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EveryO

The Open Machine Learning & Deep Learning Framework

Build · Train · Optimize · Accelerate · Deploy

GitHub Python License Version Development

An open-source Python framework for machine learning, automatic differentiation, neural networks, and accelerated computation.

Designed for developers, researchers, students, and engineers who want to understand and build intelligent systems.

Get Started · Architecture · API Reference · Roadmap · Contribute

Maintained By & Owner: EveryO Contributors & @krishanth7

Important

Welcome to the official EveryO Wiki.

This wiki is the central knowledge hub for understanding EveryO, exploring its architecture, learning its development workflows, and following its evolution.

EveryO is under active development. Features, APIs, and implementation details may change before a stable release.

1. Welcome to EveryO

EveryO is an open-source machine learning and deep learning framework implemented in Python.

The project provides foundational components for numerical computation, automatic differentiation, neural network construction, model training, optimization, and experimentation.

EveryO is designed to support developers who want to explore how machine learning frameworks operate internally while providing reusable tools for practical research and development.

Our Vision

To make the foundations of machine learning more transparent, extensible, and accessible through open-source engineering.

Our Mission

EveryO aims to develop a modular and understandable machine learning framework that brings together:

  • Numerical computing through tensor operations.
  • Automatic differentiation for gradient-based learning.
  • Neural network modules for model development.
  • Optimization algorithms for training.
  • Hardware acceleration through optional backends.
  • Model interoperability through supported export mechanisms.
  • Developer tooling for testing, benchmarking, and diagnostics.
  • Open collaboration through community-driven development.

2. Wiki Navigation

Explore the project through the following documentation areas.

Section Description Link
Getting Started Installation and first steps Explore
Installation Dependencies and setup Explore
Quick Start First EveryO program Explore
Architecture Framework design and components Explore
Tensor Engine Tensor operations and numerical computing Explore
Autograd Automatic differentiation Explore
Neural Networks Layers and model construction Explore
Optimization Optimizers and training Explore
Backend System NumPy, TensorFlow, and CUDA integration Explore
API Reference Developer API documentation Explore
Examples Practical implementation guides Explore
Roadmap Planned development milestones Explore
Contributing Community contribution guidelines Read

Note

Wiki navigation links become available as the corresponding pages are created. The main repository documentation is available immediately through the repository links.

3. Framework Capabilities

EveryO brings together several essential machine learning components.

3.1 Tensor Computing

EveryO provides tensor-based numerical computation with support for common mathematical operations.

Key capabilities:

  • Multidimensional tensors.
  • Element-wise operations.
  • Broadcasting.
  • Matrix multiplication.
  • Shape transformations.
  • Numerical computation workflows.

Learn More: Tensor Engine

3.2 Automatic Differentiation

Automatic differentiation supports gradient computation for trainable operations.

Key capabilities:

  • Computational graphs.
  • Gradient propagation.
  • Backward differentiation.
  • Gradient-based optimization.
  • Integration with neural network training.

Learn More: Automatic Differentiation

3.3 Neural Network Modules

EveryO includes components for constructing and training neural network architectures.

Supported areas include:

  • Fully connected layers.
  • Convolutional neural networks.
  • Recurrent neural networks.
  • LSTM and GRU modules.
  • Attention mechanisms.
  • Transformer building blocks.
  • Activation functions.
  • Loss functions.

Learn More: Neural Networks

3.4 Training and Optimization

EveryO provides functionality for gradient-based model training.

Key capabilities:

  • Optimization algorithms, including SGD and Adam.
  • Loss computation.
  • Parameter updates.
  • Training workflows.
  • Model evaluation.
  • Training-related utilities.

Learn More: Optimization

3.5 Hardware and Backend Support

EveryO uses NumPy as its foundational numerical backend and provides optional integration paths for additional computation environments.

Backend Purpose
NumPy Foundational CPU numerical computation
TensorFlow Optional backend integration
CUDA Optional GPU acceleration

Warning

Hardware acceleration depends on compatible hardware, software dependencies, and build configuration. CUDA functionality should be validated in the intended GPU environment before production use.

Learn More: Backend System

3.6 Model Interoperability and Tooling

EveryO also includes functionality related to:

  • Model serialization.
  • ONNX export.
  • Quantization.
  • Profiling.
  • Benchmarking.
  • Distributed training.
  • Command-line diagnostics.

Specific capabilities and limitations depend on the installed version and optional dependencies.

4. Getting Started

4.1 System Requirements

Minimum Python version: Python 3.9

A supported Python environment and the project's required dependencies are necessary.

4.2 Installation

Install EveryO from the repository:

git clone https://github.com/krishanth7/EveryO.git
cd EveryO
python -m pip install -e .

4.3 Optional Development Dependencies

For development and testing:

python -m pip install -e ".[dev]"

Optional features may require additional dependencies.

Consult the repository's pyproject.toml and documentation for available extras.

4.4 Verify Installation

Run:

everyo info

For additional diagnostics:

everyo doctor

4.5 Explore the Framework

Continue with:

5. Framework Architecture

EveryO follows a modular approach to machine learning framework development.

5.1 Conceptual Architecture

                    EveryO Framework
                           |
          +----------------+----------------+
          |                |                |
       Developer        Neural          Developer
         APIs           Networks          Tools
          |                |                |
          +----------------+----------------+
                           |
                   Training Engine
                           |
                  Automatic Differentiation
                           |
                    Tensor Operations
                           |
                  Computation Backends
                           |
              +------------+------------+
              |            |            |
            NumPy      TensorFlow      CUDA
              |            |            |
             CPU       Integration      GPU

This diagram is a conceptual overview, not an exact representation of source-code dependencies.

