Scaling TensorFlow Models on Kubernetes

Scaling TensorFlow Models on Kubernetes

Kubernetes and Kubeflow can meet scalability requirements for TensorFlow ML models. I’ll walk you through several practical examples, describing how to scale ML models with Kubeflow on Kubernetes.

Kubernetes and Kubeflow can meet scalability requirements for TensorFlow ML models

With the growing integration of AI/ML into applications and business processes, production-grade ML models require more scalable infrastructure and compute power for training and deployment.

Modern ML algorithms train on large volumes of data and require billions of iterations to minimize their cost functions. Vertical scaling of such models involves OS-level bottlenecks — including the number of CPUs, GPUs, and storage that can be provisioned — and has proven to be inefficient for such models. More efficient parallel processing algorithms, such as asynchronous training and allreduce-style training, require a distributed cluster system where different workers learn simultaneously in a coordinated fashion.

Scalability is also important for serving DL models in production. Processing a single API request to the model prediction endpoint may trigger a complex processing logic that can take a significant amount of time. As more users are hitting the model’s endpoints, more serving instances are required to process client requests efficiently. Being able to serve ML models in a distributed and scalable way becomes essential to ensuring the usability of ML applications.

Addressing these scalability challenges in a distributed cloud environment is hard. MLOps engineers face the challenge of configuring interactions between multiple nodes and inference services while ensuring fault tolerance, high availability, and application health.

In this blog post, I’ll discuss how Kubernetes and Kubeflow can meet these scalability requirements for TensorFlow ML models. I’ll walk you through several practical examples, describing how to scale ML models with Kubeflow on Kubernetes.

First, I’ll discuss how to use TensorFlow training jobs (TFJobs) abstraction to orchestrate distributed training of TensorFlow models on Kubernetes via Kubeflow. Then I’ll show how to implement TF distribution strategies for synchronous and asynchronous distributed training. Finally, I’ll discuss various options for scaling TF models serving in Kubernetes — including KFServing, Seldon Core, and BentoML.

By the end of the article, you’ll have a better understanding of the basic K8s and Kubeflow abstractions as well as the tools available to scale your TensorFlow models, both for training and production-grade serving.

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