rocket science on the cloud. Novartis Institutes for Biomedical Research talks about a scientific computing environment to do performance benchmark workloads and large HPC clusters, including a 30,000-core environment for research in the fight against cancer, using the Cancer Genome Atlas (TCGA)."
Speeding Up Atlas Deep Learning Platform with Alluxio + FluidAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Speeding Up Atlas Deep Learning Platform with Alluxio + Fluid
Yuandong Xie, Platform Researcher (Unisound)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Introducing the Hub for Data OrchestrationAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Introducing the Hub for Data Orchestration
Danny Linden, Chapter Lead Software Engineer (Ryte)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Modernizing Global Shared Data Analytics Platform and our Alluxio JourneyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Sandipan Chakraborty, Director of Engineering (Rakuten)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Unified Data Access with Gimel
Deepak Chandramouli, Engineering Lead
Anisha Nainani, Sr. Software Engineer
Dr. Vladimir Bacvanski, Principal Architect (Paypal)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
How to teach your data scientist to leverage an analytics cluster with Presto...Alluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
How to teach your data scientist to leverage an analytics cluster with Presto, Spark, and Alluxio
Katarzyna Orzechowska, Data Scientist (ING Tech)
Mariusz Derela, DevOps Engineer (ING Tech)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Alluxio Use Cases and Future DirectionsAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Data Orchestration for Analytics and AI in the Cloud Era
Calvin Jia, Founding Engineer (Alluxio)
Bin Fan, Founding Engineer, VP of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Realizing the promise of portable data processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Real-world Cloud HPC at Scale, for Production Workloads (BDT212) | AWS re:Inv...Amazon Web Services
"Running high-performance scientific and engineering applications is challenging no matter where you do it. Join IT executives from Hitachi Global Storage Technology, The Aerospace Corporation, Novartis, and Cycle Computing and learn how they have used the AWS cloud to deploy mission-critical HPC workloads.
Cycle Computing leads the session on how organizations of any scale can run HPC workloads on AWS. Hitachi Global Storage Technology discusses experiences using the cloud to create next-generation hard drives. The Aerospace Corporation provides perspectives on running MPI and other simulations, and offer insights into considerations like security while running rocket science on the cloud. Novartis Institutes for Biomedical Research talks about a scientific computing environment to do performance benchmark workloads and large HPC clusters, including a 30,000-core environment for research in the fight against cancer, using the Cancer Genome Atlas (TCGA)."
Speeding Up Atlas Deep Learning Platform with Alluxio + FluidAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Speeding Up Atlas Deep Learning Platform with Alluxio + Fluid
Yuandong Xie, Platform Researcher (Unisound)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Introducing the Hub for Data OrchestrationAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Introducing the Hub for Data Orchestration
Danny Linden, Chapter Lead Software Engineer (Ryte)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
The hidden engineering behind machine learning products at HelixaAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The hidden engineering behind machine learning products at Helixa
Gianmario Spacagna, (Helixa)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Building Modern Data Pipelines on GCP via a FREE online BootcampData Con LA
Data Con LA 2020
Description
You just got hired by a large "tech startup". They're a hip travel agency like Kayak, "revolutionizing the airline industry" by developing an A/I that negotiates best airline deals on behalf of passengers. But in reality they are developing the AI to jack up ticket prices as it finds the passengers' preferences. They run their tech on the latest Google Cloud technologies, so you figured it's a great place to sharpen your skills as a Data Engineer despite the company's broken ethical compass. We teach Cloud Data Engineering to beginner/intermediate developers via a fun and engaging story. You will build a complete data-driven A/I pipeline. Ingest 6 years worth of real flight records, profile 30M+ user profiles and process 100M+ live streaming events while learning tools such as BigQuery, Dataflow (Apache Beam), DataProc (Apache Spark), Pub/Sub (Kafka), BigTable, and Airflow (Cloud Composer). During our talk, we will:
*Discuss the latest Serverless Data Architecture on GCP
*Explore the architectural decisions behind our Data Pipeline
*Run a live demo from our course
Speaker
Parham Parvizi, Tura Labs, Founder / Data Engineer
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
High Performance Data Lake with Apache Hudi and Alluxio at T3GoAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
High Performance Data Lake with Apache Hudi and Alluxio at T3Go
Trevor Zhang & Vino Yang (T3Go)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manis...Spark Summit
Redis accelerates Apache Spark execution by 45 times, when used as a shared distributed in-memory datastore for Spark in analyses like time series data range queries. With the redis module for machine learning, redis-ml, implementation of spark-ml models gains a new real time serving layer that offloads processing of models directly in Redis, allows multiple applications to reuse the same models and speeds up classification and execution of these models by 13x. Join this session to learn more about the Redis Labs’ connector for Apache Spark that enhances production implementations of real-time big data processing.
