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Frequently Asked Questions (FAQ)
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Answers to common questions about EveryO's architecture, installation, numerical computing, machine learning capabilities, interoperability, performance, security, and community.
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Project Maintainer: @krishanth7
Repository: github.com/krishanth7/EveryO
Note
Documentation Accuracy
This FAQ reflects the documented capabilities and limitations of EveryO. It distinguishes implemented features from experimental behavior, optional integrations, and unverified hardware scenarios.
EveryO is classified as alpha software in its package metadata. The repository README does not present it as a production training runtime.
- General Questions
- Installation and Compatibility
- Architecture and Technology
- Tensors and Numerical Computing
- Automatic Differentiation
- Neural Networks
- Training and Optimization
- Datasets and Data Loading
- CUDA and GPU Computing
- TensorFlow Integration
- ONNX Interoperability
- Serialization and Model Files
- Performance and Profiling
- Distributed Training
- Security and Privacy
- Licensing and Commercial Use
- Development and Contributions
- Roadmap and Project Status
- Support and Reporting
- Quick Reference
- Official Resources
Q1. What is EveryO?
EveryO is an open-source neural computing framework written primarily in Python using NumPy.
It implements its own:
- Tensor abstraction.
- Reverse-mode automatic differentiation.
- Neural network modules.
- Optimization algorithms.
- Training utilities.
- Model serialization.
It also provides optional CUDA acceleration and integrations with external machine learning tools.
EveryO is designed for learning, experimentation, numerical analysis, and research.
Q2. Who should use EveryO?
EveryO is suitable for:
- Students studying machine learning.
- Developers learning neural network internals.
- Researchers performing controlled experiments.
- Educators teaching automatic differentiation.
- Engineers examining numerical correctness.
- Open-source contributors.
It is particularly useful when implementation readability and inspectability matter.
Q3. Is EveryO a wrapper around PyTorch or TensorFlow?
No.
EveryO implements its own tensor engine, autograd system, layers, optimizers, and training loop on NumPy.
TensorFlow is optional and is used for interoperability and numerical comparison.
Q4. Is EveryO free?
Yes.
EveryO is open-source software distributed under the MIT License.
You can use, modify, and distribute it subject to the license terms.
Q5. Is EveryO production-ready?
EveryO's README explicitly states that it is not a production training runtime.
The project is classified as alpha software.
Although its original feature roadmap is implemented, some hardware and multi-machine validation remains incomplete.
Q6. Does EveryO require an internet connection?
The core library does not require internet access during normal local execution.
Installing dependencies or cloning the repository may require internet access.
The bundled examples can operate locally after their dependencies are installed.
Q7. Does EveryO require an API key?
No.
EveryO's core functionality requires no API key, cloud account, or external authentication service.
Q8. Which Python versions are supported?
EveryO declares Python 3.9 or newer in its package metadata.
Its documented CI matrix includes Python 3.9, 3.10, 3.11, and 3.12.
Compatibility with newer Python versions should be verified rather than assumed.
Q9. How do I install EveryO?
Clone the repository:
git clone https://github.com/krishanth7/EveryO.git
cd EveryOCreate a virtual environment:
python -m venv .venvActivate the environment and install:
python -m pip install -e .Verify:
everyo doctorQ10. Can EveryO run on Windows?
Yes.
The project CI includes Windows testing.
Activate a virtual environment in PowerShell:
.venv\Scripts\Activate.ps1Then install EveryO using the environment's Python interpreter.
Optional CUDA support depends on additional platform-specific requirements.
Q11. Can EveryO run on Linux and macOS?
Yes.
The repository tests EveryO on Linux and macOS.
Optional backend availability depends on hardware and system configuration.
Q12. What are the main dependencies?
EveryO declares two core dependencies:
- NumPy.
- Matplotlib.
Additional packages are available through optional dependency groups.
Q13. How do I verify my installation?
