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Standardized implementation patterns for maximizing hardware utilization and training speed on specific GPU architectures.
Distinct from NVIDIA GPU Kernels: Focuses on high-level reference patterns and configurations rather than the low-level kernel code itself.
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This project is a collection of optimized scripts, deployment patterns, and reference implementations designed for scaling and accelerating state-of-the-art AI models. It serves as a multi-domain model zoo and a distributed training framework, providing PyTorch reference implementations for training and deploying models on GPU-accelerated infrastructure. The repository distinguishes itself through an optimization suite focused on NVIDIA GPU hardware, utilizing automatic mixed precision and specialized math modes to increase training speed and throughput. It provides enterprise deployment patt
Provides reference patterns for accelerating training speed through mixed precision and specialized math modes on NVIDIA hardware.