# Define sweep configuration

A Weights & Biases Sweep combines a strategy for exploring hyperparameter values with the code that evaluates them. The strategy can be as simple as trying every option or as complex as Bayesian Optimization and Hyperband (BOHB).

Define your strategy in the form of a sweep configuration. Specify the configuration either in a:

- 1.Python nested dictionary data structure if you use a Jupyter Notebook or Python script.
- 2.YAML file if you use the command line (CLI).

The following code snippets demonstrate examples of how to define a Sweep configuration in a Jupyter Notebook or Python script or within a YAML file. Configuration keys are defined in detail in subsequent sections.

Python script or Jupyter Notebook

YAML

Within your Jupyter Notebook or Python script, define a sweep in a dictionary Python data structure.

sweep_configuration = {

'method': 'random',

'name': 'sweep',

'metric': {

'goal': 'minimize',

'name': 'validation_loss'

},

'parameters': {

'batch_size': {'values': [16, 32, 64]},

'epochs': {'values': [5, 10, 15]},

'lr': {'max': 0.1, 'min': 0.0001}

}

}

Create a map in your YAML where the keys can have, as their values, further keys.

program: train.py

method: bayes

metric:

name: validation_loss

goal: minimize

parameters:

learning_rate:

min: 0.0001

max: 0.1

optimizer:

values: ["adam", "sgd"]

- 1.Ensure that you log (
`wandb.log`

) the*exact*metric name that you defined the sweep to optimize within your Python script or Jupyter Notebook. - 2.You cannot change the Sweep configuration once you start the W&B Sweep agent.

For example, suppose you want W&B Sweeps to maximize the validation accuracy during training. Within your Python script you store the validation accuracy in a variable

`validation_loss`

. In your YAML configuration file you define this as:metric:

goal: maximize

name: validation_loss

You must log the variable

`validation_loss`

(in this example) within your Python script or Jupyter Notebook to W&B.wandb.log({

'val_loss': validation_loss

})

Sweep configurations are nested; keys can have, as their values, further keys. The top-level keys are listed and briefly described below, and then detailed in the following section.

Top-Level Key | Description |
---|---|

`program` | (required) Training script to run. |

`method` | |

`parameters` | |

`name` | The name of the sweep, displayed in the W&B UI. |

`description` | Text description of the sweep. |

`metric` | Specify the metric to optimize (only used by certain search strategies and stopping criteria). |

`early_terminate` | |

`command` | |

`project` | Specify the project for this sweep. |

`entity` | Specify the entity for this sweep. |

`run_cap` | Specify a maximum number of runs in a sweep. |

The following list describes hyperparameter search methods. Specify the search strategy with the

`method`

:– Iterate over every combination of hyperparameter values. Can be computationally costly.`grid`

– Choose a random set of hyperparameter values on each iteration based on provided distributions.`random`

– Create a probabilistic model of a metric score as a function of the hyperparameters, and choose parameters with high probability of improving the metric. Bayesian hyperparameter search method uses a Gaussian Process to model the relationship between the parameters and the model metric and chooses parameters to optimize the probability of improvement. This strategy requires the`bayes`

`metric`

key to be specified. Works well for small numbers of continuous parameters but scales poorly.

Random search

Grid search

Bayes search

method: random

method: grid

method: bayes

metric:

name: val_loss

goal: minimize

Random and Bayesian searches will run forever -- until you stop the process from the command line, within your python script, or the UI. Grid search will also run forever if it searches within in a continuous search space.

Specify the search strategy with the

`method`

key in the sweep configuration.`method` | Description |

`grid` | Grid search iterates over all possible combinations of parameter values. |

`random` | Random search chooses a random set of values on each iteration. |

`bayes` | Our Bayesian hyperparameter search method uses a Gaussian Process to model the relationship between the parameters and the model metric and chooses parameters to optimize the probability of improvement. This strategy requires the `metric` key to be specified. |

Describe the hyperparameters to explore during the sweep. For each hyperparameter, specify the name and the possible values as a list of constants (for any

`method`

) or specify a `distribution`

for `random`

or `bayes`

.Values | 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` | ( `str` ) Selects a distribution from the distribution table below. If not specified, will default to `categorical` if `values` is set, to `int_uniform` if `max` and `min` are set to integers, to `uniform` if `max` and `min` are set to floats, or to`constant` if `value` is set. |

`probabilities` | Specify the probability of selecting each element of `values` when using `random` . |

`min` , `max` | ( `int` or `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. |

single value

multiple values

probabilities

distribution

nested

parameter_name:

value: 1.618

parameter_name:

values:

