uetai 0.1.1

UETAI is data validation and model debugger tools integrated with ML experiment tracking tools

Machine Learning tracking experiment and debugging tools.

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Branch	Build	Coverage	Linting	Release	License
main

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UETAI is a customize PyTorch logger which will able to help users track machine learning experiment, and esily debug raw datasets and trained models.

UETAI provided tools for helping user tracking their experiment, visualizing the dataset, results, and debuging the model (and the raw dataset also) with little effort by integrated the tools into the dashboards which users are using for logging.

In this beta version, we will only focus on integrated Comet ML, which is amazing dashboard with well-writen API and customable panel

Getting started

Firstly, you must sign up for an account from one of these supported MLTE (Machine Learning tracking experiment) tools, each dashboard will give you a unique API key to log in dashboard from any terminal or code:

Dashboard	Status
Comet ML	✅
Weights & Biases	❌
MLFlow	❌

Install uetai

You install uetai with pip by running:

pip install uetai

Or install from source repository:

git clone [e-mail]:UETAILab/uetai.git; cd uetai
pip install -e .

Basic usage

Importing and initialize your supported dashboard logger (for example: Comet ML) and start logging your experiment:

from src import CometLogger

logger = CometLogger(project_name="Uetai project")

# training process
logger.log({"loss": loss, "acc": acc})

Examples

Coming soon...

The team

UETAI is a non-profit project hosted by AI Laboratory of University of Engineering and Technology.

UETAI is currently maintained by manhdung20112000 with the support from BS. Phi Nguyen Van - gungui98 as an advisor.

License

UETAI has a MIT license, as found in the LICENSE file.