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GTC ON-DEMAND

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Abstract:
Training intelligent agents with reinforcement learning is a notoriously unstable process. Although massive parallelization on GPUs and distributed systems can reduce instabilities, the success of training remains strongly influenced by the choice of hyperparameters. We'll describe a novel meta-optimization algorithm for distributed systems that solves a set of optimization problems in parallel while looking for the optimal hyperparameters. We'll also show how it applies to deep reinforcement learning. We'll demonstrate how the algorithm can fine-tune hyperparameters while learning to play different Atari games. Compared with existing approaches, our algorithm releases more computational resources during training by means of a stochastic scheduling procedure. Our algorithm has been implemented on top of MagLev, the NVIDIA AI training and inference infrastructure.
Training intelligent agents with reinforcement learning is a notoriously unstable process. Although massive parallelization on GPUs and distributed systems can reduce instabilities, the success of training remains strongly influenced by the choice of hyperparameters. We'll describe a novel meta-optimization algorithm for distributed systems that solves a set of optimization problems in parallel while looking for the optimal hyperparameters. We'll also show how it applies to deep reinforcement learning. We'll demonstrate how the algorithm can fine-tune hyperparameters while learning to play different Atari games. Compared with existing approaches, our algorithm releases more computational resources during training by means of a stochastic scheduling procedure. Our algorithm has been implemented on top of MagLev, the NVIDIA AI training and inference infrastructure.  Back
 
Topics:
Deep Learning & AI Frameworks, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9414
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Abstract:
In this talk we will cover the essential building blocks of the AI platform Nvidia engineers are using to build a world-class automotive perception stack. Through a computer vision application example, we will see how to improve a baseline model to produce better, faster predictions. The talk will focus on: - hyper-parameter optimization, - model complexity reduction (pruning), - target platform optimizations (TensorRT integration), - automation of complex workflows
In this talk we will cover the essential building blocks of the AI platform Nvidia engineers are using to build a world-class automotive perception stack. Through a computer vision application example, we will see how to improve a baseline model to produce better, faster predictions. The talk will focus on: - hyper-parameter optimization, - model complexity reduction (pruning), - target platform optimizations (TensorRT integration), - automation of complex workflows  Back
 
Topics:
AI Application, Deployment & Inference, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8633
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