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Documentation Index

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W&B uses a Sweep Controller to manage sweeps on the cloud (standard), locally (local) across one or more machines. After a run completes, the sweep controller will issue a new set of instructions describing a new run to execute. These instructions are picked up by agents who actually perform the runs. In a typical W&B Sweep, the controller lives on the W&B server. Agents live on your machines. The following code snippets demonstrate how to initialize sweeps with the CLI and within a Jupyter Notebook or Python script.
  1. Before you initialize a sweep, make sure you have a sweep configuration defined either in a YAML file or a nested Python dictionary object in your script. For more information, see Define sweep configuration.
  2. Both the W&B Sweep and the W&B Run must be in the same project. Therefore, the name you provide when you initialize W&B (wandb.init()) must match the name of the project you provide when you initialize a W&B Sweep (wandb.sweep()).
Use the W&B SDK to initialize a sweep. Pass the sweep configuration dictionary to the sweep parameter. Optionally provide the name of the project for the project parameter (project) where you want the output of the W&B Run to be stored. If the project is not specified, the run is put in an “Uncategorized” project.
import wandb

# Example sweep configuration
sweep_configuration = {
    "method": "random",
    "name": "sweep",
    "metric": {"goal": "maximize", "name": "val_acc"},
    "parameters": {
        "batch_size": {"values": [16, 32, 64]},
        "epochs": {"values": [5, 10, 15]},
        "lr": {"max": 0.1, "min": 0.0001},
    },
}

sweep_id = wandb.sweep(sweep=sweep_configuration, project="project-name")
The wandb.sweep() function returns the sweep ID. The sweep ID includes the entity name and the project name. Make a note of the sweep ID.