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November 12-15
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Salt Palace | Level 1 | Hall DE clear filter
Wednesday, November 13
 

9:00am MST

Keynotes To Be Announced
Wednesday November 13, 2024 9:00am - 10:45am MST
Salt Palace | Level 1 | Hall DE
Wednesday November 13, 2024 9:00am - 10:45am MST
Salt Palace | Level 1 | Hall DE
  Keynote Sessions

12:10pm MST

Operationalizing High-Performance GPU Clusters in Kubernetes: A Case Study of Databricks' DBRX - Will Gleich & Wai Wu, Databricks
Wednesday November 13, 2024 12:10pm - 12:45pm MST
Salt Palace | Level 1 | Hall DE
Training large language models (LLMs) on GPUs within Kubernetes environments involves significant configuration and complexity, often leading to unique failure scenarios. This presentation will cover the lessons learned from training DBRX, a state-of-the-art LLM, that we developed on a 400-node cluster with a primary workload utilizing 3072 GPUs and the tooling needed to measure and maintain a healthy fleet of nodes and underlying interconnect fabric. This will include: * How we implemented GPU health detection leveraging Prometheus and DCGM Exporter * How we monitor GPU Direct Remote Direct Memory Access (GDRDMA) and the challenges of monitoring components that bypass CPU * Discussion of failure scenarios during training, and how they were addressed Databricks Mosaic AI Training leverages GPU clusters across many cloud providers to maximize availability; we will also discuss the variations we see and how we had to engineer around them.
Speakers
WW

Wai Wu

Databricks
avatar for Will Gleich

Will Gleich

Sr. DevOps Engineer, Databricks
Will Gleich is a Sr. DevOps engineer at Databricks specializing in MLOps and Site Reliability Engineering.
Wednesday November 13, 2024 12:10pm - 12:45pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

2:30pm MST

Optimizing LLM Performance in Kubernetes with OpenTelemetry - Ashok Chandrasekar, Google & Liudmila Molkova, Microsoft
Wednesday November 13, 2024 2:30pm - 3:05pm MST
Salt Palace | Level 1 | Hall DE
Large Language Models are increasing in popularity and their deployments on Kubernetes have steadily increased. LLM applications bring new usage patterns that the industry does not have the expertise in. At the same time, there is a lack of observability in these deployments which makes it difficult to debug performance issues. We will present an end to end walkthrough of how you can leverage client and server LLM observability using Open Telemetry based on the recent efforts in the Kubernetes and Open Telemetry communities to standardize these across LLM clients and model servers. We will also demonstrate how to troubleshoot a real-world performance issue in your LLM deployment and how to optimize your LLM server setup for better performance on Kubernetes. We'll show how to use Kubernetes autoscaling based on custom model server metrics and demonstrate how they offer a superior alternative to using GPU utilization metrics for such deployments.
Speakers
avatar for Liudmila Molkova

Liudmila Molkova

Principal Software Engineer, Microsoft
Liudmila Molkova is a Principal Software Engineer at Microsoft working on observability and Azure client libraries. She is a co-author of distributed tracing implementations across the .NET ecosystem including HTTP client instrumentation and Azure Functions. Liudmila is an active... Read More →
avatar for Ashok Chandrasekar

Ashok Chandrasekar

Senior Software Engineer, Google
Ashok Chandrasekar is a Senior Software Engineer at Google working on AI/ML experience for Google Kubernetes Engine. Previously he was a Staff Engineer at VMware where he led the cluster lifecycle management area for Tanzu Mission Control. He has 7 years of Kubernetes experience working... Read More →
Wednesday November 13, 2024 2:30pm - 3:05pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

