AI training workloads running on compute engines like PyTorch, TensorFlow, and Ray require consistent, high-throughput access to training data to maintain high GPU utilization. However, with the decoupling of compute and storage and with today’s hybrid and multi-cloud landscape, AI Platform and Data Infrastructure teams are struggling to cost-effectively deliver the high-performance data access needed for AI workloads at scale.
Join Tom Luckenbach, Alluxio Solutions Engineering Manager, to learn how Alluxio enables high-speed, cost-effective data access for AI training workloads in hybrid and multi-cloud architectures, while eliminating the need to manage data copies across regions and clouds.
What Tom will share:
- AI data access challenges in cross-region, cross-cloud architectures.
- The architecture and integration of Alluxio with frameworks like PyTorch, TensorFlow, and Ray using POSIX, REST, or Python APIs across AWS, GCP and Azure.
- A live demo of an AI training workload accessing cross-cloud datasets leveraging Alluxio's distributed cache, unified namespace, and policy-driven data management.
- MLPerf and FIO benchmark results and cost-savings analysis.
AI training workloads running on compute engines like PyTorch, TensorFlow, and Ray require consistent, high-throughput access to training data to maintain high GPU utilization. However, with the decoupling of compute and storage and with today’s hybrid and multi-cloud landscape, AI Platform and Data Infrastructure teams are struggling to cost-effectively deliver the high-performance data access needed for AI workloads at scale.
Join Tom Luckenbach, Alluxio Solutions Engineering Manager, to learn how Alluxio enables high-speed, cost-effective data access for AI training workloads in hybrid and multi-cloud architectures, while eliminating the need to manage data copies across regions and clouds.
What Tom will share:
- AI data access challenges in cross-region, cross-cloud architectures.
- The architecture and integration of Alluxio with frameworks like PyTorch, TensorFlow, and Ray using POSIX, REST, or Python APIs across AWS, GCP and Azure.
- A live demo of an AI training workload accessing cross-cloud datasets leveraging Alluxio's distributed cache, unified namespace, and policy-driven data management.
- MLPerf and FIO benchmark results and cost-savings analysis.
AI training workloads running on compute engines like PyTorch, TensorFlow, and Ray require consistent, high-throughput access to training data to maintain high GPU utilization. However, with the decoupling of compute and storage and with today’s hybrid and multi-cloud landscape, AI Platform and Data Infrastructure teams are struggling to cost-effectively deliver the high-performance data access needed for AI workloads at scale.
Join Tom Luckenbach, Alluxio Solutions Engineering Manager, to learn how Alluxio enables high-speed, cost-effective data access for AI training workloads in hybrid and multi-cloud architectures, while eliminating the need to manage data copies across regions and clouds.
What Tom will share:
- AI data access challenges in cross-region, cross-cloud architectures.
- The architecture and integration of Alluxio with frameworks like PyTorch, TensorFlow, and Ray using POSIX, REST, or Python APIs across AWS, GCP and Azure.
- A live demo of an AI training workload accessing cross-cloud datasets leveraging Alluxio's distributed cache, unified namespace, and policy-driven data management.
- MLPerf and FIO benchmark results and cost-savings analysis.
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In the rapidly evolving landscape of AI and machine learning, Platform and Data Infrastructure Teams face critical challenges in building and managing large-scale AI platforms. Performance bottlenecks, scalability of the platform, and scarcity of GPUs pose significant challenges in supporting large-scale model training and serving.
In this talk, we introduce how Alluxio helps Platform and Data Infrastructure teams deliver faster, more scalable platforms to ML Engineering teams developing and training AI models. Alluxio’s highly-distributed cache accelerates AI workloads by eliminating data loading bottlenecks and maximizing GPU utilization. Customers report up to 4x faster training performance with high-speed access to petabytes of data spread across billions of files regardless of persistent storage type or proximity to GPU clusters. Alluxio’s architecture lowers data infrastructure costs, increases GPU utilization, and enables workload portability for navigating GPU scarcity challenges.
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.
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.