As enterprises race to roll out artificial intelligence, often overlooked are the infrastructure needs to support scalable ML model development and deployment. Efforts to effectively access and utilize GPUs often lead to extensive data engineering managing data copies or specialized storage, leading to out-of-control cloud and infrastructure costs.
To address the challenges, enterprises need a new data access layer to connect compute engines to data stores wherever they reside in distributed environments.
Join this webinar with Kevin Petrie, Eckerson Group VP of Research, and Sridhar Venkatesh, Alluxio SVP of Product, to explore tools, techniques, and best practices to remove data access bottlenecks and accelerate AI/ML model training. You will learn:
- Modern requirements for AI/ML model training and data engineering
- The challenges of GPU utilization in machine learning and the need for specialized hardware
- How a new data access layer connects compute to data stores across environments
- Best practices for optimizing ML training and guiding principles for success
As enterprises race to roll out artificial intelligence, often overlooked are the infrastructure needs to support scalable ML model development and deployment. Efforts to effectively access and utilize GPUs often lead to extensive data engineering managing data copies or specialized storage, leading to out-of-control cloud and infrastructure costs.
To address the challenges, enterprises need a new data access layer to connect compute engines to data stores wherever they reside in distributed environments.
Join this webinar with Kevin Petrie, Eckerson Group VP of Research, and Sridhar Venkatesh, Alluxio SVP of Product, to explore tools, techniques, and best practices to remove data access bottlenecks and accelerate AI/ML model training. You will learn:
- Modern requirements for AI/ML model training and data engineering
- The challenges of GPU utilization in machine learning and the need for specialized hardware
- How a new data access layer connects compute to data stores across environments
- Best practices for optimizing ML training and guiding principles for success
Video:
Presentation slides:
As enterprises race to roll out artificial intelligence, often overlooked are the infrastructure needs to support scalable ML model development and deployment. Efforts to effectively access and utilize GPUs often lead to extensive data engineering managing data copies or specialized storage, leading to out-of-control cloud and infrastructure costs.
To address the challenges, enterprises need a new data access layer to connect compute engines to data stores wherever they reside in distributed environments.
Join this webinar with Kevin Petrie, Eckerson Group VP of Research, and Sridhar Venkatesh, Alluxio SVP of Product, to explore tools, techniques, and best practices to remove data access bottlenecks and accelerate AI/ML model training. You will learn:
- Modern requirements for AI/ML model training and data engineering
- The challenges of GPU utilization in machine learning and the need for specialized hardware
- How a new data access layer connects compute to data stores across environments
- Best practices for optimizing ML training and guiding principles for success
Video:
Presentation slides:
Videos:
Presentation Slides:
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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.