Merge pull request aws#133 from awslabs/mvs-change-waiters

renamed waiters

Author: djarpin

advanced_functionality/data_distribution_types/data_distribution_types.ipynb

@@ -374,7 +374,7 @@
  "\n",
  "status = sm.describe_training_job(TrainingJobName=replicated_job)['TrainingJobStatus']\n",
  "print(status)\n",
- "sm.get_waiter('TrainingJob_Created').wait(TrainingJobName=replicated_job)\n",
+ "sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=replicated_job)\n",
  "status = sm.describe_training_job(TrainingJobName=replicated_job)['TrainingJobStatus']\n",
  "print(\"Training job ended with status: \" + status)\n",
  "if status == 'Failed':\n",
@@ -577,7 +577,7 @@
  "status = resp['EndpointStatus']\n",
  "print(\"Status: \" + status)\n",
  "\n",
- "sm.get_waiter('Endpoint_Created').wait(EndpointName=replicated_endpoint)\n",
+ "sm.get_waiter('endpoint_in_service').wait(EndpointName=replicated_endpoint)\n",
  "\n",
  "resp = sm.describe_endpoint(EndpointName=replicated_endpoint)\n",
  "status = resp['EndpointStatus']\n",

advanced_functionality/handling_kms_encrypted_data/handling_kms_encrypted_data.ipynb

@@ -306,7 +306,7 @@
  "\n",
  "try:\n",
  " # wait for the job to finish and report the ending status\n",
- " client.get_waiter('TrainingJob_Created').wait(TrainingJobName=job_name)\n",
+ " client.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=job_name)\n",
  " training_info = client.describe_training_job(TrainingJobName=job_name)\n",
  " status = training_info['TrainingJobStatus']\n",
  " print(\"Training job ended with status: \" + status)\n",
@@ -424,7 +424,7 @@
  "\n",
  "\n",
  "# wait until the status has changed\n",
- "client.get_waiter('Endpoint_Created').wait(EndpointName=endpoint_name)\n",
+ "client.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)\n",
  "\n",
  "\n",
  "# print the status of the endpoint\n",

advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb

@@ -270,7 +270,7 @@
  "status = resp['EndpointStatus']\n",
  "print(\"Status: \" + status)\n",
  "\n",
- "sm.get_waiter('Endpoint_Created').wait(EndpointName=kmeans_endpoint)\n",
+ "sm.get_waiter('endpoint_in_service').wait(EndpointName=kmeans_endpoint)\n",
  "\n",
  "resp = sm.describe_endpoint(EndpointName=kmeans_endpoint)\n",
  "status = resp['EndpointStatus']\n",

advanced_functionality/parquet_to_recordio_protobuf/parquet_to_recordio_protobuf.ipynb

@@ -330,7 +330,7 @@
  "\n",
  "status = sm.describe_training_job(TrainingJobName=linear_job)['TrainingJobStatus']\n",
  "print(status)\n",
- "sm.get_waiter('TrainingJob_Created').wait(TrainingJobName=linear_job)\n",
+ "sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=linear_job)\n",
  "if status == 'Failed':\n",
  " message = sm.describe_training_job(TrainingJobName=linear_job)['FailureReason']\n",
  " print('Training failed with the following error: {}'.format(message))\n",

advanced_functionality/r_bring_your_own/r_bring_your_own.ipynb

@@ -476,7 +476,7 @@
  "\n",
  "status = sm.describe_training_job(TrainingJobName=r_job)['TrainingJobStatus']\n",
  "print(status)\n",
- "sm.get_waiter('TrainingJob_Created').wait(TrainingJobName=r_job)\n",
+ "sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=r_job)\n",
  "status = sm.describe_training_job(TrainingJobName=r_job)['TrainingJobStatus']\n",
  "print(\"Training job ended with status: \" + status)\n",
  "if status == 'Failed':\n",
@@ -567,7 +567,7 @@
  "print(\"Status: \" + status)\n",
  "\n",
  "try:\n",
- " sm.get_waiter('Endpoint_Created').wait(EndpointName=r_endpoint)\n",
+ " sm.get_waiter('endpoint_in_service').wait(EndpointName=r_endpoint)\n",
  "finally:\n",
  " resp = sm.describe_endpoint(EndpointName=r_endpoint)\n",
  " status = resp['EndpointStatus']\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb

