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.

In this talk,  Zhe Zhang (NVIDIA, ex-Anyscale) introduced Ray and its applications in the LLM and multi-modal AI era. He shared his perspective on ML infrastructure, noting that it presents more unstructured challenges, and recommended using Ray and Alluxio as solutions for increasingly data-intensive multi-modal AI workloads.

Scaling experimentation in digital marketplaces is crucial for driving growth and enhancing user experiences. However, varied methodologies and a lack of experiment governance can hinder the impact of experimentation leading to inconsistent decision-making, inefficiencies, and missed opportunities for innovation.

At Poshmark, we developed a homegrown experimentation platform, Lightspeed, that allowed us to make reliable and confident reads on product changes, which led to a 10x growth in experiment velocity and positive business outcomes along the way.

This session will provide a deep dive into the best practices and lessons learned from successful implementations of large-scale experiments. We will explore the importance of experimentation, overcome scalability challenges, and gain insights into the frameworks and technologies that enable effective testing.

In the rapidly evolving world of e-commerce, visual search has become a game-changing technology. Poshmark, a leading fashion resale marketplace, has developed Posh Lens – an advanced visual search engine that revolutionizes how shoppers discover and purchase items.

Under the hood of Posh Lens lies Milvus, a vector database enabling efficient product search and recommendation across our vast catalog of over 150 million items. However, with such an extensive and growing dataset, maintaining high-performance search capabilities while scaling AI infrastructure presents significant challenges.

In this talk, Mahesh Pasupuleti shares:

• The architecture and strategies to scale Milvus effectively within the Posh Lens infrastructure
• Key considerations include optimizing vector indexing, managing data partitioning, and ensuring query efficiency amidst large-scale data growth
• Distributed computing principles and advanced indexing techniques to handle the complexity of Poshmark’s diverse product catalog

As machine learning and deep learning models grow in complexity, AI platform engineers and ML engineers face significant challenges with slow data loading and GPU utilization, often leading to costly investments in high-performance computing (HPC) storage. However, this approach can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.

A better approach is adding a data caching layer between compute and storage, like Alluxio, which offers a cost-effective alternative through its innovative data caching strategy. In this webinar, Jingwen will explore how Alluxio's caching solutions optimize AI workloads for performance, user experience and cost-effectiveness.

What you will learn:

• The I/O bottlenecks that slow down data loading in model training
• How Alluxio's data caching strategy optimizes I/O performance for training and GPU utilization, and significantly reduces cloud API costs
• The architecture and key capabilities of Alluxio
• Using Rapid Alluxio Deployer to install Alluxio and run benchmarks in AWS in just 30 minutes

In the rapidly evolving landscape of AI and machine learning, infra teams face critical challenges in managing large-scale data for AI. Performance bottlenecks, cost inefficiencies, and management complexities pose significant challenges for AI platform teams supporting large-scale model training and serving.

In this talk, Bin Fan will discuss the challenges of I/O stalls that lead to suboptimal GPU utilization during model training. He will present a reference architecture for running PyTorch jobs with Alluxio in cloud environments, demonstrating how this approach can significantly enhance GPU efficiency.

What you will learn:

• How to identify GPU utilization and I/O-related performance bottlenecks in model training
• Leverage GPU anywhere to maximize resource utilization
• Best practices for monitoring and optimizing GPU usage across training and serving pipelines

OpenAI’s developer Developer Experience Engineer, Ankit Khare, provides practical insights for AI enthusiasts on effectively customizing and leveraging LLMs in various applications through preference tuning and fine-tuning.

In today’s AI-driven world, organizations face unprecedented demands for powerful AI infrastructure to fuel their model training and serving workloads. Performance bottlenecks, cost inefficiencies, and management complexities pose significant challenges for AI platform teams supporting large-scale model training and serving. On July 9, 2024, we introduced Alluxio Enterprise AI 3.2, a groundbreaking solution designed to address these critical issues in the ever-evolving AI landscape.

In this webinar, Shouwei Chen introduced exciting new features of Alluxio Enterprise AI 3.2:

• Leveraging GPU resources anywhere accessing remote data with the same local performance
• Enhanced I/O performance with 97%+ GPU utilization for popular language model training benchmarks
• Achieving the same performance as HPC storage on existing data lake without additional HPC storage infrastructure
• New Python FileSystem API to seamlessly integrate with Python applications like Ray
• Other new features, include advanced cache management, rolling upgrades, and CSI failover

As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.

The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.

What you will learn:

• Challenges relating to the speed and costs of running Trino in the cloud
• The new Trino file system cache feature overview, including the latest development status and test results
• A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
• Real-world cases, including a large online payment firm and a top ridesharing company
• The future roadmap of Trino file system cache and Trino-Alluxio integration

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.