3:25pm MST

Optimizing Load Balancing and Autoscaling for Large Language Model (LLM) Inference on Kubernetes - David Gray, Red Hat
Wednesday November 13, 2024 3:25pm - 4:00pm MST
Salt Palace | Level 1 | Hall DE
As generative AI language models improve, they are increasingly being integrated into business-critical applications. However, large language model (LLM) inference is a compute-intensive workload that often requires expensive GPU hardware. Making efficient use of these hardware resources in the public or private cloud is critical for managing costs and power usage. This talk introduces the KServe platform for deploying LLMs on Kubernetes and provides an overview of LLM inference performance concepts. Attendees will learn techniques to improve load balancing and autoscaling for LLM inference, such as leveraging KServe, Knative, and GPU operator features. Sharing test results, we will analyze the impact of these optimizations on key performance metrics, such as latency per token and tokens per second. This talk equips participants with strategies to maximize the efficiency of LLM inference deployments on Kubernetes, ultimately reducing costs and improving resource utilization.
Speakers
avatar for David Gray

David Gray

Senior Software Engineer, Red Hat
David Gray is a Senior Software Engineer on the Performance and Scale team at Red Hat. His role involves analyzing and improving AI inference workloads on Kubernetes platforms. David is actively engaged in performance experimentation and analysis of running large language models in... Read More →
Wednesday November 13, 2024 3:25pm - 4:00pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
  • Content Experience Level Any

4:30pm MST

Platform Performance Optimization for AI - a Resource Management Perspective - Antti Kervinen, Intel & Dixita Narang, Google
Wednesday November 13, 2024 4:30pm - 5:05pm MST
Salt Palace | Level 1 | Hall DE
How much node resource management can affect AI workload performance? What options are there? What is the trade-off between total throughput and low latencies? In this talk we take a systematic approach to Platform Performance Optimization. We walk through the whole path from goal setting, gathering data, analysis, visualizations and conclusions. At each stop along the path we share our practical experiences in a case of LLM inference optimization. You will find many considerations, findings and practical tricks to take away. For instance, how to instrument PyTorch without touching the source or a container image, how to enable changing what we are measuring without new expensive benchmark reruns, and how much more we can learn from visualizations compared to numeric averages and percentiles. Finally we share real results from our case: how resource management increased total token throughput per worker node by more than 3.5x from the baseline.
Speakers
avatar for Antti Kervinen

Antti Kervinen

Cloud Orchestration Software Engineer, Intel
Antti Kervinen is a Cloud Orchestration Software Engineer working at Intel, whose interest in Linux and distributed systems has led him from academic research of concurrency to the world of Kubernetes. When unplugged, Antti spends his time outdoors discovering wonders of nature.
avatar for Dixita Narang

Dixita Narang

Software Engineer, Google
Dixita Narang is a Software Engineer at Google on the Kubernetes Node team. With a primary focus on resource management within Kubernetes, Dixita is deeply involved in the development and advancement of the Memory QoS feature, which is currently in the alpha stage. She is a new contributor... Read More →
Wednesday November 13, 2024 4:30pm - 5:05pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

5:25pm MST

Production AI at Scale: Cloudera’s Journey in Building a Robust Inference Platform - Zoram Thanga & Peter Ableda, Cloudera
Wednesday November 13, 2024 5:25pm - 6:00pm MST
Salt Palace | Level 1 | Hall DE
In this session, we talk about Cloudera AI Inference Service, a secure, large scale platform for generative AI and predictive inference workloads, built using state of the art Kubernetes, CNCF and Apache open source projects. We take the audience through our journey in building this platform and share the experiences we gained along the way. The platform is built using openness, security, scalability, performance and standards compliance as guiding principles. We demonstrate that it is possible to be open and secure at the same time, and that organizations can incorporate production grade AI inferencing into their Big Data environments. This session will cover the architecture of the platform, and explain how we handle performance, scaling, authentication, fine grained authorization and audit logging, all of which are critical considerations for production inferencing.
Speakers
avatar for Peter Ableda

Peter Ableda

Director, Product Management, Cloudera
Peter Ableda is the Director of Product Management for Cloudera’s AI product suite, bringing over a decade of experience in data management and advanced analytics. Holding a Master of Science degree in Computer Science from the Budapest University of Technology, Peter has dedicated... Read More →
avatar for Zoram Thanga

