Speed up Deep Learning Models with Minimal Effort

Описание: Speaker: Michał Kustosz
Machine Learning Engineer

Discover how to significantly speed up PyTorch models on GPU with minimal effort by tackling compute, memory, and CPU overhead. This video explores the inner workings of GPUs, identifies bottlenecks using profiling tools, and shares techniques like leveraging Tensor Cores, using the Torch Compiler, applying quantization, and enabling Automatic Mixed Precision (AMP). Boost performance and reduce memory usage while maintaining model accuracy.

00:00 Intro
02:40 Compute bound
12:35 Memory bound
19:32 Overhead bound
21:35 Profiling GPU code
23:50 Solutions

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#PyTorch #GPU #MachineLearning #DeepLearning #TensorCores #TorchCompiler #Quantization #AutomaticMixedPrecision #ModelOptimization #PerformanceBoost #DeepTalk