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We present TaskSet, a dataset of tasks for use in training and evaluatingoptimizers. TaskSet is unique in its size and diversity, containing over athousand tasks ranging from image classification with fully connected orconvolutional neural networks, to variational autoencoders, to non-volumepreserving flows on a variety of datasets. As an example application of such adataset we explore meta-learning an ordered list of hyperparameters to trysequentially. By learning this hyperparameter list from data generated usingTaskSet we achieve large speedups in sample efficiency over random search. Nextwe use the diversity of the TaskSet and our method for learning hyperparameterlists to empirically explore the generalization of these lists to newoptimization tasks in a variety of settings including ImageNet classificationwith Resnet50 and LM1B language modeling with transformers. As part of thiswork we have opensourced code for all tasks, as well as ~29 million trainingcurves for these problems and the corresponding hyperparameters.

Using a thousand optimization tasks to learn hyperparameter search strategies