Repository navigation
Quick Start
Explore · Compute · Differentiate · Train
A practical introduction to exploring EveryO, its numerical computing capabilities, automatic differentiation, and machine learning development workflows.
Home · Getting Started · Installation · Architecture
Maintained By & Owner: EveryO Contributors & @krishanth7
Important
Before You Begin
Complete the Installation Guide before proceeding.
EveryO is currently in alpha development. Public interfaces may change, so use the API exposed by your installed version and consult the repository examples for version-specific code.
- Overview
- Prerequisites
- Verify Your Environment
- Explore the Framework
- Tensor Computing Fundamentals
- Automatic Differentiation
- Neural Network Development
- Training Workflow
- Command-Line Tools
- Examples and Tutorials
- Troubleshooting
- Next Steps
EveryO provides foundational tools for machine learning and deep learning development.
This guide introduces the major concepts behind the framework and shows how to begin exploring its installed Python package.
After completing this guide, you should understand:
- How to import EveryO.
- How to inspect available public interfaces.
- How tensors are used in machine learning.
- Why automatic differentiation is important.
- How neural networks and optimizers fit together.
- How to locate and execute official examples.
- How to validate your development environment.
Ensure the following requirements are satisfied.
| Requirement | Status |
|---|---|
| Python 3.9 or newer | Required |
| EveryO installed | Required |
| pip | Required |
| NumPy | Installed through project dependencies |
| Virtual environment | Recommended |
| GPU | Optional |
If you have not installed EveryO:
git clone https://github.com/krishanth7/EveryO.git
cd EveryO
python -m pip install -e .For development dependencies:
python -m pip install -e ".[dev]"python --versionpython -m pip show everyoimport everyo
print("EveryO imported successfully.")everyo info
everyo doctoreveryo --helpTip
If the CLI is unavailable, confirm that EveryO was installed into the same virtual environment used by your terminal.
EveryO's top-level Python package exposes names that can be inspected directly.
import everyo
available_names = [
name
for name in dir(everyo)
if not name.startswith("_")
]
for name in available_names:
print(name)This helps you identify names available in your installed version.
Use Python's built-in help() function.
import everyo
help(everyo)For a specific exposed object:
import everyo
if hasattr(everyo, "Tensor"):
help(everyo.Tensor)import importlib.util
modules = [
"everyo",
"numpy",
"matplotlib",
]
for module in modules:
available = importlib.util.find_spec(module) is not None
print(f"{module}: {'Available' if available else 'Unavailable'}")Tensors are fundamental data structures used in machine learning.
They represent numerical data in one or more dimensions.
| Tensor Type | Example | Shape |
|---|---|---|
| Scalar | 5 |
() |
| Vector | [1, 2, 3] |
(3,) |
| Matrix | [[1, 2], [3, 4]] |
(2, 2) |
| Higher-Dimensional Tensor | Collection of matrices | (N, C, H, W) |
The following NumPy example demonstrates the numerical concepts underlying tensor computation.
import numpy as np
a = np.array([1.0, 2.0, 3.0])
b = np.array([4.0, 5.0, 6.0])
addition = a + b
multiplication = a * b
dot_product = np.dot(a, b)
print("Addition:", addition)
print("Multiplication:", multiplication)
print("Dot product:", dot_product)Expected output:
Addition: [5. 7. 9.]
Multiplication: [ 4. 10. 18.]
Dot product: 32.0
Note
This example uses NumPy to introduce tensor concepts. It is not a demonstration of EveryO's tensor API.
For an EveryO-specific implementation, use the tensor examples in the repository that correspond to your installed version.
- Shape: Dimensions of a tensor.
- Dtype: Numerical representation of elements.
- Broadcasting: Compatibility rules for operations involving different shapes.
- Matrix Multiplication: A fundamental operation in neural networks.
- Reduction: Operations such as sum, mean, and maximum.
Continue: Tensor Engine
Automatic differentiation enables machine learning frameworks to calculate gradients efficiently.
Gradients describe how a function changes with respect to its inputs.
