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.
Privacy & security
Can the data scientist see my raw data?
No. Data scientists submit model code; the code runs on your client, against your data, in an isolated sandbox. They only ever see the metrics you decide to expose. Raw data, in-flight model state, and intermediate artifacts never leave your infrastructure.What gets shared with the data scientist or with tracebloc?
Only the evaluation metrics you configure on the use case (accuracy, F1, latency, and so on). No raw data, no trained weights, no logs.Where do trained model weights end up?
On your storage. Trained weights stay on the persistent volume claim attached to your client. Data scientists never receive them.Is the client’s egress restricted?
Yes — the chart applies a KubernetesNetworkPolicy that only allows training pods to reach the tracebloc backend (for orchestration metadata) and the in-cluster proxy that handles result and FLOPs reporting. The egress lockdown only takes effect on a CNI that enforces NetworkPolicy. EKS’s default VPC CNI does not, out of the box — see the EKS deployment guide for what to install.
Infrastructure
Do I need a GPU?
For most ML workloads, yes. The standalone installer detects NVIDIA and AMD GPUs and installs the right drivers automatically. CPU-only is fine for small tabular and text models, but expect long training times on anything image- or sequence-heavy.What Kubernetes versions do you support?
Kubernetes 1.24 and above. See the setup guide for the full prerequisites.Can I run on-premise only?
Yes. The standalone installer provisions a local cluster on your machine, or you can deploy the unified Helm chart into your existing on-prem Kubernetes. The client never depends on cloud infrastructure for training.Do you support air-gapped clusters?
Yes — see the packaged-chart install path in tracebloc/client INSTALL.md for the offline flow.Training & models
How do I monitor training?
Through the tracebloc dashboard — every experiment, every model, every metric. Or query the API if you prefer programmatic access.What if a training run fails?
The client retries transient failures automatically. Persistent failures show up in the dashboard with logs and exit codes. For ingestion-time failures, check the Job logs in the namespace you deployed into.Can I bring my own model?
Yes. Use the tracebloc Python package to upload a model file (PyTorch, TensorFlow, or a custom container). For ready-made starting points, see the model zoo.Do you support fine-tuning?
Yes — the same upload flow handles full training, fine-tuning with pretrained weights, and inference-only evaluation.Cost & support
How much does it cost?
See the pricing page on the website.How do I get help?
- Email [email protected]
- Open an issue on the relevant GitHub repo: client, data-ingestors, docs
- Join the Discord
Next steps
- Get started — install the client
- Browse key terms
- Use the Python SDK