On-Demand Videos
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.
In this talk, Sandeep Manchem discussed big data and AI, covering typical platform architecture and data challenges. We had engaging discussions about ensuring data safety and compliance in Big Data and AI applications.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.
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ALLUXIO DAY 2021 January 19, 2021
Over the years, Alluxio has grown significantly to be the data orchestration framework for the cloud. The community developers and users have contributed a lot of effort and innovation to make Alluxio the system it is today. There are many users and companies deploying Alluxio at very large scale, and with the large scale, comes different types of challenges.
In this talk, I will introduce the high-level architecture of the current system, and present the various components of Alluxio. Also, I will discuss some of the main challenges of large scale Alluxio deployments, and the lessons we learned from those environments. This talk will detail some of the major scalability improvements added in the past several months, and how users can benefit from the changes.
In this keynote, you will learn about the evolution of the global data platform at Rakuten spread across multiple regions, and clouds. In addition, you will hear about the journey across the years, and the use of data orchestration for multiple use cases.
We introduce Data Orchestration Hub, a management service that makes it easy to build an analytics or machine learning platform on data sources across regions to unify data lakes. Easy to use wizards connect compute engines, such as Presto or Spark, to data sources across data centers or from a public cloud to a private data center. In this session, you will witness the use of “The Hub” to connect a compute cluster in the cloud with data sources on-premises using Alluxio. This new service allows you to build a hybrid cloud on your own, without the expertise needed to manage or configure Alluxio.
Distributed applications are not new. The first distributed applications were developed over 50 years ago with the arrival of computer networks, such as ARPANET. Since then, developers have leveraged distributed systems to scale out applications and services, including large-scale simulations, web serving, and big data processing. However, until recently, distributed applications have been the exception, rather than the norm. However, this is changing quickly. There are two major trends fueling this transformation: the end of Moore’s Law and the exploding computational demands of new machine learning applications. These trends are leading to a rapidly growing gap between application demands and single-node performance which leaves us with no choice but to distribute these applications. Unfortunately, developing distributed applications is extremely hard, as it requires world-class experts. To make distributed computing easy, we have developed Ray, a framework for building and running general-purpose distributed applications.
Video: Presentation Slides: The Pandemic Changes Everything, the Need for Speed and Resiliency from Alluxio, Inc.
In this keynote, Calvin Jia will share some of the hottest use cases in Alluxio 2 and discuss the future directions of the project being pioneered by Alluxio and the community. Bin Fan will provide an overview of the growth of Alluxio open-source community with highlights on community-driven collaboration with engineering teams from Microsoft and Alibaba to advance the technology.
Data platforms span multiple clusters, regions and clouds to meet the business needs for agility, cost effectiveness, and efficiency. Organizations building data platforms for structured and unstructured data have standardized on separation of storage and compute to remain flexible while avoiding vendor lock-in. Data orchestration has emerged as the foundation of such a data platform for multiple use cases all the way from data ingestion to transformations to analytics and AI.
In this keynote from Haoyuan Li, founder and CEO of Alluxio, we will showcase how organizations have built data platforms based on data orchestration. The need to simplify data management and acceleration across different business personas has given rise to data orchestration as a requisite piece of the modern data platform. In addition, we will outline typical journeys for realizing a hybrid and multi-cloud strategy.
JD.com is one of the largest e-commerce corporations. In big data platform of JD.com, there are tens of thousands of nodes and tens of petabytes off-line data which require millions of spark and MapReduce jobs to process everyday. As the main query engine, thousands of machines work as Presto nodes and Presto plays an import role in the field of In-place analysis and BI tools. Meanwhile, Alluxio is deployed to improve the performance of Presto. The practice of Presto & Alluxio in JD.com benefits a lot of engineers and analysts.
In this talk, Baolong Mao from Tencent will share his experience in developing Apache Ozone under file system, showing how to create a new Under File System in a few steps with minimal lines of code.
At PayPal & any other data driven enterprise – data users & applications work with a variety of data sources (RDBMS, NoSQL, Messaging, Documents, Big Data, Time Series Databases), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive) to process petabytes of data. Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM).
To solve this problem and to make product development more effective, PayPal Data Platforms developed “Gimel”, an open source, unified analytics data platform which provides access to any storage through a single unified data API and SQL, which are powered by a centralized data catalog.
In most of the distributed storage systems, the data nodes are decoupled from compute nodes. This is motivated by an improved cost efficiency, storage utilization and a mutually independent scalability of computation and storage. While this consideration is indisputable, several situations exist where moving computation close to the data brings important benefits. Whenever the stored data is to be processed for analytics purposes, all the data needs to be repeatedly moved from the storage to the compute cluster, which leads to reduced performance.
In this talk, we will present how using Alluxio computation and storage ecosystems can better interact benefiting of the “bringing the data close to the code” approach. Moving away from the complete disaggregation of computation and storage, data locality can enhance the computation performance. During this talk, we will present our observations and testing results that will show important enhancements in accelerating Spark Data Analytics on Ceph Objects Storage using Alluxio.