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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.

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Installation

pip install tracebloc_package>=0.6.32

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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

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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()

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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.

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Google Colab Quickstart

The fastest way to get started is our Google Colab notebook — runs entirely in your browser, no local setup needed.

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Next Steps