AI & GPU Workloads
20 articles from official documentation
Building a Reliable Cloud Native Foundation for Distributed AI Training
Unlock the potential of distributed AI training with Kubernetes. By leveraging RDMA for high-throughput communication and Lustre for efficient data access, you can streamline your ML workflows. Discover how to set up a robust infrastructure that minimizes management overhead.
- →Leverage RDMA for lower-overhead, high-throughput communication between GPU nodes.
- →Utilize Lustre for high-throughput shared access to training data and checkpoints.
Secure Multi-Tenant GPU Metrics in Kubernetes: A Deep Dive
In a multi-tenant Kubernetes environment, managing GPU metrics securely is crucial. By leveraging MetricAccess and kube-rbac-proxy, you can ensure that each team only sees its own metrics. This article breaks down how to implement these features effectively.
- →Implement MetricAccess to declare specific metrics for each team.
- →Use kube-rbac-proxy for authentication and authorization in a Kubernetes-native way.
Transforming Kubernetes: From Cloud Native to AI Native
As AI continues to evolve, so must our infrastructure. Discover how Kubernetes can adapt to support AI-native applications while avoiding common pitfalls. Learn why relying solely on simplified tools can lead to serious security issues.
- →Recognize that AI-native infrastructure often lacks the robustness of true cloud-native setups.
- →Monitor for security vulnerabilities, as seen in the exposure of user data due to misconfigured applications.
Scaling AI with Kubernetes: The Role of CNCF Silver Members
As enterprises scale AI from training to inference, operational efficiency becomes critical. Kubernetes plays a vital role in managing these workloads, and the support from CNCF Silver Members enhances this infrastructure.
- →Understand the role of CNCF Silver Members in building cost-efficient AI infrastructure.
- →Leverage Kubernetes for automating deployment and scaling of AI workloads.
Unifying AI Training and Inference on Kubernetes: Lessons from China Merchants Bank
China Merchants Bank has cracked the code on unifying AI training and inference using Kubernetes. By leveraging tools like Kueue and Fluid, they efficiently manage nearly 10,000 heterogeneous accelerator cards. Dive into how this unified control plane can revolutionize your AI workflows.
- →Leverage Kueue to manage training admission and prevent resource wastage.
- →Utilize KEDA and Prometheus for dynamic scaling of online inference based on demand.
China's Cloud Native Surge: Kubernetes in the Age of AI Inference
China is rapidly advancing in cloud native technologies, especially as AI transitions to inference. Kubernetes plays a pivotal role in this evolution, providing the infrastructure for scalable applications and resilient data pipelines.
- →Leverage Kubernetes for scalable application infrastructure across various cloud environments.
- →Implement chaos engineering to enhance system resilience during distributed inference.
Navigating Heterogeneous Infrastructure for AI with Kubernetes
AI workloads are complex, requiring both CPU and GPU resources to function optimally. Understanding how Dynamic Resource Allocation (DRA) can help you manage these resources is crucial for effective AI platform engineering.
- →Understand the distinct roles of CPU and GPU in AI workloads.
- →Utilize Dynamic Resource Allocation (DRA) to manage specialized device requests.
Kubernetes and AI: Navigating the New Frontier
Kubernetes isn't new, but the rise of AI workloads is reshaping how we think about it. Managing job placement and GPU utilization is crucial to prevent resource starvation and maintain application performance. Dive into the complexities of running AI in Kubernetes and what you need to watch out for.
- →Manage job placement actively to optimize resource usage.
- →Utilize GPUs efficiently to avoid costly idling.
Accelerate AI Inference: Fast Model Loading on Amazon EKS
Speed is crucial for AI inference, and inefficient model loading can bottleneck your applications. By leveraging tools like Run:ai Model Streamer and torch.compile, you can significantly reduce startup times. Discover how to optimize your Kubernetes deployments for faster performance.
- →Leverage Run:ai Model Streamer for efficient model loading.
- →Optimize weights loading from S3 to reduce startup time.
Predictive Autoscaling for GPU Workloads: Stay Ahead of Demand in Kubernetes
In a world where GPU workloads can spike unexpectedly, predictive autoscaling is a game changer. By leveraging a Bi-LSTM model, Kubernetes can forecast demand and pre-provision capacity, ensuring your applications are ready when it matters most.
- →Leverage the Predictive Controller to forecast demand every 60 seconds.
- →Utilize Bi-LSTM models to analyze past metrics for better predictions.
