Deploying PyTorch models into production requires balancing performance, hardware efficiency, and cost optimization. Developers face challenges such as GPU memory constraints during training, suboptimal inference latency, and escalating cloud infrastructure expenses. For instance, modern models like LLMs demand specialized strategies to reduce GPU memory footprint while maintaining throughput.
PyTorch basics
Deploying PyTorch models into production requires balancing performance, hardware efficiency, and cost optimization. Developers face challenges such as GPU memory constraints during training, suboptimal inference latency, and escalating cloud infrastructure expenses. For instance, modern models like LLMs demand specialized strategies to reduce GPU memory footprint while maintaining throughput. Meanwhile, serving architectures often suffer from poor hardware utilization due to rigid microservice designs, leading to unnecessary costs. This session addresses these pain points by providing a roadmap for transitioning PyTorch models from research to production, emphasizing GPU optimization and cost-aware serving.
We will go over two aspects in this talk:
PyTorch Model Development & Optimization We begin with PyTorch’s native tools for model construction, including torch.nn.Module for modular architecture design and torch.nn.Parameter for efficient weight management. To minimize GPU memory during training, we demonstrate techniques like gradient checkpointing, mixed precision via torch.amp, and batch size tuning. For instance, replacing optimizer.zero_grad() with param.grad = None reduces memory operations by 15–20%. We’ll also explore PyTorch 2.X’s compiler (torch.compile) to accelerate training cycles by 30–40% through graph optimizations.
GPU-Centric Model Lowering Maximizing GPU utilization requires hardware-aware optimizations. We detail how to enable Tensor Cores for FP16/INT8 operations, align layer dimensions to CUDA kernel requirements (e.g., multiples of 8), and leverage DeepSpeed’s ZeRO-3 for sharding billion-parameter models across GPUs. Attendees will learn to profile models using PyTorch Profiler, identifying bottlenecks like excessive H2D/D2H transfers or underutilized SM cores. A case study will show how offline autotuning in torch.inductor reduces compilation overhead by 60% in production pipelines.
Bugra Akyildiz is an Senior Engineering Manager at Meta where his team builds large scale recommender system models for recommendation producst. He received B.S from Bilkent University and M.Sc from New York University focusing signal processing and machine learning.