> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Troubleshoot common sweep issues including CommError, CUDA out of memory, and wandb agent failures.

# Sweeps troubleshooting

This page helps you diagnose and resolve common error messages you might encounter when you run W\&B Sweeps. The following sections describe an error, explain why it occurs, and recommend how to fix it.

## `CommError, Run does not exist` and `ERROR Error uploading`

Your W\&B run ID might be defined if W\&B returns both of these error messages. For example, you might have a similar code snippet defined somewhere in your Jupyter Notebooks or Python script:

```python theme={null}
wandb.init(id="some-string")
```

You can't set a run ID for sweeps because W\&B automatically generates random, unique IDs for runs that sweeps create.

W\&B run IDs need to be unique within a project.

If you want to set a custom name that appears on tables and graphs, pass a name to the `name` parameter when you initialize W\&B. For example:

```python theme={null}
wandb.init(name="a helpful readable run name")
```

After you remove the `id` argument from `wandb.init()`, the sweep can assign its own unique run IDs, and the upload errors stop.

## `CUDA out of memory`

If you see this error message, refactor your code to use process-based executions. When you run each trial in its own process, W\&B releases GPU memory between runs.

To refactor your code, complete the following steps:

1. Rewrite your code as a Python script called `train.py`. Add the name of the training script (`train.py`) to your YAML sweep configuration file (`config.yaml` in this example):

   ```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"]
   ```

2. Add the following to your `train.py` Python script:

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

3. From your CLI, initialize a sweep with `wandb sweep`:

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

4. Note the sweep ID that W\&B returns. Start the sweep job with [`wandb agent`](/models/ref/cli/wandb-agent) from the CLI instead of the Python SDK ([`wandb.agent()`](/models/ref/python/functions/agent)). Replace `[SWEEP-ID]` in the following code snippet with the sweep ID that W\&B returned in the previous step:

   ```bash theme={null}
   wandb agent [SWEEP-ID]
   ```

With your training code running as a script under the CLI agent, each trial executes in its own process, and W\&B releases GPU memory between runs.

## `anaconda 400 error`

The following error usually occurs when you don't log the metric you're optimizing:

```text theme={null}
wandb: ERROR Error while calling W&B API: anaconda 400 error: 
{"code": 400, "message": "TypeError: bad operand type for unary -: 'NoneType'"}
```

Within your YAML file or nested dictionary, you specify a key named `metric` to optimize. Ensure that you log this metric with `wandb.Run.log()`. Also, ensure you use the exact metric name you defined the sweep to optimize within your Python script or Jupyter Notebook. For more information about configuration files, see [Define sweep configuration](/models/sweeps/define-sweep-configuration).