Zoram Thanga

Principal Engineer, Cloudera
Zoram is a Principal Engineer, Enterprise AI Platform in Cloudera. He has been working in the software industry for over 23 years, and has been involved in building clustering software, containers, file systems, analytical query engines, and ML/AI platforms. He is a committer in the... Read More →
Wednesday November 13, 2024 5:25pm - 6:00pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
 
Thursday, November 14
 

9:00am MST

Keynotes To Be Announced
Thursday November 14, 2024 9:00am - 10:30am MST
Salt Palace | Level 1 | Hall DE
Thursday November 14, 2024 9:00am - 10:30am MST
Salt Palace | Level 1 | Hall DE
  Keynote Sessions

11:55am MST

Democratizing AI Model Training on Kubernetes with Kubeflow TrainJob and JobSet - Andrey Velichkevich, Apple & Yuki Iwai, CyberAgent, Inc.
Thursday November 14, 2024 11:55am - 12:30pm MST
Salt Palace | Level 1 | Hall DE
Running model training on Kubernetes is challenging due to the complexity of AI/ML models, large training datasets, and various distributed strategies like data and model parallelism. It is crucial to configure failure handling, success criteria, and gang-scheduling for large-scale distributed training to ensure fault tolerance and elasticity. This talk will introduce the new Kubeflow TrainJob API, which democratizes distributed training and LLM fine-tuning on Kubernetes. The speakers will demonstrate how TrainJob integrates with Kubernetes JobSet to ensure scalable and efficient AI model training with simplified Python experience for Data Scientists. Additionally, they will explain the innovative concept of reusable and extendable training runtimes within TrainJob. The speakers will highlight how these capabilities empower data scientists to rapidly iterate on their ML development, making Kubernetes more accessible and beneficial for the entire ML ecosystem.
Speakers
avatar for Andrey Velichkevich

Andrey Velichkevich

Senior Software Engineer, Apple
Andrey Velichkevich is a Senior Software Engineer at Apple and is a key contributor to the Kubeflow open-source project. He is a member of Kubeflow Steering Committee and a co-chair of Kubeflow AutoML and Training WG. Additionally, Andrey is an active member of the CNCF WG AI. He... Read More →
avatar for Yuki Iwai

Yuki Iwai

Software Engineer, CyberAgent, Inc.
Yuki is a Software Engineer at CyberAgent, Inc. He works on the internal platform for machine-learning applications and high-performance computing. He is currently a Technical Lead for Kubeflow WG AutoML / Training. He is also a Kubernetes WG Batch active member and a Kubernetes... Read More →
Thursday November 14, 2024 11:55am - 12:30pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
  • Content Experience Level Any

2:30pm MST

Unlocking Potential of Large Models in Production - Yuan Tang, Red Hat & Adam Tetelman, NVIDIA
Thursday November 14, 2024 2:30pm - 3:05pm MST
Salt Palace | Level 1 | Hall DE
The recent paradigm shift from traditional ML to GenAI and LLMs has brought with it a new set of non-trivial LLMOps challenges around deployment, scaling, and operations that make building an inference platform to meet all business requirements an unsolved problem. This talk highlights these new challenges along with best-practices and solutions for building out large, scalable, and reliable inference platforms on top of cloud native technologies such as Kubernetes, Kubeflow, Kserve, and Knative. Which tools help effectively benchmark and assess the quality of an LLM? What type of storage and caching solutions enable quick auto-scaling and model downloads? How can you ensure your model is optimized for the specialized accelerators running in your cluster? How can A/B testing or rolling upgrades be accomplished with limited compute? What exactly do you monitor in an LLM? In this session we will use KServe as a case study to answer these questions and more.
Speakers
avatar for Yuan Tang

Yuan Tang

Principal Software Engineer, Red Hat
Yuan is a principal software engineer at Red Hat, working on OpenShift AI. Previously, he has led AI infrastructure and platform teams at various companies. He holds leadership positions in open source projects, including Argo, Kubeflow, and Kubernetes. He's also a maintainer and... Read More →
avatar for Adam Tetelman

