As the amount of data analyzed and stored continues to grow exponentially, fixed on-premises infrastructure like Apache Hadoop data lakes becomes costly. Add to that the need to support newer and popular frameworks on an already busy data lake, it is not uncommon to see Hadoop-based data lakes running at beyond 100% utilization and hybrid processing split between physical and cloud infrastructure. As a result, companies are looking to leverage the flexibility and cost savings of the cloud.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
We’ll also show a demo on how to get up and running with Alluxio and Dataproc, including how to:
- Setup your hybrid environment between your private datacenter and Google Cloud Platform
- Burst a Spark based machine learning algorithm to Dataproc while accessing on-prem data
- Scale analytic workloads directly on data on-prem without copying and synchronizing the data into the cloud
ALLUXIO TECH TALK
As the amount of data analyzed and stored continues to grow exponentially, fixed on-premises infrastructure like Apache Hadoop data lakes becomes costly. Add to that the need to support newer and popular frameworks on an already busy data lake, it is not uncommon to see Hadoop-based data lakes running at beyond 100% utilization and hybrid processing split between physical and cloud infrastructure. As a result, companies are looking to leverage the flexibility and cost savings of the cloud.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
We’ll also show a demo on how to get up and running with Alluxio and Dataproc, including how to:
- Setup your hybrid environment between your private datacenter and Google Cloud Platform
- Burst a Spark based machine learning algorithm to Dataproc while accessing on-prem data
- Scale analytic workloads directly on data on-prem without copying and synchronizing the data into the cloud
ALLUXIO TECH TALK
As the amount of data analyzed and stored continues to grow exponentially, fixed on-premises infrastructure like Apache Hadoop data lakes becomes costly. Add to that the need to support newer and popular frameworks on an already busy data lake, it is not uncommon to see Hadoop-based data lakes running at beyond 100% utilization and hybrid processing split between physical and cloud infrastructure. As a result, companies are looking to leverage the flexibility and cost savings of the cloud.
Join us for this tech talk where we will show you how Alluxio can help burst your private computing environment to Google Cloud, minimizing costs and I/O overhead. Alluxio coupled with Google’s open source data and analytics processing engine, Dataproc, enables zero-copy burst for faster query performance in the cloud so you can take advantage of resources that are not local to your data, without the need for managing the copying or syncing of that data.
We’ll also show a demo on how to get up and running with Alluxio and Dataproc, including how to:
- Setup your hybrid environment between your private datacenter and Google Cloud Platform
- Burst a Spark based machine learning algorithm to Dataproc while accessing on-prem data
- Scale analytic workloads directly on data on-prem without copying and synchronizing the data into the cloud
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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.