- 8

- 6

- 7

- 5

- 3

- 0

- 9

parameter_name:

values: [1, 2, 3, 4, 5]

probabilities: [0.1, 0.2, 0.1, 0.25, 0.35]

parameter_name:

distribution: normal

mu: 100

sigma: 10

optimizer:

parameters:

learning_rate:

values: [0.01, 0.001]

momentum:

value: 0.9

Specify how to distribute values if you choose a random (

`random)`

or Bayesian (`bayes)`

search method. Value | Description |
---|---|

`constant` | Constant distribution. Must specify `value` . |

`categorical` | Categorical distribution. Must specify `values` . |

`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` . |

`inv_log_uniform` | Inverse log uniform distribution. Returns `X` , where `log(1/X)` 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` . |

constant

categorical

uniform

q_uniform

parameter_name:

distribution: constant

value: 2.71828

parameter_name:

distribution: categorical

values:

- elu

- celu

- gelu

- selu

- relu

- prelu

- lrelu

- rrelu

- relu6

parameter_name:

distribution: uniform

min: 0

max: 1

parameter_name:

distribution: q_uniform

min: 0

max: 256

q: 1

Describes the metric to optimize. This metric should be logged explicitly to W&B by your training script.

Key | Description |
---|---|

`name` | Name of the metric to optimize. |

`goal` | Either `minimize` or `maximize` (Default is `minimize` ). |

`target` | Goal value for the metric you're optimizing. When any run in the sweep achieves that target value, the sweep's state will be set to `finished` . This means all agents with active runs will finish those jobs, but no new runs will be launched in the sweep. |

For example, if you want to minimize the validation loss of your model:

# model training code that returns validation loss as valid_loss

wandb.log({"val_loss" : valid_loss})

Maximize

Minimize

Target

metric:

name: val_acc

goal: maximize

metric:

name: val_loss

goal: minimize

metric:

name: val_acc

goal: maximize

target: 0.95

The metric you optimize must be a top-level metric.

Do not log the metric for your sweep inside of a sub-directory. In the proceeding code example, we want to log the validation loss (

`"loss": val_loss`

). First we define it in a dictionary. However, the dictionary passed to `wandb.log`

does not specify the key-value pair to track.val_metrics = {

"loss": val_loss,

"acc": val_acc

}

# Incorrect. Dictionary key-value paired is not provided.

wandb.log({"val_loss", val_metrics})

Instead, log the metric at the top level. For example, after you create a dictionary, specify the key-value pair when you pass the dictionary to the

`wandb.log`

method:val_metrics = {

"loss": val_loss,

"acc": val_acc

}

wandb.log({"val_loss", val_metrics["loss"]})

Early termination is an optional feature that speeds up hyperparameter search by stopping poorly-performing runs. When the early stopping is triggered, the agent stops the current run and gets the next set of hyperparameters to try.

Key | Description |
---|---|

`type` | Specify the stopping algorithm |

We support the following stopping algorithm(s):

`type` | Description |

`hyperband` |

Hyperband stopping evaluates if a program should be stopped or permitted to continue at one or more pre-set iteration counts, called "brackets". When a run reaches a bracket, its metric value is compared to all previous reported metric values and the W&B Run is terminated if its value is too high (when the goal is minimization) or 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.

Specify either

`min_iter`

or `max_iter`

to create a bracket schedule.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` ). |

The hyperband early terminator checks what W&B Runs to terminate once every few minutes. The end run timestamp might differ from the specified brackets if your run or iteration are short.

Hyperband (min_iter)

Hyperband (max_iter)

early_terminate:

type: hyperband

min_iter: 3

The brackets for this example are:

`[3, 3*eta, 3*eta*eta, 3*eta*eta*eta]`

, which equals `[3, 9, 27, 81]`

.early_terminate:

type: hyperband

max_iter: 27

s: 2

The brackets for this example are

`[27/eta, 27/eta/eta]`

, which equals `[9, 3]`

.UNIX

Windows

/usr/bin/env python train.py --param1=value1 --param2=value2

python train.py --param1=value1 --param2=value2

On UNIX systems,

`/usr/bin/env`

ensures the right Python interpreter is chosen based on the environment.The format and contents can be modified by specifying values under the

`command`

key. Fixed components of the command, such as filenames, can be included directly (see examples below).We support 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. |

The default command format is defined as:

command:

- ${env}

- ${interpreter}

- ${program}

- ${args}

Set python interpreter

Add extra parameters

Omit arguments

Use the Hydra

Remove the

`{$interpreter}`

macro and provide a value explicitly in order to hardcode the python interpreter. For example, the following code snippet demonstrates how to do this:command:

- ${env}

- python3

- ${program}

- ${args}

To add extra command line arguments not specified by sweep configuration parameters:

command:

- ${env}

- ${interpreter}

- ${program}

- "--config"

- "your-training-config.json"

- ${args}

If your program does not use argument parsing you can avoid passing arguments all together and take advantage of

`wandb.init`

picking up sweep parameters into `wandb.config`

automatically:command:

- ${env}

- ${interpreter}

- ${program}

You can change the command to pass arguments the way tools like Hydra expect. See Hydra with W&B for more information.

command:

- ${env}

- ${interpreter}

- ${program}

- ${args_no_hyphens}

Last modified 18d ago