Sweep configuration options

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A sweep configuration consists of nested key-value pairs. Use top-level keys within your sweep configuration to define qualities of your sweep search such as the parameters to search through (parameter key), the methodology to search the parameter space (method key), and more.

The proceeding table lists top-level sweep configuration keys and a brief description. See the respective sections for more information about each key.

Top-level keys Description
program (required) Training script to run
entity The entity for this sweep
project The project for this sweep
description Text description of the sweep
name The name of the sweep, displayed in the W&B UI.
method (required) The search strategy
metric The metric to optimize (only used by certain search strategies and stopping criteria)
parameters (required) Parameter bounds to search
early_terminate Any early stopping criteria
command Command structure for invoking and passing arguments to the training script
run_cap Maximum number of runs for this sweep

See the Sweep configuration structure for more information on how to structure your sweep configuration.

metric

Use the metric top-level sweep configuration key to specify the name, the goal, and the target metric to optimize.

Key Description
name Name of the metric to optimize.
goal Either minimize or maximize (Default is minimize).
target Goal value for the metric you are optimizing. The sweep does not create new runs when if or when a run reaches a target value that you specify. Active agents that have a run executing (when the run reaches the target) wait until the run completes before the agent stops creating new runs.

parameters

In your YAML file or Python script, specify parameters as a top level key. Within the parameters key, provide the name of a hyperparameter you want to optimize. Common hyperparameters include: learning rate, batch size, epochs, optimizers, and more. For each hyperparameter you define in your sweep configuration, specify one or more search constraints.

The proceeding table shows supported hyperparameter search constraints. Based on your hyperparameter and use case, use one of the search constraints below to tell your sweep agent where (in the case of a distribution) or what (value, values, and so forth) to search or use.

Search constraint Description
values Specifies all valid values for this hyperparameter. Compatible with grid.
value Specifies the single valid value for this hyperparameter. Compatible with grid.
distribution Specify a probability distribution. See the note following this table for information on default values.
probabilities Specify the probability of selecting each element of values when using random.
min, max (intor float) Maximum and minimum values. If int, for int_uniform -distributed hyperparameters. If float, for uniform -distributed hyperparameters.
mu (float) Mean parameter for normal - or lognormal -distributed hyperparameters.
sigma (float) Standard deviation parameter for normal - or lognormal -distributed hyperparameters.
q (float) Quantization step size for quantized hyperparameters.
parameters Nest other parameters inside a root level parameter.

method

Specify the hyperparameter search strategy with the method key. There are three hyperparameter search strategies to choose from: grid, random, and Bayesian search.

Iterate over every combination of hyperparameter values. Grid search makes uninformed decisions on the set of hyperparameter values to use on each iteration. Grid search can be computationally costly.

Grid search executes forever if it is searching within in a continuous search space.

Choose a random, uninformed, set of hyperparameter values on each iteration based on a distribution. Random search runs forever unless you stop the process from the command line, within your python script, or the W&B App UI.

Specify the distribution space with the metric key if you choose random (method: random) search.

In contrast to random and grid search, Bayesian models make informed decisions. Bayesian optimization uses a probabilistic model to decide which values to use through an iterative process of testing values on a surrogate function before evaluating the objective function. Bayesian search works well for small numbers of continuous parameters but scales poorly. For more information about Bayesian search, see the Bayesian Optimization Primer paper.

Bayesian search runs forever unless you stop the process from the command line, within your python script, or the W&B App UI.

Within the parameter key, nest the name of the hyperparameter. Next, specify the distribution key and specify a distribution for the value.

The proceeding tables lists distributions W&B supports.

Value for distribution key Description
constant Constant distribution. Must specify the constant value (value) to use.
categorical Categorical distribution. Must specify all valid values (values) for this hyperparameter.
int_uniform Discrete uniform distribution on integers. Must specify max and min as integers.
uniform Continuous uniform distribution. Must specify max and min as floats.
q_uniform Quantized uniform distribution. Returns round(X / q) * q where X is uniform. q defaults to 1.
log_uniform Log-uniform distribution. Returns a value X between exp(min) and exp(max)such that the natural logarithm is uniformly distributed between min and max.
log_uniform_values Log-uniform distribution. Returns a value X between min and max such that log(X) is uniformly distributed between log(min) and log(max).
q_log_uniform Quantized log uniform. Returns round(X / q) * q where X is log_uniform. q defaults to 1.
q_log_uniform_values Quantized log uniform. Returns round(X / q) * q where X is log_uniform_values. q defaults to 1.
inv_log_uniform Inverse log uniform distribution. Returns X, where log(1/X) is uniformly distributed between min and max.
inv_log_uniform_values Inverse log uniform distribution. Returns X, where log(1/X) is uniformly distributed between log(1/max) and log(1/min).
normal Normal distribution. Return value is normally distributed with mean mu (default 0) and standard deviation sigma (default 1).
q_normal Quantized normal distribution. Returns round(X / q) * q where X is normal. Q defaults to 1.
log_normal Log normal distribution. Returns a value X such that the natural logarithm log(X) is normally distributed with mean mu (default 0) and standard deviation sigma (default 1).
q_log_normal Quantized log normal distribution. Returns round(X / q) * q where X is log_normal. q defaults to 1.

early_terminate

Use early termination (early_terminate) to stop poorly performing runs. If early termination occurs, W&B stops the current run before it creates a new run with a new set of hyperparameter values.

Stopping algorithm

Hyperband hyperparameter optimization evaluates if a program should stop or if it should to continue at one or more pre-set iteration counts, called brackets.

When a W&B run reaches a bracket, the sweep compares that run’s metric to all previously reported metric values. The sweep terminates the run if the run’s metric value is too high (when the goal is minimization) or if the run’s metric is too low (when the goal is maximization).

Brackets are based on the number of logged iterations. The number of brackets corresponds to the number of times you log the metric you are optimizing. The iterations can correspond to steps, epochs, or something in between. The numerical value of the step counter is not used in bracket calculations.

Key Description
min_iter Specify the iteration for the first bracket
max_iter Specify the maximum number of iterations.
s Specify the total number of brackets (required for max_iter)
eta Specify the bracket multiplier schedule (default: 3).
strict Enable ‘strict’ mode that prunes runs aggressively, more closely following the original Hyperband paper. Defaults to false.

command

Modify the format and contents with nested values within the command key. You can directly include fixed components such as filenames.

W&B supports the following macros for variable components of the command:

Command macro Description
${env} /usr/bin/env on Unix systems, omitted on Windows.
${interpreter} Expands to python.
${program} Training script filename specified by the sweep configuration program key.
${args} Hyperparameters and their values in the form --param1=value1 --param2=value2.
${args_no_boolean_flags} Hyperparameters and their values in the form --param1=value1 except boolean parameters are in the form --boolean_flag_param when True and omitted when False.
${args_no_hyphens} Hyperparameters and their values in the form param1=value1 param2=value2.
${args_json} Hyperparameters and their values encoded as JSON.
${args_json_file} The path to a file containing the hyperparameters and their values encoded as JSON.
${envvar} A way to pass environment variables. ${envvar:MYENVVAR} __ expands to the value of MYENVVAR environment variable. __

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