{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution exercise a."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the cell below and paste in [the main notebook](../modeling.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some arbitrary hyperparameters\n",
    "min_child_weights = [1, 2, 4, 8, 10]\n",
    "\n",
    "for weight in min_child_weights:\n",
    "    hyperparams[\"min_child_weight\"] = weight\n",
    "    \n",
    "    run_name = f\"algorithm-mode-run-{create_date()}-weight-{weight}\"\n",
    "    \n",
    "    xgb = sagemaker.estimator.Estimator(image_uri=docker_image_name,\n",
    "                                        role=role,\n",
    "                                        hyperparameters=hyperparams,\n",
    "                                        instance_count=1, \n",
    "                                        instance_type='ml.m4.xlarge',\n",
    "                                        output_path=f's3://{bucket}/{prefix}/output',\n",
    "                                        base_job_name=\"demo-xgboost-customer-churn\",\n",
    "                                        sagemaker_session=sm_sess)\n",
    "    \n",
    "    # Look at the wait=False below\n",
    "    print(f\"Starting training job for trial {run_name}!\")\n",
    "    with Run(\n",
    "    experiment_name=experiment_name,\n",
    "    run_name=run_name, \n",
    "    sagemaker_session=sm_sess,) as run:\n",
    "        xgb.fit(inputs={\n",
    "                          'train': s3_input_train,\n",
    "                          'validation': s3_input_validation\n",
    "                        },\n",
    "                wait=False\n",
    "               )"
   ]
  }
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