Adam Tetelman

Principal Product Architect, NVIDIA
Adam Tetelman is a principal architect at NVIDIA leading cloud native initiatives and CNCF engagements across the company; building inference platforms for NVIDIA AI Enterprise and DGX Cloud. He has degrees in computational robotics, computer & systems engineering, and cognitive science... Read More →
Thursday November 14, 2024 2:30pm - 3:05pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

3:25pm MST

Unlocking the Future of GPU Scheduling in Kubernetes with Reinforcement Learning - Nikunj Goyal, Adobe Systems & Aditi Gupta, Disney Plus Hotstar
Thursday November 14, 2024 3:25pm - 4:00pm MST
Salt Palace | Level 1 | Hall DE
Scaling up Multi GPU setup using Kubernetes for large scale ML projects has been a hot topic equally stressed upon among both the AI and cloud community. While Kubernetes is able to providing computing power by scheduling GPU nodes, certain issues like resource fragmentation and low utilization plague the performance and results in cost issues. Why Reinforcement Learning (RL) in particular one would ask. Unlike the other algorithms, RL shines in its unique ability to continuously adapt to changing environments and efficiently handle Complex and Multi-dimensional Objectives making it particularly suitable for the dynamic and heterogeneous nature of Kubernetes clusters. In this talk, we shall explore the current landscape of GPU scheduling and some state of the art RL algorithms proposed for scheduling. Their current impact on Kubernetes and the possible use of RLHF shall be dived deep into. We hope that audience gain more insights into these new ways of scheduling GPUs on Kubernetes.
Speakers
avatar for Aditi Gupta

Aditi Gupta

Aditi Gupta, Software Developer at Disney + Hotstar, Disney Plus Hotstar
I'm Aditi Gupta, a Software Developer Engineer at Disney+ Hotstar. Graduated from Asia's largest tech college for women, Indira Gandhi Delhi Technical University,I've been deeply immersed in cloud-native technologies and AI/ML advancements. Skilled in containerisation, micro-service... Read More →
avatar for Nikunj Goyal

Nikunj Goyal

Developer at Adobe, AI and Machine Learning Specialist, Adobe Systems
Hi, I am Nikunj Goyal, working as a developer at Adobe and a Maths major from IIT Roorkee. I am working with AI and Machine Learning for some time mainly with Generative AI and graph based methods. I am a core part of Text-to-vector generation team at my org and previously worked... Read More →
Thursday November 14, 2024 3:25pm - 4:00pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

4:30pm MST

Which GPU Sharing Strategy Is Right for You? a Comprehensive Benchmark Study Using DRA - Kevin Klues & Yuan Chen, NVIDIA
Thursday November 14, 2024 4:30pm - 5:05pm MST
Salt Palace | Level 1 | Hall DE
Dynamic Resource Allocation (DRA) is one of the most anticipated features to ever make its way into Kubernetes. It promises to revolutionize the way hardware devices are consumed and shared between workloads. In particular, DRA unlocks the ability to manage heterogeneous GPUs in a unified and configurable manner without the need for awkward solutions shoehorned on top of the existing device plugin API. In this talk, we use DRA to benchmark various GPU sharing strategies including Multi-Instance GPUs, Multi-Process Service (MPS), and CUDA Time-Slicing. As part of this, we provide guidance on the class of applications that can benefit from each strategy as well as how to combine different strategies in order to achieve optimal performance. The talk concludes with a discussion of potential challenges, future enhancements, and a live demo showcasing the use of each GPU sharing strategy with real-world applications.
Speakers
avatar for Kevin Klues

Kevin Klues

Distinguished Engineer, NVIDIA
Kevin Klues is a distinguished engineer on the NVIDIA Cloud Native team. Kevin has been involved in the design and implementation of a number of Kubernetes technologies, including the Topology Manager, the Kubernetes stack for Multi-Instance GPUs, and Dynamic Resource Allocation (DRA... Read More →
avatar for Yuan Chen