Run:
everyo info
everyo doctoreveryo doctor performs a real gradient calculation to check core numerical behavior.
Q14. Why does Python say EveryO is not installed?
The most common cause is an environment mismatch.
Check:
python -m pip show everyoAnd:
python -c "import sys; print(sys.executable)"Ensure EveryO is installed in the interpreter running your program.
Q15. What is EveryO's architecture?
EveryO is organized into cooperating subsystems:
Application / Training Code
|
v
Neural Modules
|
v
Tensor Operations
|
v
Autograd Engine
|
v
NumPy CPU
|
Optional CUDA
Supporting components include datasets, optimizers, serialization, profiling, and interoperability utilities.
Q16. Why does EveryO use NumPy?
NumPy provides reliable numerical arrays while allowing EveryO's own automatic differentiation and neural network logic to remain readable.
This makes it easier to inspect operations and understand gradient computation.
Q17. Can I inspect EveryO's source code?
Yes.
EveryO is open source.
The repository includes implementations of tensors, autograd, neural layers, optimizers, and training utilities.
Q18. Does EveryO support type hints?
Yes.
The repository includes a py.typed package marker, and its contribution guidelines require type hints on public functions and methods.
Q19. What is a Tensor in EveryO?
A Tensor is EveryO's primary numerical data structure.
It stores numerical values and supports mathematical operations, shape information, device information, and optional gradient tracking.
Q20. How do I create a tensor?
import everyo as eo
x = eo.tensor([1.0, 2.0, 3.0])
print(x)Q21. Does EveryO support matrix multiplication?
Yes.
import everyo as eo
a = eo.tensor([[1.0, 2.0]])
b = eo.tensor([[3.0], [4.0]])
result = eo.matmul(a, b)
print(result)The numerical result is 11.
Q22. Does EveryO support broadcasting?
Yes.
EveryO supports NumPy-style broadcasting for compatible tensor shapes.
Gradient propagation must correctly account for broadcast dimensions.
Q23. Can EveryO use float32 and float64?
Yes.
Tensor construction supports numerical dtype selection.
The appropriate dtype depends on accuracy, memory, and performance requirements.
Q24. How do I inspect tensor properties?
print(x.shape)
print(x.ndim)
print(x.dtype)
print(x.device)
print(x.size)Q25. What is automatic differentiation?
Automatic differentiation computes derivatives by applying the chain rule through a graph of mathematical operations.
EveryO implements reverse-mode automatic differentiation, which is useful for neural network training.
Q26. How do I calculate a gradient?
import everyo as eo
x = eo.tensor(
[2.0],
requires_grad=True,
)
y = x * x
y.backward()
print(x.grad)Expected gradient:
[4.]
Q27. What does requires_grad mean?
requires_grad=True tells EveryO to track operations involving the tensor so gradients can be computed during backpropagation.
Q28. Why is my gradient missing?
Possible causes:
- Gradient tracking was disabled.
- The tensor did not require gradients.
- Backward was not called.
- The computation occurred in a
no_grad()context.
Review the graph construction and backward call.
Q29. What is no_grad used for?
no_grad() disables gradient graph construction for operations inside its context.
It is useful for inference.
with eo.no_grad():
predictions = model(features)Q30. How does EveryO verify gradient correctness?
The repository uses central finite-difference checks and comparisons with TensorFlow where applicable.
For a scalar function:
[ f'(x) \approx \frac{f(x+h)-f(x-h)}{2h} ]
Numerical tolerances are necessary because floating-point computations are not exact.
Q31. Can I build a neural network?
Yes.
import everyo as eo
model = eo.Sequential(
eo.Linear(4, 16),
eo.ReLU(),
eo.Linear(16, 3),
)Q32. Which neural network layers are available?
Documented layer families include:
- Linear layers.
- Activation functions.
- Dropout.
- Convolution.
- Pooling.
- Normalization.
- Recurrent layers.
- Attention and transformer components.
