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.

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

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.

Explore the project through the following documentation areas.

EveryO brings together several essential machine learning components.

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

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

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

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

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

Learn More: Backend System

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.

Minimum Python version: Python 3.9

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

Install EveryO from the repository:

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

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.

Run:

everyo info

For additional diagnostics:

everyo doctor

Continue with:

• Installation Guide
• Quick Start
• First Neural Network
• Examples and Tutorials

EveryO follows a modular approach to machine learning framework development.

                    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.

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

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

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.

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.

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

See:

CONTRIBUTING.md

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

Start with:

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

Explore:

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

Review:

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

EveryO follows an iterative development approach.

Potential engineering priorities include:

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

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

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

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

For development discussions and implementation work, visit:

• Issues
• Pull Requests
• Changelog

EveryO welcomes constructive contributions from developers and researchers.

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

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

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

Start Here: Contribution Guidelines

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

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

Read Security Policy

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

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

Read Code of Conduct

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

Read Governance

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.

Need help understanding EveryO or contributing to its development?

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

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.