Architecting a Heterogeneous Data Platform Across Clusters, Regions, and CloudsAlluxio, Inc.
Alluxio Product School Webinar
January 27, 2022
For more Alluxio events: https://www.alluxio.io/events/
Speaker:
Adit Madan
Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.
Join Alluxio’s Sr. Product Mgr., Adit Madan, to learn:
- Key challenges with architecting a successful heterogeneous data platform
- How data orchestration can overcome data access challenges in a distributed, heterogeneous environment
- How to identify ways to use Alluxio to meet the needs of your own data environment and workload requirements
Automatski - RSA-2048 Cryptography Cracked using Shor's Algorithm on a Quantu...Aditya Yadav
Cracking RSA-2048 Cryptography using Shor's Algorithm on a Quantum Computer
We demonstrate live a Pure/Undiluted Implementation of Shor's Algorithm on a 100,000+ Qubit Quantum Computer Simulator by Automatski.
We have hence cracked RSA-2048 and all Existing Cryptography in The World
A New “Sparkitecture” for Modernizing your Data Warehouse: Spark Summit East ...Spark Summit
Legacy enterprise data warehouse (EDW) architecture, geared toward day-to-day workloads associated with operational querying, reporting, and analytics, are often ill-equipped to handle the volume of data, traffic, and varied data types associated with a modern, ad-hoc analytics platform. Faced with challenges of increasing pipeline speed, aggregation, and visualization in a simplified, self-service fashion, organizations are increasingly turning to some combination of Spark, Hadoop, Kafka, and proven analytical databases like Vertica as key enabling technologies to optimize their EDW architecture. Join us to learn how successful organizations have developed real-time streaming solutions with these technologies for range of use cases, including IOT predictive maintenance.
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
This Big Data case study outlines the Hadoop infrastructure deployment for a Fortune 100 media and telecommunications company.
Hadoop adoption in this company had grown organically across multiple different teams, starting with “science projects” and lab initiatives that quickly grew and expanded. Going forward, some of the options they considered for their Big Data deployment included expanding their on-premises infrastructure and using a Hadoop-as-a-Service cloud offering.
Fortunately, they realized that there is a third option: providing the benefits of Hadoop-as-a-Service with on-premises infrastructure. They selected the BlueData EPIC software platform to virtualize their Hadoop infrastructure and provide on-demand access to virtual Hadoop clusters in a secure, multi-tenant model.
Learn more about this case study in the blog post at: http://www.bluedata.com/blog/2015/05/big-data-case-study-hadoop-infrastructure
Deep Learning on Apache Spark at CERN’s Large Hadron Collider with Intel Tech...Databricks
In this session, you will learn how CERN easily applied end-to-end deep learning and analytics pipelines on Apache Spark at scale for High Energy Physics using BigDL and Analytics Zoo open source software running on Intel Xeon-based distributed clusters.
Technical details and development learnings will be shared using an example of topology classification to improve real-time event selection at the Large Hadron Collider experiments. The classifier has demonstrated very good performance figures for efficiency, while also reducing the false positive rate compared to the existing methods. It could be used as a filter to improve the online event selection infrastructure of the LHC experiments, where one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives.
This is part of CERN’s research on applying Deep Learning and Analytics using open source and industry standard technologies as an alternative to the existing customized rule based methods. We show how we could quickly build and implement distributed deep learning solutions and data pipelines at scale on Apache Spark using Analytics Zoo and BigDL, which are open source frameworks unifying Analytics and AI on Spark with easy to use APIs and development interfaces seamlessly integrated with Big Data Platforms.
Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.
This talk will cover Ray’s overview, architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem.
Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference—from a single node to a cluster, with tangible performance difference when using Ray.