Refer to the API Reference for exact constructors.
Q33. Does EveryO support CNNs?
Yes.
The repository includes convolution and pooling layers.
Image tensors use the NHWC layout:
(batch, height, width, channels)
Q34. Does EveryO support RNN, LSTM, and GRU?
Yes.
These recurrent architectures are implemented and included in the documented roadmap.
Q35. Does EveryO support transformers?
Yes.
EveryO includes attention and transformer-related modules, including multi-head attention and transformer encoder components.
Q36. Can I create custom neural network layers?
Yes.
Custom layers can subclass EveryO's Module.
For serializable custom modules, follow the module registration and configuration requirements in the contributor documentation.
Q37. Does EveryO include a training loop?
Yes.
EveryO provides a Trainer class for model training and evaluation.
Q38. Which optimizers are available?
The repository documents optimizers including:
- SGD.
- Momentum SGD.
- Nesterov momentum.
- Adam.
- AMSGrad.
Q39. How do I train a model?
import everyo as eo
model = eo.Sequential(
eo.Linear(4, 16),
eo.ReLU(),
eo.Linear(16, 3),
)
trainer = eo.Trainer(
model,
eo.Adam(model.parameters(), lr=0.01),
eo.CrossEntropyLoss(),
metrics=["accuracy"],
)
history = trainer.fit(
train_loader,
epochs=20,
validation_loader=validation_loader,
)This example assumes train_loader and validation_loader have already been created.
Q40. Does EveryO support early stopping?
Yes.
The training utilities include callback support, including early stopping.
Q41. Does EveryO support learning-rate scheduling?
Yes.
Learning-rate scheduling is part of the documented training capabilities.
Q42. Why is my training loss not decreasing?
Possible causes:
- Learning rate is unsuitable.
- Model outputs do not match labels.
- Data preprocessing is incorrect.
- Gradients are not being computed.
- The model architecture is insufficient.
Start with a small reproducible dataset and inspect intermediate values.
Q43. Does EveryO support mixed-precision training?
Yes.
EveryO includes autocast and GradScaler.
However, CPU float16 execution is not guaranteed to be faster than float32 execution.
Q44. Does EveryO include a DataLoader?
Yes.
EveryO provides DataLoader and ArrayDataset.
Q45. Can I shuffle training data?
Yes.
loader = eo.DataLoader(
dataset,
batch_size=32,
shuffle=True,
seed=42,
)Q46. Does EveryO provide sample datasets?
Yes.
The repository includes locally generated datasets and examples for experimentation.
Q47. Can I use my own dataset?
Yes.
You can use EveryO's dataset abstractions to supply numerical features and labels.
Check shape, dtype, and label compatibility with the selected model and loss function.
Q48. Does EveryO support NVIDIA CUDA?
Yes.
EveryO includes an optional CUDA extension with native kernels.
It requires compatible hardware and an appropriate build environment.
Q49. Is CUDA required?
No.
The core framework runs on CPU without CUDA.
Q50. How do I check CUDA availability?
import everyo as eo
print(eo.cuda.is_available())You can also run:
everyo infoQ51. Why is CUDA unavailable?
Possible causes:
- No compatible NVIDIA GPU.
- Missing driver.
- Missing CUDA toolkit.
- Native extension not built.
- Incompatible environment.
Use everyo doctor for more information.
Q52. Are CUDA kernels fully hardware-validated?
The repository documents CUDA implementation and CPU-side tests, but its standard CI does not provide real GPU execution.
Hardware validation remains an important limitation.
Q53. Will CUDA always be faster?
No.
Performance depends on:
- Tensor size.
- Transfer overhead.
- Kernel implementation.
- Hardware.
- Workload characteristics.
Small workloads may not benefit from GPU acceleration.
Q54. Does EveryO require TensorFlow?
No.
TensorFlow is optional.
Q55. Why does EveryO use TensorFlow comparisons?
TensorFlow provides an external reference for checking selected numerical and gradient results.
