Admin Menus

Logging in with an admin account will reveal an extra Administration menu on the bottom left of the sidebar. User information registered in Backend.AI is listed in the Users tab. super-admin role user can see all users' information, create and deactivate a user.

User ID (email), Name (username), Role and Description(User Description) can be filtered by typing text in the search box on each column header.

Create and update users

A user can be created by clicking the '+Create User' button. Note that the password must be longer or equal to 8 characters and at least 1 alphabet/special character/ number should be included. The maximum length allowed for E-Mail and Username is 64.

Note

If a user with the same email or username already exists, it is not possible to creat user account. Please try other email and username.

Check if the user is created.

Click the green button in the Controls panel for more detailed user information. You can also check the domain and project information where the user belongs.

Click the 'Setting (Gear)' in the Controls panel to update information of a user who already exists. User's name, password, activation state, etc. can be changed. User ID cannot be changed.

Each of the five items at the bottom of the dialog has the following functions.

User Status: Indicates the user's status. Inactive users cannot log in. Before Verification is a status indicates that a user needs an additional step to activate the account such as email verification or an approval from an admin. Note that the inactive users are listed in the Inactive tab separately.
Require password change?: If the admin has chosen random passwords while creating users in batches, this field can be set to ON to indicate that password change is required. The users will see the top bar that notify user to update their password, but this is a kind of descriptive flag which has no effect on actual use.
Enable sudo session: Allow the user to use sudo in the compute session. This is useful when the user needs to install packages or run commands that require root privileges. However, it is not recommended to enable this option for all users, as it may cause security issues.
2FA Enabled: A flag indicating whether the user uses two-factor authentication. When using two-factor authentication, users are additionally required to enter an OTP code when logging in. Administrators can only disable two-factor authentication for other users.
Resource Policy: From Backend.AI version 24.09, you can select the user resource policy to which the user belongs. For more information about user resource policies, please refer user resource policy section.

Inactivate user account

Deleting user accounts is not allowed even for superadmins, to track usage statistics per user, metric retention, and accidental account loss. Instead, admins can inactivate user accounts to keep users from logging in. Click the delete icon in the Controls panel. A popover asking confirmation appears, and you can deactivate the user by clicking the Deactivate button.

To re-activate users, go to Users - Inactive tab, and select the status of the target user to Active.

Warning

Please note that deactivating or reactivating the user does not change the user's credentials, since the user account can have multiple keypairs, which brings it hard to decide which credential should be reactivated.

Manage User's Keypairs

Each user account usually have one or more keypairs. A keypair is used for API authentication to the Backend.AI server, after user logs in. Login requires authentication via user email and password, but every request the user sends to the server is authenticated based on the keypair.

A user can have multiple keypairs, but to reduce the user's burden of managing keypairs, we are currently using only one of the user's keypairs to send requests. Also, when you create a new user, a keypair is automatically created, so you do not need to create and assign a keypair manually in most cases.

Keypairs can be listed on the Credentials tab of in the Users page. Active keypairs are shown immediately, and to see the inactive keypairs, click the Inactive panel at the bottom.

Like in Users tab, you can use the buttons in the Controls panel to view or update keypair details. Click the green info icon button to see specific details of the keypair. If necessary, you can copy the secret key by clicking the copy button.

You can modify the resource policy and rate limit of the keypair by clicking the blue 'Setting (Gear)' button. Please keep in mind that if the 'Rate Limit' value is small, API operations such as login may be blocked.

You can also deactivate or reactivate the keypair by clicking red 'Deactivate' button or black 'Activate' button in control column. Unlike the User tab, the Inactive tab allows permanent deletion of key pairs. However, you cannot permanently delete a key pair if it is currently being used as a user's main access key.

If you accidentally deleted a keypair, you can re-create keypair for the user by clicking the '+ ADD CREDENTIAL' button at the upper right corner.