Yuan Chen

Principal Software Engineer, NVIDIA
Yuan Chen is a Principal Software Engineer at NVIDIA, working on building NVIDIA GPU Cloud for AI. He served as a Staff Software Engineer at Apple from 2019 to 2024, where he contributed to the development of Apple's Kubernetes infrastructure. Yuan has been an active code contributor... Read More →
Thursday November 14, 2024 4:30pm - 5:05pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
  • Content Experience Level Any

5:25pm MST

Managing and Distributing AI Models Using OCI Standards and Harbor - Steven Zou & Steven Ren, VMware by Broadcom
Thursday November 14, 2024 5:25pm - 6:00pm MST
Salt Palace | Level 1 | Hall DE
Just as container images are vital to cloud-native technology, AI models are crucial to AI technology. Effectively, conveniently, and safely managing, maintaining, and distributing AI models is critical for supporting workflows like AI model training, inference, and application deployment. This presentation explores AI model management based on OCI standards and the Harbor project. Standardizing AI model structures and characteristics using OCI specifications and extension mechanisms like OCI Reference to link datasets and dependencies. When large models require efficient loading or privacy considerations, model replication or proxy with upstream repositories like Hugging Face becomes essential. Enhancing model distribution security through signing, vulnerability scanning, and policy-based governance is often necessary. Additionally, introducing acceleration mechanisms such as P2P can significantly improve the efficiency of large model loading.
Speakers
avatar for Steven Ren

Steven Ren

Senior Manager, Broadcom
avatar for Steven Zou

Steven Zou

Staff II Engineer, VMware by Broadcom
Steven Zou is a senior engineer with years of experience in cloud computing and cloud-native technology. He is currently working as a Staff II engineer at VMware, focusing on cloud-native and Kubernetes-related platform services. In addition, he is a core maintainer of the CNCF open-source... Read More →
Thursday November 14, 2024 5:25pm - 6:00pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
  • Content Experience Level Any
 
Friday, November 15
 

9:00am MST

Keynotes To Be Announced
Friday November 15, 2024 9:00am - 10:30am MST
Salt Palace | Level 1 | Hall DE
Friday November 15, 2024 9:00am - 10:30am MST
Salt Palace | Level 1 | Hall DE
  Keynote Sessions

11:55am MST

Improving Service Availability: Scaling Ahead with Machine Learning for HPA Optimization - Avni Sharma & Estela Ramirez, Intuit
Friday November 15, 2024 11:55am - 12:30pm MST
Salt Palace | Level 1 | Hall DE
In this talk, we will explore employing machine learning (ML) algorithms to enhance the Kubernetes autoscaling capabilities beyond the traditional, reactive horizontal pod autoscaler (HPA). Attendees will be introduced to how to leverage recommendation algorithms to predict future load and usage patterns, allowing for smarter, proactive scaling decisions. This approach not only ensures high availability and responsiveness of applications but also offers a pathway to substantial cost optimizations by preventing over-provisioning and minimizing resource wastage.
Speakers
avatar for Avni Sharma

Avni Sharma

Product Manager, Intuit
Avni is a Product Manager at Intuit, working on Intuit’s Modern SaaS Kubernetes platform. She also worked on ArgoCD as a PM. Avni is passionate about Developer tooling and strives to make developers' life easier by delivering them delightful experiences. She is also an Open Source... Read More →
avatar for Estela Ramirez

Estela Ramirez

Software Engineer, Intuit Kubernetes Service, Intuit
Estela is a Software Engineer at Intuit focusing on Intuit Kubernetes Developer Platform. She works on abstracting the autoscaling for developers.
Friday November 15, 2024 11:55am - 12:30pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