Consider:
[ y = x^2 + 3x ]
Its derivative is:
[ \frac{dy}{dx} = 2x + 3 ]
At (x = 2):
[ \frac{dy}{dx} = 7 ]
Input Data
|
v
Forward Computation
|
v
Loss Calculation
|
v
Backward Differentiation
|
v
Gradient Computation
|
v
Parameter Update
Locate the automatic differentiation examples and inspect the current API before running them.
Important concepts include:
- Computational graphs.
- Differentiable operations.
- Backward propagation.
- Gradient accumulation.
- Gradient-based optimization.
Continue: Automatic Differentiation
Neural networks are composed of trainable layers and mathematical operations.
Input Features
|
v
Linear Layer
|
v
Activation Function
|
v
Hidden Layer
|
v
Output Layer
|
v
Prediction
| Component | Responsibility |
|---|---|
| Input | Receives training features |
| Linear Layer | Applies trainable transformations |
| Activation | Introduces nonlinearity |
| Loss Function | Measures prediction error |
| Optimizer | Updates model parameters |
| Training Loop | Coordinates repeated learning steps |
Machine learning frameworks may support architectures such as:
- Feedforward neural networks.
- Convolutional neural networks.
- Recurrent neural networks.
- Attention-based models.
- Transformers.
Availability and implementation details should be confirmed against the EveryO version in use.
Continue: Neural Networks
A standard supervised machine learning training workflow contains several stages.
Prepare Dataset
|
v
Initialize Model
|
v
Select Loss Function
|
v
Configure Optimizer
|
v
Forward Pass
|
v
Calculate Loss
|
v
Backward Pass
|
v
Update Parameters
|
v
Evaluate Performance
One complete pass through the training dataset.
A subset of training examples processed together.
A parameter that influences the size of optimization updates.
A numerical measure of model prediction error.
The derivative used to guide parameter updates.
The following is conceptual pseudocode, not executable EveryO API code.
# Conceptual example only
model = create_model()
optimizer = create_optimizer(model.parameters())
for epoch in range(num_epochs):
for inputs, targets in training_data:
predictions = model(inputs)
loss = calculate_loss(predictions, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
evaluate(model)Use the repository's current training examples for executable EveryO implementations.
Continue: Neural Networks
EveryO provides a command-line interface for developer workflows.
everyo infoeveryo doctoreveryo benchmark --helpeveryo test --helpeveryo demo --helpNote
Inspect command-specific help to determine which options are available in your installed version.
The repository includes resources for exploring framework capabilities.
| Resource | Link |
|---|---|
| Source Code | everyo/ |
| Examples | examples/ |
| Documentation | docs/ |
| Tests | tests/ |
| Benchmarks | benchmarks/ |
- Inspect tensor examples.
- Review automatic differentiation examples.
- Explore neural network components.
- Study optimization workflows.
- Run relevant tests.
- Examine advanced examples.
EveryO cannot be imported
Check the active Python environment:
python -m pip show everyoIf necessary, reinstall from the repository root:
python -m pip install -e .CLI command is unavailable
Verify that the correct virtual environment is active.
Run:
python -m pip show everyoThen inspect the environment's executable directory.
An API example does not work
The example may target a different framework version.
Check:
- Installed package version.
- Current public API.
- Repository examples.
- Required optional dependencies.
- Relevant error messages.
GPU functionality is unavailable
GPU acceleration depends on compatible hardware, drivers, optional dependencies, and native backend configuration.
The base installation does not require a GPU.
Tests fail or are skipped
Install development dependencies:
python -m pip install -e ".[dev]"Then run:
python -m pytestReview failures and skipped tests individually.
You have now explored the main concepts and developer tools associated with EveryO.
Continue with the following Wiki pages:
| Next Page | Purpose |
|---|---|
| Tensor Engine | Tensor operations and numerical computing |
| Automatic Differentiation | Gradient computation |
| Neural Networks | Model development |
| Framework Architecture | Internal system design |
| API Reference | Public API documentation |
| Examples and Tutorials | Practical examples |
Linked Wiki pages become available once created.
Next Recommended Page: Framework Architecture →
Home · Getting Started · Installation · Repository
EveryO — Open Machine Learning. Built for Developers.
Maintained By & Owner:
EveryO Contributors & @krishanth7
MIT Licensed · Open Source · Community Driven
© 2026 EveryO Contributors