There is a Kubernetes orchestrator for managing containers. From the moment of its appearance it has been overgrown with the software necessary for ML. K s tools allow ML professionals to use the spee of the GPU in containers Ajit Raina Senior Development Manager Reis Labs . Lets highlight the main advantages of Kubernetes which are important for deploying ML services. Separate working environment Each ML model must have an environment modules libraries etc. And the versions of the libraries depend on the versions of the drivers on the node. In Kubernetes you can run models on nodes with pre installe driver versions.
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Moreover do it automatically. Easy container administration Container management is more convenient and simpler than in the same Docker Swarm. Moreover containers are autonomous. K s itself manages resources depending on the nees of the application . Kubernetes makes it easy to organize and manage containers and applications. The technology allows you to automate operational tasks managing the availability of applications and scaling them Algeria Email List Thomas DiGiacomo director of technology and products at SUSE . Availability of tools for ML experiments Kubeflow a machine learning platform that runs ML pipelines on Kubernetes clusters is responsible for training comparing and selecting optimal models. Kubeflow allows you to isolate the conduct of various experiments by creating pods as well as automate the selection of reference models.
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This is important since in practice it is not known which approach and which mathematical model to choose for solving the problem. This is determine by the results of experiments. Several ML engineers and mathematicians can test their hypotheses BM Leads and results on the same dataset select the best models and not be afraid of conflicts between experiments in the namespace. But this is just one of the benefits. Autoscaling Kubernetes can automatically adjust the number of resources use base on workloads. Autoscaling is done in conjunction with application scaling pod level and adjusting the number of nodes within a cluster cluster level . Safety Also in K s the network policy settings system and the use of namespaces are being actively develope.