Hyperband on NNI¶
Hyperband is a popular autoML algorithm. The basic idea of Hyperband is to create several buckets, each having
n randomly generated hyperparameter configurations, each configuration using
r resources (e.g., epoch number, batch number). After the
n configurations are finished, it chooses the top
n/eta configurations and runs them using increased
r*eta resources. At last, it chooses the best configuration it has found so far.
2. Implementation with full parallelism¶
First, this is an example of how to write an autoML algorithm based on MsgDispatcherBase, rather than Tuner and Assessor. Hyperband is implemented in this way because it integrates the functions of both Tuner and Assessor, thus, we call it Advisor.
Second, this implementation fully leverages Hyperband’s internal parallelism. Specifically, the next bucket is not started strictly after the current bucket. Instead, it starts when there are available resources.
To use Hyperband, you should add the following spec in your experiment’s YAML config file:
advisor: #choice: Hyperband builtinAdvisorName: Hyperband classArgs: #R: the maximum trial budget R: 100 #eta: proportion of discarded trials eta: 3 #choice: maximize, minimize optimize_mode: maximize
Note that once you use Advisor, you are not allowed to add a Tuner and Assessor spec in the config file. If you use Hyperband, among the hyperparameters (i.e., key-value pairs) received by a trial, there will be one more key called
TRIAL_BUDGET defined by user. By using this
TRIAL_BUDGET, the trial can control how long it runs.
report_final_result(metric) in your trial code,
metric should be either a number or a dict which has a key
default with a number as its value. This number is the one you want to maximize or minimize, for example, accuracy or loss.
eta are the parameters of Hyperband that you can change.
R means the maximum trial budget that can be allocated to a configuration. Here, trial budget could mean the number of epochs or mini-batches. This
TRIAL_BUDGET should be used by the trial to control how long it runs. Refer to the example under
examples/trials/mnist-advisor/ for details.
n/eta configurations from
n configurations will survive and rerun using more budgets.
Here is a concrete example of
|i||n r||n r||n r||n r||n r|
|0||81 1||27 3||9 9||6 27||5 81|
|1||27 3||9 9||3 27||2 81|
|2||9 9||3 27||1 81|
|3||3 27||1 81|
s means bucket,
n means the number of configurations that are generated, the corresponding
r means how many budgets these configurations run.
i means round, for example, bucket 4 has 5 rounds, bucket 3 has 4 rounds.
For information about writing trial code, please refer to the instructions under
4. Future improvements¶
The current implementation of Hyperband can be further improved by supporting a simple early stop algorithm since it’s possible that not all the configurations in the top
n/eta perform well. Any unpromising configurations should be stopped early.
In the current implementation, configurations are generated randomly which follows the design in the paper. As an improvement, configurations could be generated more wisely by leveraging advanced algorithms.