Parallelize agents ↗

    wandb agent sweep_id
    ```
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  <span class="tab-start" data-tab-title="Jupyter Notebook"></span>
Use the W\&B Python SDK library to parallelize your W\&B Sweep agent across multiple CPUs within Jupyter Notebooks. Ensure you have the sweep ID that was returned when you [initialized the sweep](./initialize-sweeps).  In addition, provide the name of the function the sweep will execute for the `function` parameter:

1. Open more than one Jupyter Notebook.
2. Copy and past the W\&B Sweep ID on multiple Jupyter Notebooks to parallelize a W\&B Sweep. For example, you can paste the following code snippet on multiple jupyter notebooks to paralleliz your sweep if you have the sweep ID stored in a variable called `sweep_id` and the name of the function is `function_name`:

```python
    wandb.agent(sweep_id=sweep_id, function=function_name)
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

### Parallelize on a multi-GPU machine

Follow the procedure outlined to parallelize your W\&B Sweep agent across multiple GPUs with a terminal using CUDA Toolkit:

1. Open more than one terminal window on your local machine.
2. Specify the GPU instance to use with `CUDA_VISIBLE_DEVICES` when you start a W\&B Sweep job ([`wandb agent`](/models/ref/cli/wandb-agent)). Assign `CUDA_VISIBLE_DEVICES` an integer value corresponding to the GPU instance to use.

For example, suppose you have two NVIDIA GPUs on your local machine. Open a terminal window and set `CUDA_VISIBLE_DEVICES` to `0` (`CUDA_VISIBLE_DEVICES=0`). Replace `sweep_ID` in the proceeding example with the W\&B Sweep ID that is returned when you initialized a W\&B Sweep:

Terminal 1

```bash
CUDA_VISIBLE_DEVICES=0 wandb agent sweep_ID

CUDA_VISIBLE_DEVICES=1 wandb agent sweep_ID