Merge pull request #154 from awslabs/mvs-scalar-data

ResNet CIFAR 10 generates scalar data faster

sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb

@@ -116,6 +116,7 @@
     "estimator = TensorFlow(entry_point='resnet_cifar_10.py',\n",
     "                       source_dir=source_dir,\n",
     "                       role=role,\n",
+    "                       hyperparameters={'min_eval_frequency': 10},\n",
     "                       training_steps=1000, evaluation_steps=100,\n",
     "                       train_instance_count=2, train_instance_type='ml.c4.xlarge', \n",
     "                       base_job_name='tensorboard-example')\n",
@@ -137,7 +138,8 @@
     "\n",
     "It takes a few minutes to provision containers and start the training job.**TensorBoard** will start to display metrics shortly after that.\n",
     "\n",
-    "You can access **TensorBoard** locally at [http://localhost:6006](http://localhost:6006) or using your SageMaker notebook instance [proxy/6006/](/proxy/6006/)(TensorBoard will not work if forget to put the slash, '/', in end of the url). If TensorBoard started on a different port, adjust these URLs to match."
+    "You can access **TensorBoard** locally at [http://localhost:6006](http://localhost:6006) or using your SageMaker notebook instance [proxy/6006/](/proxy/6006/)(TensorBoard will not work if forget to put the slash, '/', in end of the url). If TensorBoard started on a different port, adjust these URLs to match.",
+    "This example uses the optional hyperparameter **```min_eval_frequency```** to generate training evaluations more often, allowing to visualize **TensorBoard** scalar data faster. You can find the available optional hyperparameters [here](https://github.com/aws/sagemaker-python-sdk#optional-hyperparameters)**."
    ]
   },
   {