These comparisons help validate EveryO's own implementations.
Q56. How do I install TensorFlow support?
python -m pip install -e ".[tensorflow]"Q57. Does installing TensorFlow automatically enable CUDA?
No.
TensorFlow GPU support and EveryO's native CUDA extension are separate capabilities with their own requirements.
Q58. Does EveryO support ONNX?
Yes.
EveryO includes optional ONNX export and ONNX Runtime integration.
Q59. How do I install ONNX support?
python -m pip install -e ".[onnx]"Q60. Can I export a model to ONNX?
Yes, for supported architectures.
eo.export_onnx(
model,
"model.onnx",
input_shape=(1, 4),
)Q61. What is the tracing ONNX exporter?
The tracing exporter records primitive operations executed during a forward pass.
It supports architectures that may not be covered by the layer-based exporter.
Q62. Does ONNX tracing support every dynamic shape?
No.
Tracing can specialize to the example execution path and shapes.
Dynamic-batch support is conditional on validation.
Q63. Can EveryO save trained models?
Yes.
eo.save(model, "model.evo")Q64. How do I load a saved model?
restored = eo.load("model.evo")Q65. What is an .evo file?
An .evo file is EveryO's model archive format.
It contains structured model metadata and numerical parameters.
The archive is ZIP-based and uses JSON and NumPy parameter storage.
Q66. Does EveryO use pickle for model loading?
No.
EveryO's model serialization is designed to avoid pickle-based code execution.
The loader uses NumPy with allow_pickle=False and reconstructs models through registered module classes.
Q67. Can I load any external .evo file safely?
Treat downloaded model files as untrusted input.
The pickle-free design reduces a major code-execution risk but does not prove that every malformed or malicious archive is harmless.
Report any unexpected code execution privately.
Q68. Is EveryO faster than PyTorch or TensorFlow?
EveryO does not claim to outperform optimized production frameworks.
Its primary goals are readability, experimentation, and numerical verification.
Performance should be assessed using reproducible benchmarks.
Q69. Does EveryO include profiling tools?
Yes.
with eo.profile() as run:
model(batch)
run.summary()Q70. Can I export profiling traces?
Yes.
run.export_chrome_trace("trace.json")Q71. Does EveryO support quantization?
Yes.
The repository documents dynamic int8 weight quantization.
compact = eo.quantize_dynamic(model)Q72. Does quantization guarantee faster inference?
No.
The documented implementation focuses on compact weights and numerical fidelity.
It does not currently provide a dedicated optimized int8 inference kernel.
Q73. Does EveryO support distributed training?
Yes.
The repository implements data-parallel training and TCP-based process-group functionality.
Q74. Has multi-machine training been fully validated?
Not in the documented CI environment.
The repository reports TCP communication testing across separate processes on one host, but not completed testing across two physical machines.
Q75. Is distributed communication encrypted?
The documented TCP process-group protocol assumes a trusted network and does not provide transport encryption.
It should not be exposed to untrusted networks.
Q76. Does using multiple workers always improve performance?
No.
Distributed execution introduces synchronization, communication, and process-management overhead.
Benefits depend on workload and environment.
Q77. Does EveryO collect telemetry?
The official Security Policy states that EveryO's core library and CLI do not collect or transmit usage telemetry.
Q78. Does EveryO transmit my data?
The official policy states that the core library makes no network requests.
Applications built around EveryO may introduce their own network functionality and require separate review.
Q79. How do I report a security vulnerability?
Use:
GitHub Private Vulnerability Reporting
Do not disclose vulnerability details in a public issue.
Q80. Does EveryO have a bug bounty?
The verified security policy does not establish a monetary bug-bounty program.
Responsible vulnerability reporting is welcome.
Q81. What license does EveryO use?
EveryO uses the MIT License.
Q82. Can I use EveryO commercially?
Yes.
The MIT License permits commercial use, subject to its terms.