The takeaways from this talk are :
Learn Ray architecture, core concepts, and Ray primitives and patterns
Why Distributed computing will be the norm not an exception
How to scale your ML workloads with Ray libraries:
Training on a single node vs. Ray cluster, using XGBoost with/without Ray
Hyperparameter search and tuning, using XGBoost with Ray Tune
Inferencing at scale, using XGBoost with/without Ray
The hidden engineering behind machine learning products at HelixaAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The hidden engineering behind machine learning products at Helixa
Gianmario Spacagna, (Helixa)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Building Modern Data Pipelines on GCP via a FREE online BootcampData Con LA
Data Con LA 2020
Description
You just got hired by a large "tech startup". They're a hip travel agency like Kayak, "revolutionizing the airline industry" by developing an A/I that negotiates best airline deals on behalf of passengers. But in reality they are developing the AI to jack up ticket prices as it finds the passengers' preferences. They run their tech on the latest Google Cloud technologies, so you figured it's a great place to sharpen your skills as a Data Engineer despite the company's broken ethical compass. We teach Cloud Data Engineering to beginner/intermediate developers via a fun and engaging story. You will build a complete data-driven A/I pipeline. Ingest 6 years worth of real flight records, profile 30M+ user profiles and process 100M+ live streaming events while learning tools such as BigQuery, Dataflow (Apache Beam), DataProc (Apache Spark), Pub/Sub (Kafka), BigTable, and Airflow (Cloud Composer). During our talk, we will:
*Discuss the latest Serverless Data Architecture on GCP
*Explore the architectural decisions behind our Data Pipeline
*Run a live demo from our course
Speaker
Parham Parvizi, Tura Labs, Founder / Data Engineer
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
High Performance Data Lake with Apache Hudi and Alluxio at T3GoAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
High Performance Data Lake with Apache Hudi and Alluxio at T3Go
Trevor Zhang & Vino Yang (T3Go)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manis...Spark Summit
Redis accelerates Apache Spark execution by 45 times, when used as a shared distributed in-memory datastore for Spark in analyses like time series data range queries. With the redis module for machine learning, redis-ml, implementation of spark-ml models gains a new real time serving layer that offloads processing of models directly in Redis, allows multiple applications to reuse the same models and speeds up classification and execution of these models by 13x. Join this session to learn more about the Redis Labs’ connector for Apache Spark that enhances production implementations of real-time big data processing.
Architecting a Heterogeneous Data Platform Across Clusters, Regions, and CloudsAlluxio, Inc.
Alluxio Product School Webinar
January 27, 2022
For more Alluxio events: https://www.alluxio.io/events/
Speaker:
Adit Madan
Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.
Join Alluxio’s Sr. Product Mgr., Adit Madan, to learn:
- Key challenges with architecting a successful heterogeneous data platform
- How data orchestration can overcome data access challenges in a distributed, heterogeneous environment
- How to identify ways to use Alluxio to meet the needs of your own data environment and workload requirements
Automatski - RSA-2048 Cryptography Cracked using Shor's Algorithm on a Quantu...Aditya Yadav
Cracking RSA-2048 Cryptography using Shor's Algorithm on a Quantum Computer
We demonstrate live a Pure/Undiluted Implementation of Shor's Algorithm on a 100,000+ Qubit Quantum Computer Simulator by Automatski.
We have hence cracked RSA-2048 and all Existing Cryptography in The World
A New “Sparkitecture” for Modernizing your Data Warehouse: Spark Summit East ...Spark Summit
Legacy enterprise data warehouse (EDW) architecture, geared toward day-to-day workloads associated with operational querying, reporting, and analytics, are often ill-equipped to handle the volume of data, traffic, and varied data types associated with a modern, ad-hoc analytics platform. Faced with challenges of increasing pipeline speed, aggregation, and visualization in a simplified, self-service fashion, organizations are increasingly turning to some combination of Spark, Hadoop, Kafka, and proven analytical databases like Vertica as key enabling technologies to optimize their EDW architecture. Join us to learn how successful organizations have developed real-time streaming solutions with these technologies for range of use cases, including IOT predictive maintenance.
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
This Big Data case study outlines the Hadoop infrastructure deployment for a Fortune 100 media and telecommunications company.
Hadoop adoption in this company had grown organically across multiple different teams, starting with “science projects” and lab initiatives that quickly grew and expanded. Going forward, some of the options they considered for their Big Data deployment included expanding their on-premises infrastructure and using a Hadoop-as-a-Service cloud offering.
Fortunately, they realized that there is a third option: providing the benefits of Hadoop-as-a-Service with on-premises infrastructure. They selected the BlueData EPIC software platform to virtualize their Hadoop infrastructure and provide on-demand access to virtual Hadoop clusters in a secure, multi-tenant model.
Learn more about this case study in the blog post at: http://www.bluedata.com/blog/2015/05/big-data-case-study-hadoop-infrastructure
Deep Learning on Apache Spark at CERN’s Large Hadron Collider with Intel Tech...Databricks
In this session, you will learn how CERN easily applied end-to-end deep learning and analytics pipelines on Apache Spark at scale for High Energy Physics using BigDL and Analytics Zoo open source software running on Intel Xeon-based distributed clusters.
Technical details and development learnings will be shared using an example of topology classification to improve real-time event selection at the Large Hadron Collider experiments. The classifier has demonstrated very good performance figures for efficiency, while also reducing the false positive rate compared to the existing methods. It could be used as a filter to improve the online event selection infrastructure of the LHC experiments, where one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives.
