onnxruntime/docs/api/python/downloads/594159f58c2d97bee3f22ee659067d5a/plot_backend.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# ONNX Runtime Backend for ONNX\n\n*ONNX Runtime* extends the \n`onnx backend API <https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md>`_\nto run predictions using this runtime.\nLet's use the API to compute the prediction\nof a simple logistic regression model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nfrom onnxruntime import datasets\nfrom onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument\nimport onnxruntime.backend as backend\nfrom onnx import load\n\nname = datasets.get_example(\"logreg_iris.onnx\")\nmodel = load(name)\n\nrep = backend.prepare(model, 'CPU')\nx = np.array([[-1.0, -2.0]], dtype=np.float32)\ntry:\n    label, proba = rep.run(x)\n    print(\"label={}\".format(label))\n    print(\"probabilities={}\".format(proba))\nexcept (RuntimeError, InvalidArgument) as e:\n    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The device depends on how the package was compiled,\nGPU or CPU.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from onnxruntime import get_device\nprint(get_device())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The backend can also directly load the model\nwithout using *onnx*.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "rep = backend.prepare(name, 'CPU')\nx = np.array([[-1.0, -2.0]], dtype=np.float32)\ntry:\n    label, proba = rep.run(x)\n    print(\"label={}\".format(label))\n    print(\"probabilities={}\".format(proba))\nexcept (RuntimeError, InvalidArgument) as e:\n    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The backend API is implemented by other frameworks\nand makes it easier to switch between multiple runtimes\nwith the same API.\n\n"
      ]
    }
  ],
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