Kubernetes is widely used across enterprises to orchestrate computation. And while Kubernetes helps improve flexibility and portability for computation in public/hybrid cloud environments across infrastructure providers, running data-intensive workloads can be challenging.
When it comes to efficiently moving data closer to Spark or Presto frameworks, co-locating data with these frameworks and accessing data from multiple or remote clouds is hard to do. That’s where Alluxio, an open source data orchestration platform, can help.
Alluxio enables data locality with your Spark and Presto workloads for faster performance and better data accessibility in Kubernetes. It also provides portability across storage providers.
In this on demand tech talk we’ll give a quick overview of Alluxio and the use cases it powers for Spark/Presto in Kubernetes. We’ll show you how to set up Alluxio and Spark/Presto to run in Kubernetes as well.
Kubernetes is widely used across enterprises to orchestrate computation. And while Kubernetes helps improve flexibility and portability for computation in public/hybrid cloud environments across infrastructure providers, running data-intensive workloads can be challenging.
When it comes to efficiently moving data closer to Spark or Presto frameworks, co-locating data with these frameworks and accessing data from multiple or remote clouds is hard to do. That’s where Alluxio, an open source data orchestration platform, can help.
Alluxio enables data locality with your Spark and Presto workloads for faster performance and better data accessibility in Kubernetes. It also provides portability across storage providers.
In this on demand tech talk we’ll give a quick overview of Alluxio and the use cases it powers for Spark/Presto in Kubernetes. We’ll show you how to set up Alluxio and Spark/Presto to run in Kubernetes as well.
Kubernetes is widely used across enterprises to orchestrate computation. And while Kubernetes helps improve flexibility and portability for computation in public/hybrid cloud environments across infrastructure providers, running data-intensive workloads can be challenging.
When it comes to efficiently moving data closer to Spark or Presto frameworks, co-locating data with these frameworks and accessing data from multiple or remote clouds is hard to do. That’s where Alluxio, an open source data orchestration platform, can help.
Alluxio enables data locality with your Spark and Presto workloads for faster performance and better data accessibility in Kubernetes. It also provides portability across storage providers.
In this on demand tech talk we’ll give a quick overview of Alluxio and the use cases it powers for Spark/Presto in Kubernetes. We’ll show you how to set up Alluxio and Spark/Presto to run in Kubernetes as well.
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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.
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.