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raoberman authored and savitamittal1 committed Aug 30, 2023
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218 changes: 216 additions & 2 deletions ...rain-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-with-sklearn.ipynb
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"Now, create the file in the dependencies directory."
"Now, create the file in the dependencies directory. You can also optionally install Intel® Extension for Scikit-Learn in your yaml file for additional performance no your Intel hardware. More details can be found at the end of this section."
]
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"### **[Optional] Install Intel® Extension for Scikit-Learn optimizations for more performance on Intel hardware**"
]
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"source": [
"Want to speed up your scikit-learn scripts on Intel hardware? Try adding [Intel® Extension for Scikit-Learn](https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html) into your conda yaml file. We will show you how to enable these optimizations later in this example:"
]
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"%%writefile {dependencies_dir}/conda.yaml\n",
"name: sklearn-env\n",
"channels:\n",
" - conda-forge\n",
"dependencies:\n",
" - python=3.8\n",
" - pip=21.2.4\n",
" - scikit-learn=0.24.2\n",
" - scikit-learn-intelex\n",
" - scipy=1.7.1\n",
" - pip: \n",
" - mlflow== 1.26.1\n",
" - azureml-mlflow==1.42.0\n",
" - mlflow-skinny==2.3.2"
]
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"The specification contains some usual packages, that you'll use in your job (numpy, pip), along with Intel® Extension for Scikit-Learn.\n",
"\n",
"\n",
"Use the *yaml* file to create and register this custom environment in your workspace:"
]
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{
"cell_type": "code",
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"source": [
"from azure.ai.ml.entities import Environment\n",
"\n",
"custom_env_name = \"sklearn-env\"\n",
"\n",
"job_env = Environment(\n",
" name=custom_env_name,\n",
" description=\"Custom environment for sklearn image classification\",\n",
" conda_file=os.path.join(dependencies_dir, \"conda.yaml\"),\n",
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
")\n",
"job_env = ml_client.environments.create_or_update(job_env)\n",
"\n",
"print(\n",
" f\"Environment with name {job_env.name} is registered to workspace, the environment version is {job_env.version}\"\n",
")"
]
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"Now, create the script file in the source directory."
"Now, create the script file in the source directory. If you want to use Intel® Extension for Scikit-Learn optimizations as part of this script, take a look at the alternative script file found at the end of this section."
]
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{
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"### **[Optional]** Enable Intel® Extension for Scikit-Learn optimizations for more performance on Intel hardware**"
]
},
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"attachments": {},
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"metadata": {},
"source": [
"If you have installed Intel® Extension for Scikit-Learn (as demonstrated in the previous section), you can enable the performance optimizations by adding the two lines of code to the top of the script file, as shown below.\n",
"\n",
"To learn more about Intel® Extension for Scikit-Learn, visit the package's [documentation](https://intel.github.io/scikit-learn-intelex/)."
]
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"cell_type": "code",
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"metadata": {
"name": "create_sklearnex_script_file"
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"%%writefile {src_dir}/train_iris.py\n",
"# Modified from https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/\n",
"\n",
"import argparse\n",
"import os\n",
"\n",
"# Import and enable Intel Extension for Scikit-learn optimizations\n",
"# where possible\n",
"\n",
"from sklearnex import patch_sklearn\n",
"patch_sklearn()\n",
"\n",
"# importing necessary libraries\n",
"import numpy as np\n",
"\n",
"\n",
"from sklearn import datasets\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import joblib\n",
"\n",
"import mlflow\n",
"import mlflow.sklearn\n",
"\n",
"def main():\n",
" parser = argparse.ArgumentParser()\n",
"\n",
" parser.add_argument('--kernel', type=str, default='linear',\n",
" help='Kernel type to be used in the algorithm')\n",
" parser.add_argument('--penalty', type=float, default=1.0,\n",
" help='Penalty parameter of the error term')\n",
"\n",
" # Start Logging\n",
" mlflow.start_run()\n",
"\n",
" # enable autologging\n",
" mlflow.sklearn.autolog()\n",
"\n",
" args = parser.parse_args()\n",
" mlflow.log_param('Kernel type', str(args.kernel))\n",
" mlflow.log_metric('Penalty', float(args.penalty))\n",
"\n",
" # loading the iris dataset\n",
" iris = datasets.load_iris()\n",
"\n",
" # X -> features, y -> label\n",
" X = iris.data\n",
" y = iris.target\n",
"\n",
" # dividing X, y into train and test data\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"\n",
" # training a linear SVM classifier\n",
" from sklearn.svm import SVC\n",
" svm_model_linear = SVC(kernel=args.kernel, C=args.penalty)\n",
" svm_model_linear = svm_model_linear.fit(X_train, y_train)\n",
" svm_predictions = svm_model_linear.predict(X_test)\n",
"\n",
" # model accuracy for X_test\n",
" accuracy = svm_model_linear.score(X_test, y_test)\n",
" print('Accuracy of SVM classifier on test set: {:.2f}'.format(accuracy))\n",
" mlflow.log_metric('Accuracy', float(accuracy))\n",
" # creating a confusion matrix\n",
" cm = confusion_matrix(y_test, svm_predictions)\n",
" print(cm)\n",
"\n",
" registered_model_name=\"sklearn-iris-flower-classify-model\"\n",
"\n",
" ##########################\n",
" #<save and register model>\n",
" ##########################\n",
" # Registering the model to the workspace\n",
" print(\"Registering the model via MLFlow\")\n",
" mlflow.sklearn.log_model(\n",
" sk_model=svm_model_linear,\n",
" registered_model_name=registered_model_name,\n",
" artifact_path=registered_model_name\n",
" )\n",
"\n",
" # # Saving the model to a file\n",
" print(\"Saving the model via MLFlow\")\n",
" mlflow.sklearn.save_model(\n",
" sk_model=svm_model_linear,\n",
" path=os.path.join(registered_model_name, \"trained_model\"),\n",
" )\n",
" ###########################\n",
" #</save and register model>\n",
" ###########################\n",
" mlflow.end_run()\n",
"\n",
"if __name__ == '__main__':\n",
" main()"
]
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