2:00pm MST

From Vectors to Pods: Integrating AI with Cloud Native - Rajas Kakodkar, Broadcom; Kevin Klues, NVIDIA; Joseph Sandoval, Adobe; Ricardo Rocha, CERN; Cathy Zhang, Intel
Friday November 15, 2024 2:00pm - 2:35pm MST
Salt Palace | Level 1 | Hall DE
The rise of AI is challenging long-standing assumptions about running cloud native workloads. AI demands hardware accelerators, vast data, efficient scheduling and exceptional scalability. Although Kubernetes remains the de facto choice, feedback from end users and collaboration with researchers and academia are essential to drive innovation, address gaps and integrate AI in cloud native. This panel features end users, AI infra researchers and leads of the CNCF AI and Kubernetes device management working groups focussed on: - Expanding beyond LLMs to explore AI for cloud native workload management, memory usage and debugging - Challenges with scheduling and scaling of AI workloads from the end user perspective - OSS Projects and innovation in AI and cloud native in the CNCF landscape - Improving resource utilisation and performance of AI workloads The next decade of Kubernetes will be shaped by AI. We don’t yet know what this will look like, come join us to discover it together.
Speakers
avatar for Ricardo Rocha

Ricardo Rocha

Lead Platforms Infrastructure, CERN
Ricardo leads the Platform Infrastructure team at CERN with a strong focus on cloud native deployments and machine learning. He has led for several years the internal effort to transition services and workloads to use cloud native technologies, as well as dissemination and training... Read More →
avatar for Kevin Klues

Kevin Klues

Distinguished Engineer, NVIDIA
Kevin Klues is a distinguished engineer on the NVIDIA Cloud Native team. Kevin has been involved in the design and implementation of a number of Kubernetes technologies, including the Topology Manager, the Kubernetes stack for Multi-Instance GPUs, and Dynamic Resource Allocation (DRA... Read More →
avatar for Joseph Sandoval

Joseph Sandoval

Principal Product Manager, Adobe Inc.
Joseph Sandoval, a seasoned tech expert with 25 years in various roles running distributed systems, infrastructure platforms and thrives on empowering developers to scale their applications. An advocate for OpenSource software, he harnesses its transformative power to champion change... Read More →
avatar for Cathy Zhang

Cathy Zhang

senior principal engineer, Intel
As a member of the CNCF TOC, Cathy has been sponsoring and guiding projects' applications for graduation/incubating, and reviewing/approving new sandbox projects. She has been a committee member for several KubeCon. Cathy is a currently Senior Principal Engineer at Intel, leading... Read More →
avatar for Rajas Kakodkar

Rajas Kakodkar

Senior Member of Technical Staff | Tech Lead TAG Runtime CNCF, Broadcom
Rajas is a senior member of technical staff at Broadcom and a tech lead of the CNCF Technical Advisory Group, Runtime. He is actively involved in the AI working group in the CNCF. He is a Kubernetes contributor and has been a maintainer of the Kube Proxy Next Gen Project. He has also... Read More →
Friday November 15, 2024 2:00pm - 2:35pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
  • Content Experience Level Any

2:55pm MST

Enabling Fault Tolerance for GPU Accelerated AI Workloads in Kubernetes - Arpit Singh & Abhijit Paithankar, NVIDIA
Friday November 15, 2024 2:55pm - 3:30pm MST
Salt Palace | Level 1 | Hall DE
In K8s based ML platforms, job failures from hardware errors such as GPU malfunctions, network disruptions, ECC errors, and OOM events pose significant challenges. These failures cause resource underutilization, wasted engineering time, and high operational costs, often requiring users to resubmit jobs. Current AI/ML frameworks lack adequate fault tolerance strategies, typically requiring manual intervention and causing delays before jobs can resume. This talk explores fault tolerance strategies including naive job restarts on failure, job restarts with hot spares, and job restarts by replacing faulty nodes. We discuss how to achieve fault propagation by leveraging node and pod conditions and address gaps in fault discovery and error propagation in the existing Kubernetes ecosystem. Our talk will also include ways to enhance components like the node-problem-detector and introduce new elements to close the gaps in fault detection , propagation reaction and remediation.
Speakers
avatar for Abhijit Paithankar

Abhijit Paithankar

Tech Lead and Engineering Manager, NVIDIA
Abhijit Paithankar is the AI and HPC Systems Tech Lead and Engineering Manager at NVIDIA, focusing on advanced computing technologies. Previously, he co-founded Crave.IO and served as CTO, and held key roles at Nutanix and VMware, developing critical hypervisor and storage solutions... Read More →
avatar for Arpit Singh (SW-CLOUD) US

