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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.

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). These are the minimum specs to run a workspace comfortably:
MinimumRecommended
CPU4 cores8+ cores
RAM8 GB16+ GB
Disk20 GB free50+ GB free
Supported platforms: macOS (Intel & Apple Silicon) · Linux (x86_64 & arm64) · Windows (x86_64 & arm64) Outbound access needed: The installer downloads container images and connects to the tracebloc platform. Make sure your network allows traffic to *.docker.io, *.tracebloc.io, github.com, and pypi.org.

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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 ”+”.
FieldWhat to enter
NameA name for your workspace, e.g. my-team
LocationWhere this machine is deployed
PasswordA secure password (not your account password)
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:
StatusMeaning
PendingRegistration received, being provisioned
OnlineDeployed and connected to the platform
OfflineDisconnected or not 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. 
bash <(curl -fsSL https://tracebloc.io/i.sh)
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: 
tracebloc client installed successfully

Workspace : <workspace>
Mode      : CPU      # or GPU

Logs:       ~/.tracebloc/
Data:       /tracebloc/<workspace>
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 auto-detects GPU hardware and configures the cluster accordingly:
  • 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: 
kubectl get pods -n <workspace>
You should see two pods in Running state:
PodRole
mysql-...Local metadata store — tracks jobs, metrics, and configuration
tracebloc-jobs-manager-...The client — executes training jobs and communicates with the platform
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.