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Overcoming Cloud Native Infrastructure Challenges for AI Workloads

Table of Contents

Defining Cloud Native AI with Kubernetes

Cloud native AI refers to running AI workloads on cloud native infrastructure, typically orchestrated by Kubernetes. Kubernetes provides portability and scalability for deploying AI models into production. However, running stateful AI workloads on Kubernetes can be challenging.

Major cloud providers like AWS, GCP, and Azure provide managed Kubernetes services optimized for AI workloads. The CNCF landscape includes many AI/ML projects like Kubeflow, Seldon Core, and more. However, there are still gaps around model governance, distributed training, and simplicity.

Open Source Licensing Considerations

An open question around open source AI is how to handle licensing for machine learning models, which are more than just code. Most ML models today do not specify a license. CNCF projects follow open source licenses like MIT or Apache 2.0. More clarity is needed on licensing best practices for open source AI models.

Kubernetes as the Core Platform

The CNCF scope focuses on cloud native technologies centered around Kubernetes. Kubernetes provides portability across cloud providers and on-prem infrastructure. It allows abstracting away the underlying infrastructure. Kubernetes helps with scaling AI workloads across clusters of heterogeneous hardware, including GPUs and TPUs. However, Kubernetes was not originally designed for stateful apps, presenting challenges for distributed training.

Current Usage Patterns and Benefits

Many organizations run ML workflows on Kubernetes in production for benefits like portability and autoscaling. Kubernetes helps with deployment, monitoring, and scaling of machine learning pipelines.

The Kubeflow project provides a ML stack on Kubernetes, including Jupyter Notebooks, ML pipelines, and model serving. Many companies use managed Kubernetes services from cloud providers to run Kubeflow and other ML tools.

Performance and Scalability Challenges

While Kubernetes helps operationally, it can present performance challenges for certain AI workloads:

  • Stateful workload support: Long-running stateful apps like distributed training are difficult on Kubernetes.

  • Distributed training: Low latency and high throughput is needed for model training across clusters.

Stateful Workload Support

Kubernetes was designed for stateless applications in order to support elastic scalability. Storing state outside Kubernetes eases scheduling. AI training workloads are very stateful, requiring checkpointing to handle failures. Extended runtimes for training models make orchestration complex.

Distributed Training

Training complex ML models requires distributing work across clusters of machines with accelerators like GPUs. Frameworks like PyTorch handle parallel model training. But keeping high efficiency across Kubernetes clusters remains challenging.

Simplifying the User Experience

Many AI developers come from a data science background and may not be devops experts. There is a gap between data scientists who create ML models and IT teams managing the infrastructure.

Better abstractions are needed to simplify running AI workloads on Kubernetes without deep cloud native expertise.

Bridging AI Developers and Cloud Engineers

Organizations need to bridge the gap between data scientists and IT teams to successfully adopt cloud native AI. Improved interfaces, tutorials, documentation, and automation tools can help remove infrastructure burdens from ML developers.

FAQ

Q: How is cloud native technology currently being used for AI workloads?
A: Current usage patterns leverage the portability and scalability of Kubernetes for model training and serving. Key projects like Kubeflow facilitate these workloads.

Q: What are some key challenges with running AI on Kubernetes?
A: Stateful, distributed workloads for model training can be difficult on Kubernetes. Simplifying the infrastructure for AI developers is also an issue.

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