The Rate Limit field is where you specify the maximum number of requests that can be sent to the Backend.AI server in 15 minutes. For example, if set to 1000, and the keypair sends more than 1000 API requests in 15 minutes, and the server throws an error and does not accept the request. It is recommended to use the default value and increase it when the API request frequency goes up high according to the user's pattern.

Manage Models cards

All of the Model cards in model store are managed by project admin. After uploading model store with model-definition file, any user in the project can access to model card and clone it if needed.

Following method is to add model cards from the Hugging Face.

Note

Before creating a model card, getting an access to the specific model on Hugging Face is needed. For more information, please refer to Gated models .

First, Set the project to 'model-store'.

Move to data page and click the 'Create Folder' button on the right side. Enter the folder name, and set the rest of folder configuration as shown below:

Usage Mode: Model

Type: project

Permission: Read-Write

Cloneable: True

After creating the folder, you need to set and upload the model-definition.yaml file to the folder that you've just created. Following is the example of the model-definition file. If you want to know how to write model-definition file, please refer to Model definition guide section.

models:
- name: "Llama-3.1-8B-Instruct"
   model_path: "/models/Llama-3.1-8B-Instruct"
   service:
      pre_start_actions:
      - action: run_command
         args:
            command:
            - huggingface-cli
            - download
            - --local-dir
            - /models/Llama-3.1-8B-Instruct
            - --token
            - hf_****
            - meta-llama/Llama-3.1-8B-Instruct
         start_command:
         - /usr/bin/python
         - -m
         - vllm.entrypoints.openai.api_server
         - --model
         - /models/Llama-3.1-8B-Instruct
         - --served-model-name
         - Llama-3.1-8B-Instruct
         - --tensor-parallel-size
         - "1"
         - --host
         - "0.0.0.0"
         - --port
         - "8000"
         - --max-model-len
         - "4096"
      port: 8000
      health_check:
         path: /v1/models
         max_retries: 500

Once the model-definition file is uploaded, the model card will appear in the model store page.

Note

You need to download model manually after setting model-definition file. For downloading the model file to folder, you can mount the model folder to session creation and download file to there by referring Downloading models .

Clicking on the model card you've just created will display the details of the model-definition file you uploaded. Now, every member of the project can access the model card and clone it.

Manage Resource Policy

Keypair Resource Policy

In Backend.AI, administrators have the ability to set limits on the total resources available for each keypair, user, and project. Resource policies enable you to define the maximum allowed resources and other compute session-related settings. Additionally, it is possible to create multiple resource policies for different needs, such as user or research requirements, and apply them on an individual basis.

The Resource Policy page allows administrators to view a list of all registered resource policies. Administrators can review the resource policies established for keypairs, users, and projects directly on this page. Let's begin by examining the resource policies for keypairs. In the figure below, there are three policies in total (gardener, student, default). The infinity symbol (∞) indicates that no resource restrictions have been applied to those resources.

The user account being used in this guide is currently assigned to the default resource policy. This can be verified in the Credentials tab on the Users page. You can also confirm that all resource policies are set to default in the Resource Policy panel.

To modify resource policies, click the 'Setting (Gear)' in the Control column of the default policy group. In the Update Resource Policy dialog, every option is editable except for Policy Name, which serves as the primary key for distinguishing resource policies in the list. Uncheck the Unlimited checkbox at the bottom of CPU, RAM, and fGPU, and set the resource limits to the desired values. Ensure that the allocated resources are less than the total hardware capacity. In this case, set CPU, RAM, and fGPU to 2, 4, and 1 respectively. Click the OK button to apply the updated resource policy.

About details of each option in resource policy dialog, see the description below.

