Cerebrium

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---
name: cerebrium
description: >-
  Use for any Cerebrium task: deploying Python code to serverless GPU or CPU, writing or fixing
  cerebrium.toml, choosing hardware and regions, calling deployed endpoints (REST, streaming,
  WebSocket, async), autoscaling and concurrency, cold starts, secrets, CI/CD, and debugging a
  build or a running app from the terminal. Covers the cerebrium CLI, configuration defaults the
  API actually applies, accepted GPU identifiers with per-plan limits, and troubleshooting.
license: MIT
metadata:
  author: cerebrium
  version: "0.1.0"
---

# Cerebrium

Cerebrium runs Python workloads on serverless GPU and CPU: REST endpoints, SSE streaming,
WebSockets, and async jobs, with scale-to-zero and per-second billing. One `cerebrium.toml`
describes hardware, scaling, dependencies and runtime; one CLI (`cerebrium`) drives everything.

Reach for it when the workload is an inference API for an LLM, an embedding model or a vision
model; a real-time voice, video or streaming app; bursty traffic that should scale from zero
without holding idle GPUs; a deployment that has to run in several regions for latency or data
residency; or a migration off Replicate, Hugging Face or Mystic, each of which has a guide under
`https://cerebrium.ai/docs/migrations`.

This file carries the workflow and the rules. Load the reference that matches the task:

| Read | When |
| --- | --- |
| [`references/cli.md`](https://github.com/CerebriumAI/cerebrium-skills/blob/master/skills/cerebrium/references/cli.md) | Running any `cerebrium` command: the full surface, flags, non-interactive auth, CI/CD, which commands cost money. |
| [`references/config.md`](https://github.com/CerebriumAI/cerebrium-skills/blob/master/skills/cerebrium/references/config.md) | Writing or fixing `cerebrium.toml`: every key, the default the API applies when it is omitted, accepted ranges, rebuild triggers. |
| [`references/hardware.md`](https://github.com/CerebriumAI/cerebrium-skills/blob/master/skills/cerebrium/references/hardware.md) | Choosing `compute`, `gpu_count`, `region`, `provider`, `compute_tier`: accepted GPU identifiers, per-GPU and per-plan limits, regional availability, storage. |
| [`references/troubleshooting.md`](https://github.com/CerebriumAI/cerebrium-skills/blob/master/skills/cerebrium/references/troubleshooting.md) | A build that failed, an app that 5xxs or queues, slow cold starts, settings that reverted. |

## Rules for agents

1. **Deploys cost money.** `cerebrium deploy`, `cerebrium run` and `cerebrium apps scale` start
   billable compute, and `cerebrium apps delete` is destructive. State what will run on what
   hardware and get the user's confirmation before the first one in a session.
2. **`cerebrium run` is not local.** It packages the working directory, uploads it, and executes
   in the cloud on the hardware in `cerebrium.toml`. There is no local emulator.
3. **A `cerebrium.toml` key you leave out is reset to its default on deploy**, not left alone,
   and a misspelled key does nothing in the CLI while still reaching the backend. Keep every
   value that matters in the file, spelled as in [references/config.md](references/config.md).
4. **Never invent config keys or GPU identifiers.** Both are validated server-side and a wrong
   value fails the deploy. The accepted sets are in the references.
5. **Adapt an example before writing from scratch.** `https://github.com/CerebriumAI/examples`
   holds runnable references (vLLM, SDXL, Pipecat voice agents, ASGI apps), each with a working
   `cerebrium.toml`.
6. **Check the live docs when this skill does not cover it**, rather than guessing: the
   `cerebrium-docs` MCP server (search plus docs filesystem), any docs page as markdown by
   appending `.md` to its URL, or the index at `https://cerebrium.ai/docs/llms.txt`.

## First run: check state before acting

```bash
cerebrium version                   # installed? if not: pip install cerebrium
cerebrium projects current          # authen

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