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Clusterone - distributed deep learning without complications

  • Kubernetes

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.

about Clusterone

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

  • Kubernetes

    The 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

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.

Benefits

  • 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.

  • flexibility and scalability

    the implemented solution has enabled us to run machine learning tasks efficiently on distributed resources, with the ability to scale easily.

  • process automation

    The team implemented automated resource management and task scheduling, which greatly simplified the platform.

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