Merge pull request #115 from awslabs/arpin_r_byo_dkr_restart

Arpin r byo dkr restart

README.md

@@ -44,6 +44,7 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Connecting to Redshift](advanced_functionality/working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
 - [Bring Your Own XGBoost Model](advanced_functionality/xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
 - [Bring Your Own k-means Model](advanced_functionality/kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.
+- [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
 - [Installing the R Kernel](advanced_functionality/install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
 - [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
 
@@ -59,7 +60,7 @@ These examples focus on the Amazon SageMaker Python SDK which allows you to writ
 - [TensorFlow Networks with Keras](sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras)
 - [Introduction to Estimators in TensorFlow](sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators)
 - [TensorFlow and TensorBoard](sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard)
-- [Distributed TensorFlow)(sagemaker-python-sdk/tensorflow_distributed_mnist)
+- [Distributed TensorFlow](sagemaker-python-sdk/tensorflow_distributed_mnist)
 
 ### Under Development
 
@@ -68,7 +69,6 @@ These Amazon SageMaker examples fully illustrate a concept, but may require some
 - [Bring Your Own MXNet Model](under_development/tensorflow_iris_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
 - [Bring Your Own TensorFlow Model](under_development/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
 - [Ensembling Multiple Models](under_development/modeling) creates two different models for prediction, hosts them independently and shows how their outputs can be combined for better accuracy than either one alone.
-- [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
 
 ## FAQ
 

under_development/r_bring_your_own/r_bring_your_own.ipynb → advanced_functionality/r_bring_your_own/r_bring_your_own.ipynb

@@ -324,6 +324,12 @@
     "# Get the login command from ECR and execute it directly\n",
     "$(aws ecr get-login --region ${region} --no-include-email)\n",
     "\n",
+    "# On a SageMaker Notebook Instance, the docker daemon may need to be restarted in order\n",
+    "# to detect your network configuration correctly.  (This is a known issue.)\n",
+    "if [ -d \"/home/ec2-user/SageMaker\" ]; then\n",
+    "  sudo service docker restart\n",
+    "fi\n",
+    "\n",
     "# Build the docker image locally with the image name and then push it to ECR\n",
     "# with the full name.\n",
     "docker build  -t ${algorithm_name} .\n",

introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb

@@ -13,7 +13,7 @@
     "  2. [Data ingestion](#Data-ingestion)\n",
     "  3. [Data inspection](#Data-inspection)\n",
     "  4. [Data conversion](#Data-conversion)\n",
-    "3. [Training the K-Means model](#Training-the-K-Means-model)\n",
+    "3. [Training the FM model](#Training-the-FM-model)\n",
     "4. [Set up hosting for the model](#Set-up-hosting-for-the-model)\n",
     "  1. [Import model into hosting](#Import-model-into-hosting)\n",
     "  2. [Create endpoint configuration](#Create-endpoint-configuration)\n",

introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb

@@ -13,7 +13,7 @@
     "  2. [Data ingestion](#Data-ingestion)\n",
     "  3. [Data inspection](#Data-inspection)\n",
     "  4. [Data conversion](#Data-conversion)\n",
-    "3. [Training the K-Means model](#Training-the-K-Means-model)\n",
+    "3. [Training the PCA model](#Training-the-PCA-model)\n",
     "4. [Set up hosting for the model](#Set-up-hosting-for-the-model)\n",
     "  1. [Import model into hosting](#Import-model-into-hosting)\n",
     "  2. [Create endpoint configuration](#Create-endpoint-configuration)\n",

introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb

@@ -570,7 +570,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#sagemaker.Session().delete_endpoint(linears_predictor.endpoint)"
+    "#sagemaker.Session().delete_endpoint(linear_predictor.endpoint)"
    ]
   }
  ],