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Getting Started
Install · Configure · Verify · Build · Explore
A practical introduction to installing and exploring the EveryO machine learning framework.
Home · Installation · Quick Start · Architecture · Repository
Maintained By & Owner: EveryO Contributors & @krishanth7
Important
Welcome to EveryO.
This guide walks you through preparing your Python environment, installing EveryO, verifying its command-line tools, and beginning development.
EveryO is currently classified as Alpha software. APIs, optional backend behavior, and implementation details may change during development.
- Introduction
- Who Should Use EveryO
- System Requirements
- Prepare Your Environment
- Install EveryO
- Verify Your Installation
- Explore Your First Python Session
- Optional Dependencies and Backends
- Development Environment
- Testing and Validation
- Command-Line Tools
- Troubleshooting
- Learning Path
- Contribution Workflow
- Support and Resources
- Next Steps
EveryO is an open-source Python framework for machine learning and deep learning development.
It provides foundational components for numerical computation, automatic differentiation, neural network construction, training, and experimentation.
The framework uses NumPy as a foundational computation backend and includes optional integrations for additional computational capabilities.
| Capability | Description |
|---|---|
| Tensor Operations | Multidimensional numerical computation |
| Automatic Differentiation | Gradient computation for supported operations |
| Neural Networks | Reusable layers and model components |
| Optimization | Gradient-based parameter updates |
| Training | Model training and evaluation utilities |
| Backend Integration | NumPy, optional TensorFlow and CUDA paths |
| Model Interoperability | Serialization and supported export functionality |
| Developer Tools | Diagnostics, testing, profiling, and benchmarking |
By the end of this guide, you should be able to:
- Prepare a Python development environment.
- Obtain the EveryO source code.
- Install the framework.
- Verify the package installation.
- Explore the command-line interface.
- Locate examples and documentation.
- Run the project's tests.
- Understand how to continue learning.
EveryO is intended for people interested in developing or studying machine learning systems.
Explore tensor operations, neural network implementations, and training utilities.
Experiment with computational workflows and investigate framework behavior.
Study the fundamentals of machine learning frameworks and automatic differentiation.
Contribute to framework implementation, documentation, testing, and developer tooling.
Note
EveryO is not presented as a drop-in replacement for mature production machine learning frameworks. Evaluate its capabilities, compatibility, and limitations for your intended use.
| Requirement | Specification |
|---|---|
| Programming Language | Python |
| Minimum Python Version | 3.9 |
| Package Manager | pip |
| Source Control | Git, for repository-based installation |
| Primary Numerical Dependency | NumPy |
| Visualization Dependency | Matplotlib |
| GPU | Not required for base installation |
Development may be performed in compatible Python environments on:
- Windows.
- Linux.
- macOS.
Platform-specific functionality, particularly CUDA acceleration, depends on compatible hardware and software.
Not every combination of operating system, Python version, and optional backend is guaranteed to have been tested.
Consider using:
- A supported Python interpreter.
- Git.
- A terminal.
- A code editor or IDE.
- A Python virtual environment.
Popular editor choices include Visual Studio Code and PyCharm, although neither is required.
Open your terminal and run:
python --versionOn systems where Python is invoked as python3:
python3 --versionEnsure that the installed Python version is 3.9 or newer.
python -m pip --versiongit --versionIf Git is unavailable, install it from:
Choose a location for your development projects.
Example:
mkdir everyo-workspace
cd everyo-workspacegit clone https://github.com/krishanth7/EveryO.git
cd EveryOThis downloads the EveryO source repository.
Using a dedicated virtual environment is recommended.
python -m venv .venvActivate it in PowerShell:
.\.venv\Scripts\Activate.ps1python3 -m venv .venv
source .venv/bin/activatepython --version
python -m pip --versionThe active Python interpreter should correspond to your intended environment.
python -m pip install --upgrade pip setuptools wheelThis step is recommended for development setup but may be unnecessary in managed environments.
From the repository root:
python -m pip install -e .This installs EveryO in editable mode.
Editable installation is useful for contributors because source changes can be reflected without repeatedly reinstalling the project.
To install the project's development dependencies:
python -m pip install -e ".[dev]"Development dependencies include tools used for testing, linting, and related engineering workflows.