5.2 Architectural Objectives

The framework emphasizes:

  1. Modularity — Clear separation of major components.
  2. Extensibility — Support for additional capabilities.
  3. Understandability — Transparent implementation concepts.
  4. Testability — Components suitable for independent validation.
  5. Interoperability — Integration with supported tools.
  6. Performance Awareness — Benchmarking and profiling.
  7. Developer Experience — Documentation and diagnostics.

Detailed Architecture: Framework Architecture

6. Developer Experience

EveryO includes tools intended to support development, testing, and framework exploration.

6.1 Command-Line Interface

Examples of available CLI commands:

everyo info
everyo doctor
everyo benchmark
everyo test
everyo demo

Command availability and options should be checked against the installed version.

6.2 Testing

The repository includes tests covering areas such as:

  • Tensor operations.
  • Automatic differentiation.
  • Neural network layers.
  • Optimization.
  • Serialization.
  • Backend integration.
  • Distributed functionality.
  • Model export.
  • CLI functionality.

To execute the test suite in a development environment:

python -m pytest

Some tests may require optional dependencies or compatible hardware.

6.3 Code Quality

Contributors should follow the project's established linting, formatting, testing, and review requirements.

See:

CONTRIBUTING.md

7. Documentation and Learning

EveryO documentation is organized around both practical usage and internal framework concepts.

For Beginners

Start with:

  1. Installation.
  2. Tensor fundamentals.
  3. Basic operations.
  4. Automatic differentiation.
  5. Neural network construction.
  6. Model training.

For Experienced Developers

Explore:

  1. Framework architecture.
  2. Computational graph behavior.
  3. Backend implementation.
  4. Performance profiling.
  5. Model serialization.
  6. Distributed training.
  7. Extending framework functionality.

For Contributors

Review:

  1. Contribution guidelines.
  2. Repository organization.
  3. Coding conventions.
  4. Test requirements.
  5. Pull request procedures.
  6. Project governance.

8. Project Development and Roadmap

EveryO follows an iterative development approach.

8.1 Current Project Information

Attribute Value
Project EveryO
Language Python
Current Package Version 0.2.0
Development Classification Alpha
License MIT
Source Code Public GitHub repository
Development Model Open source

8.2 Development Priorities

Potential engineering priorities include:

Reliability

  • Expand regression coverage.
  • Improve error handling.
  • Validate serialization behavior.
  • Strengthen compatibility testing.

Performance

  • Improve benchmarking.
  • Validate hardware acceleration.
  • Identify computational bottlenecks.
  • Document performance characteristics.

Developer Experience

  • Expand API documentation.
  • Improve onboarding.
  • Enhance CLI diagnostics.
  • Improve examples and tutorials.

Release Engineering

  • Improve packaging validation.
  • Establish release readiness criteria.
  • Strengthen compatibility documentation.
  • Maintain clear change logs.

Note

These are proposed improvement areas rather than approved or scheduled release commitments.

For development discussions and implementation work, visit:

9. Open-Source Contribution

EveryO welcomes constructive contributions from developers and researchers.

Ways to Contribute

  • Improve documentation.
  • Report bugs.
  • Propose enhancements.
  • Add tests.
  • Improve performance.
  • Review pull requests.
  • Improve accessibility.
  • Share reproducible examples.

Contribution Workflow

Discover an Issue
       |
       v
Review Guidelines
       |
       v
Create a Branch
       |
       v
Implement Changes
       |
       v
Run Relevant Tests
       |
       v
Open Pull Request
       |
       v
Maintainer Review
       |
       v
Approval and Merge

All contributions remain subject to the project's review and governance procedures.

Start Here: Contribution Guidelines

10. Security, Accessibility and Governance

EveryO recognizes that responsible open-source engineering requires more than functional software.

10.1 Security

Security-related findings should follow the project's documented vulnerability reporting process.

Read Security Policy

10.2 Accessibility

EveryO aims to reduce accessibility barriers across documentation, CLI tools, APIs, visualizations, and contribution workflows.

WCAG 2.2 Level AA is a target for applicable web interfaces, not a verified project-wide conformance claim.

Read Accessibility Policy

10.3 Community Conduct

All contributors are expected to follow the project's community standards.

Read Code of Conduct

10.4 Governance

Project decisions and contribution processes are governed by the repository's published policies.

Read Governance

11. Licensing

EveryO is distributed under the MIT License.

The MIT License permits broad use, modification, distribution, and commercial integration, subject to preservation of the required copyright and permission notices.

Read the Full License

Third-party dependencies remain subject to their respective licenses.

12. Support and Community

Need help understanding EveryO or contributing to its development?

Resource Purpose
GitHub Issues Bug reports and feature requests
Pull Requests Code contributions and reviews
Support Policy Support guidance
Contributing Guide Development participation
Security Policy Security vulnerability reporting

When requesting support, provide relevant environment details, reproducible steps, and error messages where possible.

13. Project Ownership and Maintenance

Project Governance and Stewardship

Maintained By & Owner

EveryO Contributors & @krishanth7

Open-Source Collaboration · Engineering Quality · Community Development

The EveryO project is maintained through its repository governance and contribution processes.

The named maintainer and project contributors support the framework's development, documentation, testing, and continued improvement.

This maintenance designation does not independently determine legal copyright ownership.

14. Useful Links

Destination Link
Repository Home EveryO
Source Code Browse Code
Documentation docs/
Examples examples/
Tests tests/
Issues GitHub Issues
Pull Requests GitHub Pull Requests
License MIT License

Build with EveryO

Explore the fundamentals. Develop intelligent systems. Contribute to open machine learning.

Getting Started · Developer Documentation · Contribute

EveryO — Open Machine Learning. Built for Developers.

Maintained By & Owner: EveryO Contributors & @krishanth7

Licensed under the MIT License.

© 2026 EveryO Contributors

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