exalsius-operator

exalsius-operator

Features

The exalsius-operator is a Kubernetes operator that extends Kubernetes with Custom Resource Definitions (CRDs) to facilitate the creation of further ephemeral Kubernetes clusters and installation of dependencies necessary for distributed AI training workloads across public cloud providers and on-premise machines.

With exalsius, AI practitioners and engineers can dynamically provision infrastructure, execute machine learning workloads, and efficiently manage resources while minimizing cloud costs through automatic resource termination after job completion.

Features

• Colony-Based Resource Management: Define and manage Colony resources, consisting of Kubernetes clusters spanning multiple cloud providers or on-premise hosts.
• Ephemeral AI Clusters: Automatically launch and tear down short-lived Kubernetes clusters for AI workloads, optimizing cloud cost efficiency.
• Automated Dependency Installation: Deploy key components required for distributed AI training, such as:
  • NVIDIA CUDA drivers
  • Volcano for gang scheduling
  • Observability stack (Prometheus, Grafana, etc.)
  • Distributed ML frameworks (Ray, Kubeflow, etc.)
• Multi-Cloud GPU Cost Optimization: Deploy AI workloads on the cheapest available GPUs across AWS, Azure, GCP, and other providers, using exalsius-cli.

Architecture

exalsius-operator follows a declarative approach to infrastructure management using Kubernetes CRDs:

1. User defines a Colony CRD, specifying compute resources and dependencies.
2. Exalsius Operator provisions cloud/on-premise resources and sets up AI infrastructure.
3. User specifies a training job (e.g. a DDPJob CRD), specifying the AI workload to execute and the colony infrastructure to use.
4. AI workload is executed on the provisioned infrastructure.
5. Cloud resources are automatically terminated after job completion to save costs.

In addition, exalsius-cli can be used to submit a job to the operator which automatically provisions the infrastructure - based on requirements provided by the user - and executes the job

TBA

Contributing

We welcome contributions! Please check the CONTRIBUTING.md file for guidelines.

License

Copyright 2025.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.