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Documentation Index and Project Handbook
Explore · Learn · Build · Contribute · Advance
One Framework. One Knowledge Base. Forty Documentation Pages.
A unified technical reference for EveryO's tensor engine, automatic differentiation, neural network development, experimentation, interoperability, and open-source contribution.
GitHub Repository · Getting Started · API Reference · Contributing
Project: EveryO
Repository: github.com/krishanth7/EveryO
Maintainer: @krishanth7
Documentation Collection: 40 pages
Document Type: Master documentation index and handbook
Framework Maturity: Experimental / Alpha
License: MIT
Important
Documentation Status
This handbook organizes a 40-page documentation collection prepared for the EveryO GitHub Wiki.
Page titles and links describe the intended Wiki structure. Links will work after the corresponding pages are created using the specified page names.
The handbook does not certify that all pages have been published, that every example has been executed against the current repository, or that every documented capability is production-ready.
Before publication, maintainers should verify technical accuracy, supported APIs, example commands, cross-page links, and compatibility with the current EveryO release.
- Welcome to EveryO
- Project Overview
- Documentation Architecture
- Complete 40-Page Documentation Index
- Getting Started Documentation
- Core Framework Documentation
- Machine Learning Documentation
- Engineering and Performance Documentation
- Development and Contribution Documentation
- Governance and Community Documentation
- Advanced Engineering Documentation
- Recommended Learning Paths
- Documentation Navigation System
- GitHub Wiki Sidebar
- GitHub Wiki Footer
- Documentation Standards
- Code Example Standards
- API Documentation Standards
- Mathematical Documentation Standards
- Documentation Versioning
- Documentation Maintenance
- Documentation Review Process
- Documentation Quality Assurance
- Community Documentation Contributions
- Support and Issue Reporting
- Security and Responsible Use
- Release Documentation
- Project Maturity and Limitations
- Final Documentation Checklist
- Official Project Resources
- Closing Statement
EveryO is an open-source neural computing framework built around Python and NumPy.
It provides an environment for exploring fundamental machine learning concepts and implementing neural network computations.
The project emphasizes:
- Understandable implementations.
- Mathematical transparency.
- Automatic differentiation.
- Reusable neural network components.
- Numerical validation.
- Reproducible experimentation.
- Community-driven development.
The EveryO Wiki exists to help developers understand not only how to use the framework, but also how its computational systems operate.
The documentation should make it easier to:
- Install and explore EveryO.
- Understand tensor and gradient computations.
- Build neural network experiments.
- Evaluate numerical correctness.
- Diagnose performance limitations.
- Contribute improvements.
- Participate in the project community.
| Attribute | Description |
|---|---|
| Project | EveryO |
| Category | Neural computing / machine learning |
| Primary language | Python |
| Numerical foundation | NumPy |
| Automatic differentiation | Reverse-mode autograd |
| Model development | Modular neural network components |
| CPU execution | Primary execution path |
| CUDA | Optional, selected operations |
| ONNX | Optional interoperability |
| Model serialization | .evo |
| License | MIT |
| Project maturity | Experimental / Alpha |
EveryO is designed for:
- Python developers.
- Machine learning students.
- Neural network researchers.
- Open-source contributors.
- Developers exploring framework internals.
EveryO should not be represented as a fully validated production-scale alternative to established machine learning platforms.
Its documented strengths are educational transparency, experimentation, and numerical verification.
The 40-page documentation collection is organized into seven major areas.
EVERYO DOCUMENTATION
│
├── 01. GETTING STARTED
│ ├── Introduction
│ ├── Installation
│ ├── Quick Start
│ └── Architecture
│
├── 02. CORE COMPUTING
│ ├── Tensor Engine
│ ├── Automatic Differentiation
│ ├── Neural Networks
│ ├── Optimization
│ └── Backend System
│
├── 03. MACHINE LEARNING
│ ├── Model Training
│ ├── Data Pipeline
│ ├── Model Serialization
│ └── Examples
│
├── 04. ENGINEERING QUALITY
│ ├── Performance
│ ├── Testing
│ ├── Benchmarking
│ └── Numerical Validation
│
├── 05. CONTRIBUTING
│ ├── Development Setup
│ ├── Coding Standards
│ ├── Pull Requests
│ └── Release Process
│
├── 06. PROJECT OPERATIONS
│ ├── Security
│ ├── Governance
│ ├── Roadmaps
│ ├── Support
│ └── Community
│
└── 07. ADVANCED REFERENCE
├── Advanced Tutorials
├── Custom Modules
├── Model Deployment
├── Experiment Tracking
└── Master Handbook
This is a documentation taxonomy, not a representation of the repository's filesystem.
