Guides articles

Work interactively with Polyaxon sandboxes

Choose between sandbox shells and exec, understand PTY lifetime and output replay, and manage terminal attachment explicitly through the Python SDK.

Connect your local tools to Polyaxon with SSH

Connect native terminals and IDEs to a Polyaxon service, forward application ports, and use tmux explicitly when you want to resume a shell.

Reconnect to your Polyaxon shell

Learn how ordinary ops shells differ from tmux sessions, then detach and reconnect to the same Polyaxon shell from the CLI or UI.

Optimize LLM inference with repeatable benchmarks

Compare inference optimizations against a fixed workload, latency limits, and quality checks, with benchmark configurations and results tracked in Polyaxon.

Combine run filters with AND and OR

Use OR to combine metric thresholds, negated conditions, and independent groups of filters in Polyaxon queries.

Automate multiple runs with async Python clients

Monitor several Polyaxon runs concurrently with async Python clients, retrieve recent logs, and manage concurrency, timeouts, and client cleanup.

Tune LLM hyperparameters within a compute budget

Design a small LoRA fine-tuning sweep, control its execution in Polyaxon, and compare validation quality, resource use, and repeatability.

Write shorter Polyaxonfiles

Use direct parameter values and let input and output defaults imply optionality, with a look ahead at simpler workload definitions planned for Polyaxon 2.18.

Route ML jobs to regional infrastructure with Polyaxon

Configure regional execution with Polyaxon agents, queues, and storage connections, then measure data-transfer costs without assuming same-region traffic is free.

Upload multiple files and folders with Polyaxon CLI

Upload code, configuration, and small datasets in one Polyaxon CLI command, then save the same path mappings in a declarative mount section.

What is sovereign AI? Control across the AI lifecycle

Define sovereign AI as control over data, models, compute, operations, providers, and evidence, then implement it with Kubernetes.

Fine-tune Mistral 7B with LoRA on Kubernetes

Plan a Mistral 7B LoRA fine-tuning workflow on Kubernetes with versioned data, GPU scheduling, Polyaxon tracking, evaluation, and adapter packaging.

What are your ML jobs connecting to?

Trace image pulls, Git clones, S3 and GCS access, Hugging Face downloads, and artifact uploads across the lifecycle of Kubernetes jobs and sandboxes.

Pods, Jobs, or Services for AI agents?

Choose the Kubernetes execution shape for an AI agent by separating logical task identity from Pods, Jobs, Services, workflows, and sandbox lifecycles.

ML infrastructure explained for business teams

Understand what ML infrastructure pays for, how it affects delivery and reliability, and how to evaluate an investment using measurable workflow outcomes.

An AI agent deployment checklist for the first production release

Build an agent release checklist around Polyaxon qualification jobs, versioned components, artifact reports, termination settings, and manual approval.

Place ML workloads close to their data

Design region-aware ML execution around dataset, registry, model, cache, artifact, and service locality without confusing proximity with data residency.

Production LLM systems: Where to invest after the prototype

Use Polyaxon run tracking, comparison dashboards, resource monitoring, and repeatable evaluation to decide what to improve after an LLM prototype.

Docker build caching for ML workloads on Kubernetes

Reduce container build and startup time for ML jobs with reusable dependency layers, persistent build caches, and deliberate image and model caching.

Durable execution for AI agents

Build agent recovery around committed checkpoints, versioned inputs, safe side effects, and approval state so interrupted tasks can continue reliably.

Designing the runtime layer for AI agents

Design agent execution around durable state, safe tool retries, approval waits, and recovery, with clear responsibilities for Polyaxon and your agent framework.

Run ML workloads on your existing Kubernetes cluster

Plan a Polyaxon deployment on an existing Kubernetes cluster, covering GPUs, storage, identity, networking, workload ownership, and recovery.

How to improve GPU utilization

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

Prompt versioning for production AI systems

Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.