Hyperparameter Searches

Model hyperparameters are free parameters of a model which control different aspects of the learning process of your model. Hyperparameter search is the process of finding the model hyperparameters which result in the most performant model.

Spell lets you automate hyperparameter searches with the spell hyper command.

Note

For an interactive, runnable tutorial on hyperparameter search refer to our blog post: "An introduction to hyperparameter search with CIFAR10".

The spell hyper command kicks off your hyperparameter search. You can choose between a grid search, random search, or bayesian search.

This command is very similar to the spell run command and takes all of the same command line options, with the addition of hyperparameter specifications. For more info on the spell run command, see What Is a Run.

Let's take a look at the example command below.

$ spell hyper grid -t T4  \
    --param rate=0.001,0.01,0.1,1 \
    --param layers=2,3,5,10 -- \
    "python train.py --learning_rate :rate: --num_layers :layers:"

The first part should be familiar. We request a grid search running on T4 machines.

Next are two --param options, which list the values that we want our hyperparameter search to test for each specified parameter. Here we specify two parameters: rate and layers, and the values we want for each. The way to specify the values to search is different for the different types of hyperparameter searches.

Finally, we have our python command: python train.py --learning_rate :rate: --num_layers :layers:.

The parameters in colon bracket form, :rate: and :layers:, are replaced in individual runs with specific values of the respective parameter.

In grid search, a set of discrete values are provided for each hyperparameter and a run is created for all possible combinations of hyperparameters. For example:

$ spell hyper grid \
    --param rate=0.001,0.01,0.1,1 \
    --param layers=2,3,5,10 -- \
    python train.py --learning_rate :rate: --num_layers :layers:
Everything up-to-date
💫 Casting hyperparameter search #59…
rate    layers    Run ID
0.001   2         362
0.001   3         363
0.001   5         364
0.001   10        365
0.01    2         366
0.01    3         367
0.01    5         368
0.01    10        369
0.1     2         370
0.1     3         371
0.1     5         372
0.1     10        373
1       2         374
1       3         375
1       5         376
1       10        377

Hyperparameters are specified with the --param NAME=VALUE[,VALUE,VALUE...] flag. NAME corresponds to the name of the hyperparameter. One or more comma separated VALUEs can be provided after the =, corresponding to the values for the hyperparameter. The values can consist of string, integer, or floating point values.

Note

The hyperparameter NAME provided must exist in the run command surrounded by colons. This tells Spell where to substitute specific values for the hyperparameter in the run command when making the individual runs for the hyperparameter search.

In random search, each hyperparameter is randomly sampled to determine specific values for each run. Additionally, the --num-runs option must be specified to indicate the total number of runs to create. Hyperparameters are specified with the --param option flag. The specification can consist of either:

  1. A set of discrete values, specified with --param NAME=VALUE[,VALUE,VALUE...], similar to grid search. In this case one of the discrete values is randomly selected for the hyperparameter value when constituting a run.
  2. A range specification, specified with --param NAME=MIN:MAX[:SCALING[:TYPE]]. In this case the hyperparameter value is randomly selected from the specified range when constituting a run. MIN and MAX are required and correspond to the minimum and maximum value of the range of this hyperparameter.

SCALING is optional and can consist of 3 different values (linear is the default if not specified):

  • linear: the hyperparameter range (i.e., MIN to MAX) is sampled uniformly at random to determine a hyperparameter value.
  • log: the hyperparameter range is scaled logarithmically during the sampling (i.e., the range log(MIN) to log(MAX) is sampled uniformly at random and then exponentiated to yield the hyperparameter value). This results in a higher probability density for the sampling towards the lower end of the range.
  • reverse_log: this is the opposite scaling as that described in log, resulting in a higher probability density for the sampling at the higher end of the range.

