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Distributed Training Infrastructure

Multi-GPU Pipeline — PyTorch DDP, HDF5, ETL

High-performance distributed training system using PyTorch DDP across multiple GPUs with optimized ETL pipelines and HDF5 compression for large datasets.

PythonPyTorch DDPHDF5ETLWeights & Biases

01 — Problem

What was hard about this

Training on 32GB+ datasets was bottlenecked by single-GPU processing and inefficient data loading that wasted compute time.

02 — Solution

How it works

Engineered a multi-GPU pipeline using PyTorch DDP across 3 GPUs with parallelized gradient synchronization. Built an ETL pipeline with HDF5 compression and parallel feature extraction to eliminate I/O bottlenecks. Monitored with Weights & Biases dashboards.

03 — Impact

What shipped

  • 39% reduction in training time via multi-GPU parallelization
  • 25x faster data loading with HDF5 compression pipeline
  • 25% improved GPU utilization through batch size tuning
  • Real-time experiment tracking with Weights & Biases
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