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change the job names to be unified with all the other jobs in JumpSta…
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…rt (aws#3631)

Co-authored-by: atqy <[email protected]>
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@Neo9061 @atqy
Neo9061 and atqy committed Oct 28, 2022
1 parent e3afd11 commit 004040c
Showing 1 changed file with 16 additions and 16 deletions.
32 changes: 16 additions & 16 deletions ...sformer_autogluon_churn/churn-prediction-lightgbm-catboost-tabtransformer-autogluon.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -1854,4 +1854,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}

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