> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-1917.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# How do I fix `Cuda out of memory` during a sweep?

If you see `Cuda out of memory` during a sweep, refactor your code to use process-based execution. Rewrite your code as a Python script and call the sweep agent from the CLI instead of the Python SDK.

1. Add your training logic to a Python script (for example, `train.py`):

   ```python theme={null}
   if __name__ == "__main__":
       train()
   ```

2. Reference the script in your YAML sweep configuration:

   ```yaml theme={null}
   program: train.py
   method: bayes
   metric:
     name: validation_loss
     goal: maximize
   parameters:
     learning_rate:
       min: 0.0001
       max: 0.1
     optimizer:
       values: ["adam", "sgd"]
   ```

3. Initialize the sweep with the CLI:

   ```shell theme={null}
   wandb sweep config.yaml
   ```

4. Start the sweep agent with the CLI, replacing `sweep_ID` with the ID returned in the previous step:

   ```shell theme={null}
   wandb agent sweep_ID
   ```

Using the CLI-based agent (`wandb agent`) instead of the Python SDK (`wandb.agent`) ensures each run is a separate process with its own memory allocation, preventing CUDA memory from accumulating across runs.

For more information, see [Sweeps troubleshooting](/models/sweeps/troubleshoot-sweeps/).

***

<Badge stroke shape="pill" color="orange" size="md">[Sweeps](/support/models/tags/sweeps)</Badge><Badge stroke shape="pill" color="orange" size="md">[Run Crashes](/support/models/tags/run-crashes)</Badge>