As more and more companies turn to AI / ML / DL to unlock insight, AI has become this mythical word that adds unnecessary barriers to new adaptors. Oftentimes it was regarded as luxury for those big tech companies only – this should not be the case.
In this talk, Jingwen will first dissect the ML life cycle into five stages – starting from data collection, to data cleansing, model training, model validation, and end at model inference / deployment stages. For each stage, Jingwen will then go over its concept, functionality, characteristics, and use cases to demystify ML operations. Finally, Jingwen will showcase how Alluxio, a virtual data lake, could help simplify each stage.
As more and more companies turn to AI / ML / DL to unlock insight, AI has become this mythical word that adds unnecessary barriers to new adaptors. Oftentimes it was regarded as luxury for those big tech companies only – this should not be the case.
In this talk, Jingwen will first dissect the ML life cycle into five stages – starting from data collection, to data cleansing, model training, model validation, and end at model inference / deployment stages. For each stage, Jingwen will then go over its concept, functionality, characteristics, and use cases to demystify ML operations. Finally, Jingwen will showcase how Alluxio, a virtual data lake, could help simplify each stage.
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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.
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.