Hyperparameter Tuning With Determined

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. 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.

Other Supported Methods

Determined also supports other common hyperparameter search algorithms:

  1. Single is appropriate for manual hyperparameter tuning, as it trains a single hyperparameter configuration.

  2. Grid brute force evaluates all possible hyperparameter configurations and returns the best.

  3. Random evaluates a set of hyperparameter configurations chosen at random and returns the best.

  4. Population-based training (PBT) begins as random search but periodically replaces low-performing hyperparameter configurations with ones near the high-performing points in the hyperparameter space.

Handling Trial Errors and Early Stopping Requests

When a trial encounters an error or fails unexpectedly, Determined will restart it from the latest checkpoint unless we have done so max_restarts times, which is configured in the experiment configuration. Once we have reached max_restarts, any further trials that fail will be marked as errored and will not be restarted. For search methods that adapt to validation metric values (Adaptive (Simple), Adaptive (Advanced), and Population-based training (PBT)), we do not continue training errored trials, even if the search method would typically call for us to continue training. This behavior is useful when some parts of the hyperparameter space result in models that cannot be trained successfully (e.g., the search explores a range of batch sizes and some of those batch sizes cause GPU OOM errors). An experiment can complete successfully as long as at least one of the trials within it completes successfully.

Trial code can also request that training be stopped early, e.g., via a framework callback such as tf.keras.callbacks.EarlyStopping or manually by calling determined.TrialContext.set_stop_requested(). When early stopping is requested, Determined will finish the current training or validation step and checkpoint the trial. Trials that are stopped early are considered to be “completed”, whereas trials that fail are marked as “errored”.