Resource Policy
- CPU: Specify the maximum amount of CPU cores. (max value: 512)
- Memory: Specify the maximum amount of memory in GB. It would be good practice to set memory twice as large as the maximum value of GPU memory. (max value: 1024)
- CUDA-capable GPU: Specify the maximum amount of physical GPUs. If fractional GPU is enabled by the server, this setting has no effect. (max value: 64)
- CUDA-capable GPU (fractional): Fractional GPU (fGPU) is literally split a single GPU to multiple partitions in order to use GPU efficiently. Notice that the minimum amount of fGPU required is differed by each image. If fractional GPU is not enabled by the server, this settings has no effect. (max value: 256)
Sessions
- Cluster Size: Set the maximum limit for the number of multi-containers or multi-nodes that can be configured when creating a session.
- Session Lifetime (sec.): Limits the maximum lifetime of a compute session from the reservation in the active status, including PENDING and RUNNING statuses. After this time, the session will be force-terminated even if it is fully utilized. This will be useful to prevent the session from running indefinitely.
- Max Pending Session Count: Maximum number of compute sessions that can be in the PENDING status simultaneously.
- Concurrent Jobs: Maximum number of concurrent compute session per keypair. If this value is set to 3, for example, users bound to this resource policy cannot create more than 3 compute sessions simultaneously. (max value: 100)
- Idle timeout (sec.): Configurable period of time during which the user can leave their session untouched. If there is no activity at all on a compute session for idle timeout, the session will be garbage collected and destroyed automatically. The criteria of the "idleness" can be various and set by the administrators. (max value: 15552000 (approx. 180 days))
- Max Concurrent SFTP Sessions: Maximum number of concurrent SFTP sessions.
Folders
- Allowed hosts: Backend.AI supports many NFS mountpoint. This field limits the accessibility to them. Even if a NFS named "data-1" is mounted on Backend.AI, users cannot access it unless it is allowed by resource policy.
- (Deprecated since 23.09.4) Max. #: the maximum number of storage folders that can be created/invited. (max value: 100).

In the keypair resource policy list, check that the Resources value of the default policy has been updated.

../_images/keypair_resource_policy_update_check.png

You can create a new resource policy by clicking the '+ Create' button. Each setting value is the same as described above.

To create a resource policy and associate it with a keypair, go to the Credentials tab of the Users page, click the gear button located in the Controls column of the desired keypair, and click the Select Policy field to choose it.

You can also delete each of resource keypairs by clicking trash can icon in the Control column. When you click the icon, the confirmation popup will appears. Click 'Delete' button to erase."

../_images/resource_policy_delete_dialog.png

Note

If there's any users (including inactive users) following a resource policy to be deleted, deletion may not be done. Before deleting a resource policy, please make sure that no users remain under the resource policy.

If you want to hide or show specific columns, click the 'Setting (Gear)' at the bottom right of the table. This will bring up a dialog where you can select the columns you want to display.

User Resource Policy

Starting from version 24.03, Backend.AI supports user resource policy management. While each user can have multiple keypairs, a user can only have one user resource policy. In the user resource policy page, users can set restrictions on various settings related to folders such as Max Folder Count and Max Folder Size, as well as individual resource limits like Max Session Count Per Model Session and Max Customized Image Count.

To create a new user resource policy, click the Create button.

Name: The name of the user resource policy.
Max Folder Count: The maximum number of folders that the user can create. If the user's folder count exceeds this value, user cannot create a new folder. If set to Unlimited, it is displayed as "∞".
Max Folder Size: The maximum size of the user's storage space. If user's storage space exceeds this value, user cannot create a new data folder. If set to Unlimited, it is displayed as "∞".
Max Session Count Per Model Session: The maximum number of available sessions per model service created by a user. Increasing this value can put a heavy load on the session scheduler and potentially lead to system downtime, so please caution when adjusting this setting.
Max Customized Image Count: The maximum number of customized images that user can create. If user's customized image count exceeds this value, user cannot create a new customized image. If you want to know more about customized images, please refer to the My Environments section.

To update, click the 'Setting (Gear)' button in the control column. To delete, click the trash can button.

Note

Changing a resource policy may affect all users who use that policy, so use it with caution.

Similar to keypair resource policy, users can select and display only columns users want by clicking the 'Setting (Gear)' button at the bottom right of the table.

