# Worker queue
URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/wb-troubleshooting.html

> Describes how DataRobot uses modeling workers and how to troubleshoot problems.

# Worker queue

> [!NOTE] Note
> Trial accounts have a maximum of four workers available.

If you expect to be able to increase your worker count but cannot, the reasons may be:

- You have hit your worker limit .
- Your workers are part of a shared pool .
- Your workers are in use by another project .

## Worker limit

[Modeling worker allocations](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#modeling-worker-allocation) are set by your administrator. Each worker processes a modeling job. This job count applies across all projects in the cluster, that is, multiple browser windows building models are all a part of your personal worker count—more windows does not provide more workers.

## Pooled workers

If you are in an organization, it may implement a [shared pool of workers](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#what-are-organizations). In this case, workers are allocated across all jobs for all users in the organization on a first-in, first-out basis. While you may not have to wait for jobs of other users in your organization to complete, your (and their) jobs will be seeded in the queue and processed as they were received.

## Workers in use

If you believe you should be able to increase the worker count but you cannot, for example, "using X of Y workers," there are two values to consider for debugging. When Y is lower than you expect, check your worker limit with your administrator. When X is less than you expect, check whether workers are being allocated to other projects or users in your organization.

To check worker use in your projects, navigate to the [Manage Projectsinventory](https://docs.datarobot.com/en/docs/classic-ui/modeling/manage-projects.html#manage-projects-control-center) in DataRobot Classic and look for queued jobs.

See the DataRobot Classic [documentation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/worker-queue.html) for more information.

## Workers for registration and deployment

Available workers are required for registered model version creation and deployment. If the [number of workers](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-basic-experiment.html#start-modeling) for the model's experiment is set to zero, model registration and deployment fails, as these workers are required to calculate the training data baseline and run other registration and deployment-related jobs.

## Failed experiments

If an experiment or dataset is not correctly configured for the selected problem type, the experiment will fail. For example, if only two classes remain after aggregation for multiclass experiments, which require at least three classes, the experiment fails.

Failure results in:

- An error notification and an error message displayed on the experiment Leaderboard.
- AFailedbadge next to the experiment in the Use Case.

To understand why the experiment failed, open the experiment and navigate to View experiment info. The error message is displayed in the central window or use the icons to:

- Delete the experiment from the Use Case and start over.
- Duplicate the experiment with or without its settings.

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