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In recent versions the orchestrator has consolidate protection at all levels. We talke about this in more detail in our article . How Manage Kubernetes Helps Machine Learning Deploying services is very complicate due to the specifics of Kubernetes. To help administrators work with containers there is Manage Kubernetes which allows you to automate basic tasks relate to application support. In addition the processing of requests for clusters is reuce to several minutes. Consider the properties of ready made Kubernetes clusters which also facilitate the deployment of ML services. fault tolerance In Manage Kubernetes you can back up application replicas. Before deploying the application it is enough to indicate the number of running replicas that will work on the hook.

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This is useful for highly loade ML services. If there is an unexpecte influx of incoming requests from clients the service will scale under load automatically start new replicas to avoid downtime. Wide choice of graphics cards All cloud platform GPUs are available in Manage Kubernetes . You can create preconfigure nodes with NVIDIA Tesla and A Andorra Email List graphics cards. Of these and A are accelerators designe for AI tasks. If you nee to train a large model the best option is A . For the smallest models T is suitable. Through choice you can save money and reconfigure the cluster on occasion. For example during model training use a group of nodes with a powerful video card and during inference use nodes with a weaker video card.

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Relative productivity of accelerators. Source Each of the video cards has a certain number of tensor and CUDA cores. The more of them the faster high performance computing model training and work in inference. On the diagram you can see the BM Leads characteristics of each of the video cards. Graphics card specifications The most complete information about available video cards in Manage Kubernetes clusters is at the link . You can calculate the cost of a cluster with a GPU in the calculator . Autoscaling for groups of GPU nodes is currently not available as it is a rare request today. Even relatively weak video cards can withstand heavy loads.

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