Hyperparameter Search: Adaptive (Advanced)

The adaptive search method employs the same underlying algorithm as the Adaptive (Simple) method, but it allows users to control the behavior of the search in a more fine-grained way at the cost of being more difficult to configure. This section explains the configuration settings that influence the behavior of the adaptive searcher and gives recommendations for how to configure those settings.

Quick start

Here are some suggested initial settings for adaptive that typically work well.

Search mode:

  • mode: Set to standard.

Resource budget:

  • max_length: The maximum training length (see Training Units) of any trial that survives to the end of the experiment. This quantity is domain-specific and should roughly reflect the number of minibatches the model must be trained on for it to converge on the data set. For users who would like to determine this number experimentally, train a model with reasonable hyperparameters using the single search method.

  • budget: Set budget to roughly 10 times max_length. A higher budget will result in hyperparameter search that consumes more resources and takes longer to complete, but may produce better-performing models.


Conceptually, the adaptive searcher is a carefully tuned strategy for spawning multiple SHA (successive halving algorithm) searchers, themselves hyperparameter search algorithms. SHA can be configured to make different tradeoffs between exploration and exploitation, i.e., how many trials are explored versus how long a single trial is trained for. Because the right tradeoff between exploration and exploitation is hard to know in advance, the adaptive algorithm tries several SHA searches with different tradeoffs.

The configuration settings available to Determined experiments running in adaptive mode mostly affect the SHA subroutines directly. The mode configuration is the only one affecting the decisions of the adaptive searcher, by changing the number and types of SHA subroutines spawned.

The first section here gives a description of SHA. The second section describes the configuration parameters that influence how this search method behaves. The third section gives a summary of the adaptive configuration settings.


At a high level, SHA prunes (“halves”) a set of trials in successive rounds we call rungs. SHA starts with an initial set of trials. (A trial means one model, with a fixed set of hyperparameter values.) SHA trains all the trials for some length and the trials with the worst validation performance are discarded. In the next rung, the remaining trials are trained for a longer period of time, and then trials with the worst validation performance are pruned once again. This is repeated until the maximum training length is reached.

First, an example of SHA.

  • Rung 1: SHA creates N initial trials; the hyperparameter values for each trial are randomly sampled from the hyperparameters defined in the experiment configuration file. Each trial is trained for 1 epoch, and then validation metrics are computed.

  • Rung 2: SHA picks the N/4 top-performing trials according to validation metrics. These are trained for 4 epochs.

  • Rung 3: SHA picks the N/16 top-performing trials according to validation metrics. These are trained for 16 epochs.

At the end, the trial with best performance has the hyperparameter setting the SHA searcher returns.

In the example above, divisor is 4, which determines what fraction of trials are kept in successive rungs, as well as the training length in successive rungs. max_length is 16 epochs, which is the maximum length a trial is trained for.

The remaining degree of freedom in this SHA example is the number N of trials initialized. This is determined by the top-level adaptive algorithm, through budget and the number/types of SHA subroutines called.

In general, SHA has a fixed divisor d. In the first rung, it generates an initial set of randomly chosen trials and runs until each trial has trained for the same length. In the next rung, it keeps 1/d of those trials and closes the rest. Then it runs each remaining trial until it has trained for d times as long as the previous rung. SHA iterates this process until some stopping criterion is reached, such as completing a specified number of rungs or having only one trial remaining. The total training length, rungs, and trials within rungs are fixed within each SHA searcher, but vary across different calls to SHA by the adaptive algorithm. Note that although the name “SHA” includes the phrase “halving”, the fraction of trials pruned after every rung is controlled by divisor.

Adaptive over SHA

The adaptive algorithm calls SHA subroutines with varying parameters. The exact calls are configured through the choice of mode, which specifies how aggressively to perform early stopping. One way to think about this behavior is as a spectrum that ranges from “one SHA run” (aggressive early stopping; eliminate most trials every rung) to “searcher: random” (no early stopping; all initialized trials are allowed to run to completion).

On one end, aggressive applies early stopping in a very eager manner; this mode essentially corresponds to only making a single call to SHA. With the default divisor of 4, 75% of the remaining trials will be eliminated in each rung after only being trained for 25% the length of the next rung. This implies that relatively few trials will be allowed to finish even a small fraction of the length needed train to convergence (max_length). This aggressive early stopping behavior allows the searcher to start more trials for a wider exploration of hyperparameter configurations, at the risk of discarding a configuration too soon.

On the other end, conservative mode is more similar to a random search, in that it performs significantly less pruning. Extra SHA subroutines are spawned with fewer rungs and longer training lengths to account for the high percentage of trials eliminated after only a short time. However, a conservative adaptive search will only explore a small fraction of the configurations explored by an aggressive search, given the same budget.

Once the number and types of calls to SHA are determined (via mode), the adaptive algorithm will allocate training length budgets to the SHA subroutines, from the overall budget for the adaptive algorithm (user-specified through budget). This determines the number of trials at each rung (N in the above SHA example).


Users specify configurations for the adaptive searcher through the Experiment Configuration. They fall into two categories described below.

Parameters for SHA:

  • max_length: The maximum training length (see Training Units) for any one trial.

  • (optional, for advanced users only) divisor: The multiplier for eliminating trials and increasing time trained at each rung. The default is 4.

  • (optional, for advanced users only) max_rungs: The maximum number of rungs. The default is 5.

Parameters for adaptive mode:

  • mode: Options are aggressive, standard, or conservative. Specifies how aggressively to perform early stopping. We suggest using either aggressive or standard mode.

  • budget: A budget for the total training length across all trials and SHA calls. The budget is split evenly between SHA calls. The recommendation above was to set budget = 10 * max_length.


The table below illustrates the difference between aggressive, standard, and conservative for an otherwise fixed configuration. While aggressive tries out 64 hyperparameter configurations, conservative tries only 31 hyperparameter configurations but has the budget to run more of them to the full 16 epochs. More SHA instances are generated by conservative, which are responsible for creating the trials run for the full 16 epochs.

The settings are divisor: 4, max_rungs: 3, max_length: {epochs: 16}, and budget: {epochs: 160}.

Total epochs trained

Number of trials






























For an experiment generated by a specific .yaml experiment configuration file, this information (SHA instances and number of trials vs. training length) can be found with the command

det preview-search <file_name.yaml>


Q: How do I control how long a trial is trained for before it is potentially discarded?

The training length is guaranteed to be at least max_length / 256 by default, or max_length / divisor ^ max_rungs-1 in general. It is recommended to configure this in records or epochs if the global_batch_size hyperparameter is not constant, to ensure each trial trains on the same amount of data.

Q: How do I set the initial number of trials? How do I make sure ``x`` trials are run the full training length (``max_length``)?

The number of initial trials is determined by a combination of mode, budget, divisor, max_rungs, and max_length. Here is a rule of thumb for the default configuration of max_rungs: 5 and divisor: 4, with mode: aggressive and a large enough budget:

  • The initial number of trials is budget / (4 * max_length).

  • To ensure that x trials are run max_length, set budget to be 4 * x * max_length.

A configuration setting that meets set goals can also be found by trial and error. The command

det preview-search <file_name.yaml>

will display information on the number of trials versus training length for the configuration specified in file_name.yaml. Increasing budget increases both the initial number of trials and the number of trials that train the full length. On the other hand, max_length decreases both. The mode decides on allocation of training length between trials; mode: conservative runs more trials for longer, whereas mode: aggressive eliminates the most trials early in training.

Q: The adaptive algorithm sounds great so far. What are its weaknesses?

One downside of adaptive is that it results in doing more validations, which might be expensive.