FAQ

Accounts and permissions#

A button is missing#

Buttons you lack the permission for are not shown, and a few screens show a read-only view instead. A disabled button is unavailable because of the current state. Check your role in the current tenant under the avatar menu → Account settings (계정 설정), and find the permission you need in Roles and permissions. Roles are changed by the tenant administrator → Members

I only see "접근 가능한 테넌트가 없습니다" (no accessible tenant)#

The account does not belong to any tenant. Ask a tenant administrator to add you as a member. Right after installation, when no tenant exists yet, even the global administrator sees only this screen, so create the first tenant with the command-line client (First sign-in and initial setup).

I pressed production promotion but the version did not become Production#

That is expected. Promotion to Production always creates an approval request, even for roles holding the permission, and takes effect only when someone with the approve permission approves it → Approvals

I forgot my password#

The sign-in screen has no reset function. Ask a global administrator to reset it → Users, Command-line tools

Installation and operations#

System Health never leaves "Needs attention"#

Signing keys, the pepper (a secret mixed in when tokens are hashed) and the admin password left at their defaults stay CRITICAL — even when every connection is healthy → Mandatory security settings

Large dataset or image uploads fail halfway#

Usually the request-size or response-time limit of a front proxy such as a CDN or reverse proxy. Use chunked CLI upload for datasets → Reverse proxy caveats, Uploading a large dataset

A deleted tenant comes back after a restart#

Bootstrap tenants (tenants listed in the server settings and created automatically) are recreated every time the server starts. Empty the bootstrap settings before the first start → Installing with docker compose

Training#

A training run stays queued#

The server limits how many runs execute at once, across all tenants, to GEO_MLOPS_TRAINING_MAX_CONCURRENT (default 1). The next run starts only when an earlier one ends. The current limit and queue size are shown in Training operations, and the progress stages in the run detail (Watching training progress). To raise the limit, change it in Server settings and restart the app.

GPU slots are not a reason to wait. If the tenant has too few free GPU slots or no GPU wiring at submission, the submission is rejected. If another workload takes the slots while the run waits, it fails just before starting with "GPU 를 확보하지 못했습니다" (could not secure a GPU). Check slots in GPU slots.

Training fails in preparation / my own training image fails#

→ Training container troubleshooting

Can I continue a stopped run?#

There is no "continue" feature. Upload the checkpoint as pretrained weights and start a new run from it → Stopping and continuing training

The metric chart is empty#

Metrics are read from MLflow. Make sure the trainer logs them with mlflow.log_metrics(..., step=epoch) → What the trainer must do

Deployment and edge#

I deployed, but the model on the device did not change#

The deployment record and delivery to devices are separate. Send the Pull model (모델 내려받기) command from the device detail → Commands in Edge Fleet → Deploying to field devices, Rolling back a deployment

A device shows as unhealthy#

Its heartbeats stopped. Check the agent on the device, the network and the token → Edge SDK troubleshooting

Alerts keep firing after I silenced them#

Silences apply only to Alertmanager alerts. Handle edge-device and drift alerts with Acknowledge and Resolve → Silencing alerts

Integration#

DataOps sync does not run by itself#

Only manual sync exists today. Press Sync on the dataset screen → Sync rules

Logging to MLflow fails with "active experiment ID does not match"#

Inside a platform training container, do not call set_experiment() or set_tracking_uri(); the trainer must inherit the run the server created → What the trainer must do