Project Resource Policy

Starting from version 24.03, Backend.AI supports project resource policy management. Project resource policies manage storage space (quota) and folder-related limitations for projects.

When clicking the Project tab of the Resource Policy page, you can see the list of project resource policy.

To create a new project resource policy, click the '+ Create' button at the top right of the table.

Name: The name of the project resource policy.
Max Folder Count: The maximum number of project folders that an administrator can create. If the project folder count exceeds this value, the administrator will not be able to create a new project folder. If set to Unlimited, it will be displayed as "∞".
Max Folder Size: The maximum size of the project's storage space. If the project's storage space exceeds this value, the administrator cannot create a new project folder. If set to Unlimited, it is displayed as "∞".
Max Network Count: The maximum number of networks that can be created for the project since Backend.AI version 24.12. If set to Unlimited, it is displayed as "∞".

The meaning of each field is similar to the user resource policy. The difference is that the project resource policy is applied to the project folders, while the user resource policy is applied to the user folders.

If you want to make changes, click the 'Setting (Gear)' button in the control column. Resource policy names cannot be edited. Deletion can be done by clicking the trash can icon button.

Note

Changing a resource policy may affect all users who use that policy, so use it with caution.

You can select and display only the columns you want by clicking the 'Setting (Gear)' button at the bottom right of the table.

To save the current resource policy as a file, click on the 'Tools' menu located at the top left of each tab. Once you click the menu, download dialog will appear.

Unified View for Pending Sessions

From Backend.AI version 25.13.0, a unified view for pending sessions is available in the Admin Menu. Unlike the Session page, the Scheduler page provides a unified view of all pending sessions within a selected resource group. The index number displayed next to the status indicates the queue position in which the session will be created once sufficient resources become available.

Similar to the Session page, you can click the session name to open a drawer that displays detailed information about the session.

Manage Images

Admins can manage images, which are used in creating a compute session, in the Images tab of the Environments page. In the tab, meta information of all images currently in the Backend.AI server is displayed. You can check information such as registry, namespace, image name, image's based OS, digest, and minimum resources required for each image. For images downloaded to one or more agent nodes, there will be a installed tag in each Status column.

Note

The feature to install images by selecting specific agents is currently under development.

You can change the minimum resource requirements for each image by clicking the 'Setting (Gear)' in the Controls panel. Each image has hardware and resource requirements for minimal operation. (For example, for GPU-only images, there must be a minimum allocated GPU.) The default value for the minimum resource amount is provided as embedded in the image's metadata. If an attempt is made to create a compute session with a resource that is less than the amount of resources specified in each image, the request is automatically adjusted to the minimum resource requirements for the image and then generated, not cancelled.

Warning

Don't change the minimum resource requirements to an amount less than the predefined value! The minimum resource requirements included in the image metadata are values that have been tested and determined. If you are not really sure about the minimum amount of resources you want to change, leave it in the default.

Additionally, you can add or modify the supported apps for each image by clicking the 'Apps' icon located in the Controls column. Once you click the icon, the name of the app and its corresponding port number will be displayed accordingly.

In this interface, you can add supported custom applications by clicking the '+ Add' button below. To delete an application, simply click the 'red trash can' button on the right side of each row.

Note

You need to reinstall the image after changing the managed app.

Confirmation dialog for manage app change in image

Manage docker registry

You can click on the Registries tab in Environments page to see the information of the docker registry that are currently connected. cr.backend.ai is registered by default, and it is a registry provided by Harbor.

Note

In the offline environment, the default registry is not accessible, so click the trash icon on the right to delete it.

Click the refresh icon in Controls to update image metadata for Backend.AI from the connected registry. Image information which does not have labels for Backend.AI among the images stored in the registry is not updated.

