my_library_script_mode_trainer.ipynb

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   "source": [
    "from time import gmtime, strftime\n",
    "\n",
    "from sagemaker.sklearn.estimator import SKLearn\n",
    "\n",
    "job_name = \"custom-library-script-ctr-prediction-\" + strftime(\"%Y%m%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "hyperparameters = {\"alpha\": 0.0002, \"eta0\": 3.0}\n",
    "enable_local_mode_training=False\n",
    "\n",
    "if enable_local_mode_training:\n",
    "    train_instance_type = \"local\"\n",
    "    inputs = {\"train\": f\"file://{train_file}\", \"test\": f\"file://{test_file}\"}\n",
    "else:\n",
    "    train_instance_type = \"ml.m5.large\"\n",
    "    inputs = {\"train\": s3_train_data, \"test\": s3_test_data}\n",
    "\n",
    "estimator_parameters = {\n",
    "    \"entry_point\": \"my_library_script_mode.py\",\n",
    "    \"source_dir\": \"custom_script\",\n",
    "    \"dependencies\": [\"my_custom_library\"],\n",
    "    \"framework_version\": \"0.23-1\",\n",
    "    \"py_version\": \"py3\",\n",
    "    \"instance_type\": train_instance_type,\n",
    "    \"instance_count\": 1,\n",
    "    \"hyperparameters\": hyperparameters,\n",
    "    \"output_path\": output_location,\n",
    "    \"role\": role,\n",
    "    \"base_job_name\": job_name,\n",
    "}\n",
    "\n",
    "estimator = SKLearn(**estimator_parameters)\n",
    "estimator.fit(inputs)"
   ]
  }
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