Define a sweep configuration

A W&B 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 a sweep configuration either in a Python dictionary or a YAML file. How you define your sweep configuration depends on how you want to manage your sweep.

The following guide describes how to format your sweep configuration. See Sweep configuration options for a comprehensive list of top-level sweep configuration keys.

Basic structure

Both sweep configuration format options (YAML and Python dictionary) utilize key-value pairs and nested structures.

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

For example, the proceeding code snippets show the same sweep configuration defined within a YAML file and within a Python dictionary. Within the sweep configuration there are five top level keys specified: program, name, method, metric and parameters.

• CLI
• Python script or Jupyter notebook

program: train.py
name: sweepdemo
method: bayes
metric:
  goal: minimize
  name: validation_loss
parameters:
  learning_rate:
    min: 0.0001
    max: 0.1
  batch_size:
    values: [16, 32, 64]
  epochs:
    values: [5, 10, 15]
  optimizer:
    values: ["adam", "sgd"]

sweep_configuration = {
    "name": "sweepdemo",
    "method": "bayes",
    "metric": {"goal": "minimize", "name": "validation_loss"},
    "parameters": {
        "learning_rate": {"min": 0.0001, "max": 0.1},
        "batch_size": {"values": [16, 32, 64]},
        "epochs": {"values": [5, 10, 15]},
        "optimizer": {"values": ["adam", "sgd"]},
    },
}

Within the top level parameters key, the following keys are nested: learning_rate, batch_size, epoch, and optimizer. For each of the nested keys you specify, you can provide one or more values, a distribution, a probability, and more. For more information, see the parameters section in Sweep configuration options.

Double nested parameters

Sweep configurations support nested parameters. To delineate a nested parameter, use an additional parameters key under the top level parameter name. Sweep configs support multi-level nesting.

Specify a probability distribution for your random variables if you use a Bayesian or random hyperparameter search. For each hyperparameter:

1. Create a top level parameters key in your sweep config.
2. Within the parameterskey, nest the following:
   1. Specify the name of hyperparameter you want to optimize.
   2. Specify the distribution you want to use for the distribution key. Nest the distribution key-value pair underneath the hyperparameter name.
   3. Specify one or more values to explore. The value (or values) should be inline with the distribution key.
      1. (Optional) Use an additional parameters key under the top level parameter name to delineate a nested parameter.

def main():
    run = wandb.init(config={"nested_param": {"manual_key": 1}})


sweep_configuration = {
    "top_level_param": 0,
    "nested_param": {
        "learning_rate": 0.01,
        "double_nested_param": {"x": 0.9, "y": 0.8},
    },
}

# Initialize sweep by passing in config.
sweep_id = wandb.sweep(sweep=sweep_configuration, project="<project>")

# Start sweep job.
wandb.agent(sweep_id, function=main, count=4)

Sweep configuration template

The following template shows how you can configure parameters and specify search constraints. Replace hyperparameter_name with the name of your hyperparameter and any values enclosed in <>.

program: <insert>
method: <insert>
parameter:
  hyperparameter_name0:
    value: 0  
  hyperparameter_name1: 
    values: [0, 0, 0]
  hyperparameter_name: 
    distribution: <insert>
    value: <insert>
  hyperparameter_name2:  
    distribution: <insert>
    min: <insert>
    max: <insert>
    q: <insert>
  hyperparameter_name3: 
    distribution: <insert>
    values:
      - <list_of_values>
      - <list_of_values>
      - <list_of_values>
early_terminate:
  type: hyperband
  s: 0
  eta: 0
  max_iter: 0
command:
- ${Command macro}
- ${Command macro}
- ${Command macro}
- ${Command macro}      

Sweep configuration examples

• CLI
• Python script or Jupyter notebook

program: train.py
method: random
metric:
  goal: minimize
  name: loss
parameters:
  batch_size:
    distribution: q_log_uniform_values
    max: 256 
    min: 32
    q: 8
  dropout: 
    values: [0.3, 0.4, 0.5]
  epochs:
    value: 1
  fc_layer_size: 
    values: [128, 256, 512]
  learning_rate:
    distribution: uniform
    max: 0.1
    min: 0
  optimizer:
    values: ["adam", "sgd"]

sweep_config = {
    "method": "random",
    "metric": {"goal": "minimize", "name": "loss"},
    "parameters": {
        "batch_size": {
            "distribution": "q_log_uniform_values",
            "max": 256,
            "min": 32,
            "q": 8,
        },
        "dropout": {"values": [0.3, 0.4, 0.5]},
        "epochs": {"value": 1},
        "fc_layer_size": {"values": [128, 256, 512]},
        "learning_rate": {"distribution": "uniform", "max": 0.1, "min": 0},
        "optimizer": {"values": ["adam", "sgd"]},
    },
}

Bayes hyperband example

program: train.py
method: bayes
metric:
  goal: minimize
  name: val_loss
parameters:
  dropout:
    values: [0.15, 0.2, 0.25, 0.3, 0.4]
  hidden_layer_size:
    values: [96, 128, 148]
  layer_1_size:
    values: [10, 12, 14, 16, 18, 20]
  layer_2_size:
    values: [24, 28, 32, 36, 40, 44]
  learn_rate:
    values: [0.001, 0.01, 0.003]
  decay:
    values: [1e-5, 1e-6, 1e-7]
  momentum:
    values: [0.8, 0.9, 0.95]
  epochs:
    value: 27
early_terminate:
  type: hyperband
  s: 2
  eta: 3
  max_iter: 27

The proceeding tabs show how to specify either a minimum or maximum number of iterations for early_terminate:

• Maximum number of iterations
• Minimum number of iterations

early_terminate:
  type: hyperband
  min_iter: 3

early_terminate:
  type: hyperband
  max_iter: 27
  s: 2

Command example

program: main.py
metric:
  name: val_loss
  goal: minimize

method: bayes
parameters:
  optimizer.config.learning_rate:
    min: !!float 1e-5
    max: 0.1
  experiment:
    values: [expt001, expt002]
  optimizer:
    values: [sgd, adagrad, adam]

command:
- ${env}
- ${interpreter}
- ${program}
- ${args_no_hyphens}

• Unix
• Windows

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

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

The proceeding tabs show how to specify common command macros:

• Set Python interpreter
• Add extra parameters
• Omit arguments
• Hydra

command:
  - ${env}
  - python3
  - ${program}
  - ${args}

command:
  - ${env}
  - ${interpreter}
  - ${program}
  - "--config"
  - "your-training-config.json"
  - ${args}

command:
  - ${env}
  - ${interpreter}
  - ${program}

command:
  - ${env}
  - ${interpreter}
  - ${program}
  - ${args_no_hyphens}