This section provides the authoritative navigation order for the planned EveryO Wiki collection.
| # | Documentation Page | Primary Purpose |
|---|---|---|
| 01 | Home | Project introduction and Wiki entry point |
| 02 | Getting Started | First steps with EveryO |
| 03 | Installation | Installation and environment setup |
| 04 | Quick Start | First tensor and model examples |
| 05 | Framework Architecture | Internal framework design |
| 06 | Tensor Engine | Tensor computation and operations |
| 07 | Automatic Differentiation | Autograd and gradient computation |
| 08 | Neural Networks | Neural network components |
| 09 | Optimization | Optimizers and parameter updates |
| 10 | Backend System | Execution backends and devices |
| 11 | API Reference | Public programming interfaces |
| 12 | Examples and Tutorials | Practical framework examples |
| 13 | Model Training | Training workflows |
| 14 | Data Pipeline | Data preparation and loading |
| 15 | Model Serialization | Saving and restoring models |
| 16 | Performance Optimization | Performance engineering |
| 17 | Testing and Quality Assurance | Testing methodology |
| 18 | Development Setup | Contributor environment |
| 19 | Contributing | Contribution process |
| 20 | Coding Standards | Code quality conventions |
| 21 | Pull Request Guidelines | Pull request review standards |
| 22 | Release Process | Release engineering |
| 23 | Security Policy | Security reporting and practices |
| 24 | Project Governance | Project decision-making |
| 25 | Roadmap | Project direction |
| 26 | Support and Troubleshooting | User support overview |
| 27 | Frequently Asked Questions | Common questions |
| 28 | Glossary | Technical terminology |
| 29 | Community Guidelines | Community participation |
| 30 | Acknowledgements | Contributor recognition |
| 31 | Development Roadmap | Engineering milestones |
| 32 | Troubleshooting Guide | Detailed diagnostics |
| 33 | Best Practices | Recommended engineering practices |
| 34 | Benchmarking Guide | Reproducible performance measurement |
| 35 | Numerical Accuracy and Validation | Mathematical verification |
| 36 | Advanced Tutorials | Advanced learning |
| 37 | Custom Modules and Layers | Framework extensibility |
| 38 | Model Deployment and Inference | Model packaging and inference |
| 39 | Experiment Tracking and Reproducibility | Experiment management |
| 40 | Documentation Index and Project Handbook | Master documentation reference |
Total planned documentation pages: 40
The numbering represents the intended reading and organizational order. It does not imply that each page has already been published.
New users should begin with the foundational documentation.
| Page | Description |
|---|---|
| Home | Understand the project |
| Getting Started | Learn the basic workflow |
| Installation | Install the framework |
| Quick Start | Execute introductory examples |
| Framework Architecture | Understand major components |
git clone https://github.com/krishanth7/EveryO.git
cd EveryO
python -m pip install -e .
everyo info
everyo doctorFor a complete first-time setup, consult the Installation page.
The core framework pages explain EveryO's computational foundations.
Topics:
- Tensor creation.
- Shapes.
- Elementwise operations.
- Matrix multiplication.
- Broadcasting.
- Reductions.
Topics:
- Computational graphs.
- Chain rule.
- Reverse-mode differentiation.
- Gradient accumulation.
- Backward propagation.
Topics:
- Modules.
- Parameters.
- Linear layers.
- Activations.
- Network composition.
Topics:
- Gradient descent.
- Momentum.
- Adam.
- Parameter updates.
- Optimization behavior.
Topics:
- CPU execution.
