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https://github.com/quantco/ndonnx
ONNX-backed array library that is compliant with the Array API standard.
https://github.com/quantco/ndonnx
array numpy onnx python
Last synced: 6 days ago
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ONNX-backed array library that is compliant with the Array API standard.
- Host: GitHub
- URL: https://github.com/quantco/ndonnx
- Owner: Quantco
- License: bsd-3-clause
- Created: 2024-06-10T11:52:01.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-11T13:23:02.000Z (12 days ago)
- Last Synced: 2024-12-16T20:10:15.828Z (7 days ago)
- Topics: array, numpy, onnx, python
- Language: Python
- Homepage: https://ndonnx.readthedocs.io/
- Size: 1.39 MB
- Stars: 42
- Watchers: 5
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
# ndonnx
[](https://github.com/quantco/ndonnx/actions/workflows/ci.yml)
[](https://ndonnx.readthedocs.io/en/latest/?badge=latest)
[](https://anaconda.org/conda-forge/ndonnx)
[](https://pypi.org/project/ndonnx)
An ONNX-backed array library that is compliant with the [Array API](https://data-apis.org/array-api/) standard.
## Installation
Releases are available on PyPI and conda-forge.
```bash
# using pip
pip install ndonnx
# using conda
conda install ndonnx
# using pixi
pixi add ndonnx
```
## Development
You can install the package in development mode using:
```bash
git clone https://github.com/quantco/ndonnx
cd ndonnx
# For Array API tests
git submodule update --init --recursive
pixi shell
pre-commit run -a
pip install --no-build-isolation --no-deps -e .
pytest tests -n auto
```
## Quick start
`ndonnx` is an ONNX based python array library.
It has a couple of key features:
- It implements the [`Array API`](https://data-apis.org/array-api/) standard. Standard compliant code can be executed without changes across numerous backends such as like `NumPy`, `JAX` and now `ndonnx`.
```python
import numpy as np
import ndonnx as ndx
import jax.numpy as jnp
def mean_drop_outliers(a, low=-5, high=5):
xp = a.__array_namespace__()
return xp.mean(a[(low < a) & (a < high)])
np_result = mean_drop_outliers(np.asarray([-10, 0.5, 1, 5]))
jax_result = mean_drop_outliers(jnp.asarray([-10, 0.5, 1, 5]))
onnx_result = mean_drop_outliers(ndx.asarray([-10, 0.5, 1, 5]))
assert np_result == onnx_result.to_numpy() == jax_result == 0.75
```
- It supports ONNX export. This allows you persist your logic into an ONNX computation graph.
```python
import ndonnx as ndx
import onnx
# Instantiate placeholder ndonnx array
x = ndx.array(shape=("N",), dtype=ndx.float32)
y = mean_drop_outliers(x)
# Build and save ONNX model to disk
model = ndx.build({"x": x}, {"y": y})
onnx.save(model, "mean_drop_outliers.onnx")
```
You can then make predictions using a runtime of your choice.
```python
import onnxruntime as ort
import numpy as np
inference_session = ort.InferenceSession("mean_drop_outliers.onnx")
prediction, = inference_session.run(None, {
"x": np.array([-10, 0.5, 1, 5], dtype=np.float32),
})
assert prediction == 0.75
```
In the future we will be enabling a stable API for an extensible data type system. This will allow users to define their own data types and operations on arrays with these data types.
## Array API coverage
Array API compatibility is tracked in `api-coverage-tests`. Missing coverage is tracked in the `skips.txt` file. Contributions are welcome!
Summary(1119 total):
- 961 passed
- 107 failed
- 51 deselected
Run the tests with:
```bash
pixi run arrayapitests
```