> ## 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.

# tracebloc Python SDK

> Python library for uploading models, linking them with datasets, configuring training parameters, and launching training runs on the tracebloc platform.

`tracebloc` is a Python library for uploading models, linking them with datasets, configuring training parameters, and launching training runs on the tracebloc platform.

<Note>
  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.
</Note>

## 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):

```bash theme={null}
pip install "tracebloc[pytorch]>=0.8.1"      # most users
# pip install "tracebloc[tensorflow]>=0.8.1" # TensorFlow
# pip install "tracebloc[sklearn]>=0.8.1"    # scikit-learn only
# pip install "tracebloc[boosting]>=0.8.1"   # XGBoost / CatBoost / LightGBM
# pip install "tracebloc[survival]>=0.8.1"   # lifelines / scikit-survival
# pip install "tracebloc[all]>=0.8.1"        # everything
```

## 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

```python theme={null}
from tracebloc import User

# 1. Log in to your tracebloc account
user = User()  # prompts for email and password

# 2. Upload a model file (must be in your current directory or provide a path)
user.upload_model(model_name="densenet.py")

# To upload with pretrained weights (weights file must be in the same directory):
# user.upload_model(model_name="densenet.py", weights=True)

# 3. Link model with a dataset from your use case
#    Find the Dataset ID on your use case page at ai.tracebloc.io
training_plan = user.link_model_dataset(dataset_id="YOUR_DATASET_ID")

# 4. Configure training parameters
training_plan.experiment_name("My first experiment")
training_plan.epochs(10)
training_plan.optimizer("adam")
training_plan.learning_rate({"type": "constant", "value": 0.001})
training_plan.validation_split(0.2)

# 5. Review your training plan
training_plan.get_training_plan()

# 6. Start training
training_plan.start()

# 7. Log out when done
user.logout()
```

## Model Zoo

Use a ready-made model from the [tracebloc model zoo](https://github.com/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](https://colab.research.google.com/drive/1N00idtpoaq1lk9OJE6g4bMqd8o-Qex2C) — runs entirely in your browser, no local setup needed.

## Next Steps

* [Start training](/join-use-case/start-training) — detailed walkthrough of the training workflow
* [Hyperparameters](/join-use-case/hyperparameters) — full reference for all training configuration options
* [FAQs](/tools-help/faqs)
* [Key terms](/tools-help/key-terms)