This is part of CERN’s research on applying Deep Learning and Analytics using open source and industry standard technologies as an alternative to the existing customized rule based methods. We show how we could quickly build and implement distributed deep learning solutions and data pipelines at scale on Apache Spark using Analytics Zoo and BigDL, which are open source frameworks unifying Analytics and AI on Spark with easy to use APIs and development interfaces seamlessly integrated with Big Data Platforms.
Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.
This talk will cover Ray’s overview, architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem.
Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference—from a single node to a cluster, with tangible performance difference when using Ray.
The takeaways from this talk are :
Learn Ray architecture, core concepts, and Ray primitives and patterns
Why Distributed computing will be the norm not an exception
How to scale your ML workloads with Ray libraries:
Training on a single node vs. Ray cluster, using XGBoost with/without Ray
Hyperparameter search and tuning, using XGBoost with Ray Tune
Inferencing at scale, using XGBoost with/without Ray
Ray (https://github.com/ray-project/ray) is a framework developed at UC Berkeley and maintained by Anyscale for building distributed AI applications. Over the last year, the broader machine learning ecosystem has been rapidly adopting Ray as the primary framework for distributed execution. In this talk, we will overview how libraries such as Horovod (https://horovod.ai/), XGBoost, and Hugging Face Transformers, have integrated with Ray. We will then showcase how Uber leverages Ray and these ecosystem integrations to simplify critical production workloads at Uber. This is a joint talk between Anyscale and Uber.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
BigDL: Bringing Ease of Use of Deep Learning for Apache Spark with Jason Dai ...Databricks
BigDL is a distributed deep learning framework for Apache Spark open sourced by Intel. BigDL helps make deep learning more accessible to the Big Data community, by allowing them to continue the use of familiar tools and infrastructure to build deep learning applications. With BigDL, users can write their deep learning applications as standard Spark programs, which can then directly run on top of existing Spark or Hadoop clusters.
In this session, we will introduce BigDL, how our customers use BigDL to build End to End ML/DL applications, platforms on which BigDL is deployed and also provide an update on the latest improvements in BigDL v0.1, and talk about further developments and new upcoming features of BigDL v0.2 release (e.g., support for TensorFlow models, 3D convolutions, etc.).
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Cassandra Summit 2014: Apache Spark - The SDK for All Big Data PlatformsDataStax Academy
Apache Spark has grown to be one of the largest open source communities in big data, with over 190 developers and dozens of companies contributing. The latest 1.0 release alone includes contributions from 117 people. A clean API, interactive shell, distributed in-memory computation, stream processing, interactive SQL, and libraries delivering everything from machine learning to graph processing make it an excellent unified platform to solve a number of problems. Apache Spark works very well with a growing number of big data solutions, including Cassandra and Hadoop. Come learn about Apache Spark and see how easy it is for you to get started using Spark to build your own high performance big data applications today.
Accelerating open science and AI with automated, portable, customizable and r...Grigori Fursin
Validating experimental results from articles has finally become a norm at many systems and ML conferences. Nowadays, more than half of accepted papers pass artifact evaluation and share related code and data. Unfortunately, lack of a common experimental framework, common research methodology and common formats places an increasing burden on evaluators to validate a growing number of ad-hoc artifacts. Furthermore, having too many ad-hoc artifacts and Docker snapshots is almost as bad as not having any (!), since they cannot be easily reused, customized and built upon.
While overviewing more than 100 papers during artifact evaluation at PPoPP, CGO, PACT, Supercomputing and other conferences, we noticed that many of them use similar experimental setups, benchmarks, models, data sets, environments and platforms. This motivated us to develop Collective Knowledge (CK), an open workflow framework with a unified Python API to automate common researchers’ tasks such as detecting software and hardware dependencies, installing missing packages, downloading data sets and models, compiling and running programs, performing autotuning and co-design, crowdsourcing time-consuming experiments across computing resources provided by volunteers similar to SETI@home, applying statistical analysis and machine learning, validating results and plotting them on a common scoreboard for open and fair comparison, automatically generating interactive articles, and so on: http://cKnowledge.org.
In this presentation we will introduce CK concepts and present several real world use cases from General Motors and Arm
on collaborative benchmarking, autotuning and co-design of efficient software/hardware stacks for deep learning. We also present results and reusable CK components from the 1st ACM ReQuEST optimization tournament: http://cKnowledge.org/request. Finally, we introduce our latest initiative to create
an open repository of reusable research components and workflows to reboot and accelerate open science, quantum computing and AI!
Taboola's experience with Apache Spark (presentation @ Reversim 2014)tsliwowicz
At taboola we are getting a constant feed of data (many billions of user events a day) and are using Apache Spark together with Cassandra for both real time data stream processing as well as offline data processing. We'd like to share our experience with these cutting edge technologies.
