Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Deep Learning in the Cloud at Scale: A Data Orchestration Story
Mickey Zhang, Software Engineer (Microsoft)
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
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
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
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
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
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
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
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
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
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
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
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
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
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
The Practice of Presto & Alluxio in E-Commerce Big Data PlatformAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Practice of Presto & Alluxio in E-Commerce Big Data Platform
Wenjun Tao, Sr. Software Engineer, JD.com
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
The Pandemic Changes Everything, the Need for Speed and ResiliencyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Pandemic Changes Everything, the Need for Speed and Resiliency
Parviz Peiravi, Global CTO of Financial Services Solutions, Intel
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Octo and the DevSecOps Evolution at Oracle by Ian Van HovenInfluxData
The transition from 40 years of successful licensed software development to an agile-based SaaS business involves many challenges. Octo, a real-time streaming metrics framework built around InfluxDB time series database, is aimed specifically at one: simplifying the collection and visualization of mission-critical operational data to enable a culture change toward metrics immersion and product ownership. Learn more by viewing this InfluxDays NYC 2019 presentation.
How to Build a new under filesystem in Alluxio: Apache Ozone as an exampleAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
How to Build a new under filesystem in Alluxio: Apache Ozone as an example
Baolong Mao, Sr. System Engineer (Tencent)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
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.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Challenges for running Hadoop on AWS - AdvancedAWS MeetupAndrei Savu
Nowadays we've got all the tools we need to spin-up and tear-down clusters with hundreds of nodes in minutes and this puts more pressure on the tools we use to configure and monitor our applications. This challenge is even more interesting when we have to deal with long running distributed data storage and processing systems like Hadoop. In this talk we will look into some of the challenges we need to deal with when creating and managing Hadoop clusters in AWS, we will discuss improvement opportunities in monitoring (e.g. detecting and dealing with instance failure, resource contention & noisy neighbors) and a bit about the future and how we should go about disconnecting workload dispatch from cluster lifecycle.
R&D to Product Pipeline Using Apache Spark in AdTech: Spark Summit East talk ...Spark Summit
The central premise of DataXu is to apply data science to better marketing. At its core, is the Real Time Bidding Platform that processes 2 Petabytes of data per day and responds to ad auctions at a rate of 2.1 million requests per second across 5 different continents. Serving on top of this platform is Dataxu’s analytics engine that gives their clients insightful analytics reports addressed towards client marketing business questions. Some common requirements for both these platforms are the ability to do real-time processing, scalable machine learning, and ad-hoc analytics. This talk will showcase DataXu’s successful use-cases of using the Apache Spark framework and Databricks to address all of the above challenges while maintaining its agility and rapid prototyping strengths to take a product from initial R&D phase to full production. The team will share their best practices and highlight the steps of large scale Spark ETL processing, model testing, all the way through to interactive analytics.
How Adobe uses Structured Streaming at ScaleDatabricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing. We want to share some of our learnings and hard earned lessons and as we reached this scale specifically with Structured Streaming.
Know thy Lag
While consuming off a Kafka topic which sees sporadic loads, its very important to monitor the Consumer lag. Also makes you respect what a beast backpressure is.