- Optional CUDA functionality.
- Backend selection.
- Device limitations.
These pages cover practical machine learning development.
| Topic | Documentation |
|---|---|
| Public interfaces | API Reference |
| Example programs | Examples and Tutorials |
| Training | Model Training |
| Data preparation | Data Pipeline |
| Model persistence | Model Serialization |
DATA
|
v
PREPROCESSING
|
v
MODEL
|
v
FORWARD PASS
|
v
LOSS
|
v
BACKWARD PASS
|
v
OPTIMIZER
|
v
EVALUATION
|
v
SERIALIZATION
EveryO emphasizes numerical correctness and reproducible evaluation.
Learn to identify bottlenecks and evaluate improvements.
Learn the testing strategy and quality requirements.
Learn how to:
- Define workloads.
- Measure latency.
- Report throughput.
- Compare hardware.
- Preserve benchmark evidence.
Numerical Accuracy and Validation
Learn how to:
- Measure numerical error.
- Verify gradients.
- Validate floating-point operations.
- Compare CPU and CUDA results.
- Evaluate ONNX compatibility.
The development documentation supports engineers contributing to EveryO.
| Page | Purpose |
|---|---|
| Development Setup | Prepare a development environment |
| Contributing | Understand the contribution process |
| Coding Standards | Follow project conventions |
| Pull Request Guidelines | Prepare reviewable changes |
| Release Process | Understand release procedures |
DISCOVER ISSUE
|
v
REVIEW REQUIREMENTS
|
v
CREATE BRANCH
|
v
IMPLEMENT CHANGE
|
v
ADD TESTS
|
v
RUN QUALITY CHECKS
|
v
OPEN PULL REQUEST
|
v
CODE REVIEW
|
v
MAINTAINER DECISION
python -m pytest -q
ruff check .
ruff format --check .Project operations require clear expectations and transparent processes.
| Page | Focus |
|---|---|
| Security Policy | Responsible vulnerability reporting |
| Project Governance | Maintainer decisions |
| Roadmap | Project priorities |
| Support and Troubleshooting | Support channels |
| Frequently Asked Questions | Common answers |
| Glossary | Shared terminology |
| Community Guidelines | Respectful collaboration |
| Acknowledgements | Contributor recognition |
| Development Roadmap | Engineering milestones |
| Troubleshooting Guide | Diagnostic procedures |
The Wiki should reflect the repository's current governance documentation.
A Wiki summary should not override the authoritative repository governance policy.
Security vulnerabilities should be reported privately through the established security reporting channel.
Advanced documentation supports deeper experimentation and framework development.
| Page | Subject |
|---|---|
| Best Practices | Engineering recommendations |
| Benchmarking Guide | Performance methodology |
| Numerical Accuracy and Validation | Numerical verification |
| Advanced Tutorials | Advanced model development |
| Custom Modules and Layers | Framework extension |
| Model Deployment and Inference | Inference integration |
| Experiment Tracking and Reproducibility | Research reproducibility |
| Documentation Index and Project Handbook | Complete navigation |
BEST PRACTICES
|
v
BENCHMARKING
|
v
NUMERICAL VALIDATION
|
v
ADVANCED TUTORIALS
|
v
CUSTOM MODULES
|
v
MODEL INFERENCE
|
v
EXPERIMENT TRACKING
The documentation supports different learning objectives.
Goal: Learn EveryO fundamentals.
Outcome: Understand basic tensor and neural network execution.
Goal: Build and evaluate neural network experiments.
Outcome: Build reproducible model workflows.
Goal: Understand and extend the computational engine.
- Framework Architecture
- Tensor Engine
- Automatic Differentiation
- Backend System
- Custom Modules and Layers
- Testing and Quality Assurance
Outcome: Develop and validate framework components.
Goal: Conduct transparent experiments.
- Numerical Accuracy and Validation
- Benchmarking Guide
- Advanced Tutorials
- Experiment Tracking and Reproducibility
- Performance Optimization
Outcome: Produce reproducible and interpretable experimental results.
Goal: Contribute safely and effectively.
