| Name | ndonnx JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | ONNX backed array library compliant with Array API standard. |
| upload_time | 2024-06-15 10:47:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
numpy
onnx
array-api
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ndonnx
[](https://github.com/quantco/ndonnx/actions/workflows/ci.yml)
[](https://ndonnx.readthedocs.io/en/latest/?badge=latest)
[](https://prefix.dev/channels/conda-forge/packages/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.array_api as npx
import ndonnx as ndx
from jax.experimental import array_api as jxp
def mean_drop_outliers(a, low=-5, high=5):
xp = a.__array_namespace__()
return xp.mean(a[(low < a) & (a < high)])
arr = [-12.12, 1.12, 2.12, 2.13, 123.,]
np_result = mean_drop_outliers(npx.asarray(arr))
jax_result = mean_drop_outliers(jxp.asarray(arr))
ndx_result = mean_drop_outliers(ndx.asarray(arr))
print(np_result) # 1.79
print(jax_result) # 1.79
print(ndx_result) # Array(1.79, dtype=ndx.Float64)
assert np_result == ndx_result.to_numpy()
```
- It supports ONNX export. This allows you persist your logic into an ONNX computation graph for convenient and performant inference.
```python
import onnx
import ndonnx as ndx
a = ndx.array(shape=("N",), dtype=ndx.float64)
b = ndx.array(shape=("N",), dtype=ndx.float64)
out = a[:2] + b[:2]
model_proto = ndx.build({"a": a, "b": b}, {"c": out})
onnx.save(model_proto, "model.onnx")
# Having serialised your model to disk, perform
# inference using a runtime of your choosing.
import onnxruntime as ort
import numpy as np
inference_session = ort.InferenceSession("model.onnx")
prediction, = inference_session.run(None, {
"a": np.array([1, 2, 3], dtype=np.float64),
"b": np.array([4, 5, 6], dtype=np.float64),
})
print(prediction) # array([5., 7.])
```
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 the array-api coverage test suite in `api-coverage-tests`. Missing coverage is tracked in the `skips.txt` file. Contributions are welcome!
Summary(1119 total):
- 898 passed
- 210 failed
- 11 deselected
Run the tests with:
```bash
pixi run arrayapitests
```
Raw data
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"description": "# ndonnx\n\n[](https://github.com/quantco/ndonnx/actions/workflows/ci.yml)\n[](https://ndonnx.readthedocs.io/en/latest/?badge=latest)\n[](https://prefix.dev/channels/conda-forge/packages/ndonnx)\n\nAn ONNX-backed array library that is compliant with the [Array API](https://data-apis.org/array-api/) standard.\n\n## Installation\n\nReleases are available on PyPI and conda-forge.\n\n```bash\n# using pip\npip install ndonnx\n# using conda\nconda install ndonnx\n# using pixi\npixi add ndonnx\n```\n\n## Development\n\nYou can install the package in development mode using:\n\n```bash\ngit clone https://github.com/quantco/ndonnx\ncd ndonnx\n\n# For Array API tests\ngit submodule update --init --recursive\n\npixi shell\npre-commit run -a\npip install --no-build-isolation --no-deps -e .\npytest tests -n auto\n```\n\n## Quick start\n\n`ndonnx` is an ONNX based python array library.\n\nIt has a couple of key features:\n\n- 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`.\n\n ```python\n import numpy.array_api as npx\n import ndonnx as ndx\n from jax.experimental import array_api as jxp\n\n def mean_drop_outliers(a, low=-5, high=5):\n xp = a.__array_namespace__()\n return xp.mean(a[(low < a) & (a < high)])\n\n arr = [-12.12, 1.12, 2.12, 2.13, 123.,]\n\n np_result = mean_drop_outliers(npx.asarray(arr))\n jax_result = mean_drop_outliers(jxp.asarray(arr))\n ndx_result = mean_drop_outliers(ndx.asarray(arr))\n print(np_result) # 1.79\n print(jax_result) # 1.79\n print(ndx_result) # Array(1.79, dtype=ndx.Float64)\n assert np_result == ndx_result.to_numpy()\n ```\n\n- It supports ONNX export. This allows you persist your logic into an ONNX computation graph for convenient and performant inference.\n\n ```python\n import onnx\n import ndonnx as ndx\n\n a = ndx.array(shape=(\"N\",), dtype=ndx.float64)\n b = ndx.array(shape=(\"N\",), dtype=ndx.float64)\n out = a[:2] + b[:2]\n model_proto = ndx.build({\"a\": a, \"b\": b}, {\"c\": out})\n onnx.save(model_proto, \"model.onnx\")\n\n # Having serialised your model to disk, perform\n # inference using a runtime of your choosing.\n import onnxruntime as ort\n import numpy as np\n inference_session = ort.InferenceSession(\"model.onnx\")\n prediction, = inference_session.run(None, {\n \"a\": np.array([1, 2, 3], dtype=np.float64),\n \"b\": np.array([4, 5, 6], dtype=np.float64),\n })\n print(prediction) # array([5., 7.])\n ```\n\nIn 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.\n\n## Array API coverage\n\nArray API compatibility is tracked in the array-api coverage test suite in `api-coverage-tests`. Missing coverage is tracked in the `skips.txt` file. Contributions are welcome!\n\nSummary(1119 total):\n\n- 898 passed\n- 210 failed\n- 11 deselected\n\nRun the tests with:\n\n```bash\npixi run arrayapitests\n```\n",
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