Deep Learning Training Infrastructure—GPU & Distributed Training

I will set up a deep learning training infrastructure for your project — configuring GPU instances on your cloud provider, optimising batch size and mixed precision training for your GPU memory, implementing distributed data-parallel training across multiple GPUs if required, and establishing a training job management system enabling repeatable, resumable training runs. Deep learning training that runs on a single GPU without mixed precision, gradient accumulation, and DataLoader optimisation consistently underutilises available hardware — a properly configured training setup can reduce training time by 3-5x on the same hardware through configuration alone.

The setup covers cloud GPU instance configuration (AWS, GCP, or Azure), mixed precision training (torch.cuda.amp or tf.keras.mixed_precision), DataLoader worker and prefetch optimisation, gradient accumulation for effective large batch training, checkpoint saving and resumption, and Weights & Biases or MLflow experiment tracking integration.