Q83. Can I modify and redistribute EveryO?
Yes.
The MIT License permits modification and redistribution, provided applicable copyright and license notices are retained.
Q84. Can I create a fork?
Yes.
You can fork the repository and develop your own modifications.
Do not imply official project endorsement without authorization.
Q85. Can I contribute to EveryO?
Yes.
EveryO welcomes:
- Bug fixes.
- Documentation.
- Tests.
- Features.
- Performance work.
- Code reviews.
Follow CONTRIBUTING.md.
Q86. How do I set up a development environment?
git clone https://github.com/krishanth7/EveryO.git
cd EveryO
python -m venv .venv
python -m pip install -e ".[dev]"Activate the virtual environment before installation.
Q87. How do I run tests?
python -m pytest -qQ88. How do I check code formatting?
ruff check .
ruff format --check .Q89. Does passing CI guarantee a pull request will be merged?
No.
The maintainer also considers correctness, scope, documentation, security, maintainability, and project direction.
Q90. Who maintains EveryO?
The project is independently maintained.
The current maintainer is @krishanth7.
Q91. Is EveryO's original roadmap complete?
Yes.
The repository README marks all ten original roadmap objectives as implemented.
Q92. Does roadmap completion mean EveryO is production-ready?
No.
Implementation completion and production validation are different.
The README explicitly documents hardware, distributed-computing, and performance limitations.
Q93. What should EveryO focus on next?
Potential future priorities include:
- Real CUDA hardware validation.
- Cross-machine distributed testing.
- Broader numerical regression coverage.
- Performance benchmarking.
- Expanded interoperability testing.
- Documentation improvements.
These are recommendations, not confirmed commitments.
Q94. Are release dates guaranteed?
No.
Roadmap items and proposals do not guarantee specific delivery dates.
Release decisions belong to the project maintainer.
Q97. Where should I ask technical questions?
Use GitHub Discussions.
Q98. What should a bug report contain?
Include:
- EveryO version.
- Python version.
- Operating system.
- Relevant hardware.
- Installation method.
- Minimal reproduction.
- Complete sanitized traceback.
- Optional backend availability.
- Commands already attempted.
Q99. Is support guaranteed?
No.
EveryO provides public, best-effort community support.
It does not guarantee response times, private consulting, custom development, or hardware support.
Q100. How can I help improve EveryO?
You can:
- Report reproducible bugs.
- Improve documentation.
- Add tests.
- Review pull requests.
- Validate optional backends.
- Suggest technical improvements.
- Contribute focused code changes.
Start with the repository's contribution guide and open issues.
| Topic | Answer |
|---|---|
| Framework | EveryO |
| Language | Python |
| Numerical foundation | NumPy |
| Declared version | 0.2.0 |
| Python requirement | 3.9+ |
| Tested CI versions | 3.9–3.12 |
| License | MIT |
| Development status | Alpha |
| Autograd | Implemented |
| Neural networks | Implemented |
| CUDA | Optional |
| TensorFlow | Optional |
| ONNX | Optional |
| Serialization | .evo |
| Quantization | Implemented |
| Distributed training | Implemented with validation limits |
| Original roadmap | 10/10 implemented |
| Production readiness | Not claimed |
| API keys | Not required |
| Telemetry | None in core/CLI |
| Support | Public, best effort |
| Maintainer | @krishanth7 |
| Resource | Link |
|---|---|
| GitHub Repository | EveryO |
| README | README.md |
| Getting Started | Documentation |
| Architecture | Architecture Guide |
| API Reference | API Documentation |
| Support | SUPPORT.md |
| Security | SECURITY.md |
| Contributing | CONTRIBUTING.md |
| Governance | GOVERNANCE.md |
| Project Policy | PROJECT_POLICY.md |
| Issues | GitHub Issues |
| Discussions | GitHub Discussions |
| Releases | GitHub Releases |
| Maintainer | @krishanth7 |
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