tracebloc is a Python library for uploading models, linking them with datasets, configuring training parameters, and launching training runs on the tracebloc platform.
The package was renamed from
tracebloc_package to tracebloc in 0.8.0. The old name keeps working — pip install tracebloc_package resolves via a redirect, and from tracebloc_package import User still works with a DeprecationWarning. New code should use the canonical tracebloc name; the shim is removed in 1.0.0.Installation
Pick the extra that matches your ML framework — the default install ships the core SDK only (~140 MB, ~30 sec) instead of every framework (~8 GB):Key Features
- Upload model files and pretrained weights
- Link models with datasets from your use cases
- Configure training parameters (epochs, optimizer, learning rate, augmentation, callbacks)
- Review training plans before starting
- Launch remote training on secure infrastructure
Quick Start
Model Zoo
Use a ready-made model from the tracebloc model zoo or bring your own. Supported tasks include image classification, object detection, text classification, tabular classification/regression, time series forecasting, semantic segmentation, keypoint detection, and time-to-event prediction.Google Colab Quickstart
The fastest way to get started is our Google Colab notebook — runs entirely in your browser, no local setup needed.Next Steps
- Start training — detailed walkthrough of the training workflow
- Hyperparameters — full reference for all training configuration options
- FAQs
- Key terms