Keep inference available during Kubernetes node drains
Set a PodDisruptionBudget around usable inference capacity, inspect blocked evictions, and account for replacement GPUs and model warmup.
KubernetesInferenceReliability
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Optimize one model three ways with NVIDIA Model Optimizer on Polyaxon
Compare INT8 quantization, FastNAS pruning, and distillation against one tracked baseline, then measure the candidate that meets your accuracy floor.
MLOpsModel OptimizationTraining
Serve multiple LoRA adapters with vLLM on Polyaxon
Deploy two task-specific LoRA adapters over one Qwen base model, select them by name, and measure quality and shared GPU capacity with Polyaxon.
LLMOpsvLLMInference
Run persistent Dask and Ray clusters on Polyaxon
Launch Dask or Ray clusters with Polyaxonfiles, submit repeated work, and control worker capacity, durable outputs, and cluster lifetimes.
ProductDaskRay
Finish Indexed Jobs after enough attempts succeed
Use Kubernetes Job success policies for chosen indexes or success counts, preserve durable results, and distinguish them from Polyaxon metric early stopping.
KubernetesPipelinesScheduling
Transfer files to Polyaxon workspaces over SSH
Use Polyaxon's generated SSH host with native SCP and SFTP to move files into and out of a running service workspace.
ProductCliRemote Development
Spread inference replicas across Kubernetes zones
Use topology spread constraints for inference availability, understand minDomains and GPU capacity, and preserve placement intent in Polyaxon.
KubernetesSchedulingInference
Train a decision classifier on Polyaxon
Fine-tune an open Qwen model for bounded support-routing decisions with Polyaxon jobs, track the adapter, and serve the reviewed version through vLLM.
LLMOpsFine TuningClassification
Deploy Laya typed decisions on Polyaxon
Serve the open Laya typed-decisions checkpoint through Polyaxon, with a CPU classifier endpoint, explicit model scope, and a review path for uncertain classifications.
LLMOpsInferenceClassification
Serve a Jev-style Qwen classifier on Polyaxon
Deploy an open Qwen model behind a typed classification API on Polyaxon, then check the decision schema and calibration before routing real work.
LLMOpsInferenceClassification
Serve DiffusionGemma on Polyaxon
Adapt Google's Gemma-on-Kubernetes deployment choices to Polyaxon with a DiffusionGemma vLLM service, a batch evaluation job, and an explicit compatibility checklist.
LLMOpsInferenceKubernetes
Align CPU and GPU resources with Kubernetes NUMA policies
Combine CPU Manager eligibility, full-core allocation, and Topology Manager policy, then compare CPU/GPU workloads with Polyaxon.
KubernetesGPUPerformance
Run background commands in Polyaxon sandboxes
Start a background process, save its execution ID, reconnect to status and logs, and distinguish command limits from the lifetime of a Polyaxon sandbox.
PolyaxonSandboxesPython
Schedule mapped runs in the order you specify
Use an ordered Polyaxon mapping for a curated list of experiments, control concurrency, and distinguish scheduling order from dependencies and completion order.
PolyaxonOrchestrationExperimentation
Separate evaluator changes from application improvements
Compare old and new application outputs under both evaluator versions, inspect changed decisions, and retain the four comparisons in Polyaxon.
LLMOpsEvaluationExperimentation
Make vLLM prefix caching work for repeated prompts
Understand exact prefix reuse in vLLM, structure repeated context, account for replica routing, and compare cold and warm workloads with Polyaxon.
LLMOpsInferencevLLM
Resize CPU and memory without replacing Kubernetes Pods
Use Kubernetes in-place resource resizing for running workloads, inspect whether changes took effect, and understand the limits for Polyaxon services.
KubernetesSchedulingResources
Native gang scheduling reaches beta in Kubernetes 1.37
Explore native gang scheduling in Kubernetes 1.37, its potential for Polyaxon training and sandboxes, and how it compares with KAI, Kueue, and Volcano.
KubernetesSchedulingDistributed Training
Keep failed candidates visible in pipeline reports
Reconcile evaluation results against an expected candidate manifest, preserve failed and missing outcomes, and separate pipeline reporting from release approval.
MLOpsOrchestrationEvaluation
Work inside a team from Python and the CLI
Scope Polyaxon Python clients and CLI commands to a team, review runs across its projects, and keep explicit project targets and saved defaults clear.
PolyaxonTeamsPython
Design a search space before launching the sweep
Use Polyaxon matrices to choose parameter scales, separate meaningful combinations, and control the size of a hyperparameter sweep.
Hyperparameter TuningMLOpsExperimentation
Version the dataset behind every evaluation
Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.
DataOpsMLOpsLLMOps
Keep distributed training workers close together
Understand native topology-aware workload scheduling, combine rack locality with gang placement, and assess the tradeoff between waiting and communication.
KubernetesSchedulingDistributed Training
Resume interrupted training without losing progress
Build recoverable PyTorch training jobs with complete checkpoints, durable storage, and explicit restoration. Practice recovery locally and with Polyaxon.
MLOpsKubernetesPyTorch