Launch an ML training experiment on tracebloc in under 10 minutes. Connect your account, upload a model, link it to a dataset, configure training parameters, and start benchmarking — all from a single notebook.
No local setup. Click the badge above or:
Copy the notebook to your Drive and start running cells.
git clone https://github.com/tracebloc/start-training.git
cd start-training
# Pick the extra that matches your ML framework:
pip install "tracebloc[pytorch]>=0.8.1" # most common
# pip install "tracebloc[tensorflow]>=0.8.1" # TensorFlow
# pip install "tracebloc[all]>=0.8.1" # everything
jupyter notebook notebooks/traceblocTrainingGuide.ipynb| Step | What you do |
|---|---|
| 1 | Connect to tracebloc with your email + password |
| 2 | Upload a model from the model zoo or your own |
| 3 | Link it to a dataset from your use case |
| 4 | Configure training — epochs, batch size, learning rate, augmentation |
| 5 | Start training — model runs inside your secure Kubernetes environment |
Results appear on the use case leaderboard in the tracebloc web app.
- A tracebloc account — sign up free
- An active use case with a dataset — how to join one
- A model file — grab one from the model zoo or build your own
Platform · Docs · Model zoo · PyPI package · Discord
Apache 2.0 — see LICENSE.
Need help? [email protected] or open an issue.