Is Your Kubernetes Cluster AI-Ready? Here's What You Need to Know
As AI workloads surge, Kubernetes must adapt. Dynamic Resource Allocation (DRA) offers a game-changing way to request specialized hardware for these demanding tasks. Discover how to leverage this feature effectively.
- →Leverage Dynamic Resource Allocation (DRA) for flexible hardware requests.
- →Monitor resource utilization to optimize performance for AI workloads.
Building an AI Factory on Kubernetes: Optimizing Resource Allocation
Transform your AI workloads with Kubernetes by leveraging Dynamic Resource Allocation and HAMi. Discover how these tools can optimize resource use and tenant isolation in your AI factory setup.
- →Leverage Dynamic Resource Allocation (DRA) to treat accelerators as rich devices with attributes.
- →Implement HAMi to enforce per-pod memory and compute limits for better resource management.
Kubeflow's Graduation: The New Standard for Cloud Native AI Operations
Kubeflow has officially graduated, marking a significant milestone for cloud native AI operations. This platform standardizes the entire AI and ML lifecycle, from data processing to model serving, making it a game-changer for production environments.
- →Leverage Kubeflow's capabilities for standardizing the AI and ML lifecycle.
- →Utilize native tools for data processing and model serving to streamline operations.
Who Owns the AI Pipeline? Navigating LLMOps and Platform Engineering
Understanding who should own the AI pipeline is crucial for effective LLMOps. This article dives into the lifecycle of large language model operations, from data prep to monitoring, and highlights the importance of treating prompts as versioned artifacts.
- →Understand LLMOps as a comprehensive lifecycle from data prep to monitoring.
- →Treat prompts as versioned artifacts to ensure consistency in model performance.
Unlocking AI Model Interoperability with Docker and ModelPack
AI model management is often fragmented, but Docker and ModelPack are changing that. By leveraging OCI artifacts, you can standardize model packaging and distribution. Discover how to efficiently use the Docker Model Runner to streamline your AI workflows.
- →Leverage OCI artifacts for standardized AI model packaging.
- →Use the command `docker model package –format=cncf` to format models in ModelPack format.
Unlocking Cost Efficiency: OpenCost 1.121.0 for Kubernetes Inference Tracking
OpenCost 1.121.0 introduces a groundbreaking way to track inference costs in Kubernetes, making it easier to optimize your spending. It leverages metrics from your existing deployments to provide detailed cost insights per model, including GPU usage and infrastructure costs.
- →Understand allocation-based cost per model, which includes GPU memory and shared infrastructure costs.
- →Utilize usage-based cost per model to focus on active inference expenses and optimize for token processing.
Efficient GPU Batch Inference on ECS: Scale to Zero
Unlock the power of GPU batch inference on Amazon ECS with zero idle costs. Learn how to leverage Amazon SQS for job buffering and AWS Application Auto Scaling to optimize your resource usage.
- →Utilize Amazon SQS to buffer inference requests, ensuring durability across instance lifecycle events.
- →Set TRANSFORMERS_OFFLINE to 1 for fully air-gapped container operation after image pull.
Subaru's Kubernetes Revolution: 60x Faster AI Development
Subaru has transformed its AI development process using cloud-native infrastructure on Kubernetes, slashing container image pull times from three hours to just three minutes. Discover how they leveraged Envoy Gateway and GitOps practices to achieve this remarkable efficiency.
- →Leverage Envoy Gateway and MetalLB to optimize Kubernetes for AI workloads.
- →Adopt GitOps with Argo CD and Helmfile to standardize application delivery.
How Japan's 250% Training Surge is Shaping Global AI Talent
Japan's explosive growth in AI training is not just a regional phenomenon—it's influencing global talent dynamics. The Kubestronaut program is at the forefront, certifying essential skills for managing AI infrastructure and workloads.
- →Understand the Kubestronaut program's role in certifying AI infrastructure skills.
- →Aim for Golden Kubestronaut status by completing all CNCF certifications and obtaining LFCS.
Supercharge Your AI Workflows with Kubeflow's Latest Innovations
Kubeflow is evolving, and its latest features are game-changers for AI development. With Kale, you can turn Jupyter notebooks into production-ready pipelines without writing any KFP SDK code. This article dives into how these innovations can streamline your AI workflows.
- →Utilize Kale to convert Jupyter notebooks into production-ready pipelines effortlessly.
- →Leverage the unified Kubeflow SDK for managing end-to-end AI workloads.
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