1  {
  2   "cells": [
  3    {
  4     "cell_type": "markdown",
  5     "id": "ec6a8805",
  6     "metadata": {},
  7     "source": [
  8      "# Restore model dependencies with mlflow.pyfunc.get_model_dependencies()"
  9     ]
 10    },
 11    {
 12     "cell_type": "code",
 13     "execution_count": null,
 14     "id": "70a2b95e",
 15     "metadata": {},
 16     "outputs": [],
 17     "source": [
 18      "from pathlib import Path\n",
 19      "\n",
 20      "from sklearn import datasets\n",
 21      "from sklearn.neighbors import KNeighborsClassifier\n",
 22      "\n",
 23      "import mlflow\n",
 24      "\n",
 25      "X, y = datasets.load_iris(as_frame=True, return_X_y=True)\n",
 26      "model = KNeighborsClassifier()\n",
 27      "model.fit(X, y)\n",
 28      "\n",
 29      "model_path = \"/tmp/sk_model_01\"\n",
 30      "\n",
 31      "mlflow.sklearn.save_model(model, model_path)\n",
 32      "\n",
 33      "model_requirements_file_path = mlflow.pyfunc.get_model_dependencies(model_path)"
 34     ]
 35    },
 36    {
 37     "cell_type": "code",
 38     "execution_count": null,
 39     "id": "238d6445",
 40     "metadata": {},
 41     "outputs": [],
 42     "source": [
 43      "print(Path(model_requirements_file_path).read_text())"
 44     ]
 45    },
 46    {
 47     "cell_type": "code",
 48     "execution_count": null,
 49     "id": "1f9b51b9",
 50     "metadata": {},
 51     "outputs": [],
 52     "source": [
 53      "%pip install -r $model_requirements_file_path"
 54     ]
 55    },
 56    {
 57     "cell_type": "code",
 58     "execution_count": null,
 59     "id": "9b0a183b",
 60     "metadata": {},
 61     "outputs": [],
 62     "source": [
 63      "# In order to enable the environment restored by %pip command above,\n",
 64      "# you need to manually click the kernel restart button."
 65     ]
 66    },
 67    {
 68     "cell_type": "code",
 69     "execution_count": null,
 70     "id": "1262da7f",
 71     "metadata": {},
 72     "outputs": [],
 73     "source": [
 74      "import mlflow\n",
 75      "\n",
 76      "model = mlflow.pyfunc.load_model(\"/tmp/sk_model_01\")\n",
 77      "\n",
 78      "from sklearn import datasets\n",
 79      "\n",
 80      "X, y = datasets.load_iris(as_frame=True, return_X_y=True)\n",
 81      "result = model.predict(X)\n",
 82      "\n",
 83      "print(result)"
 84     ]
 85    }
 86   ],
 87   "metadata": {
 88    "kernelspec": {
 89     "display_name": "Python 3 (ipykernel)",
 90     "language": "python",
 91     "name": "python3"
 92    },
 93    "language_info": {
 94     "codemirror_mode": {
 95      "name": "ipython",
 96      "version": 3
 97     },
 98     "file_extension": ".py",
 99     "mimetype": "text/x-python",
100     "name": "python",
101     "nbconvert_exporter": "python",
102     "pygments_lexer": "ipython3",
103     "version": "3.8.12"
104    }
105   },
106   "nbformat": 4,
107   "nbformat_minor": 5
108  }