Comet-ml

Comet codes for Colloquia

Machine Learning with Comet.ml Integration

This repository contains machine learning experiments focused on classification tasks, parameter optimization, and efficient logging using Comet.ml. It includes tools for tracking gradients, weights, confusion matrices, and training metrics while optimizing performance for carbon-efficient computation.

Features

• Deep Learning Workflows:

  • Logs gradients, training metrics, and confusion matrices.
  • Tracks parameter optimization for classification models.

• Machine Learning Workflows:

  • Classification models for various datasets.
  • Automated hyperparameter tuning.

• Carbon-Efficient Code:

  • Focused on energy-efficient machine learning computations.

Requirements

• Python 3.10+
• Comet.ml API Key
• Required libraries:
• Required libraries (install with pip install -r requirements.txt):
  • TensorFlow or PyTorch
  • scikit-learn
  • Comet.ml

Usage

1. Clone the repository:

   git clone https://github.com/nastaransh/comet-ml.git
   cd comt-ml

2. Open the codes in Jupyter (Colab or any other platforms) and run them