tracebloc is a collaborative AI workspace you deploy on your own infrastructure. Invite researchers, partners, vendors — anyone — to train, fine-tune, and benchmark models on your private data. Your data never moves.
Mac / Linux
bash <(curl -fsSL https://tracebloc.io/i.sh)Windows
irm https://tracebloc.io/i.ps1 | iexOne script. Your Mac, your server, your Kubernetes cluster. A shared workspace where contributors build on your data — without your data ever leaving.
| Step | What happens |
|---|---|
| 1 | Deploy — install on your Mac, Linux, bare metal, or any Kubernetes cluster |
| 2 | Define — set up a use case with datasets, metrics, and evaluation criteria |
| 3 | Invite — onboard contributors globally in minutes via email whitelisting |
| 4 | Build — contributors train and fine-tune models inside your environment |
| 5 | Compare — one leaderboard. Accuracy, latency, robustness, cost. Ship the winner. |
Your data stays on your infrastructure. Fine-tuned weights stay on your infrastructure. Always.
| model-zoo | Pre-built models for vision, NLP, tabular, time series — ready to train |
| quickstart | Jupyter notebook to launch training in minutes |
| tracebloc-ingestor | Pipelines to validate, prepare, and ingest your datasets — the dataset templates show one per task |
| client | Deploy the tracebloc workspace on your Kubernetes cluster |
Deploy your workspace → Install tracebloc · Explore use cases → ai.tracebloc.io/explore · Train a model → Open the Colab notebook · Read the docs → docs.tracebloc.io
PyTorch · TensorFlow · DeepSpeed · Docker · Kubernetes · AWS · Azure · GCP · On-Prem
Website · Documentation · LinkedIn · Discord · X · support@tracebloc.io