- Development Setup
- Contributing
- Coding Standards
- Pull Request Guidelines
- Project Governance
- Community Guidelines
Outcome: Prepare maintainable contributions aligned with project requirements.
A consistent navigation structure helps readers move between related topics.
[Home](Home) · [Getting Started](Getting-Started) · [API Reference](API-Reference) · [Documentation Index](Documentation-Index-and-Project-Handbook)---
[← Previous Page](Previous-Page) · [Documentation Index](Documentation-Index-and-Project-Handbook) · [Next Page →](Next-Page)Replace the placeholder page names with the appropriate Wiki slugs.
- Keep page names consistent.
- Avoid broken links.
- Use descriptive link text.
- Link related concepts.
- Maintain a clear entry point.
- Keep the master index synchronized.
GitHub Wikis support a special _Sidebar.md page.
The following is a recommended navigation structure.
## EveryO Documentation
**[Home](Home)**
### Getting Started
- [Getting Started](Getting-Started)
- [Installation](Installation)
- [Quick Start](Quick-Start)
- [Framework Architecture](Framework-Architecture)
### Core Framework
- [Tensor Engine](Tensor-Engine)
- [Automatic Differentiation](Automatic-Differentiation)
- [Neural Networks](Neural-Networks)
- [Optimization](Optimization)
- [Backend System](Backend-System)
- [API Reference](API-Reference)
### Machine Learning
- [Examples and Tutorials](Examples-and-Tutorials)
- [Model Training](Model-Training)
- [Data Pipeline](Data-Pipeline)
- [Model Serialization](Model-Serialization)
- [Performance Optimization](Performance-Optimization)
### Development
- [Testing and Quality Assurance](Testing-and-Quality-Assurance)
- [Development Setup](Development-Setup)
- [Contributing](Contributing)
- [Coding Standards](Coding-Standards)
- [Pull Request Guidelines](Pull-Request-Guidelines)
- [Release Process](Release-Process)
### Project & Community
- [Security Policy](Security-Policy)
- [Project Governance](Project-Governance)
- [Roadmap](Roadmap)
- [Support and Troubleshooting](Support-and-Troubleshooting)
- [Frequently Asked Questions](Frequently-Asked-Questions)
- [Glossary](Glossary)
- [Community Guidelines](Community-Guidelines)
- [Acknowledgements](Acknowledgements)
- [Development Roadmap](Development-Roadmap)
- [Troubleshooting Guide](Troubleshooting-Guide)
### Advanced Engineering
- [Best Practices](Best-Practices)
- [Benchmarking Guide](Benchmarking-Guide)
- [Numerical Accuracy and Validation](Numerical-Accuracy-and-Validation)
- [Advanced Tutorials](Advanced-Tutorials)
- [Custom Modules and Layers](Custom-Modules-and-Layers)
- [Model Deployment and Inference](Model-Deployment-and-Inference)
- [Experiment Tracking and Reproducibility](Experiment-Tracking-and-Reproducibility)
---
**[Complete Documentation Index](Documentation-Index-and-Project-Handbook)**
[GitHub Repository](https://github.com/krishanth7/EveryO)Update _Sidebar.md whenever a Wiki page is renamed or removed.
The sidebar should reflect published pages rather than drafts that are not yet accessible.
GitHub Wikis also support a special _Footer.md page.
---
**EveryO — Open Machine Learning. Built for Developers.**
[Home](Home) · [Documentation Index](Documentation-Index-and-Project-Handbook) · [Contributing](Contributing) · [Security](Security-Policy) · [Support](Support-and-Troubleshooting)
[GitHub](https://github.com/krishanth7/EveryO) · [Issues](https://github.com/krishanth7/EveryO/issues) · [Discussions](https://github.com/krishanth7/EveryO/discussions)
*Open Source · MIT Licensed · Community Driven*
© 2026 EveryO ContributorsKeep the footer:
- Short.
- Consistent.
- Accessible.
- Easy to maintain.
All documentation should follow consistent editorial standards.
- Use clear technical language.
- Define specialized terminology.
- Explain assumptions.
- Include practical examples.
- Avoid unsupported claims.
- State limitations explicitly.