Arpit Singh (SW-CLOUD) US

Senior Software Engineer, Nvidia
Arpit Singh specializes in AI infrastructure at Nvidia, enhancing deep learning applications. Besides being a Kubernetes contributor, Arpit has 10+ years of experience spanning Nvidia, Nutanix and Cisco. He holds multiple patents (2 granted, 4+ pending) and has dual master's degr... Read More →
Friday November 15, 2024 2:55pm - 3:30pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

4:00pm MST

Divide and Conquer: Master GPU Partitioning and Visualize Savings with OpenCost - Kaysie Yu & Ally Ford, Microsoft
Friday November 15, 2024 4:00pm - 4:35pm MST
Salt Palace | Level 1 | Hall DE
Kubernetes is the ideal platform for running AI and ML workloads, such as LLMs. GPU nodes are often used for their parallel processing capabilities and higher performance benefits; however, they are known to be costly. Many factors impact the cost of running AI/ML workloads such as GPU utilization, GPU VM size, idle time, etc. These costs are often ignored and considered inherent in running GPU workloads. But if running workloads at scale and left unoptimized, costs will quickly spin out of control. In this talk, we leverage NVIDIA DCGM exporter with Prometheus for GPU metrics monitoring alongside OpenCost to measure the Kubernetes spend of our GPU workloads. We will provide an overview of OpenCost, highlighting its role in bridging the gap between the developer and platform teams through visibility and accountability of spend. We will demonstrate how to use the NVIDIA GPU Operator and how techniques such as partitioning can lead to significant cost savings.
Speakers
avatar for Ally Ford

Ally Ford

Product Manager, Microsoft
Ally is a Product Manager on the Azure Kubernetes Service (AKS) team at Microsoft Azure. She spends her days collaborating with customers to design features that improve the end to end operator experience for both Linux and Windows users. Formerly she was a UX designer and project... Read More →
avatar for Kaysie

Kaysie

Product Manager, Microsoft
Kaysie Yu is a Product Manager on the Azure Kubernetes Service team at Microsoft. She works on cost management and optimization and is passionate about the convergence of FinOps and GreenOps, advocating for best practices that help organizations achieve cost efficiency while contributing... Read More →
Friday November 15, 2024 4:00pm - 4:35pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML

4:55pm MST

Distributed Multi-Node Model Inference Using the LeaderWorkerSet API - Abdullah Gharaibeh & Rupeng Liu, Google
Friday November 15, 2024 4:55pm - 5:30pm MST
Salt Palace | Level 1 | Hall DE
Large Language Models have shown remarkable capabilities in various tasks, from text generation to code writing. However, the inference process for these models presents significant challenges. LLMs are computationally intensive, often requiring specialized hardware like TPUs or GPUs to achieve reasonable response times. In some cases their substantial size can strain the resources of a single machine. Specifically, models such as Gemini, Claude, and GPT4 are too large to fit on any single GPU or TPU device, let alone on any single multi-accelerator machine, necessitating what we refer to as multi-node server deployment where a single model server “backend” runs as a distributed process on multiple nodes to harness enough accelerator memory to fit and run the model. This talk presents LeaderWorkerSet, a new k8s API that enables multi-node model inference. We demonstrate its capabilities by orchestrating state of the art model servers such as vLLM and JetStream on both GPUs and TPUs.
Speakers
avatar for Abdullah Gharaibeh

Abdullah Gharaibeh

Staff Software Engineer, Google
Abdullah is a staff software engineer at Google and sig-scheduling and working group batch co-chair. He works on Kubernetes and Google Kubernetes Engine, focusing on scheduling and batch workloads.
avatar for Rupeng Liu

Rupeng Liu

Software engineer, Google
Rupeng Liu, a software engineer from the Google's Kubernetes inference team
Friday November 15, 2024 4:55pm - 5:30pm MST
Salt Palace | Level 1 | Hall DE
  AI + ML
 
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