How key features of Kubernetes naturally meet the needs of AI inference, and how they benefit inference workloads.
译自 5 Reasons To Use Kubernetes for AI Inference，作者 Zulyar Ilakhunov。
Many of the key features of Kubernetes are a natural fit for AI inference, whether it's AI-powered microservices or ML models, and it's almost as if they're designed specifically for this purpose. Let's take a look at these features and how they benefit inference workloads.
1. Scalability
The scalability of AI-driven applications and ML models ensures that they can handle the required load, such as the number of concurrent user requests. Kubernetes has three native autoscaling mechanisms, each of which is beneficial for scalability: horizontal pod autoscaler (HPA), vertical pod autoscaler (VPA), and cluster autoscaler (CA).
- The Horizontal Pod Autoscaler scales the number of pods running an application or ML model based on various metrics, such as CPU, GPU, and memory utilization. When demand increases, such as a spike in user requests, HPA scales resources up. When the load decreases, HPA scales resources down.
- The Vertical Pod Autoscaler adjusts the CPU, GPU, and memory requirements and limits of the containers in the pod based on the actual usage of the pod. By changing the limits in the pod specification, you can control the specific amount of resources that a pod can receive. It is useful for maximizing the utilization of each available resource on a node.
- Cluster Autoscaler adjusts the pool of compute resources available throughout the cluster to meet workload demands. It dynamically adds or removes worker nodes to the cluster based on the pod's resource needs. That's why CAs are critical for inferencing large ML models with large user bases.
Here are the key benefits of K8s scalability for AI inference:
- Ensure high availability for AI workloads by automatically scaling the number of pod replicas up and down as needed
- Support product growth by automatically sizing clusters as needed
- Optimize resource utilization based on the actual needs of your application, ensuring that you only pay for the resources your pods use
2. Resource optimization
By thoroughly optimizing the resource utilization of your inference workloads, you can provide them with the right amount of resources. This can save you money, which is especially important when renting a GPU that is often expensive. The key Kubernetes features that allow you to optimize resource usage for your inference workloads are efficient resource allocation, detailed control over limits and requests, and autoscaling.
- Efficient resource allocation: You can allocate a specific amount of GPU, CPU, and RAM to a pod by specifying it in the pod manifest. However, only NVIDIA accelerators currently support GPU time slicing and multi-instance partitioning. If you're using Intel or AMD accelerators, the pod can only request the entire GPU.
- Detailed control over resource "limits" and "requests": Requests defines the minimum resources required by the container, while limits prevent the container from using more resources than specified. This provides fine-grained control over compute resources.
- Auto-scaling: HPAs, VPAs, and CAs prevent wasted idle resources. If you configure these features correctly, you won't have any idle resources.
With these Kubernetes features, your workloads get the compute power they need, no more, no less. Since renting a mid-range GPU in the cloud can cost between $1 and $2 per hour, you can save a lot of money in the long run.
3. Performance optimization
While AI inference is typically less resource-intensive than training, it still requires GPUs and other computing resources to run efficiently. HPAs, VPAs, and CAs are key contributors to Kubernetes' ability to improve inference performance. They ensure that AI-powered applications are allocated optimal resources even when the load changes. However, you can use other tools to help you control and predict the performance of your AI workloads, such as StormForge or Magalix Agent.
Overall, Kubernetes' elasticity and ability to fine-tune resource usage enables you to achieve the best performance for your AI applications, regardless of their size and load.
4. Portability
Portability is critical for AI workloads, such as ML models. This enables you to run them consistently across different environments without worrying about infrastructure differences, saving time and money. Kubernetes primarily achieves portability through two built-in features: containerization and compatibility with any environment.
- Containerization: Kubernetes uses containerization technologies, such as containerd and Docker, to package ML models and AI-powered applications into portable containers along with their dependencies. You can then use those containers in any cluster, in any environment, and even with other container orchestration tools.
- Support for multi-cloud and hybrid environments: Kubernetes clusters can be distributed across multiple environments, including public clouds, private clouds, and on-premises infrastructure. This gives you flexibility and reduces vendor lock-in.
Here are the key benefits of K8s portability:
- Consistent ML model deployment across environments
- Migrate and update AI workloads more easily
- The flexibility to choose a cloud provider or on-premise infrastructure
When running AI inference, infrastructure failures and downtime can lead to significant degradation in accuracy, unpredictable model behavior, or simply service interruptions. This is unacceptable for many AI-driven applications, including safety-critical applications such as robotics, autonomous driving, and medical analytics. Kubernetes' self-healing and fault-tolerant capabilities help prevent these issues.
- Pod-level and node-level fault tolerance: If a pod fails or becomes unresponsive, Kubernetes automatically detects the problem and restarts the pod. This ensures that the application remains available and responsive. If the node running the pod fails, Kubernetes automatically schedules the pod to a healthy node.
- Rolling updates: Kubernetes supports rolling updates, so you can update container images with minimal downtime. This enables you to quickly deploy bug fixes or model updates without disrupting running inference services.
- Readiness and survivability probes: These probes are health checks that detect when a container is unable to receive traffic or becomes unhealthy, and triggers a reboot or replacement if necessary.
- Cluster self-healing: K8s can automatically fix control plane and minion issues, such as replacing failed nodes or restarting unhealthy components. This helps maintain the overall health and availability of the clusters running AI inference.
Here are the key benefits of K8s fault tolerance:
- Improves application resiliency by keeping AI-driven applications highly available and responsive
- Minimal downtime and disruption when problems arise
- Improves user satisfaction by making applications and models highly available and more resilient to unexpected infrastructure failures
conclusion
As organizations continue to incorporate AI into their applications, use large ML models, and face dynamic loads, it's critical to adopt Kubernetes as the foundational technology. As a managed Kubernetes provider, we're seeing a growing need for scalable, fault-tolerant, and cost-effective infrastructure that can handle AI inference scale. Kubernetes is a tool that natively provides all of these capabilities.
Want to learn more about accelerating AI with Kubernetes? Explore this Gcore eBook.