How to speed up a Deep Learning Language model by almost 50X at half the cost - KDnuggets

One of the big headaches in deep learning is that models take forever to train. As an ML engineer, waiting hours or days for training to complete makes iteratively improving your model a slow and frustrating process. In this blog post, we show how to accelerate fine-tuning the ALBERT language model while also reducing costs by using Determined's built-in support for distributed training with AWS spot instances. Originally, ALBERT took over 36 hours to train on a single V100 GPU and cost $112 on AWS. With distributed training and spot instances, training the model using 64 V100 GPUs took only 48 minutes and cost only $47! That's both a 46x performance improvement and a 58% reduction in cost!