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ndonnx\n\n[![CI](https://img.shields.io/github/actions/workflow/status/quantco/ndonnx/ci.yml?style=flat-square\u0026branch=main)](https://github.com/quantco/ndonnx/actions/workflows/ci.yml)\n[![Documentation](https://readthedocs.org/projects/ndonnx/badge/?version=latest)](https://ndonnx.readthedocs.io/en/latest/?badge=latest)\n[![conda-forge](https://img.shields.io/conda/vn/conda-forge/ndonnx?style=flat-square\u0026logoColor=white\u0026logo=conda-forge)](https://anaconda.org/conda-forge/ndonnx)\n[![pypi](https://img.shields.io/pypi/v/ndonnx.svg?logo=pypi\u0026logoColor=white)](https://pypi.org/project/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 as np\n  import ndonnx as ndx\n  import jax.numpy as jnp\n\n  def mean_drop_outliers(a, low=-5, high=5):\n      xp = a.__array_namespace__()\n      return xp.mean(a[(low \u003c a) \u0026 (a \u003c high)])\n\n  np_result = mean_drop_outliers(np.asarray([-10, 0.5, 1, 5]))\n  jax_result = mean_drop_outliers(jnp.asarray([-10, 0.5, 1, 5]))\n  onnx_result = mean_drop_outliers(ndx.asarray([-10, 0.5, 1, 5]))\n\n  assert np_result == onnx_result.unwrap_numpy() == jax_result == 0.75\n  ```\n\n- It supports ONNX export. This allows you persist your logic into an ONNX computation graph.\n\n  ```python\n  import ndonnx as ndx\n  import onnx\n\n  # Instantiate placeholder ndonnx array\n  x = ndx.array(shape=(\"N\",), dtype=ndx.float32)\n  y = mean_drop_outliers(x)\n\n  # Build and save ONNX model to disk\n  model = ndx.build({\"x\": x}, {\"y\": y})\n  onnx.save(model, \"mean_drop_outliers.onnx\")\n  ```\n\n  You can then make predictions using a runtime of your choice.\n\n  ```python\n  import onnxruntime as ort\n  import numpy as np\n\n  inference_session = ort.InferenceSession(\"mean_drop_outliers.onnx\")\n  prediction, = inference_session.run(None, {\n      \"x\": np.array([-10, 0.5, 1, 5], dtype=np.float32),\n  })\n  assert prediction == 0.75\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 tested against the official `array-api-tests` suite.\nMissing coverage is tracked in the `skips.txt` file.\nContributions are welcome!\n\nRun the tests with:\n\n```bash\npixi run arrayapitests\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantco%2Fndonnx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantco%2Fndonnx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantco%2Fndonnx/lists"}