@ -34,7 +34,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"from azureml.core import Workspace"
"from azureml.core import Workspace, Experiment\n",
"from azureml.core.compute import AmlCompute\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.widgets import RunDetails\n",
"from azureml.core.model import InferenceConfig, Model\n",
"from azureml.core.webservice import AciWebservice"
],
"outputs": [],
"metadata": {}
@ -71,7 +76,226 @@
"execution_count": null,
"source": [
"experiment_name = 'aml-experiment'\n",
"experiment = Experiment(ws, experiment_name)"
"experiment = Experiment(ws, experiment_name)\n",
"experiment"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Create a Compute Cluster\n",
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target) for your AutoML run."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"aml_name = \"heart-f-cluster\"\n",
"try:\n",
" aml_compute = AmlCompute(ws, aml_name)\n",
" print('Found existing AML compute context.')\n",
"except:\n",
" print('Creating new AML compute context.')\n",
" aml_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\", min_nodes=1, max_nodes=3)\n",
" aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)\n",
" aml_compute.wait_for_completion(show_output = True)\n",
"\n",
"cts = ws.compute_targets\n",
"compute_target = cts[aml_name]"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Data\n",
"Make sure you have uploaded the dataset to Azure ML and that the key is the same name as the dataset."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"key = 'heart-failure-records'\n",
"dataset = ws.datasets[key]\n",
"df = dataset.to_pandas_dataframe()\n",
"df.describe()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## AutoML Configuration"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"automl_settings = {\n",
" \"experiment_timeout_minutes\": 20,\n",
" \"max_concurrent_iterations\": 3,\n",
" \"primary_metric\" : 'AUC_weighted'\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(compute_target=compute_target,\n",
" task = \"classification\",\n",
" training_data=dataset,\n",
" label_column_name=\"DEATH_EVENT\",\n",
" enable_early_stopping= True,\n",
" featurization= 'auto',\n",
" debug_log = \"automl_errors.log\",\n",
" **automl_settings\n",
" )"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## AutoML Run"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"remote_run = experiment.submit(automl_config)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"RunDetails(remote_run).show()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Save the best model"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"best_run, fitted_model = remote_run.get_output()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"best_run.get_properties()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"model_name = best_run.properties['model_name']\n",
"script_file_name = 'inference/score.py'\n",
"best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
"description = \"aml heart failure project sdk\"\n",
"model = best_run.register_model(model_name = model_name,\n",
" description = description,\n",
" tags = None)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Deploy the Best Model\n",
"\n",
"Run the following code to deploy the best model. You can see the state of the deployment in the Azure ML portal. This step can take a few minutes."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,\n",
" memory_gb = 1,\n",
" tags = {'type': \"automl-heart-failure-prediction\"},\n",
" description = 'Sample service for AutoML Heart Failure Prediction')\n",
"\n",
"aci_service_name = 'automl-hf-sdk'\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Consume the Endpoint\n",
"You can add inputs to the following input sample. "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"data = {\n",
" \"data\":\n",
" [\n",
" {\n",
" 'age': \"60\",\n",
" 'anaemia': \"false\",\n",
" 'creatinine_phosphokinase': \"500\",\n",
" 'diabetes': \"false\",\n",
" 'ejection_fraction': \"38\",\n",
" 'high_blood_pressure': \"false\",\n",
" 'platelets': \"260000\",\n",
" 'serum_creatinine': \"1.40\",\n",
" 'serum_sodium': \"137\",\n",
" 'sex': \"false\",\n",
" 'smoking': \"false\",\n",
" 'time': \"130\",\n",
" },\n",
" ],\n",
"}\n",
"\n",
"test_sample = str.encode(json.dumps(data))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"response = aci_service.run(input_data=test_sample)\n",
"response"
],
"outputs": [],
"metadata": {}
Amagash
3 years ago