Welcome to MLFusionLab! This repository houses a Django project designed to streamline your machine learning workflow, providing a user-friendly interface for building, training, and managing models.
Why MLFusionLab?
- Structured Environment: Organize your machine learning endeavors within a robust Django project, leveraging Django's structure for models, views, and templates.
- Customizable Interface: Interact with your models and data through a dynamic web interface, tailoring the experience to your specific needs.
- Simplified Experiment Tracking: Manage datasets, track experiment parameters, and compare model performance with ease.
Who is this for?
- Machine learning practitioners seeking a more organized and efficient way to develop and deploy models using PyTorch and scikit-learn.
- Data scientists and engineers who prefer a visual interface for interacting with their machine learning projects.
- Teams collaborating on machine learning tasks, benefitting from centralized model management. (todo)
Getting Started:
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Clone the repository:
git clone https://github.com/harikris001/MLFusionLab.git
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Navigate to the project directory:
cd MLFusionLab -
Create a virtual environment (recommended):
python -m venv .venv source .venv/bin/activate -
Install project dependencies:
pip install -r requirements.txt
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Apply database migrations:
python manage.py migrate
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Start the development server:
python manage.py runserver
Access the application in your browser at
http://127.0.0.1:8000/.
Key Features (Potential):
- User authentication and authorization for secure access and project management.
- Data upload and management capabilities through a user-friendly interface.
- Model training and evaluation workflows with integrated visualization tools specifically designed for PyTorch and scikit-learn.
- Support for data analysis, cleaning, and visualization using popular Python libraries like Pandas, NumPy, and Matplotlib.
Libraries Used:
- Django (Core framework)
- Django REST framework (For building APIs - optional if needed)
- PyTorch (Deep Learning library)
- Scikit-learn (Machine learning library)
- Pandas (Data analysis and manipulation)
- NumPy (Numerical computing)
- Matplotlib (Data visualization)
- Other libraries as needed for your specific machine learning tasks.
Contributing:
We welcome contributions! Share your ideas, report issues, or submit pull requests to help us improve MLFusionLab.