> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tracebloc.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> How tracebloc runs on your infrastructure — and why your data never leaves it.

**Models come to your data — your data never leaves your infrastructure.** You run a tracebloc *workspace* on your own hardware. Contributors submit models that are scanned and run in isolation against your data, on your machines. Only results leave — and trained model weights only if you choose to share them.

## The trust boundary

<Frame>
  <img src="https://mintcdn.com/tracebloc/EVB0IGskWPJcKhjR/images/traceblocfederatedlearning.png?fit=max&auto=format&n=EVB0IGskWPJcKhjR&q=85&s=25bcf5a575b409f8d337be15cefde5cc" alt="How tracebloc keeps your data on your infrastructure — models come to the data, only results leave" width="1320" height="631" data-path="images/traceblocfederatedlearning.png" />
</Frame>

Raw data stays inside your infrastructure. Your workspace opens an **outbound-only** connection — nothing reaches in to pull your data out.

## How it works

<Steps>
  <Step title="Deploy your workspace">
    One command sets up your private workspace on your own infrastructure — a laptop, an on-prem server, or a cloud cluster.
  </Step>

  <Step title="Ingest your datasets">
    Stage your training and test data locally. Metadata syncs to the platform so contributors can see what's available — **the raw data never moves.**
  </Step>

  <Step title="Create a use case">
    Define a use case from your datasets and set how submitted models are evaluated.
  </Step>

  <Step title="Invite contributors">
    Whitelist contributors by email. Only the people you invite can take part.
  </Step>

  <Step title="Contributors submit and train models">
    Each model is **scanned for vulnerabilities**, then trains against your data in an **isolated container**, on your hardware.
  </Step>

  <Step title="Only results are shared">
    Training and evaluation results flow back to you over TLS. Trained model weights are shared **only if you choose to** — you control that in the admin panel.
  </Step>
</Steps>

## What stays, what leaves

| Data                                  | Shared with the platform?                                                        |
| ------------------------------------- | -------------------------------------------------------------------------------- |
| Raw training & test data              | **Never** — it stays on your infrastructure                                      |
| Dataset metadata (schema, row counts) | Yes — so contributors know what's available                                      |
| Training & evaluation results         | Yes — the metrics models are judged on                                           |
| Trained model weights                 | **Only if you allow it** — your choice per collaboration, set in the admin panel |

Enforced by per-job container isolation, a NetworkPolicy that blocks data egress from training pods, a vulnerability scan before any model runs, and TLS on all traffic.

<Note>
  **The mental model:** `1 machine = 1 workspace = n datasets`. One deployment per machine; as many datasets inside it as you like.
</Note>

## Vocabulary

| Term            | What it is                                                                                                                                                   |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Workspace**   | Your private, dedicated AI environment — tracebloc's software running on your own infrastructure, where you invite contributors to train models on your data |
| **Client ID**   | The credential that connects your workspace to the platform (created on the clients page)                                                                    |
| **Dataset**     | Data you've staged locally for use cases                                                                                                                     |
| **Use case**    | A task contributors build models for, against your datasets                                                                                                  |
| **Contributor** | An external data scientist who submits models — and never sees your raw data                                                                                 |

## What it touches

The installer changes only **Docker** and **`~/.tracebloc`** on your host — no system-wide changes. Uninstalling is a single command, and your data is yours throughout.

## Get started

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="/environment-setup/quickstart">
    A running workspace in about 10 minutes, with one command.
  </Card>

  <Card title="Deployment environments" icon="server" href="/environment-setup/deployment-environments">
    Deploy on local / k3d, bare-metal, EKS, AKS, or OpenShift.
  </Card>
</CardGroup>

<Note>
  Want the guarantees in detail for your security team? See [Security & data handling](/environment-setup/configuration#security).
</Note>