How do I fix `Cuda out of memory` during a sweep? - Weights & Biases Documentation

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.

Add your training logic to a Python script (for example, train.py):
```
if __name__ == "__main__":
    train()
```

Reference the script in your YAML sweep configuration:

program: train.py
method: bayes
metric:
  name: validation_loss
  goal: maximize
parameters:
  learning_rate:
    min: 0.0001
    max: 0.1
  optimizer:
    values: ["adam", "sgd"]

Initialize the sweep with the CLI:
```
wandb sweep config.yaml
```
Start the sweep agent with the CLI, replacing sweep_ID with the ID returned in the previous step:
```
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.

Sweeps Run Crashes

⌘I