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NVIDIA FLARE Q3 Webinar: From Skills to Scale with AI Agents

Event artwork for NVIDIA FLARE Q3 Webinar: From Skills to Scale with AI Agents

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What to expect

Learn how NVIDIA FLARE uses AI agents to simplify federated learning workflows and scale experiments efficiently across shared HPC infrastructure.

Taking place online on 20 August 2026, from 9:00–10:00 AM PDT, this expert webinar from AICamp explores how NVFLARE is reducing friction across the full journey from federated learning development to large-scale deployment. It is designed for AI and machine learning developers, researchers, infrastructure teams and technical practitioners working with federated workflows, PyTorch or high-performance computing.

From agentic skills to reliable workflows

The first part of the session focuses on Agentic Skills for Federated Workflows. Federated learning newcomers often need to understand specialist concepts, APIs, job structures, configuration patterns and validation processes before they can run a dependable experiment. FLARE skills package this domain knowledge into reusable agent workflows that can help users:

  • Inspect data and convert existing PyTorch code
  • Generate federated learning jobs
  • Validate results and diagnose failures
  • Work effectively without first mastering the full NVFLARE API surface

The webinar will also introduce newly added Auto-FL skills. These extend agentic assistance into iterative experiment development by managing candidate jobs, tracking campaign state, comparing outcomes and guiding subsequent iterations. For experienced users, this promises a more structured way to handle repeated code changes, runs, results and diagnosis.

Scaling NVIDIA FLARE on HPC

The second section examines Scaling NVFLARE on HPC Infrastructure. The discussion will consider how Slurm integration can move beyond basic sbatch submission towards a more complete execution model for shared clusters. Topics include isolated studies, Apptainer and other container backends, improved lifecycle management, GPU allocation and resource patterns that prevent orchestration processes from unnecessarily occupying accelerator nodes.…

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