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Start sweep agents

Start a W&B Sweep on one or more agents on one or more machines. W&B Sweep agents query the Weights & Biases server you launched when you initialized a W&B Sweep (wandb sweep) for hyperparameters and use them to run model training.

To start a W&B Sweep agent, provide the W&B Sweep ID that was returned when you initialized a W&B Sweep. The W&B Sweep ID has the form:



  • entity: Your Weights & Biases username or team name.
  • project: The name of the 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.
  • sweep_ID: The pseudo random, unique ID generated by W&B.

Provide the name of the function the W&B Sweep will execute if you start a W&B Sweep agent within a Jupyter Notebook or Python script.

The proceeding code snippets demonstrate how to start an agent with Weights & Biases. We assume you already have a configuration file and you have already initialized a W&B Sweep. For more information about how to define a configuration file, see Define sweep configuration.

Use the wandb agent command to start a sweep. Provide the sweep ID that was returned when you initialized the sweep. Copy and paste the code snippet below and replace sweep_id with your sweep ID:

wandb agent sweep_id

Stop W&B agentโ€‹


Random and Bayesian searches will run forever. You must stop the process from the command line, within your python script, or the Sweeps UI.

Optionally specify the number of W&B Runs a Sweep agent should try. The following code snippets demonstrate how to set a maximum number of W&B Runs with the CLI and within a Jupyter Notebook, Python script.

First, initialize your sweep. For more information, see Initialize sweeps.

sweep_id = wandb.sweep(sweep_config)

Next, start the sweep job. Provide the sweep ID generated from sweep initiation. Pass an integer value to the count parameter to set the maximum number of runs to try.

sweep_id, count = "dtzl1o7u", 10
wandb.agent(sweep_id, count=count)

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