Tutorial: Define, initialize, and run a sweep

This page shows how to define, initialize, and run a sweep. There are four main steps:

1. Set up your training code
2. Define the search space with a sweep configuration
3. Initialize the sweep
4. Start the sweep agent

Copy and paste the following code into a Jupyter Notebook or Python script:

# Import the W&B Python Library and log into W&B
import wandb

# 1: Define objective/training function
def objective(config):
    score = config.x**3 + config.y
    return score

def main():
    with wandb.init(project="my-first-sweep") as run:
        score = objective(run.config)
        run.log({"score": score})

# 2: Define the search space
sweep_configuration = {
    "method": "random",
    "metric": {"goal": "minimize", "name": "score"},
    "parameters": {
        "x": {"max": 0.1, "min": 0.01},
        "y": {"values": [1, 3, 7]},
    },
}

# 3: Start the sweep
sweep_id = wandb.sweep(sweep=sweep_configuration, project="my-first-sweep")

wandb.agent(sweep_id, function=main, count=10)

The following sections break down and explains each step in the code sample.

Set up your training code

Define a training function that takes in hyperparameter values from wandb.Run.config and uses them to train a model and return metrics.

Optionally provide the name of the project where you want the output of the W&B Run to be stored (project parameter in wandb.init()). If the project is not specified, the run is put in an “Uncategorized” project.

# 1: Define objective/training function
def objective(config):
    score = config.x**3 + config.y
    return score


def main():
    with wandb.init(project="my-first-sweep") as run:
        score = objective(run.config)
        run.log({"score": score})

Define the search space with a sweep configuration

Specify the hyperparameters to sweep in a dictionary. For configuration options, see Define sweep configuration.

The proceeding example demonstrates a sweep configuration that uses a random search ('method':'random'). The sweep will randomly select a random set of values listed in the configuration for the batch size, epoch, and the learning rate.

W&B minimizes the metric specified in the metric key when "goal": "minimize" is associated with it. In this case, W&B will optimize for minimizing the metric score ("name": "score").

# 2: Define the search space
sweep_configuration = {
    "method": "random",
    "metric": {"goal": "minimize", "name": "score"},
    "parameters": {
        "x": {"max": 0.1, "min": 0.01},
        "y": {"values": [1, 3, 7]},
    },
}

Initialize the Sweep

W&B uses a Sweep Controller to manage sweeps on the cloud (standard), locally (local) across one or more machines. For more information about Sweep Controllers, see Search and stop algorithms locally.

A sweep identification number is returned when you initialize a sweep:

sweep_id = wandb.sweep(sweep=sweep_configuration, project="my-first-sweep")

For more information about initializing sweeps, see Initialize sweeps.

Start the Sweep

Use the wandb.agent API call to start a sweep.

wandb.agent(sweep_id, function=main, count=10)

Visualize results (optional)

Open your project to see your live results in the W&B App dashboard. With just a few clicks, construct rich, interactive charts like parallel coordinates plots, parameter importance analyzes, and additional chart types.

For more information about how to visualize results, see Visualize sweep results. For an example dashboard, see this sample Sweeps Project.

Stop the agent (optional)

In the terminal, press Ctrl+C to stop the current run. Press it again to terminate the agent.