Use a consistent hierarchy:
# Page Title
## Major Section
### Subsection
#### Detailed TopicUse consistent names for:
- EveryO.
- Tensor.
- Module.
- Parameter.
- Automatic differentiation.
- Backend.
- Checkpoint.
- Model artifact.
Use descriptive links, readable tables, meaningful headings, and text alternatives for informative images.
Code examples should be accurate and easy to reproduce.
- Valid syntax.
- Explicit imports.
- Defined variables.
- Supported public APIs.
- Clear expected behavior.
- Appropriate error handling.
import everyo as eo
x = eo.tensor(
[2.0],
requires_grad=True,
)
y = x * x
y.backward()
print(x.grad)Expected gradient:
[4.]
Label examples clearly as:
- Runnable.
- Illustrative.
- Pseudocode.
- Experimental.
- Hardware-dependent.
Before publishing a runnable example, execute it against the intended supported EveryO version.
Public API documentation should explain:
- Function or class name.
- Purpose.
- Parameters.
- Return value.
- Expected shapes.
- Supported dtypes.
- Exceptions.
- Example usage.
- Limitations.
## API Name
### Description
Explain the operation.
### Signature
Provide the verified signature.
### Parameters
Document each argument.
### Returns
Document the result.
### Example
Provide runnable code.
### Limitations
Describe unsupported behavior.The current repository implementation and verified public API should take precedence over outdated Wiki examples.
EveryO documentation frequently describes mathematical operations.
Define every variable and unit where applicable.
For a linear transformation:
[ Y=XW+b ]
where:
- (X): Input tensor.
- (W): Weight matrix.
- (b): Bias vector.
- (Y): Output tensor.
Where practical, provide:
- Analytical reference.
- Numerical example.
- Expected output.
- Error tolerance.
- Edge cases.
Do not claim universal numerical accuracy percentages without a defined measurement method and supporting evidence.
Documentation should evolve with the framework.
Record:
- Applicable framework version.
- Documentation revision date.
- Relevant API changes.
- Known compatibility limitations.
Avoid implying that all examples work across every past or future release.
Important documentation changes should be reviewable through the repository or Wiki's version history.
When an API changes:
- Identify affected pages.
- Update examples.
- Explain migration requirements.
- Remove obsolete guidance when appropriate.
Documentation requires ongoing maintenance.
| Responsibility | Expected Activity |
|---|---|
| API accuracy | Review against implementation |
| Installation | Verify commands |
| Examples | Execute representative programs |
| Navigation | Check links |
| Security | Review policy references |
| Releases | Update compatibility information |
| Community | Address documentation feedback |
Review relevant pages after:
- Public API changes.
- New releases.
- Dependency changes.
- Backend changes.
- Security policy updates.
- Significant architecture changes.
Do not allow Wiki guidance to silently diverge from the repository's supported behavior.
AUTHOR DRAFT
|
v
TECHNICAL REVIEW
|
v
EXAMPLE VALIDATION
|
v
EDITORIAL REVIEW
|
v
LINK VERIFICATION
|
v
MAINTAINER APPROVAL
|
v
PUBLICATION
|
v
POST-PUBLICATION REVIEW
Reviewers should check:
- Technical accuracy.
- API compatibility.
- Mathematical correctness.
- Readability.
- Completeness.
- Security implications.
- Reproducibility.
Only authorized maintainers or collaborators should publish approved changes according to the repository's access controls.
Documentation quality should be evaluated systematically.
| Category | Validation |
|---|---|
| Accuracy | Matches current implementation |
| Completeness | Covers required concepts |
| Consistency | Uses standard terminology |
| Accessibility | Supports readable navigation |
| Reproducibility | Examples can be repeated |
| Security | Avoids unsafe recommendations |
| Maintainability | Easy to update |
Check:
- Internal Wiki links.
- Repository file links.
- External references.
- Anchor links.
Run examples against a supported installation.
Verify formulas and worked calculations.
Documentation improvements are valuable open-source contributions.
- Correcting inaccurate explanations.
- Improving examples.
- Fixing broken links.
- Adding verified troubleshooting guidance.