Reading Data In
Fan Out Pattern using minPartitions to Use Kafka Efficiently
Overload protection using maxOffsetsPerTrigger
More Apache Spark Settings used to optimize Throughput
MicroBatching Best Practices
Map() +ForEach() vs MapPartitons + forEachPartition
Adobe Spark Speculation and its Effects
Calculating Streaming Statistics
Windowing
Importance of the State Store
RocksDB FTW
Broadcast joins
Custom Aggegators
OffHeap Counters using Redis
Pipelining
Never late again! Job-Level deadline SLOs in YARNDataWorks Summit
Modern resource management frameworks for large scale analytics leave unresolved the problematic tension between high cluster utilization and job’s performance predictability—respectively coveted by operators and users. We address this in Morpheus a system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred from historical data, 2) enforces SLOs using novel scheduling techniques that isolate jobs from sharing-induced performance variability, and 3) mitigates inherent performance variance (e.g., due to failures) by means of dynamic reprovisioning of jobs. We validate these ideas against production traces from a 50k node cluster, and show that Morpheus can lower the number of deadline violations, while retaining cluster-utilization, and lowering cluster footprint. We demonstrate the scalability of our implementation by deploying Morpheus on a 2700-node cluster and running it against production-derived workloads. The extensions to the YARN ReservationSystem (2,3) are being open-sourced as part of Apache Hadoop----jira number: YARN-5326.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
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
Build Real-Time Applications with Databricks StreamingDatabricks
In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
The Practice of Presto & Alluxio in E-Commerce Big Data PlatformAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Practice of Presto & Alluxio in E-Commerce Big Data Platform
Wenjun Tao, Sr. Software Engineer, JD.com
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
The Pandemic Changes Everything, the Need for Speed and ResiliencyAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Pandemic Changes Everything, the Need for Speed and Resiliency
Parviz Peiravi, Global CTO of Financial Services Solutions, Intel
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Octo and the DevSecOps Evolution at Oracle by Ian Van HovenInfluxData
The transition from 40 years of successful licensed software development to an agile-based SaaS business involves many challenges. Octo, a real-time streaming metrics framework built around InfluxDB time series database, is aimed specifically at one: simplifying the collection and visualization of mission-critical operational data to enable a culture change toward metrics immersion and product ownership. Learn more by viewing this InfluxDays NYC 2019 presentation.
How to Build a new under filesystem in Alluxio: Apache Ozone as an exampleAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
How to Build a new under filesystem in Alluxio: Apache Ozone as an example
Baolong Mao, Sr. System Engineer (Tencent)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
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.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Challenges for running Hadoop on AWS - AdvancedAWS MeetupAndrei Savu
Nowadays we've got all the tools we need to spin-up and tear-down clusters with hundreds of nodes in minutes and this puts more pressure on the tools we use to configure and monitor our applications. This challenge is even more interesting when we have to deal with long running distributed data storage and processing systems like Hadoop. In this talk we will look into some of the challenges we need to deal with when creating and managing Hadoop clusters in AWS, we will discuss improvement opportunities in monitoring (e.g. detecting and dealing with instance failure, resource contention & noisy neighbors) and a bit about the future and how we should go about disconnecting workload dispatch from cluster lifecycle.
R&D to Product Pipeline Using Apache Spark in AdTech: Spark Summit East talk ...Spark Summit
The central premise of DataXu is to apply data science to better marketing. At its core, is the Real Time Bidding Platform that processes 2 Petabytes of data per day and responds to ad auctions at a rate of 2.1 million requests per second across 5 different continents. Serving on top of this platform is Dataxu’s analytics engine that gives their clients insightful analytics reports addressed towards client marketing business questions. Some common requirements for both these platforms are the ability to do real-time processing, scalable machine learning, and ad-hoc analytics. This talk will showcase DataXu’s successful use-cases of using the Apache Spark framework and Databricks to address all of the above challenges while maintaining its agility and rapid prototyping strengths to take a product from initial R&D phase to full production. The team will share their best practices and highlight the steps of large scale Spark ETL processing, model testing, all the way through to interactive analytics.
How Adobe uses Structured Streaming at ScaleDatabricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing. We want to share some of our learnings and hard earned lessons and as we reached this scale specifically with Structured Streaming.
Know thy Lag
While consuming off a Kafka topic which sees sporadic loads, its very important to monitor the Consumer lag. Also makes you respect what a beast backpressure is.
