> ## 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 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](../../data-science-workflows/projects/main#creating-a-new-project) or [deploy](../../data-science-workflows/deployments/main#deploying-a-project) a project. You can create as many resource profiles as needed. 1. Connect to your instance of Workbench. 2. View a list of your configmaps by running the following command: ``` kubectl get cm ``` 3. Edit the `anaconda-enterprise-anaconda-platform.yml` file. ``` kubectl edit cm anaconda-enterprise-anaconda-platform ``` Anaconda recommends making a backup copy of this file before you edit it. Any changes you make will impact how Workbench functions. 4. Find the `resource-profiles:` section of the file. 5. Add any additional resources using the following examples as a template for your resource profiles, then customize them for your environment: ``` resource-profiles: resource-profile: description: Custom resource profile (global) resources: limits: cpu: "2" memory: 4096Mi user_visible: true roles_profile: description: Custom resource profile (roles) resources: limits: cpu: "3" memory: 4096Mi user_visible: true acl: roles: - ae-creator groups_profile: description: Custom resource profile (group) resources: limits: cpu: "4" memory: 4096Mi user_visible: true acl: groups: - managers users_profile: description: Custom resource profile (users) resources: limits: cpu: "1" memory: 4096Mi user_visible: true acl: users: - user2 not_visible_to_anyone: description: Custom resource profile (nv) resources: limits: cpu: "3" memory: 4096Mi user_visible: false 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 ``` Resource profiles display their `description:` as their name. Profiles are listed in alphabetical order, after the default profile. 6. (Optional) 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:` specification in the file as shown: 7. (Optional) 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. ``` 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 ``` 8. Save your changes to the file. 9. Restart the workspace and deploy services by running the following command: ``` kubectl delete pods -l 'app in (ap-workspace, ap-deploy)' ``` Anaconda’s Kubernetes Helm chart encapsulates application definitions, dependencies, and configurations into a single `values.yaml` file. For more information about the Helm chart, see [Helm values template](../../environment-prep/byok8s-prep#helm-chart). 1. Connect to your instance of Workbench. 2. Save your current configurations using the `extract_config.sh` script by running the following command: ``` # Replace with the namespace Workbench is installed in NAMESPACE= ./extract_config.sh ``` The `extract_config.sh` script creates a file called `helm_values.yaml` and saves it in the directory where the script was run. 3. Verify that the information captured in `helm_values.yaml` file contains your current cluster configuration settings. 4. Create a new section at the bottom of the `helm_values.yaml` file and add any additional resources using the following examples as a template for your resource profiles, then customize them for your environment: ``` resource-profiles: resource-profile: description: Custom resource profile (global) resources: limits: cpu: "2" memory: 4096Mi user_visible: true roles_profile: description: Custom resource profile (roles) resources: limits: cpu: "3" memory: 4096Mi user_visible: true acl: roles: - ae-creator groups_profile: description: Custom resource profile (group) resources: limits: cpu: "4" memory: 4096Mi user_visible: true acl: groups: - managers users_profile: description: Custom resource profile (users) resources: limits: cpu: "1" memory: 4096Mi user_visible: true acl: users: - user2 not_visible_to_anyone: description: Custom resource profile (nv) resources: limits: cpu: "3" memory: 4096Mi user_visible: false 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 ``` Resource profiles display their `description:` as their name. Profiles are listed in alphabetical order, after the default profile. 5. (Optional) 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: 6. (Optional) 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. ``` 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 ``` 7. Perform a Helm upgrade by running the following command: ``` helm upgrade --values ./helm_values.yaml anaconda-enterprise ./Anaconda-Enterprise/ ``` Open a project and view its settings to verify that the resource profiles you added appear in the **Resource Profile** dropdown.