Hyperparameter tuning is a common machine learning workflow that involves appropriately configuring the data, model architecture, and learning algorithm to yield an effective model. Hyperparameter tuning is a challenging problem in deep learning given the potentially large number of hyperparameters to consider.
In machine learning, hyperparameter tuning is the process of selecting the features, model architecture, and learning process parameters that yield an effective model.
Why Do Hyperparameters Matter? During the model development lifecycle, a machine learning engineer makes a wide range of decisions impacting model performance. For example, a computer vision model requires decisions on sample features, model architecture, and training algorithm parameters, e.g.:
Should we consider features aside from the raw images in the training set?
Would synthetic data augmentation techniques like image rotation or horizontal flipping yield a better performing model?
Should we populate additional features via advanced image processing techniques such as shape edge extraction?
What model architecture works best?
How many layers?
What kind of layers (e.g., dense, dropout, pooling)?
How should we parameterize each layer (e.g., size, activation function)?
What learning algorithm hyperparameters should we use?
What gradient descent batch size should we set?
What optimizer should we utilize, and how should we parameterize it (e.g., learning rate)?
A machine learning engineer can manually guess and test hyperparameters, or they might narrow the search space by using a pretrained model. However, even if the machine learning engineer achieves seemingly good model performance, they’re left wondering how much better they might do with additional tuning.
Hyperparameter tuning is a crucial phase in the model development lifecycle. However, it is rife with obstacles covered in the next section.
Tuning deep learning models is difficult because:
A deep learning model’s objective (e.g., validation loss) as a function of the hyperparameters is non-continuous and noisy, so we can’t apply analytical or continuous optimization techniques to calculate the validation objective given a set of hyperparameters. Thus, hyperparameter tuning is a black box optimization problem in that we must train a model under a set of hyperparameters in order to evaluate the objective.
Hyperparameter tuning suffers from the curse of dimensionality, as the number of possible hyperparameter configurations is exponential in the number of hyperparameters. For instance, even if a model has just ten categorical hyperparameters with five values per hyperparameter, and each hyperparameter configuration takes one minute to train on average, it would take 5^10 minutes, or nearly 20 years, to evaluate all possible hyperparameter configurations.
Deep learning model training is computationally expensive. It’s not uncommon for a model to require hours or days to train on expensive hardware.
Fortunately, there are automatic hyperparameter tuning techniques that the machine learning engineer can leverage to find an effective model. Determined provides support for hyperparameter search as a first-class workflow that is tightly integrated with Determined’s job scheduler, which allows for efficient execution of state-of-the-art early-stopping based approaches as well as seamless parallelization of these methods.
An intuitive interface is provided to use hyperparameter searching as described in the following sections.