tracebloc-package 0.6.33

Build AI with anyone. On data that can't move. SDK for the tracebloc collaborative AI workspace.

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.

pip install tracebloc_package

Quick Start

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.

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
• Quickstart Notebook
• Website
• GitHub

License

MIT