Run:
python -m pip show everyoYou should see installed package metadata, including the package name and version.
Run:
python -c "import everyo; print('EveryO import successful')"If the import succeeds, the package is available to the active Python interpreter.
Tip
Always use python -m pip with the Python interpreter associated with your intended environment. This reduces the risk of installing packages into the wrong environment.
EveryO includes command-line utilities for inspecting and diagnosing the environment.
everyo infoUse this command to inspect framework information exposed by the installed version.
everyo doctorThis command is intended to help diagnose installation and environment configuration.
everyo --helpUse the CLI help output to inspect available commands and options.
- Python version meets the minimum requirement.
- Virtual environment is active, if used.
- Package installation completed.
-
import everyosucceeds. -
everyo infoexecutes. -
everyo doctorexecutes. - CLI help is accessible.
Important
A successful installation does not automatically verify every optional backend, GPU operation, or advanced framework capability.
From the activated environment:
pythonInside the Python interpreter:
import everyo
print("EveryO is available.")This confirms that Python can import the installed package.
For an introductory inspection:
import everyo
public_names = [
name for name in dir(everyo)
if not name.startswith("_")
]
print(public_names)This displays public-looking names exposed by the installed top-level package.
Not every displayed name is necessarily a documented stable API.
The EveryO repository includes an examples/ directory.
Browse the available examples:
Select an example that matches your installed dependencies and current learning objectives.
Read its requirements and instructions before execution.
Start with:
- Tensor creation and shapes.
- Mathematical operations.
- Broadcasting.
- Automatic differentiation.
- Neural network layers.
- Loss functions.
- Optimizers.
- Model training.
For exact Python API signatures and supported parameters, refer to the source code and version-specific documentation.
EveryO provides optional dependency groups for additional capabilities.
Install the TensorFlow extra:
python -m pip install -e ".[tensorflow]"Actual compatibility depends on the Python version, platform, and TensorFlow distribution.
Install the ONNX extra:
python -m pip install -e ".[onnx]"This installs the project's declared optional dependencies for supported ONNX functionality.
python -m pip install -e ".[yaml]"python -m pip install -e ".[cuda]"Warning
Installing the CUDA dependency extra does not by itself guarantee a functioning GPU backend.
CUDA acceleration may require an appropriate NVIDIA GPU, compatible drivers, CUDA toolchain, compiler, and native extension build configuration.
Consult the project's CUDA documentation and verify functionality in your intended environment.
| Extra | Purpose |
|---|---|
dev |
Development and testing |
tensorflow |
Optional TensorFlow integration |
onnx |
Supported ONNX workflows |
yaml |
YAML-related functionality |
cuda |
CUDA-related build dependencies |
The EveryO repository includes the following major areas:
EveryO/
├── .github/
│ └── workflows/
├── everyo/
├── tests/
├── docs/
├── examples/
├── benchmarks/
├── configs/
├── cuda/
├── README.md
├── CHANGELOG.md
├── CONTRIBUTING.md
├── SECURITY.md
├── LICENSE
└── pyproject.toml
| Directory | Purpose |
|---|---|
everyo/ |
Framework implementation |
tests/ |
Automated tests |
docs/ |
Technical documentation |
examples/ |
Usage examples |
benchmarks/ |
Performance benchmarking |
configs/ |
Configuration resources |
cuda/ |
CUDA-related source and integration |
.github/ |
Repository automation and community configuration |
Contributors should prioritize:
- Clear code organization.
- Maintainable interfaces.
- Reproducible tests.
- Consistent documentation.
- Meaningful error messages.
- Explicit dependency requirements.
- Responsible performance claims.
python -m pip install -e ".[dev]"python -m pytestExample:
python -m pytest tests/test_tensor.pyruff check .ruff format --check .Some tests may be skipped or require optional software or hardware.
A successful CPU test run does not establish correctness of CUDA-specific operations.
Test results should be reported with:
- Environment.
- Python version.
- Dependencies.
- Test command.
- Passed tests.
- Failed tests.
- Skipped tests.
EveryO exposes a CLI entry point named everyo.
| Command | Intended Purpose |
|---|---|
everyo info |
Framework information |
everyo doctor |
Environment diagnostics |
everyo benchmark |
Benchmarking utilities |
everyo test |
Test-related tooling |
everyo demo |
Demonstration functionality |
Run:
everyo --helpFor command-specific options:
everyo benchmark --help- Review command help before execution.