Apache Spark is an open source project - Hadoop-compatible computing engine that makes big data analysis drastically faster, through in-memory computing, and simpler to write, through easy APIs in Java, Scala and Python. This project was born as part of a PHD work in UC Berkley's AMPLab (part of the BDAS - pronounced "Bad Ass") and turned into an incubating Apache project with more active contributors than Hadoop. Surprisingly, Yahoo! are one of the biggest contributors to the project and already have large production clusters of Spark on YARN.
Spark can run either standalone cluster, or using either Apache mesos and ZooKeeper or YARN and can run side by side with Hadoop/Hive on the same data.
One of the biggest benefits of Spark is that the API is very simple and the same analytics code can be used for both streaming data and offline data processing.
AI/ML Infra Meetup | ML explainability in MichelangeloAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Eric Wang (Software Engineer, @Uber)
Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
AI/ML Infra Meetup | Reducing Prefill for LLM Serving in RAGAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Junchen Jiang (Assistant Professor of Computer Science, @University of Chicago)
Prefill in LLM inference is known to be resource-intensive, especially for long LLM inputs. While better scheduling can mitigate prefill’s impact, it would be fundamentally better to avoid (most of) prefill. This talk introduces our preliminary effort towards drastically minimizing prefill delay for LLM inputs that naturally reuse text chunks, such as in retrieval-augmented generation. While keeping the KV cache of all text chunks in memory is difficult, we show that it is possible to store them on cheaper yet slower storage. By improving the loading process of the reused KV caches, we can still significantly speed up prefill delay while maintaining the same generation quality.
AI/ML Infra Meetup | Perspective on Deep Learning FrameworkAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Triston Cao (Senior Deep Learning Software Engineering Manager, @NVIDIA)
From Caffe to MXNet, to PyTorch, and more, Xiande Cao, Senior Deep Learning Software Engineer Manager, will share his perspective on the evolution of deep learning frameworks.
AI/ML Infra Meetup | Improve Speed and GPU Utilization for Model Training & S...Alluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Lu Qiu (Data & AI Platform Tech Lead, @Alluxio)
- Siyuan Sheng (Senior Software Engineer, @Alluxio)
Speed and efficiency are two requirements for the underlying infrastructure for machine learning model development. Data access can bottleneck end-to-end machine learning pipelines as training data volume grows and when large model files are more commonly used for serving. For instance, data loading can constitute nearly 80% of the total model training time, resulting in less than 30% GPU utilization. Also, loading large model files for deployment to production can be slow because of slow network or storage read operations. These challenges are prevalent when using popular frameworks like PyTorch, Ray, or HuggingFace, paired with cloud object storage solutions like S3 or GCS, or downloading models from the HuggingFace model hub.
In this presentation, Lu and Siyuan will offer comprehensive insights into improving speed and GPU utilization for model training and serving. You will learn:
- The data loading challenges hindering GPU utilization
- The reference architecture for running PyTorch and Ray jobs while reading data from S3, with benchmark results of training ResNet50 and BERT
- Real-world examples of boosting model performance and GPU utilization through optimized data access
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-CloudAlluxio, Inc.
Alluxio Monthly Webinar
May. 14, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- ChanChan Mao (Developer Advocate, Alluxio)
- Bin Fan (VP of Technology, Alluxio)
Running AI/ML workloads in different clouds present unique challenges. The key to a manageable multi-cloud architecture is the ability to seamlessly access data across environments with high performance and low cost.
This webinar is designed for data platform engineers, data infra engineers, data engineers, and ML engineers who work with multiple data sources in hybrid or multi-cloud environments. Chanchan and Bin will guide the audience through using Alluxio to greatly simplify data access and make model training and serving more efficient in these environments.
You will learn:
- How to access data in multi-region, hybrid, and multi-cloud like accessing a local file system
- How to run PyTorch to read datasets and write checkpoints to remote storage with Alluxio as the distributed data access layer
- Real-world examples and insights from tech giants like Uber, AliPay and more
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio, Inc.
Alluxio Monthly Webinar
Apr. 23, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- ChanChan Mao (Developer Advocate, Alluxio)
- Shawn Sun (Tech Lead of Cloud Native, Alluxio)
Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
Optimizing Data Access for Analytics And AI with AlluxioAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Lucy Ge (Staff Software Engineer @ Alluxio)
In this presentation, Lucy Ge will discuss the data access challenges in the data pipeline and how to optimize the speed and costs of analytics and AI workloads.
Speed Up Presto at Uber with Alluxio CachingAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Chen Liang (Staff Software Engineer @ Uber)
In this presentation, Chen Liang will share the design and implementation of the Alluxio-Presto local cache to reduce query latency.
