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

# Configuring workload resource profiles

Each project editor session and deployment consumes compute resources within the Workbench cluster. If you need to run applications that require more memory or compute power than Anaconda provides by default, you can create customized resource profiles and configure the number of cores and amount of memory/RAM available for them. You can then allow users to access your customized resource profiles either globally or based on their role, assigned group, or as individual users. For more information about groups and roles in Workbench, see [Roles](/docs/data-science/latest/admin/user-mgmt/groups#roles).

For example, if your installation includes nodes with GPUs, you can add a GPU resource profile so users can access the GPUs to accelerate computation within their projects, which is essential for AI/ML model training.

Resource profiles that you create are listed for users when they [create](/docs/data-science/latest/data-science-workflows/projects/main#creating-a-new-project) or [deploy](/docs/data-science/latest/data-science-workflows/deployments/main#deploying-a-project) a project. You can create as many resource profiles as needed.

## Configuring resource profiles via Helm chart

Workbench includes either a `values.k3s.yaml` or a `values.byok.*.yaml` (depending on your implementation) file that overrides the default values in the top-level Helm chart. If you are performing your initial configurations, use the examples below to add the necessary `resource-profiles:` and `gpu-profile:` sections to the bottom of your file, then continue your environment preparation and installation.

Otherwise, follow the steps for [Setting platform configurations using the Helm chart](/docs/data-science/latest/admin/advanced/settings#setting-platform-configurations-using-the-helm-chart) to add or update the `resource-profiles:` and `gpu-profile:` configurations to the bottom of your `helm_values.yaml` file as needed for your system setup.

<Accordion title="Resource profile examples">
  ```yaml theme={null}
  resource-profiles:
    resource-profile:
      description: Custom resource profile (global)
      user_visible: true
      resources:
        limits:
          cpu: "2"
          memory: 4096Mi

    roles_profile:
      description: Custom resource profile (roles)
      user_visible: true
      resources:
        limits:
          cpu: "3"
          memory: 4096Mi
      acl:
        roles:
          - ae-creator

    groups_profile:
      description: Custom resource profile (groups)
      user_visible: true
      resources:
        limits:
          cpu: "4"
          memory: 4096Mi
      acl:
        groups:
          - managers

    users_profile:
      description: Custom resource profile (users)
      user_visible: true
      resources:
        limits:
          cpu: "1"
          memory: 4096Mi
      acl:
        users:
          - user2

    not_visible_to_anyone:
      description: Custom resource profile (not visible)
      resources:
        limits:
          cpu: "3"
          memory: 4096Mi
      user_visible: false

    gpu-profile:
      description: Custom resource profile (GPU resource)
      resources:
        limits:
          cpu: "4"
          memory: 8Gi
          nvidia.com/gpu: 1
        requests:
          cpu: "1"
          memory: 2048Mi
          nvidia.com/gpu: 1
      user_visible: true

    gpu-profile:
      description: Custom resource profile (GPU with limited idle time threshold)
      user_visible: true
      cleanup-threshold: '2 days'
      resources:
          limits:
            cpu: "4"
            memory: 8Gi
            nvidia.com/gpu: 1
          requests:
            cpu: "1"
            memory: 2048Mi
            nvidia.com/gpu: 1
  ```

  <Note>
    Resource profiles display their `description`: as their name. Profiles are listed in alphabetical order, after the default profile.
  </Note>
</Accordion>

By default, CPU sessions and deployments are allowed to run on GPU nodes. To reserve your GPU nodes for sessions and deployments that require them, comment out the `affinity:` configuration in the file as shown:

<Frame>
  <img src="https://mintcdn.com/anaconda-29683c67/QCWY8EsGZWJYinOU/images/helm_node_affinity.png?fit=max&auto=format&n=QCWY8EsGZWJYinOU&q=85&s=53a99b3191f21efa7f08795b687cf635" alt="" width="1283" height="623" data-path="images/helm_node_affinity.png" />
</Frame>

If you need to schedule user workloads on a specific node, add a `node_selector` to your resource profile. Use node selectors when running different CPU types, such as Intel and AMD; or different GPU types, such as Tesla v100 and p100. To enable a node selector, add `node_selector` to the bottom of your resource profile, with the `model:` value matching the label you have applied to your worker node.

<Accordion title="GPU node selector example">
  ```yaml theme={null}
  gpu-profile:
    description: GPU resource profile
    resources:
      limits:
        cpu: "4"
        memory: 8Gi
        nvidia.com/gpu: 1
      requests:
        cpu: "1"
        memory: 2048Mi
        nvidia.com/gpu: 1
    user_visible: true
    node_selector:
      model: v100

  ```
</Accordion>

Once you have run the Helm upgrade command, verify that the resource profiles you added appear in the **Resource Profile** dropdown under your project’s **Settings**.