Metadata-Version: 2.4
Name: tracebloc_package
Version: 0.6.33
Summary: Build AI with anyone. On data that can't move. SDK for the tracebloc collaborative AI workspace.
Home-page: https://github.com/tracebloc/tracebloc-py-package
Author: tracebloc
Author-email: lukas@tracebloc.io
License: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: absl-py
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Requires-Dist: scikit-learn
Requires-Dist: tensorflow
Requires-Dist: tensorflow-datasets
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Requires-Dist: torchlightning
Requires-Dist: torchmetrics
Requires-Dist: torchvision
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Requires-Dist: xgboost
Requires-Dist: catboost
Requires-Dist: lightgbm
Requires-Dist: lifelines
Requires-Dist: scikit-survival
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# tracebloc

**Build AI with anyone. On data that can't move.**

tracebloc is a collaborative AI workspace you deploy on your own infrastructure. Invite researchers, partners, vendors, or your own teams to train, fine-tune, and benchmark models on your private data — without the data ever leaving your environment.

```bash
pip install tracebloc_package
```

## Quick Start

```python
from tracebloc_package.user import User

# 1. Log in to your workspace
user = User()
user.login()

# 2. Upload your model to a use case
user.uploadModel(modelname="my_model")

# 3. Link your model to the dataset
user.linkModelDataset(datasetId="<your-dataset-id>")

# 4. Start training
trainingObject = user.getTrainingPlan()
trainingObject.start()
```

For a full walkthrough, open the [Quickstart Notebook on Google Colab](https://colab.research.google.com/github/tracebloc/start-training/blob/main/notebooks/traceblocTrainingGuide.ipynb).

## Supported Frameworks

| Framework | Use Cases |
|-----------|-----------|
| **PyTorch** | Image classification, object detection, semantic segmentation, tabular, text classification, time series, keypoint detection, survival analysis |
| **TensorFlow** | Image classification, tabular classification |
| **scikit-learn** | Tabular classification, tabular regression |
| **XGBoost** | Tabular classification, tabular regression |
| **CatBoost** | Tabular classification, tabular regression |
| **LightGBM** | Tabular classification, tabular regression |
| **lifelines** | Survival analysis (time-to-event) |
| **scikit-survival** | Survival analysis (time-to-event) |

## How It Works

1. **Deploy** a tracebloc workspace on any machine or Kubernetes cluster
2. **Define** a use case — select datasets, set evaluation metrics
3. **Invite** anyone — researchers, partners, your own teams across locations
4. **Build** — contributors train models inside your environment using this SDK
5. **Compare** — every submission benchmarked under identical conditions on one leaderboard

## Links

- [Documentation](https://docs.tracebloc.io/)
- [Quickstart Notebook](https://colab.research.google.com/github/tracebloc/start-training/blob/main/notebooks/traceblocTrainingGuide.ipynb)
- [Website](https://tracebloc.io)
- [GitHub](https://github.com/tracebloc)

## License

MIT