Correctly Loading Incremental Data at ScaleAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Toby Mao (CTO @ Tobiko Data)
Writing efficient and correct incremental pipelines is challenging. Data practitioners who take on this challenge are viewed as performing an "advanced" function, which discourages broader teams from adopting incremental loads. In this lightning talk, CTO of Tobiko Data, Toby Mao, will demystify incremental loading data at scale.
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLAlluxio, Inc.
Big Data Bellevue Meetup
March 21, 2024
For more Alluxio events: https://alluxio.io/events/
Speakers:
Bin Fan (VP of Open Source, Alluxio)
In this presentation, Bin Fan (VP of Open Source @ Alluxio) will address a critical challenge of optimizing data loading for distributed Python applications within AI/ML workloads in the cloud, focusing on popular frameworks like Ray and Hugging Face. Integration of Alluxio’s distributed caching for Python applications is accomplished using the fsspec interface, thus greatly improving data access speeds. This is particularly useful in machine learning workflows, where repeated data reloading across slow, unstable or congested networks can severely affect GPU efficiency and escalate operational costs.
Attendees can look forward to practical, hands-on demonstrations showcasing the tangible benefits of Alluxio’s caching mechanism across various real-world scenarios. These demos will highlight the enhancements in data efficiency and overall performance of data-intensive Python applications. This presentation is tailored for developers and data scientists eager to optimize their AI/ML workloads. Discover strategies to accelerate your data processing tasks, making them not only faster but also more cost-efficient.
Alluxio Monthly Webinar | Why a Multi-Cloud Strategy Matters for Your AI Plat...Alluxio, Inc.
Alluxio Monthly Webinar
Feb. 27, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Tarik Bennett (Senior Solutions Engineer, Alluxio)
As GenAI and AI continue to transform businesses, scaling these workloads requires optimized underlying infrastructure. A multi-cloud architecture allows organizations to leverage different cloud services to meet diverse workload demands while maximizing efficiency, reducing costs, and avoiding vendor lock-in. However, achieving a multi-cloud vision can be challenging.
In this webinar, Tarik will share how an agonistic data layer, like Alluxio, allows you to embrace the separation of storage from compute and simplify the adoption of multi-cloud for AI.
- Learn why leveraging multiple cloud providers is critical for balancing performance, scalability, and cost of your AI platform
- Discover how an agnostic data layer like Alluxio provides seamless data access in multi-cloud that bridges storage and compute without data replication
- Gain insights into real-world examples and best practices for deploying AI across on-prem, hybrid, and multi-cloud environments
Alluxio Monthly Webinar | Five Disruptive Trends that Every Data & AI Leader...Alluxio, Inc.
Alluxio Monthly Webinar
Jan. 30, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Kevin Petrie (VP of Research, Eckerson Group)
- Omid Razavi (SVP of Customer Success, Alluxio)
2024 is gearing up to be an impactful year for AI and analytics. Join us on January 30, as Kevin Petrie (VP of Research at Eckerson Group) and Omid Razavi (SVP of Customer Success at Alluxio) share key trends that data and AI leaders should know. This event will efficiently guide you with market data and expert insights to drive successful business outcomes.
- Assess current and future trends in data and AI with industry experts
- Discover valuable insights and practical recommendations
- Learn best practices to make your enterprise data more accessible for both analytics and AI applications
Data Infra Meetup | FIFO Queues are All You Need for Cache EvictionAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Juncheng Yang(Ph.D Candidate, @CMU)
As a cache eviction algorithm, FIFO has a lot of attractive properties, such as simplicity, speed, scalability, and flash-friendliness. The most prominent criticism of FIFO is its low efficiency (high miss ratio). In this talk, I will describe a simple, scalable FIFO-based algorithm with three static queues (S3-FIFO). Evaluated on 6594 cache traces from 14 datasets, we show that S3- FIFO has lower miss ratios than state-of-the-art algorithms across traces. Moreover, S3-FIFO’s efficiency is robust — it has the lowest mean miss ratio on 10 of the 14 datasets. FIFO queues enable S3-FIFO to achieve good scalability with 6× higher throughput compared to optimized LRU at 16 threads. Our insight is that most objects in skewed workloads will only be accessed once in a short window, so it is critical to evict them early (also called quick demotion). The key of S3-FIFO is a small FIFO queue that filters out most objects from entering the main cache, which provides a guaranteed demotion speed and high demotion precision.
Data Infra Meetup | Accelerate Your Trino/Presto Queries - Gain the Alluxio EdgeAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jingwen Ouyang (Product Manager, @Alluxio)
In this session, Jingwen presents an overview of using Alluxio Edge caching to accelerate Trino or Presto queries. She offers practical best practices for using distributed caching with compute engines. In addition, this session also features insights from real-world examples.
