Scheduling articles

Reduce the cost of self-hosted ML workloads

Reduce self-hosted ML costs with outcome-based accounting, right-sized resources, elastic capacity, interruption-ready workloads, local data paths, and deliberate retention.

Gang scheduling for distributed training

Understand how gang scheduling prevents partial distributed jobs from holding GPUs, how minimum membership works, and what Polyaxon supports.

GPU cluster scheduling tools compared

Compare Kueue, KAI Scheduler, Volcano, Coscheduling, and Slurm Bridge by responsibility, workload fit, and Polyaxon integration path.

How to improve GPU utilization

A practical guide to improving GPU utilization by diagnosing queue delays, input bottlenecks, resource fragmentation, sharing, and recovery overhead.

GPU utilization metrics: allocation, activity, and throughput

Learn which GPU utilization metrics explain capacity, device activity, memory pressure, and useful ML throughput—and how to avoid misleading averages.

What is GPU orchestration?

Understand how GPU orchestration connects workflows, queues, resource placement, and recovery across shared ML infrastructure.

Queue management for machine learning workloads

Why queue management matters for shared ML infrastructure and how Polyaxon handles priorities, concurrency, and workload scheduling.

Kubernetes taints and tolerations for ML workloads

Keep ordinary Pods away from specialized nodes and combine tolerations with positive placement rules for GPU and interruptible ML capacity.

Kubernetes multi-tenancy for ML platforms

Design identity, isolation, quotas, queues, networking, storage, and observability for multiple ML teams sharing Kubernetes infrastructure.

Kubernetes CronJobs for ML automation

Schedule repeatable Kubernetes jobs with explicit time zones, concurrency, deadlines, history limits, idempotency, and observable outcomes.

Kubernetes nodes for ML platforms

Understand node components, conditions, capacity, labels, taints, failure behavior, and lifecycle management for Kubernetes ML clusters.

Right-size Kubernetes resources for ML workloads

Set CPU, memory, ephemeral-storage, and GPU resources from measured ML workload behavior while preserving scheduling efficiency and reliability.

Understand Kubernetes Pod evictions for ML

Distinguish node-pressure, API-initiated, preemption, and node-failure disruptions, then design ML workloads to recover safely.