diff --git a/introduction_to_applying_machine_learning/lightgbm_catboost_tabtransformer_autogluon_churn/churn-prediction-lightgbm-catboost-tabtransformer-autogluon.ipynb b/introduction_to_applying_machine_learning/lightgbm_catboost_tabtransformer_autogluon_churn/churn-prediction-lightgbm-catboost-tabtransformer-autogluon.ipynb index 954455049b..f8a3a79f9f 100644 --- a/introduction_to_applying_machine_learning/lightgbm_catboost_tabtransformer_autogluon_churn/churn-prediction-lightgbm-catboost-tabtransformer-autogluon.ipynb +++ b/introduction_to_applying_machine_learning/lightgbm_catboost_tabtransformer_autogluon_churn/churn-prediction-lightgbm-catboost-tabtransformer-autogluon.ipynb @@ -659,7 +659,7 @@ "from sagemaker.estimator import Estimator\n", "from sagemaker.utils import name_from_base\n", "\n", - "training_job_name = name_from_base(f\"jumpstart-{train_model_id}-train\")\n", + "training_job_name = name_from_base(\"jumpstart-example-churn-lgb-g\")\n", "\n", "# Create SageMaker Estimator instance\n", "tabular_estimator = Estimator(\n", @@ -682,7 +682,7 @@ " \"auc\",\n", " hyperparameter_ranges_lgb,\n", " [{\"Name\": \"auc\", \"Regex\": \"auc: ([0-9\\\\.]+)\"}],\n", - " max_jobs=10,\n", + " max_jobs=20,\n", " max_parallel_jobs=5,\n", " objective_type=\"Maximize\",\n", " base_tuning_job_name=training_job_name,\n", @@ -721,7 +721,7 @@ "metadata": {}, "outputs": [], "source": [ - "inference_instance_type = \"ml.m5.large\"\n", + "inference_instance_type = \"ml.m5.4xlarge\"\n", "\n", "# Retrieve the inference docker container uri\n", "deploy_image_uri = image_uris.retrieve(\n", @@ -737,7 +737,7 @@ " model_id=train_model_id, model_version=train_model_version, script_scope=\"inference\"\n", ")\n", "\n", - "endpoint_name = name_from_base(f\"jumpstart-lgb-churn-{train_model_id}-\")\n", + "endpoint_name = name_from_base(\"jumpstart-example-churn-lgb-g\")\n", "\n", "# Use the estimator from the previous step to deploy to a SageMaker endpoint\n", "predictor = (tuner if use_amt else tabular_estimator).deploy(\n", @@ -1051,7 +1051,7 @@ "from sagemaker.estimator import Estimator\n", "from sagemaker.utils import name_from_base\n", "\n", - "training_job_name = name_from_base(f\"jumpstart-{train_model_id}-training\")\n", + "training_job_name = name_from_base(\"jumpstart-example-churn-cat-g\")\n", "\n", "# Create SageMaker Estimator instance\n", "tabular_estimator_cat = Estimator(\n", @@ -1074,7 +1074,7 @@ " \"AUC\",\n", " hyperparameter_ranges_cat,\n", " [{\"Name\": \"AUC\", \"Regex\": \"bestTest = ([0-9\\\\.]+)\"}],\n", - " max_jobs=10,\n", + " max_jobs=20,\n", " max_parallel_jobs=5,\n", " objective_type=\"Maximize\",\n", " base_tuning_job_name=training_job_name,\n", @@ -1103,7 +1103,7 @@ "metadata": {}, "outputs": [], "source": [ - "inference_instance_type = \"ml.m5.large\"\n", + "inference_instance_type = \"ml.m5.4xlarge\"\n", "\n", "# Retrieve the inference docker container uri\n", "deploy_image_uri = image_uris.retrieve(\n", @@ -1119,7 +1119,7 @@ " model_id=train_model_id, model_version=train_model_version, script_scope=\"inference\"\n", ")\n", "\n", - "endpoint_name_cat = name_from_base(f\"jumpstart-cat-churn-{train_model_id}-\")\n", + "endpoint_name_cat = name_from_base(\"jumpstart-example-churn-cat-g\")\n", "\n", "# Use the estimator from the previous step to deploy to a SageMaker endpoint\n", "predictor_cat = (tuner_cat if use_amt else tabular_estimator_cat).deploy(\n", @@ -1361,7 +1361,7 @@ "metadata": {}, "outputs": [], "source": [ - "training_job_name = name_from_base(f\"jumpstart-{train_model_id}-training\")\n", + "training_job_name = name_from_base(\"jumpstart-example-churn-tt-g\")\n", "\n", "# Create SageMaker Estimator instance\n", "tabular_estimator_tab = Estimator(\n", @@ -1384,7 +1384,7 @@ " \"f1_score\", # Note, TabTransformer currently does not support AUC score, thus we use its default setting F1 score as an alternative evaluation metric.\n", " hyperparameter_ranges_tab,\n", " [{\"Name\": \"f1_score\", \"Regex\": \"metrics={'f1': (\\\\S+)}\"}],\n", - " max_jobs=10,\n", + " max_jobs=20,\n", " max_parallel_jobs=5, # reduce max_parallel_jobs number if the instance type is limited in your account\n", " objective_type=\"Maximize\",\n", " base_tuning_job_name=training_job_name,\n", @@ -1414,7 +1414,7 @@ "metadata": {}, "outputs": [], "source": [ - "inference_instance_type = \"ml.m5.2xlarge\"\n", + "inference_instance_type = \"ml.m5.4xlarge\"\n", "\n", "# Retrieve the inference docker container uri\n", "deploy_image_uri = image_uris.retrieve(\n", @@ -1430,7 +1430,7 @@ " model_id=train_model_id, model_version=train_model_version, script_scope=\"inference\"\n", ")\n", "\n", - "endpoint_name_tab = name_from_base(f\"jumpstart-tabtransformer-churn-{train_model_id}-\")\n", + "endpoint_name_tab = name_from_base(\"jumpstart-example-churn-tt-g\")\n", "\n", "# Use the estimator from the previous step to deploy to a SageMaker endpoint\n", "predictor_tab = (tuner_tab if use_amt else tabular_estimator_tab).deploy(\n", @@ -1638,7 +1638,7 @@ "from sagemaker.estimator import Estimator\n", "from sagemaker.utils import name_from_base\n", "\n", - "training_job_name = name_from_base(f\"jumpstart-{train_model_id}-training\")\n", + "training_job_name = name_from_base(\"jumpstart-example-churn-ag-g\")\n", "\n", "# Create SageMaker Estimator instance\n", "tabular_estimator_ag = Estimator(\n", @@ -1676,7 +1676,7 @@ "metadata": {}, "outputs": [], "source": [ - "inference_instance_type = \"ml.m5.2xlarge\"\n", + "inference_instance_type = \"ml.m5.4xlarge\"\n", "\n", "# Retrieve the inference docker container uri\n", "deploy_image_uri = image_uris.retrieve(\n", @@ -1692,7 +1692,7 @@ " model_id=train_model_id, model_version=train_model_version, script_scope=\"inference\"\n", ")\n", "\n", - "endpoint_name_ag = name_from_base(f\"jumpstart-ag-churn-{train_model_id}-\")\n", + "endpoint_name_ag = name_from_base(\"jumpstart-example-churn-ag-g\")\n", "\n", "# Use the estimator from the previous step to deploy to a SageMaker endpoint\n", "predictor_ag = tabular_estimator_ag.deploy(\n", @@ -1854,4 +1854,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file