Data Infra Meetup | Accelerate Distributed PyTorch/Ray Workloads in the CloudAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Siyuan Sheng (Senior Software Engineer, @Alluxio)
- Chunxu Tang (Research Scientist, @Alluxio)
In this session, cloud optimization specialists Chunxu and Siyuan break down the challenges and present a fresh architecture designed to optimize I/O across the data pipeline, ensuring GPUs function at peak performance. The integrated solution of PyTorch/Ray + Alluxio + S3 offers a promising way forward, and the speakers delve deep into its practical applications. Attendees will not only gain theoretical insights but will also be treated to hands-on instructions and demonstrations of deploying this cutting-edge architecture in Kubernetes, specifically tailored for Tensorflow/PyTorch/Ray workloads in the public cloud.
Data Infra Meetup | ByteDance's Native Parquet ReaderAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Shengxuan Liu (Software Engineer, @ByteDance)
Shengxuan Liu from ByteDance presents the new ByteDance’s native Parquet Reader. The talk covers the architecture and key features of the Reader, and how the new Reader is able to facilitate data processing efficiency.
Data Infra Meetup | Uber's Data Storage EvolutionAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jing Zhao (Principal Engineer, @Uber)
Uber builds one of the biggest data lakes in the industry, which stores exabytes of data. In this talk, we will introduce the evolution of our data storage architecture, and delve into multiple key initiatives during the past several years.
Specifically, we will introduce:
- Our on-prem HDFS cluster scalability challenges and how we solved them
- Our efficiency optimizations that significantly reduced the storage overhead and unit cost without compromising reliability and performance
- The challenges we are facing during the ongoing Cloud migration and our solutions
Alluxio Monthly Webinar | Why NFS/NAS on Object Storage May Not Solve Your AI...Alluxio, Inc.
Alluxio Monthly Webinar
Nov. 15, 2023
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Tarik Bennett (Senior Solutions Engineer)
- Beinan Wang (Senior Staff Engineer & Architect)
Many companies are working with development architectures for AI platforms but have concerns about efficiency at scale as data volumes increase. They use centralized cloud data lakes, like S3, to store training data for AI platforms. However, GPU shortages add more complications. Storage and compute can be separate, or even remote, making data loading slow and expensive:
1) Optimizing a developmental setup can include manual copies, which are slow and error-prone
2) Directly transferring data across regions or from cloud to on-premises can incur expensive egress fees
This webinar covers solutions to improve data loading for model training. You will learn:
- The data loading challenges with distributed infrastructure
- Typical solutions, including NFS/NAS on object storage, and why they are not the best options
- Common architectures that can improve data loading and cost efficiency
- Using Alluxio to accelerate model training and reduce costs
AI Infra Day | Accelerate Your Model Training and Serving with Distributed Ca...Alluxio, Inc.
AI Infra Day
Oct. 25, 2023
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Adit Madan (Director of Product Management, @Alluxio)
In this session, Adit Madan, Director of Product Management at Alluxio, presents an overview of using distributed caching to accelerate model training and serving. He explores the requirements of data access patterns in the ML pipeline and offers practical best practices for using distributed caching in the cloud. This session features insights from real-world examples, such as AliPay, Zhihu, and more.
AI Infra Day | The AI Infra in the Generative AI EraAlluxio, Inc.
AI Infra Day
Oct. 25, 2023
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Bin Fan (Cheif Architect, VP of Open Source, @Alluxio)
As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for a robust AI infra stack. This opening keynote will explore the key trends of the AI infra stack in the generative AI era.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
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.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
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.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
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.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
3. Distributed computing still the exception…
Inaccessible to most developers
Few universities teach distributed computing
This will change…
Distributed computing will be the norm,
rather than the exception
4. What is different this time?
The rise of deep learning (DL)
The end of Moore’s Law
Apps becoming AI centric
15. Turing Proj. (17B)
GPT-2 (8.3 B)
GPT-1 (1.5B)
BERT
GPT-1
Transformer
ResNet-50
20x every 18 months
(https://devblogs.nvidia.com/training-bert-with-gpus/)
Memory dwarfed by demand
GPU memory
increased by just
1.45x every 18 months
No way out but distributed!
16. What is different?
The rise of deep learning (DL)
The end of Moore’s Law
Apps becoming AI centric
17. Apps becoming AI centric...
... and integrating other distributed workloads
25. Natural solution...
Stitch together existing frameworks
Distributed
systems
Model
Serving
Training
Distributed
systems
Distributed
systems
Hyperparam.
