On-Demand Videos
In this talk, Eric Wang, Senior Staff Software Engineer introduces Uber’s open-source generative end-to-end ML lifecycle management platform: Michelangelo.
In this talk, Jiani Wang, Software Engineer Meta's Pytorch Team, dives into the overview and the latest advancements in TorchTitan.
In this talk, Bin Fan, VP of Technology at Alluxio, explores how to enable efficient data access across distributed GPU infrastructure, achieving low-latency performance for feature stores and RAG workloads.
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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
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
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.
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.
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.
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, Juncheng Yangb describes 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.
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:
- Optimizing a developmental setup can include manual copies, which are slow and error-prone
- 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
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.
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 offer practical best practices for using distributed caching in the cloud. This session features insights from real-world examples, such as AliPay, Zhihu, and more.
Machine learning models power Uber’s everyday business. However, developing and deploying a model is not a one-time event but a continuous process that requires careful planning, execution, and monitoring. In this session, Sally (Mihyong) Lee, Senior Staff Engineer & TLM @ Uber, highlights Uber’s practice on the machine learning lifecycle to ensure high model quality.
In this talk, Wanchao Liang, Software Engineer at Meta Pytorch Team, explores the technology advancements of PyTorch Distributed, and dives into the details of how multi-dimensional parallelism is made possible to train Large Language Models by composing different PyTorch native distributed training APIs.
ChatGPT and other massive models represents an amazing step forward in AI, yet they do not solve real-world business problems. In this session, Jordan Plawner, Global Director of Artificial Intelligence Product Manager and Strategy at Intel, surveys how the AI ecosystem has worked non-stop over this last year to take these all-purpose multi-task models and optimize them to they can be used by organizations to address domain specific problems. He explains these new AI-for-the-real world techniques and methods such as fine tuning and how they can be applied to deliver results which are highly performant with state-of-the-art accuracy while also being economical to build and deploy everywhere to enhance products and services.