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Everything the installer does, explained. This page walks through the full setup process — what each step does, what to expect, and how to verify it worked.
The installer deploys a single-node workspace on one machine — a local Kubernetes cluster running inside Docker on that host. For multi-node or production deployments, see the EKS deployment guide.

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Requirements

The installer runs on any modern machine (one host per workspace) and checks these before it starts — below the minimum it stops on Linux with a clear message (on macOS/Windows it warns; raise Docker Desktop’s memory in Settings → Resources). “To run” keeps a workspace online; training on the same machine needs the headroom — a single training job alone reserves ~8 GB. Supported platforms: macOS (Intel & Apple Silicon) · Linux (x86_64 & arm64) · Windows (x86_64 & arm64) Outbound access needed: The installer pulls container images, the install scripts, and the Helm chart, then connects to the tracebloc platform. Allow traffic to *.docker.io, ghcr.io, raw.githubusercontent.com, *.github.io, *.tracebloc.io, and pypi.org.
Storage must be on a local disk. The data directory (HOST_DATA_DIR, default ~/.tracebloc) holds the workspace database, which corrupts on network filesystems — the installer fails fast if it is on NFS/CIFS/SMB. To keep large datasets on a network mount, point HOST_DATASET_DIR at it instead: only the dataset volume moves there, while the database stays local. See Configuration.

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1. Create an Account

Sign up at ai.tracebloc.io. Free to get started — no credit card required.

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2. Register a Client

A client is your workspace’s identity on the platform. It ties a specific machine to your account and controls what data and use cases are accessible from it. Open the client page and click ”+”. The client starts as Pending while the backend provisions resources. Note the Client ID and password — you need both in the next step.
Provisioning usually takes a few seconds. Refresh the page if the status doesn’t update.

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Client Status Reference

Your client moves through these states as it goes from registration to running:

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3. Deploy

One command sets up your entire workspace on any machine — macOS, Linux, or Windows. The installer is idempotent: it detects what’s already installed and skips it, so it’s safe to re-run at any time.
Nothing on your machine is modified outside:
  • ~/.tracebloc/ — data and config
  • Docker — container runtime

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What the Installer Does

The installer runs four clearly labelled steps: Step 1/4 — Check system requirements Verifies Docker is installed and running, detects GPU hardware (falls back to CPU mode if none), and installs missing system tools (e.g. conntrack). Step 2/4 — Set up secure compute environment Provisions a lightweight local Kubernetes cluster inside Docker. First run takes 1–2 minutes to download components. Step 3/4 — Install tracebloc client Prompts for a workspace name (e.g. berlin-team, vision-lab, ml-mardan). This identifies the client on your machine and becomes the Kubernetes namespace. Step 4/4 — Connect to tracebloc network Prompts for your Client ID and password from step 2 above. This links your secure local environment to the tracebloc platform so vendors can submit models for evaluation. When it finishes you’ll see a summary like:
Install logs are kept in ~/.tracebloc/ if you need to debug anything.
To upgrade a one-liner install later, run helm upgrade <workspace> tracebloc/client -n <namespace> --reset-then-reuse-values (append --version <version-number> to pin). See Configuration → Upgrade for details — --reset-then-reuse-values is required so the values applied by the installer are preserved.

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GPU Support

The installer detects your GPU and configures the cluster:
  • Linux (NVIDIA/AMD) — drivers, container toolkit, and Kubernetes device plugin are installed automatically. A reboot may be required after driver installation.
  • macOS — CPU-only. For GPU workloads, deploy on a Linux machine or use AWS (EKS).
  • Windows — pre-install NVIDIA drivers before running the installer. The installer detects them via nvidia-smi.
See Configuration > GPU for detailed platform-specific behavior.

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4. Verify

After the installer finishes, confirm that your workspace is running:
You should see two pods in Running state: Then open ai.tracebloc.io and check that your client status shows Online. This confirms the client has established a secure connection to the tracebloc backend.
Stuck on Offline? Check the Troubleshooting page — most issues resolve with a pod restart or credential check.

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What’s Next

Your workspace is running. Here’s where to go from here: Data owners: Ingest your dataset, then create a use case — pick from prepared datasets, define evaluation metrics, and invite contributors to submit models. Data scientists: Join a use case — connect to a workspace, train models on real data, and submit results. Advanced configuration: Configuration — customize installer options, manage the cluster, configure GPU settings, or deploy manually with Helm.