- Clarifying API limitations.
- Improving accessibility.
- Identify an issue.
- Review existing documentation.
- Prepare a focused change.
- Validate technical claims.
- Submit through an approved contribution channel.
- Respond to maintainer feedback.
Recognize meaningful contributions according to the project's established contribution and governance practices.
Use:
Use:
When reporting a documentation problem, include:
- Page title.
- Affected section.
- Incorrect or unclear content.
- Expected correction.
- Relevant framework version.
Refer to the repository's SUPPORT.md for current support guidance.
Use private vulnerability reporting:
Do not publish:
- Private credentials.
- Access tokens.
- Personal secrets.
- Sensitive training data.
- Exploit details before coordinated disclosure.
Document relevant risks and limitations of model applications.
EveryO's experimental maturity should be considered before use in high-stakes or production-critical systems.
Documentation should be reviewed as part of the release process.
- Installation commands verified.
- API changes documented.
- Examples updated.
- Compatibility information reviewed.
- Known limitations documented.
- Changelog updated.
- Relevant Wiki links checked.
Consult:
EveryO is an experimental framework.
The project includes:
- NumPy-based tensor computation.
- Automatic differentiation.
- Neural network components.
- Optimizers.
- Training workflows.
- Model serialization.
- Optional ONNX interoperability.
- Selected CUDA functionality.
- Production-scale training readiness is not established.
- CUDA functionality is limited to supported operations.
- GPU-resident operations do not imply full autograd compatibility.
- Quantization does not imply accelerated int8 execution.
- Cross-machine distributed reliability requires further validation.
- Optional dependencies may be unavailable in some environments.
Technical documentation should communicate both capabilities and limitations with equal clarity.
The 40-page Wiki should be reviewed before being declared published and verified.
- All 40 intended pages exist.
- Every page has the correct title.
- Every page has the correct Wiki slug.
- The master index links to all pages.
- No unintended duplicate pages exist.
- APIs match the current implementation.
- Commands are verified.
- Examples are tested.
- Mathematical formulas are checked.
- Unsupported features are identified.
-
_Sidebar.mdis configured. -
_Footer.mdis configured. - Internal links resolve.
- Repository links resolve.
- Page navigation is consistent.
- Installation instructions work.
- Testing instructions work.
- Benchmark claims are evidence-based.
- Numerical tolerances are justified.
- Reproducibility requirements are clear.
- Contribution guidance is current.
- Security reporting links are correct.
- Support channels are documented.
- Governance references are current.
- Community expectations are clear.
- Technical review completed.
- Editorial review completed.
- Link validation completed.
- Maintainer approval recorded.
- Publication confirmed.
Documentation should be considered published and verified only after the relevant checks are completed.
| Resource | Link |
|---|---|
| Main Repository | EveryO on GitHub |
| Repository README | README.md |
| Source Code | Repository Files |
| Issue Tracker | GitHub Issues |
| Community Discussions | GitHub Discussions |
| Pull Requests | GitHub Pull Requests |
| Releases | GitHub Releases |
| CI Workflows | GitHub Actions |
| Changelog | CHANGELOG.md |
| Contributing | CONTRIBUTING.md |
| Governance | GOVERNANCE.md |
| Security | SECURITY.md |
| Support | SUPPORT.md |
| License | LICENSE |
| NumPy | numpy.org |
| Python | python.org |
| ONNX | onnx.ai |
The EveryO documentation collection brings together the essential concepts, engineering practices, and community processes needed to explore and contribute to the framework.
Its purpose is not merely to describe available functions.
It is intended to make the project understandable, testable, maintainable, and accessible to developers with different levels of experience.
The documentation should continue to evolve as the framework evolves.
Future improvements to existing pages should be guided by implementation changes, verified evidence, community feedback, and maintainer review.
The planned 40-page documentation series is complete in draft form.
Return to Home · Browse the Master Index · Start Contributing
Open Machine Learning. Built for Developers.
GitHub · Issues · Discussions
Maintainer: @krishanth7
Open Source · MIT Licensed · Community Driven
© 2026 EveryO Contributors