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Troubleshoot common error messages with the guidance suggested.

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CommError, Run does not exist and ERROR Error uploading

Your W&B Run ID might be defined if these two error messages are both returned. As an example, you might have a similar code snippet defined somewhere in your Jupyter Notebooks or Python script: 
wandb.init(id="some-string")
You can not set a Run ID for W&B Sweeps because W&B automatically generates random, unique IDs for Runs created by W&B Sweeps. W&B Run IDs need to be unique within a project. We recommend you pass a name to the name parameter when you initialized W&B, if you want to set a custom name that will appear on tables and graphs. For example: 
wandb.init(name="a helpful readable run name")

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Cuda out of memory

Refactor your code to use process-based executions if you see this error message. More specifically, rewrite your code to a Python script. In addition, call the W&B Sweep Agent from the CLI, instead of the W&B Python SDK. As an example, suppose you rewrite your code to 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): 
program: train.py
method: bayes
metric:
  name: validation_loss
  goal: maximize
parameters:
  learning_rate:
    min: 0.0001
    max: 0.1
  optimizer:
    values: ["adam", "sgd"]
Next, add the following to your train.py Python script: 
if _name_ == "_main_":
    train()
Navigate to your CLI and initialize a W&B Sweep with wandb sweep: 
wandb sweep config.yaml
Make a note of the W&B Sweep ID that is returned. Next, start the Sweep job with wandb agent with the CLI instead of the Python SDK (wandb.agent). Replace sweep_ID in the code snippet below with the Sweep ID that was returned in the previous step: 
wandb agent sweep_ID

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anaconda 400 error

The following error usually occurs when you do not log the metric that you are optimizing: 
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 (wandb.log) this metric. In addition, ensure you use the exact metric name that you defined the sweep to optimize within your Python script or Jupyter Notebook. For more information about configuration files, see Define sweep configuration.
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