Compute Instances

Compute instances are containers running on Docker Swarm or Kubernetes clusters, managed with resource limits and optional load balancing.

Create an instance

nimbus compute run \
  --name web-1 \
  --swarm SWARM_ID \
  --image byjg/static-httpserver \
  --type small \
  --port 80:8080 \
  --env TITLE=soon \
  --env "MESSAGE=Keep In Touch"

For Kubernetes deployments, use --k8s instead of --swarm:

nimbus compute run \
  --name web-1 \
  --k8s CLUSTER_ID \
  --image nginx:latest \
  --type medium \
  --port 80:80

Edit an instance

Update the image, instance type, replicas, volumes, or other mutable configuration of a running instance. Name and target (swarm/cluster) cannot be changed after creation.

nimbus compute update --id INSTANCE_ID \
  --image nginx:1.25 \
  --type medium

List instances

nimbus compute list

Scale replicas

nimbus compute scale --id INSTANCE_ID --replicas 3

Stop, start, and terminate

nimbus compute stop INSTANCE_ID
nimbus compute start INSTANCE_ID
nimbus compute terminate INSTANCE_ID

Logs

Fetch the last 100 lines of an instance's container logs:

nimbus compute logs INSTANCE_ID

Specify a different number of lines:

nimbus compute logs INSTANCE_ID -n 500

Continuously tail new log output:

nimbus compute logs INSTANCE_ID -w

Works with both Docker Swarm and Kubernetes instances.

Attach a volume

Mount an NFS volume to a running instance:

nimbus compute volume attach --instance INSTANCE_ID --volume VOL_ID:/data
nimbus compute volume detach --instance INSTANCE_ID --volume VOL_ID

Or specify volumes at creation time:

nimbus compute run --name app --swarm SWARM_ID \
  --image myapp --type small --volume VOL_ID:/data

Custom domain

Route traffic through the load balancer with a custom domain:

nimbus compute run --name app --swarm SWARM_ID \
  --image myapp --type small --port 80:8080 \
  --domain app.example.com

GPU acceleration

Request NVIDIA GPUs for ML training, inference, or other GPU workloads. GPUs are an independent resource — pick any instance type for CPU/memory and add --gpu N:

nimbus compute run --name ml-train --swarm SWARM_ID \
  --image pytorch/pytorch:latest --type large --gpu 1

On Kubernetes:

nimbus compute run --name inference --k8s CLUSTER_ID \
  --image llama:latest --type medium --gpu 2

How it works

• Automatic provisioning — When you add a node with NVIDIA GPUs, DockNimbus automatically installs the driver, nvidia-container-toolkit, and configures the container runtime. No manual setup required.
• Placement — Nodes with GPUs are labeled automatically. The scheduler places GPU workloads only on GPU-capable nodes.
• Docker Swarm — Uses --generic-resource gpu=N for resource allocation and NVIDIA_VISIBLE_DEVICES for runtime access.
• Kubernetes — Uses nvidia.com/gpu resource limits and the NVIDIA device plugin for device assignment.

GPU sharing (time-slicing)

By default, each GPU is exclusive to one container (overcommit factor = 1). To allow multiple containers to share a GPU via time-slicing:

nimbus node gpu-overcommit NODE_ID 4

This advertises each physical GPU as 4 virtual slots. With a single GPU and overcommit=4, up to 4 containers can each request --gpu 1.

Warning: Time-slicing provides NO memory isolation. All containers sharing a GPU see the full VRAM but share it with others. Set the overcommit factor based on your workloads' known VRAM usage.

Allowed values: 1, 2, 4, 8.

Check GPU availability

nimbus node list

The GPU column shows used/total virtual slots for GPU nodes, or - for non-GPU nodes. Use nimbus node describe NODE_ID for per-device details including model, memory, and health status.

Platform constraints

Target specific architectures:

nimbus compute run --name arm-app --swarm SWARM_ID \
  --image myapp --type small --platform arm64

Instance types

nimbus compute instance-types

See Instance Types for the full list.