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Gigaversity
Gigaversity.in • 2m
One of our recent achievements showcases how optimizing code and parallelizing processes can drastically improve machine learning model training times. The Challenge: Long Training Times Our model training process was initially taking 8 hours—slowing down iterations and limiting our ability to scale quickly. We knew we needed a solution to speed things up. The Solution: Mixed-Precision & Parallelized Training Mixed-Precision Training: By switching to 16-bit floating point operations (FP16), we significantly reduced memory usage and computation time without sacrificing accuracy. Parallelization: By distributing tasks across multiple GPUs and leveraging distributed computing frameworks, we accelerated the training pipeline.Parallel training using PyTorch Distributed and DataParallel modules By implementing PyTorch’s torch.nn.DataParallel and torch.distributed, we were able to run parallelized training across available GPUs without needing complex infrastructure. This significantly reduced training time while maintaining accuracy and model performance. The Result: Training Time Cut to 1 Hour Through these efforts, we reduced model training time from 8 hours to just 1 hour! This allowed for faster iteration and quicker delivery of results, improving our overall productivity. At Gigaversity, we continue to push the boundaries of efficiency in AI and machine learning. This is just one step in our journey toward innovation. If you Know someone working on model training or optimization? Tag them below—would love to hear how others are solving similar challenges.
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