Deletion & Data Management

Properly managing the deletion of GPU instances and storage volumes is critical to prevent accidental data loss and control costs.

Deleting GPU Instances

When you delete a GPU instance that has storage attached, NeevCloud protects you from accidental data loss with clear warnings and options.

Deletion Workflow

1. You initiate GPU instance deletion from the dashboard.

2. You're presented with two options:

   • Cancel: Abort the deletion and keep everything running.

   • Delete: Proceed with instance deletion.

What Happens to Your Storage

Storage Type	What Happens
Ephemeral	Always deleted with instance
Persistent Local Disk	Deleted with instance
Persistent Network Storage	Deleted with instance

Important: Persistent local disk storage is always deleted when the GPU instance terminates, regardless of retention policy, because it's physically tied to that specific node.

Deleting Storage Volumes

When you want to delete a storage volume directly (not as part of instance deletion), NeevCloud ensures you understand the permanent nature of this action.

Deletion Workflow

1. You select a storage volume and click "Delete".

2. A warning appears:

   "Deleting storage attached to GPU instances will permanently remove data and it cannot be recovered."

3. You must type the exact Storage Name to confirm deletion.

4. You're presented with two options:

   • Cancel: Abort the deletion.

   • Delete: Permanently remove the storage volume.

Why This Is Important

Storage deletion is irreversible. Once you delete a storage volume:

• All data on that volume is permanently lost.

• You cannot recover files, checkpoints, or datasets.

• Any GPU instances using that storage will lose access.

• There is no "recycle bin" or recovery period.

Data Protection Best Practices

Before deleting any persistent storage:

1. Verify you have backups of critical data:

   # Download important checkpoints
   scp -r root@<IP>:/data/checkpoints ./local_backup/
      
   # Export final models
   scp root@<IP>:/data/final_model.pth ./production_models/

2. Check if any running instances are using the storage:

   • Review your instance dashboard.

   • Confirm no active training jobs depend on this data.

3. Document what you're deleting:

   • Note the storage name, size, and contents.

   • Keep records of why you're deleting it.

   • Update any documentation that references this storage.

4. Use external backup solutions for production data:

   • Push critical checkpoints to S3, GCS, or Zata Storage.

   • Use version control (DVC, Git LFS) for datasets and models.

   • Maintain off-platform backups of irreplaceable data.

For Production Workflows

Consider implementing a backup strategy:

# Regular backup script example
#!/bin/bash
# Sync important data to external storage
rsync -avz /data/checkpoints/ user@backup-server:/backups/project-checkpoints/
aws s3 sync /data/models/ s3://my-bucket/model-archive/

Retention Policy Management (Coming Soon)

Future NeevCloud releases will give you more granular control over data retention:

• Define how long storage persists after GPU termination.

⚠️ Warning: Always ensure important data is backed up before deleting persistent storage.