Reading Data In
Fan Out Pattern using minPartitions to Use Kafka Efficiently
Overload protection using maxOffsetsPerTrigger
More Apache Spark Settings used to optimize Throughput
MicroBatching Best Practices
Map() +ForEach() vs MapPartitons + forEachPartition
Adobe Spark Speculation and its Effects
Calculating Streaming Statistics
Windowing
Importance of the State Store
RocksDB FTW
Broadcast joins
Custom Aggegators
OffHeap Counters using Redis
Pipelining
Never late again! Job-Level deadline SLOs in YARNDataWorks Summit
Modern resource management frameworks for large scale analytics leave unresolved the problematic tension between high cluster utilization and job’s performance predictability—respectively coveted by operators and users. We address this in Morpheus a system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred from historical data, 2) enforces SLOs using novel scheduling techniques that isolate jobs from sharing-induced performance variability, and 3) mitigates inherent performance variance (e.g., due to failures) by means of dynamic reprovisioning of jobs. We validate these ideas against production traces from a 50k node cluster, and show that Morpheus can lower the number of deadline violations, while retaining cluster-utilization, and lowering cluster footprint. We demonstrate the scalability of our implementation by deploying Morpheus on a 2700-node cluster and running it against production-derived workloads. The extensions to the YARN ReservationSystem (2,3) are being open-sourced as part of Apache Hadoop----jira number: YARN-5326.
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed.
As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations.
In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Microsoft DevOps for AI with GoDataDrivenGoDataDriven
Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Software development teams know how to deliver business applications and cloud services. AI/ML teams know how to develop models that can transform a business. But when it comes to putting the two together to implement an application pipeline specific to AI/ML — to automate it and wrap it around good deployment practices — the process needs some effort to be successful.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
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.
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.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
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).
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/
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.
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.
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
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
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.
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"
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.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
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.
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!
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
De mooiste recreatieve routes ontdekken met RouteYou en FME
Deep Learning in the Cloud at Scale: A Data Orchestration Story
1. Deep Learning in the Cloud at Scale:
A Data Orchestration Story
Chao Wang, Mickey Zhang, Qianjun Xu
2. Overview Machine Learning Requirements
End-to-End lifecycle and processes
Data Scientist Workflow
Deep Learning on Azure Machine Learning
Deep Learning: Additional Requirements
Distributed Training with Azure ML Compute
Kubernetes + Alluxio
4. Machine Learning
Typical E2E Process
Prepare
Data
Register and
Manage Model
Train &
Test Model
Build
Image
…
Build Model
(your favorite IDE)
Deploy Service
Monitor Model
Prepare Experiment Deploy
Orchestrate
5. DevOps loop for data science
Prepare
Data
Prepare
Register and
Manage Model
Build
Image
…
Build Model
(your favorite IDE)
Deploy Service
Monitor Model
Train &
Test Model
7. Characteristics of Deep Learning
Massive amounts of training data
Excels with raw, unstructured data
Automatic feature extraction
Computationally expensive
8. Distributed training mode: Data parallelism
Subset 1 CNN model
Worker 1
Subset 2 CNN model
Job manager
Worker 2
Dataset
CNN model
9. Distributed training mode: Model parallelism
Dataset
CNN model
CNN model CNN modelSubset 1 Subset 2
Worker 1 Worker 2
Job manager
10. Challenges of distributed training
Dependencies and Containers
Provision clusters of VMs
Schedule jobs
Distribute data
Gather results
Handling failures
Scale resources
Secure Access
13. Typical Data Consumption Model
Storage/NFS
Deep Learning Training Platform
RAM
SSD
GPU
CPU
Azure Kubernetes Service
RAM
SSD
GPU
CPU
RAM
SSD
GPU
CPU
14. Why Alluxio?
Scalable
Performance is scalable based on the
cluster size
Lower Cost
Leverage idle resources in the cluster
Performance
Improve data access throughput by
distributing across nodes
Flexibility
Manage multiple data sources in a
unified namespace
15. Side-Car Model With Alluxio
Storage
Deep Learning Training Platform
RAM
SSD
GPU
CPU
Azure Kubernetes Service
RAM
SSD
GPU
CPU
RAM
SSD
GPU
CPU
Data Preloaded in Cluster