Tuning
Distributed
systems
Data
processing
Simulation
Distributed
systems
Business
logic
Distributed
systems
29. Three key ideas
Execute remotely functions as tasks, and
instantiate remotely classes as actors
○ Support both stateful and stateless computations
Asynchronous execution using futures
○ Enable parallelism
Distributed (immutable) object store
○ Efficient communication (send arguments by reference)
30. def read_array(file):
# read ndarray “a”
# from “file”
return a
def add(a, b):
return np.add(a, b)
a = read_array(file1)
b = read_array(file2)
sum = add(a, b)
Function
class Counter(object):
def __init__(self):
self.value = 0
def inc(self):
self.value += 1
return self.value
c = Counter()
c.inc()
c.inc()
Class
31. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
a = read_array(file1)
b = read_array(file2)
sum = add(a, b)
@ray.remote
class Counter(object):
def __init__(self):
self.value = 0
def inc(self):
self.value += 1
return self.value
c = Counter()
c.inc()
c.inc()
Function → Task Class → Actor
32. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
@ray.remote(num_gpus=1)
class Counter(object):
def __init__(self):
self.value = 0
def inc(self):
self.value += 1
return self.value
c = Counter.remote()
id4 = c.inc.remote()
id5 = c.inc.remote()
Function → Task Class → Actor
33. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
file1 file2
Node 1 Node 2
• Blue variables are Object IDs
• Similar to futures
read_array
id1
Return id1 (future) immediately,
before read_array() finishes
Task API
34. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
file1 file2
Node 1 Node 2
read_array
id1
read_array
id2
Dynamic task graph:
build at runtime
• Blue variables are Object IDs
• Similar to futures
Task API
35. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
• Blue variables are Object IDs
• Similar to futures
file1 file2
Node 1 Node 2
read_array
id1
read_array
id2
add
id
Node
3
Every task scheduled, but
not finished yet
Task API
36. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
• Blue variables are Object IDs
• Similar to futures
file1 file2
Node 1 Node 2
read_array
id1
read_array
id2
add
id
Node
3
ray.get() block until
result available
Task API
37. @ray.remote
def read_array(file):
# read ndarray “a”
# from “file”
return a
@ray.remote
def add(a, b):
return np.add(a, b)
id1 = read_array.remote(file1)
id2 = read_array.remote(file2)
id = add.remote(id1, id2)
sum = ray.get(id)
• Blue variables are Object IDs
• Similar to futures
file1
Node 1 Node 2
Node
3
read_array
file2
read_array
add
sumTask graph executed to
compute sum
Task API
39. universal framework for
distributed computing
Native Libraries Most popular scalable RL library
● PyTorch and TF support
● Largest # of algorithms
Best Distributed Library Ecosystem
40. universal framework for
distributed computing
Native Libraries
A popular hyperparameter tuning library:
“For me, and I say this as a Hyperopt maintainer,
Tune is the clear winner down the road. Tune is
fairly well architected and it integrates with
everything else, and it’s built on top of Ray so it
has all the benefits stemming from that as well.
… In 2020, I would certainly bet on Tune.”
-- Max Pumperla, HyperOpt creator
Best Distributed Library Ecosystem
41. universal framework for
distributed computing
Native Libraries
Just launched. Promising
start but a long way to go.
Best Distributed Library Ecosystem
43. universal framework for
distributed computing
Native Libraries
3rd Party Libraries
Best Distributed Library Ecosystem
●Horovod: most popular distributed
training library.
●Optuna and hyperopt: popular
hyperparameter search libraries
44. universal framework for
distributed computing
Native Libraries
3rd Party Libraries The two most popular
NLP libraries using Ray
to scale up
Best Distributed Library Ecosystem
45. universal framework for
distributed computing
Native Libraries
3rd Party Libraries
ModelArts
Best Distributed Library Ecosystem
Major ML cloud
platforms embedding
Ray/RLlib/Tune
46. universal framework for
distributed computing
Native Libraries
3rd Party Libraries
ModelArts
Best Distributed Library Ecosystem
Dask running on Ray
● Faster, more
resilient, and
more scalable
47. universal framework for
distributed computing
Native Libraries
3rd Party Libraries
ModelArts
Popular experiment tracking platform
● One-line integration with Ray Tune.
Best Distributed Library Ecosystem
48. universal framework for
distributed computing
Native Libraries
3rd Party Libraries
ModelArts
Intel’s unified Data Analytics and AI platform.
● Integrated Ray together with Spark, TF, etc
● Use cases include streaming ML with customers
such as Burger King and BMW.
Best Distributed Library Ecosystem
54. Refactored actor
management
Placement groups
Java API
Direct calls
between
workers Azure support in
cluster launcher
Original dashboard Refactor
worker to C++
Initial port to Bazel
Significant community contributions
55. Summary
Ray: universal framework for distributed computing
Comprehensive ecosystem of scalable libraries
universal framework for
distributed computing
Native Libraries 3rd Party Libraries
ModelArts
Your apps
here!
https://github.com/ray-project/ray