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By the end of this you’ll have:
  • The veri CLI installed locally.
  • An authenticated session.
When you’re ready to spend real GPU time, jump to the workflow docs at the bottom of this page.

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Install

The CLI ships the Python SDK and CLI together.
Verify:
The CLI uses ~/.config/veri/config.toml on Linux/macOS (or $XDG_CONFIG_HOME/veri/config.toml) for credentials and defaults.

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Log in

Create an API key from the Veri dashboard, then:
Confirm:
Output (rendered as a table):
Prefer environment variables? Set VERI_API_KEY=vk_... and any CLI call will pick it up. Env vars take precedence over the config file.

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Where to next

Pick the workflow you came for. Each guide covers the path against https://api.veri.studio and may spend GPU credit.

Train a model

Configure and run hosted training jobs

Deploy a model

Serve base or trained models behind an endpoint

Evaluate a model

Score models and deployments against datasets

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Stuck? Book a 15-minute call

A 15-minute call gets you unblocked faster than a docs page. Tell us what broke and we’ll fix it.

Book a 15-min beta call

Pick any time. If you’re hitting a specific issue, share your account email and any failing job IDs.

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Spot a bug?

Email daniel@veri.studio with what you expected, what happened, and any failing job IDs.