You can add your own private docker registry by clicking the '+ Add Registry' button. Note that Registry Name and Registry URL address must be set identically, and in the case of Registry URL, a scheme such as http:// or https:// must be explicitly attached. Also, images stored in the registry must have a name prefixed with Registry Name. Username and Password are optional and can be filled in if you set separate authentication settings in the registry. In Extra Information, you can pass additional information needed for each registry type as a JSON string.

GitLab Container Registry Configuration

When adding a GitLab container registry, you must specify the api_endpoint in the Extra Information field. This is required because GitLab uses separate endpoints for the container registry and the GitLab API.

For GitLab.com (public instance):

Registry URL: https://registry.gitlab.com
Extra Information: {"api_endpoint": "https://gitlab.com"}

For self-hosted (on-premise) GitLab:

Registry URL: Your GitLab registry URL (e.g., https://registry.example.com)
Extra Information: {"api_endpoint": "https://gitlab.example.com"}

The api_endpoint should point to your GitLab instance URL, not the registry URL.

Additional configuration notes:

Project path format: When specifying the project, use the full path including namespace and project name (e.g., namespace/project-name). Both components are required for the registry to function correctly.
Access token permissions: The access token used for the registry must have both read_registry and read_api scopes. The read_api scope is required for Backend.AI to query the GitLab API for image metadata during rescan operations.

You can also update the information of an existing registry, except the Registry Name.

After creating a registry and updating the image metadata, users still cannot use the images immediately. You must enable the registry by toggling the Enabled switch in the registry list to allow users to access images from the registry.

Manage resource preset

The following predefined resource presets are displayed in the Resource allocation panel when creating a compute session. Superadmin can manage these resource presets.

Resource presets in compute session launch dialog

Go to the Resource Presets tab on the Environment page. You can check the list of currently defined resource presets.

You can set resources such as CPU, RAM, fGPU, etc. to be provided by the resource preset by clicking the 'Setting (Gear)' (cogwheel) in the Controls panel. Create or Modify Resource Preset modal shows fields of the resources currently available. Depending on your server's settings, certain resources may not be visible. After setting the resources with the desired values, save it and check if the corresponding preset is displayed when creating a compute session. If available resources are less than the amount of resources defined in the preset, the corresponding preset would not be shown.

Also you can create resource preset by Clicking '+ Create Presets' button in the right top of the Resource Presets tab. You cannot create the same resource preset name that already exists, since it is the key value for distinguishing each resource preset.

Manage agent nodes

Superadmins can view the list of agent worker nodes, currently connected to Backend.AI, by visiting the Resources page. You can check agent node's IP, connecting time, actual resources currently in use, etc. The WebUI does not provide the function to manipulate agent nodes.

Query agent nodes

Also You can see exact usage about the resources in the agent worker node by Click note icon in the Control panel.

On Terminated tab, you can check the information of the agents that has been connected once and then terminated or disconnected. It can be used as a reference for node management. If the list is empty, then it means that there's no disconnection or termination occurred.

Set schedulable status of agent nodes

You may want to prevent new compute sessions from being scheduled to an Agent service without stopping it. In this case, you can disable the Schedulable status of the Agent. Then, you can block the creation of a new session while preserving the existing sessions on the Agent.

Manage resource group

Agents can be grouped into units called resource (scaling) groups. For example, let's say there are 3 agents with V100 GPUs and 2 agents with P100 GPUs. You want to expose two types of GPUs to users separately, then you can group three V100 agents into one resource group, and the remaining two P100 agents into another resource group.

Adding a specific agent to a specific resource group is not currently handled in the WebUI, and it can be done by editing agent config file from the installation location and restart the agent daemon. Management of the resource groups is possible in Resource Group tab of the Resource page.

You can edit a resource group by clicking the 'Setting (Gear)' in the Control panel. In the Select scheduler field, you can choose the scheduling method for creating a compute session. Currently, there are three types: FIFO, LIFO, and DRF. FIFO and LIFO are scheduling methods creating the first- or the last-enqueued compute session in the job queue. DRF stands for Dominant Resource Fairness, and it aims to provide resources as fair as possible for each user. You can deactivate a resource policy by turning off Active Status.

