tracebloc Package

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

tracebloc Package

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

Installation

ML framework dependencies are optional — pick the extra that matches your setup. The default install is ~140 MB (up from ~8 GB in older versions).

You use	Install command
PyTorch (most common)	pip install "tracebloc_package[pytorch]>=0.6.33"
TensorFlow	pip install "tracebloc_package[tensorflow]>=0.6.33"
scikit-learn only	pip install "tracebloc_package[sklearn]>=0.6.33"
Hugging Face LoRA / PEFT (requires PyTorch)	pip install "tracebloc_package[pytorch,huggingface]>=0.6.33"
XGBoost / CatBoost / LightGBM	pip install "tracebloc_package[boosting]>=0.6.33"
Survival analysis	pip install "tracebloc_package[survival]>=0.6.33"
Everything	pip install "tracebloc_package[all]>=0.6.33"

Upgrading from 0.6.32 or earlier? See the migration guide.

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

from tracebloc_package 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.uploadModel("densenet.py")

# To upload with pretrained weights (weights file must be in the same directory):
# user.uploadModel("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
trainingObject = user.linkModelDataset("YOUR_DATASET_ID")

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

# 5. Review your training plan
trainingObject.getTrainingPlan()

# 6. Start training
trainingObject.start()

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

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