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TAIP

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TrainX

Available

Curated, multi-tenant training on Kubernetes

TrainX is the training engine of TAIP. It turns ad-hoc kubectl job submission into a curated product: an admin builds a TrainXJobTemplate with typed parameters and an opinionated script; a user fills in a form. TrainX produces the underlying Kubernetes Job and ConfigMap, surfaces streaming logs and parsed progress, and launches TensorBoard on demand.

All products
Surface
Form rendered from template
Backed by
TrainXJob CRD · Kubernetes Job
Observability
Logs · progress · events · TensorBoard

Capabilities

What TrainX gives you

01

Self-describing templates

A TrainXJobTemplate carries typed parameter metadata. The web UI renders the form straight from the template — adding a parameter is a YAML edit, not a UI change.

02

Run, watch, browse

Streaming logs over SSE. Progress parsed from `TRAINX_PROGRESS: i/N` lines into a UI bar. K8s events tab. One-click TensorBoard. Inline PVC file browser.

03

Tenant-aware by construction

Every run is a TrainXJob CRD in the user's namespace. ConsoleX provides the namespace and live quota. TrainX never creates one — and never circumvents what's already there.

04

Air-gap friendly

No required outbound dependencies at runtime. Bundling scripts load every image into a cluster-local Zot registry; the same chart deploys connected or disconnected.

How it works

From template to running job, by hand-off.

  1. Step 01

    Admin authors a template

    Typed parameters, default config, an opinionated script. Saved as a TrainXJobTemplate CRD — auditable, reusable.

  2. Step 02

    User fills a form

    The web UI renders directly from the template's parameter metadata. No YAML, no kubectl. Quota-aware before submission.

  3. Step 03

    Watch and iterate

    Streaming logs, parsed progress bar, K8s events, one-click TensorBoard. Re-run with different params in two clicks.

Who it's for

Built for these teams

  • Research teams running fine-tunes, RLHF, and evals
  • ML engineers tired of editing Job YAML by hand
  • Platform teams curating an opinionated training surface

Pairs well with

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