https://github.com/tracebloc/client
Deployable tracebloc client for running model training pipelines
https://github.com/tracebloc/client
aks-cluster client eks-cluster kubernetes mlops tracebloc
Last synced: about 1 month ago
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Deployable tracebloc client for running model training pipelines
- Host: GitHub
- URL: https://github.com/tracebloc/client
- Owner: tracebloc
- License: apache-2.0
- Created: 2024-09-18T11:26:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-03-15T20:20:01.000Z (3 months ago)
- Last Synced: 2026-03-16T08:06:57.526Z (3 months ago)
- Topics: aks-cluster, client, eks-cluster, kubernetes, mlops, tracebloc
- Language: Shell
- Homepage:
- Size: 873 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](LICENSE) [](https://hub.docker.com/r/tracebloc/client) [](https://ai.tracebloc.io)
# tracebloc Client 🔒
The runtime that keeps your data where it belongs — on your infrastructure.
The tracebloc client deploys inside your Kubernetes cluster and executes all model training, fine-tuning, and inference locally. It connects to the tracebloc backend for orchestration only. No data, no model weights, no artifacts ever leave your environment.
## Architecture
```
Your infrastructure
┌─────────────────────────────────────────────────────────┐
│ │
│ ┌──────────────────┐ ┌───────────────────────┐ │
│ │ tracebloc │ │ Kubernetes cluster │ │
│ │ client │◄────►│ │ │
│ │ │ │ ● Training jobs │ │
│ │ Orchestrates │ │ ● Inference jobs │ │
│ │ training, │ │ ● Your datasets │ │
│ │ enforces budgets │ │ ● Fine-tuned weights │ │
│ └────────┬──────────┘ │ │ │
│ │ │ Everything stays here │ │
│ │ └───────────────────────┘ │
└────────────┼────────────────────────────────────────────┘
│
│ Encrypted (orchestration only — no data)
▼
┌─────────────────┐
│ tracebloc │
│ backend │
│ │
│ Coordinates │
│ experiments, │
│ serves web UI │
└─────────────────┘
```
## What the client manages
- **Training execution** — runs vendor models in isolated, containerized sandboxes
- **Compute budgets** — enforces per-vendor FLOPs or runtime quotas
- **Security boundaries** — namespace isolation, encrypted communication, audit logging
- **Multi-framework support** — PyTorch, TensorFlow, custom containers
- **Hardware scheduling** — CPUs, GPUs, TPUs via Kubernetes-native orchestration
## Security
For the threat model, defense layers, per-platform caveats, operator responsibilities, and verification steps, see **[docs/SECURITY.md](docs/SECURITY.md)**. The chart ships hardened defaults against untrusted user-submitted ML code; deployment still requires a CNI that enforces NetworkPolicy — that file explains exactly what to check.
## Deploy
```bash
docker pull tracebloc/client:latest
```
Deployment varies by infrastructure. Follow the guide for your setup:
- [Deployment overview](https://docs.tracebloc.io/environment-setup/deployment-overview)
- [Local — Linux](https://docs.tracebloc.io/environment-setup/local-linux)
- [Local — macOS](https://docs.tracebloc.io/environment-setup/local-macos)
- [AWS](https://docs.tracebloc.io/environment-setup/aws)
Full documentation → [docs.tracebloc.io](https://docs.tracebloc.io/)
## Links
[Platform](https://ai.tracebloc.io/) · [Docs](https://docs.tracebloc.io/) · [Discord](https://discord.gg/tracebloc)
## License
Apache 2.0 — see [LICENSE](LICENSE).
**Deployment help?** [support@tracebloc.io](mailto:support@tracebloc.io) or [open an issue](https://github.com/tracebloc/client/issues).