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Hyperband on NNI

1. Introduction

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. If you want to use full parallelism mode, set exec_mode with parallelism.

Or if you want to set exec_mode with serial according to the original algorithm. In this mode, the next bucket will start strictly after the current bucket.

parallelism mode may lead to multiple unfinished buckets, and there is at most one unfinished bucket under serial mode. The advantage of parallelism mode is to make full use of resources, which may reduce the experiment duration multiple times. The following two pictures are the results of quick verification using nas-bench-201, picture above is in parallelism mode, picture below is in serial mode.

parallelism mode serial mode

If you want to reproduce these results, refer to the example under examples/trials/benchmarking/ for details.

3. Usage

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
    #choice: serial, parallelism
    exec_mode: parallelism

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.

For report_intermediate_result(metric) and 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.

R and 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.

eta means n/eta configurations from n configurations will survive and rerun using more budgets.

Here is a concrete example of R=81 and eta=3:

  s=4 s=3 s=2 s=1 s=0
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      
4 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 examples/trials/mnist-hyperband/.

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.