TYPE is optional and can consist of 2 different values (float is the default if not specified):

  • float: the resultant hyperparameter value is a floating point number.
  • int: the resultant hyperparameter value is an integer. If this option is specified, the value after randomly sampling is rounded to the nearest integer to yield the final hyperparameter value.

An example random hyperparameter search is as follows:

$ spell hyper random \
    --num-runs 10 \
    --param rate=.001:1.0:log \
    --param layers=2:100:linear:int \
    --param cell=gru,lstm,rnn -- \
    python train.py --learning_rate :rate: --num_layers :layers: --cell_type :cell:
Everything up-to-date
💫 Casting hyperparameter search #60…
rate        layers    cell    Run ID
0.535637    68        lstm    378
0.192321    21        gru     379
0.501205    34        lstm    380
0.00103308  40        gru     381
0.0976437   49        gru     382
0.0131644   36        rnn     383
0.00139867  27        lstm    384
0.0274699   3         lstm    385
0.350886    9         rnn     386
0.23146     66        lstm    387

Bayesian search uses the results of prior runs to try to pick new parameters to test intelligently. It will often either note that a large part of the parameter space is unexplored and pick something in that region or it will observe a prior success and pick something near that. This can help you save on the total number of iterations needed to find good parameters.

Similar to the random search you must specify one or more parameters via the --param flag. These take the form --param NAME=MIN:MAX[:TYPE], where MIN is the lowest value that parameter is allowed to take, MAX is the highest, and TYPE is int or float. Similarly, you will also still need to specify --num-runs, the total number of runs that will be executed.

You will also need to provide the --metric and --metric-agg flags. --metric should be the name of the metric that the search algorithm will attempt to optimize (to learn more about metrics refer to the Metrics page in our docs). --metric-agg specifies how Spell should interpret the values of this metric observed: this can be set to min, max, last, or avg.

For example, if you select the Keras metric keras/val_accuracy and aggregation type last, Spell will use the last validation accuracy recorded in a given run and treat that as the success of your model for those parameters.

The final parameter you will need is --parallel-runs. This reflects a tradeoff: if you choose a lower number, the search will proceed incrementally and will take longer to complete. If you choose a higher number, many runs will be in progress when a new run is launched and the new run's parameters will be selected without the benefit of knowing how well the in-progress trials do.

if you are unsure what to set this value to, we recommend setting it to 3.

An example bayesian hyperparameter search is as follows:

$ spell hyper bayesian \
    --num-runs 12 \
    --parallel-runs 3 \
    --metric keras/val_acc \
    --metric-agg avg \
    --param rate=.001:1.0 \
    --param layers=2:100:int \
    python train.py --learning_rate :rate: --num_layers :layers:
Everything up-to-date
💫 Casting hyperparameter search #61…
rate        layers    Run ID
0.343882    23        388
0.294112    72        389
0.587557    64        390

Hyperparameter searches can be stopped or killed in much the same way that runs can.

$ spell hyper stop $HYPER_SEARCH_ID
$ spell hyper kill $HYPER_SEARCH_ID

spell hyper stop will send a graceful shutdown signal to all currently executing runs and dequeue all scheduled ones. spell hyper kill will send a hard reset signal instead.

The choice between stopping or killing a hyperparameter search has the same tradeoffs as stopping or killing a run. For more information see the corresponding section in What Is a Run page.

Viewing searches in the web console

You can view the results of a hyperparameter search in the web console.

A screenshot of the Spell hyperparameter search page on the web

The web console provides a high-level overview of the hyperparameter search process. You can also use it to stop or kill an in-progress search.

However, the most useful feature of the hyperparameter search page are the three built-in model performance visualizations.

A line chart allows you to visualize the model performance over time or epoch:

Screenshot of line chart

You can switch to a different metric, click-and-drag to visualize a slice of time, and filter training runs to just the ones you are interested in.

A table provides a high-level overview of final model performance:

Screenshot of table

And finally, facet charts allow you to visualize model training results on a variable-by-variable basis:

Screenshot of facet charts