WSProxy Server Address sets the WSProxy address for the resource group's Agents to use. If you set a URL in this field, WSProxy will relay the traffic of an app like Jupyter directly to the compute session via Agent bypassing Manager (v2 API). By enabling the v2 API, you can lower the Manager's burden when using app services. This also achieves the better efficiency and scalability in deploying the services. If a direct connection from WSProxy to the Agent node is not available, however, please leave this field blank to fall back to the v1 API, which relays the traffic through Manager in a traditional way.

Agent Selection Strategy

When scheduling a session, Backend.AI determines which agent (node) should host the session based on the configured strategy. There are two strategies available:

Strategy	Description	Use Cases
Dispersed (default)	Distributes workloads across multiple agents, preferring agents with more available resources for balanced GPU utilization.	Even resource distribution, balanced GPU usage across nodes
Concentrated	Packs workloads onto fewer agents, preferring agents already in use to keep other nodes idle and available for scale-down.	Cloud environments with autoscaling, reducing active node count, cost optimization

Example: With 4 GPU nodes and 2 session requests:

Dispersed: Places sessions on Node 1 and Node 2 (distributed across nodes)
Concentrated: Places both sessions on Node 1 (packed, Node 2-4 remain idle)

Note

For Inference sessions, the enforce_spreading_endpoint_replica option can be enabled to spread replicas across different agents even when using the Concentrated strategy. This ensures model serving replicas are not all on the same node.

Scheduler Options

The resource group has further Scheduler Options. The details are described below.

Allowed session types: Since user can choose the type of session, resource group can allow certain type of session. You should allow at lest one session types. The allowed session types are Interactive, Batch, and Inference.
Pending timeout: A compute session will be canceled if it stays PENDING status for longer than the Pending timeout. When you wish to prevent a session from remaining PENDING indefinitely, set this time. Set this value to zero (0) if you do not want to apply the pending timeout feature.
Retries to skip pending session: The number of retries the scheduler tries before skipping a PENDING session. It can be configured to prevent the situation where one PENDING session blocks the scheduling of the subsequent sessions indefinitely (Head-of-line blocking, HOL). If no value is specified, the global value in Etcd will be used (num retries to skip, default three times).

You can create a new resource policy by clicking the '+ Create' button. Likewise other creating options, you cannot create a resource policy with the name that already exists, since name is the key value.

Storages

On STORAGES tab, you can see what kind of mount volumes (usually NFS) exist. From 23.03 version, We provide per-user/per-project quota setting on storage that supports quota management. By using this feature, admin can easily manage and monitor the exact amount of storage usage for each user and project based folder.

In order to set quota, you need to first access to storages tab in resource page. And then, click 'Setting (Gear)' in control column.

Note

Please remind that quota setting is only available in storage that provides quota setting (e.g. XFS, CephFS, NetApp, Purestorage, etc.). Although you can see the usage of storage in quota setting page regardless of storage, you cannot configure the quota which doesn't support quota configuration internally.

Quota Setting Panel

In Quota setting page, there are two panels.

Overview panel
- Usage: Shows the actual amount usage of the selected storage.
- Endpoint: Represents the mount point of the selected storage.
- Backend Type: The type of storage.
- Capabilities: The supported feature of the selected storage.
Quota Settings
- For User: Configure per-user quota setting here.
- For Project: Configure per-project quota(project-folder) setting here.
- ID: Corresponds to user or project id.
- Hard Limit (GB): Currently set hard limit quota for selected quota.
- Control: Provides editing the hard limit or even deleting the quota setting.

Set User Quota

In Backend.AI, there are two types of vfolders created by user and admin(project). In this section, we would like to show how to check current quota setting per-user and how to configure it. First, make sure the active tab of quota settings panel is For User. Then, select user you desire to check and edit the quota. You can see the quota id that corresponds to user's id and the configuration already set in the table, if you already set the quota.