@@ -266,7 +266,7 @@
  "\n",
  "try:\n",
  " # wait for the job to finish and report the ending status\n",
- " sagemaker.get_waiter('TrainingJob_Created').wait(TrainingJobName=job_name)\n",
+ " sagemaker.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=job_name)\n",
  " training_info = sagemaker.describe_training_job(TrainingJobName=job_name)\n",
  " status = training_info['TrainingJobStatus']\n",
  " print(\"Training job ended with status: \" + status)\n",
@@ -455,7 +455,7 @@
  "\n",
  "\n",
  "# wait until the status has changed\n",
- "sagemaker.get_waiter('Endpoint_Created').wait(EndpointName=endpoint_name)\n",
+ "sagemaker.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)\n",
  "\n",
  "\n",
  "# print the status of the endpoint\n",

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb

@@ -283,7 +283,7 @@
  "\n",
  "try:\n",
  " # wait for the job to finish and report the ending status\n",
- " sagemaker.get_waiter('TrainingJob_Created').wait(TrainingJobName=job_name)\n",
+ " sagemaker.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=job_name)\n",
  " training_info = sagemaker.describe_training_job(TrainingJobName=job_name)\n",
  " status = training_info['TrainingJobStatus']\n",
  " print(\"Training job ended with status: \" + status)\n",
@@ -471,7 +471,7 @@
  "\n",
  "\n",
  "# wait until the status has changed\n",
- "sagemaker.get_waiter('Endpoint_Created').wait(EndpointName=endpoint_name)\n",
+ "sagemaker.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)\n",
  "\n",
  "\n",
  "# print the status of the endpoint\n",

introduction_to_amazon_algorithms/seq2seq_translation_en-de/SageMaker-Seq2Seq-Translation-English-German.ipynb

@@ -514,7 +514,7 @@
  "print(\"Status: \" + status)\n",
  "\n",
  "# wait until the status has changed\n",
- "sage.get_waiter('Endpoint_Created').wait(EndpointName=endpoint_name)\n",
+ "sage.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)\n",
  "\n",
  "# print the status of the endpoint\n",
  "endpoint_response = sage.describe_endpoint(EndpointName=endpoint_name)\n",

introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb

@@ -370,7 +370,7 @@
  "\n",
  "status = sm.describe_training_job(TrainingJobName=distributed_job_name)['TrainingJobStatus']\n",
  "print(status)\n",
- "sm.get_waiter('TrainingJob_Created').wait(TrainingJobName=distributed_job_name)\n",
+ "sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=distributed_job_name)\n",
  "status = sm.describe_training_job(TrainingJobName=distributed_job_name)['TrainingJobStatus']\n",
  "print(\"Training job ended with status: \" + status)\n",
  "if status == 'Failed':\n",

introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb

@@ -381,7 +381,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "Now let's kick off our training job in SageMaker's distributed, managed training, using the parameters we just created. Because training is managed, we don't have to wait for our job to finish to continue, but for this case, let's use boto3's 'TrainingJob_Created' waiter so we can ensure that the job has been started."
+ "Now let's kick off our training job in SageMaker's distributed, managed training, using the parameters we just created. Because training is managed, we don't have to wait for our job to finish to continue, but for this case, let's use boto3's 'training_job_completed_or_stopped' waiter so we can ensure that the job has been started."
  ]
  },
  {
@@ -401,7 +401,7 @@
  "\n",
  "status = sm.describe_training_job(TrainingJobName=linear_job)['TrainingJobStatus']\n",
  "print(status)\n",
- "sm.get_waiter('TrainingJob_Created').wait(TrainingJobName=linear_job)\n",
+ "sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=linear_job)\n",
  "if status == 'Failed':\n",
  " message = sm.describe_training_job(TrainingJobName=linear_job)['FailureReason']\n",
  " print('Training failed with the following error: {}'.format(message))\n",
@@ -508,7 +508,7 @@
  "status = resp['EndpointStatus']\n",
  "print(\"Status: \" + status)\n",
  "\n",
- "sm.get_waiter('Endpoint_Created').wait(EndpointName=linear_endpoint)\n",
+ "sm.get_waiter('endpoint_in_service').wait(EndpointName=linear_endpoint)\n",
  "\n",
  "resp = sm.describe_endpoint(EndpointName=linear_endpoint)\n",
  "status = resp['EndpointStatus']\n",