Blog

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Version the dataset behind every evaluation

Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.

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.

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.

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.

Did you find better hyperparameters—or a lucky seed?

Confirm hyperparameter finalists across matched training seeds, compare variation and paired differences, and reserve compute for a defensible final choice.

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.

Prevent data leakage in ML and LLM evaluation datasets

Choose evaluation boundaries, keep related examples together, audit duplicate overlap, and retain reproducible split manifests for ML and LLM experiments.

Keep long-running workloads authenticated through token rotation

Use projected ServiceAccount tokens and refresh-aware clients so long-running training, notebooks, and services can keep authenticating without static credentials.

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.

Enforce ML workload rules with CEL admission policies

Use Kubernetes ValidatingAdmissionPolicy to check workload ownership and image references, with scoped warning and enforcement stages for ML namespaces.