- Use a dedicated development environment.
- Preserve error output when reporting failures.
- Avoid assuming optional dependencies are installed.
- Verify benchmark configurations before comparing results.
Problem 1 — Python is not recognized
Python is not installed or is unavailable through the current terminal configuration.
- Confirm Python is installed.
- Check the executable path.
- Try
python3on Linux or macOS. - Reopen the terminal after installation.
- Verify the Python version.
Problem 2 — No module named everyo
The package was not installed into the active Python environment.
python -m pip install -e .
python -m pip show everyoConfirm that the intended virtual environment is active.
Problem 3 — everyo command not found
The command-line entry point is not available in the active environment.
- Activate the virtual environment.
- Confirm package installation.
- Reinstall the package if necessary.
- Check the environment's executable directory.
Problem 4 — Optional dependency missing
A feature requires an optional dependency group.
Review the project's pyproject.toml and install the appropriate extra.
Example:
python -m pip install -e ".[onnx]"Problem 5 — CUDA functionality unavailable
- No compatible NVIDIA GPU.
- Missing or incompatible drivers.
- Missing CUDA toolkit.
- Native extension build failure.
- Unsupported environment configuration.
- Check the installed GPU.
- Verify driver compatibility.
- Review CUDA build requirements.
- Inspect build errors.
- Run supported CPU functionality independently.
Problem 6 — Tests are skipped
Tests may require optional dependencies, supported hardware, or specific environment conditions.
Inspect pytest output and the relevant test markers.
Do not interpret skipped tests as successful execution of the skipped functionality.
Goal: Understand the basic framework environment.
Topics:
- Installation.
- Python imports.
- Tensor fundamentals.
- Numerical operations.
Goal: Understand gradient-based computation.
Topics:
- Computational graphs.
- Gradients.
- Backpropagation.
- Differentiable operations.
Goal: Build and train models.
Topics:
- Layers.
- Activation functions.
- Loss functions.
- Optimizers.
- Training workflows.
Goal: Explore advanced framework features.
Topics:
- Attention.
- Transformers.
- Quantization.
- ONNX export.
- Profiling.
- Distributed training.
Goal: Understand and contribute to EveryO internals.
Topics:
- Framework architecture.
- Backend integration.
- Testing.
- Performance.
- Documentation.
- Contribution workflows.
EveryO welcomes contributions from the open-source community.
Read:
Review an Issue
|
v
Prepare Development Environment
|
v
Create a Working Branch
|
v
Implement Changes
|
v
Run Tests and Quality Checks
|
v
Submit Pull Request
|
v
Review and Feedback
|
v
Approval and Merge
Contributions should be:
- Clearly scoped.
- Documented.
- Tested where appropriate.
- Compatible with project guidelines.
- Reviewed before merging.
| Resource | Link |
|---|---|
| Repository | EveryO |
| Documentation | docs/ |
| Examples | examples/ |
| Issues | GitHub Issues |
| Pull Requests | GitHub Pull Requests |
| Support Policy | SUPPORT.md |
| Security Policy | SECURITY.md |
| License | MIT License |
When reporting a technical issue, include:
- EveryO version.
- Python version.
- Operating system.
- Installation method.
- Relevant dependencies.
- Steps to reproduce.
- Expected behavior.
- Actual behavior.
- Relevant error messages.
Do not publish credentials or other sensitive information in public reports.
Congratulations on preparing your EveryO development environment.
You can now continue with the following topics:
| Next Guide | What You Will Learn |
|---|---|
| Installation | Detailed platform-specific setup |
| Quick Start | Introductory framework examples |
| Tensor Engine | Tensor operations and numerical computation |
| Automatic Differentiation | Gradients and computational graphs |
| Neural Networks | Model construction |
| Framework Architecture | Internal framework design |
| API Reference | Public API documentation |
Note
Some wiki links above are placeholders until the corresponding pages are created.
Understand the fundamentals. Explore the architecture. Build intelligent systems.
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Open Machine Learning. Built for Developers.
Maintained By & Owner:
EveryO Contributors & @krishanth7
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