Of course, if you want to edit the quota, you can simply click the Edit button in the control column. After Clicking Edit button, you may see the small modal that enables configuring quota setting. After input the exact amount, don't forget to Click OK button, unless the changes will not be applied.

Set Project Quota

Setting a quota on project-folder is similar to setting a user quota. The difference between setting project quota and user quota is to confirm setting the project quota requires one more procedure, which is selecting the domain that the project is dependent on. The rest are the same. As in the picture below, you need to first select the domain, and then select the project.

Unset Quota

We also provides the feature to unset the quota. Please remind that after removing the quota setting, quota will automatically follows user or project default quota, which cannot be set in WebUI. If you want to change the default quota setting, you may need to access to admin-only page. By Clicking Unset button in control column, the small snackbar message will show up and confirm whether you really want to delete the current quota setting. If you click OK button in the snackbar message, then it will delete the quota setting and automatically reset the quota follows to corresponding quota, which depends on the quota type(user / project).

Note

If there's no config per user/project, then corresponding values in the user/project resource policy will be set as a default value. For example, If no hard limit value for quota is set, max_vfolder_size value in the resource policy is used as the default value.

Download session lists

Note

This feature is currently not available on the default Session page. To use this feature, please enable 'Classic Session list page' option in the 'Switch back to the Classic UI' section on the User Setting page. For more details, please refer to Backend.AI User Settings section.

There's additional feature in Session page for admin. On the right side of the FINISHED tab there is a menu marked with .... When you click this menu, a sub-menu export CSV appears.

If you click this menu, you can download the information of the comcpute sessions created so far in CSV format. After the following dialog opens, enter an appropriate file name (if necessary), click the EXPORT button and you will get the CSV file. Please note that a file name can have up to 255 characters.

System settings

In the Configuration page, you can see main settings of Backend.AI server. Currently, it provides several controls which can change and list settings.

You can change image auto install and update rule by selecting one option from Digest, Tag, None. Digest is kind of checksum for the image which verifies integrity of the image and also enhances efficiency in downloading images by reusing duplicated layers. Tag is only for developing option since it does not guarantee the Integrity of the image.

Warning

Don't change rule selection unless you completely understand the meaning of each rule.

You can also change settings for scaling, plugins and enterprise features.

System setting about scaling and plugins

When a user launches a multi-node cluster session, which is introduced at version 20.09, Backend.AI will dynamically create an overlay network to support private inter-node communication. Admins can set the value of the Maximum Transmission Unit (MTU) for the overlay network, if it is certain that the value will enhance the network speed.

Server management

Go to the Maintenance page and you will see some buttons to manage the server.

RECALCULATE USAGE: Occasionally, due to unstable network connections or container management problem of Docker daemon, there may be a case where the resource occupied by Backend.AI does not match the resource actually used by the container. In this case, click the RECALCULATE USAGE button to manually correct the resource occupancy.
RESCAN IMAGES: Update image meta information from all registered Docker registries. It can be used when a new image is pushed to a Backend.AI-connected docker registry.

Note

We will continue to add other settings needed for management, such as removing unused images or registering periodic maintenance schedules.

Detailed Information

In Information page, you can see several detailed information and status of each feature. To see Manager version and API version, check the Core panel. To see whether each component for Backend.AI is compatible or not, check the Component panel.

Note

This page is only for showing current information.

Admin Menus

Create and update users

Inactivate user account

Manage User's Keypairs

Share project storage folders with project members

Manage Models cards

Manage Resource Policy

Keypair Resource Policy

User Resource Policy

Project Resource Policy

Unified View for Pending Sessions

Manage Images

Manage docker registry

GitLab Container Registry Configuration

Manage resource preset

Manage agent nodes

Query agent nodes

Set schedulable status of agent nodes

Manage resource group

Agent Selection Strategy

Scheduler Options

Storages

Quota Setting Panel

Set User Quota

Set Project Quota

Unset Quota

Download session lists

System settings

Server management

Detailed Information