Model training is arguably the most time consuming, and computationally demanding part of the Machine Learning pipeline. Depending on the complexity of your model, or search space, it can take weeks or even months to find an adequate set of parameters that allow your model to fit the data.
Predictive Early Stopping is a state-of-the-art approach for speeding up model training and hyperparameter optimization. Our benchmarking studies have shown that Predictive Early Stopping can speed up model training by up to 30% independent of the underlying infrastructure.
We build on insights gathered from projects such as Learning Curve Extrapolation, Hyperband, and Median Stopping, in order to create a predictive model that can estimate the convergence value of a loss curve.
Comet is able to leverage model data, such as hyperparameters and loss curves, from over two million models in the public section of its platform to create a model whose predictions generalize across hyperparameters and model architectures.
In some cases we are able to provide an estimate of convergence, hundreds of epochs before it actually occurs. In addition to predicting the convergence value, our Predictive Early Stopping product provides an estimate of the probability that the current model will outperform the best model result seen in the current training sweep.
In some cases we are able to provide an estimate of convergence, hundreds of epochs before it actually occurs.
These predictions allow us to terminate the training of underperforming models, so that the search process is spent evaluating only the most promising candidates.