Clusterone - distributed deep learning without complications
Devopsbay deployed a Kubernetes cluster for ClusterOne, enabling machine learning tasks to run efficiently on distributed resources. The solution enabled automated infrastructure management and scaling of AI computing.
- Kubernetes
- +1Kubernetes
about the project
ClusterOne enables researchers and companies to easily train large AI models without having to deal with the complexities of distributed computing.
The platform automates resource management and task scheduling, allowing you to focus on model development. As their advertising slogan say: “Just run deep learning experiments at scale. Anywhere.”
the challenge
Cost optimisation with no loss of efficiency and appropriate scaling as required.
technologies
Kubernetes
Ansible
Cepth
Docker
Metallb
Prometheus
Grafana
KubernetesThe infrastructure foundation, enabling container orchestration and management of a distributed computing environment. Used to deploy and manage containerised applications, ensuring scalability and reliability of platform services.
- Ansible
An automation tool used to prepare and configure servers and install a Kubernetes cluster.
- Cepth
A distributed storage system that provides scalable and efficient storage space for a cluster.
- Docker
A containerization technology used to package and isolate applications and their dependencies.
- MetalLB
Load balancer solution for Kubernetes clusters running on physical infrastructure.
- Prometheus and Grafana
Tools for monitoring and visualising cluster and application metrics.
Results
Creating a distributed infrastructure for ML- Automation of infrastructure management
- Implementation of monitoring and logging
- Researchers can focus only on their work
Creating a distributed infrastructure for ML
A Kubernetes cluster was successfully deployed to efficiently run machine learning tasks on distributed computing resources. This has provided a flexible environment for advanced AI research.
Automation of infrastructure management
The solution enabled automated resource management and scaling of AI computing. Machine provisioning and job scheduling were automated, enabling optimal use of the available infrastructure.
Implementation of monitoring and logging
Full visibility of computational processes was provided through integration with monitoring and logging systems, which facilitated the debugging of complex models.
Researchers can focus only on their work
The platform automates the management of computing resources, allowing users to focus on developing AI models without having to deal with the technical details of the infrastructure.
Creating a distributed infrastructure for ML
A Kubernetes cluster was successfully deployed to efficiently run machine learning tasks on distributed computing resources. This has provided a flexible environment for advanced AI research.
Benefits
- 1
ML & Kubernetes
Through the work of DevOpsBay, Clusterone became one of the first ML platforms built on Kubernetes, giving us a competitive advantage in the market.
- 2
flexibility and scalability
the implemented solution has enabled us to run machine learning tasks efficiently on distributed resources, with the ability to scale easily.
- 3
process automation
The team implemented automated resource management and task scheduling, which greatly simplified the platform.
client's feedback
Devopsbay provided a seamless Kubernetes solution that optimized our AI workload management. Their automation and scaling capabilities let us focus on AI development without worrying about infrastructure. The system is flexible, scalable, and has given us a competitive edge. We're very satisfied with the outcome.
CEO, Clusterone
