.. Panther documentation master file

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   .. grid-item-card:: Sketched Neural Networks
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      Replace standard linear and convolutional layers with memory-efficient sketched alternatives that maintain accuracy while reducing computational cost.

   .. grid-item-card:: Randomized Algorithms
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      Fast QR and SVD decompositions using randomized sketching, enabling efficient matrix operations on large-scale problems with theoretical guarantees.

   .. grid-item-card:: GPU Acceleration
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      Optimized CUDA kernels with Tensor Core support for maximum performance on modern GPUs, seamlessly integrated with PyTorch workflows.

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    .. grid-item-card:: :material-regular:`download;2em` Installation
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      Get started with Panther installation instructions for CPU and GPU environments.

    .. grid-item-card:: :material-regular:`rocket_launch;2em` Quick Start
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      Learn the basics with our quick start guide and first examples.

    .. grid-item-card:: :material-regular:`library_books;2em` Tutorials
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      Comprehensive tutorials covering all aspects of Panther from basics to advanced topics.

.. toctree::
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   :caption: Getting Started
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   installation
   quickstart
   tutorials/index
   benchmarking/index

.. toctree::
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   :caption: API Reference
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   api/linalg
   api/nn
   api/sketch
   api/tuner
   api/utils


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   contributing
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Quick Summary
================== 

Panther is a PyTorch library that leverages randomized numerical linear algebra (RandNLA) techniques to create sketched neural network layers, enabling significant memory savings and performance improvements for large-scale machine learning models. It provides efficient implementations of sketched linear layers, matrix decompositions, and GPU-accelerated operations, making it ideal for resource-constrained environments.

🛠️ **Key Features**
-------------------

- **Sketched Linear Layers**: Memory-efficient alternatives to standard linear layers  
- **Randomized Matrix Decompositions**: Fast QR and SVD algorithms using sketching  
- **Neural Network Operations**: Optimized convolution and attention mechanisms  
- **GPU Acceleration**: CUDA kernels with Tensor Core support  
- **AutoTuner**: Automatic hyperparameter optimization for sketching parameters  

🎯 **Why Panther?**
-------------------

Panther enables you to:

- **Reduce Memory Usage**: Sketched layers use significantly less memory than standard layers  
- **Accelerate Training**: Faster forward and backward passes with optimized kernels  
- **Scale to Larger Models**: Handle bigger networks with limited GPU memory  
- **Maintain Accuracy**: Randomized algorithms with theoretical guarantees

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`