changed instance type

Author: mvsusp

sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -33,11 +33,11 @@
     "import os\n",
     "import sagemaker\n",
     "import tensorflow\n",
+    "from sagemaker import get_execution_role\n",
     "\n",
     "sagemaker_session = sagemaker.Session()\n",
     "\n",
-    "# Replace with a role (either name or full arn) that gives SageMaker access to S3 and cloudwatch\n",
-    "role='SageMakerRole'"
+    "role = get_execution_role()"
    ]
   },
   {
@@ -50,19 +50,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cifar dataset already downloaded\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import utils\n",
     "\n",
@@ -78,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -114,7 +106,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -126,8 +117,8 @@
     "estimator = TensorFlow(entry_point='resnet_cifar_10.py',\n",
     "                       source_dir=source_dir,\n",
     "                       role=role,\n",
-    "                       training_steps=1000, evaluation_steps=100,\n",
-    "                       train_instance_count=1, train_instance_type='ml.p2.xlarge', \n",
+    "                       training_steps=3000, evaluation_steps=100,\n",
+    "                       train_instance_count=2, train_instance_type='ml.c4.xlarge', \n",
     "                       base_job_name='tensorboard-example')\n",
     "\n",
     "estimator.fit(inputs, run_tensorboard_locally=True)"
@@ -137,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The **```fit```** method will create a training job named **```tensorboard-example-{unique identifier}```**in a p2 instance. That instance will write checkpoints to the s3 bucket **```sagemaker-{your aws account number}```**.\n",
+    "The **```fit```** method will create a training job named **```tensorboard-example-{unique identifier}```** in two **ml.c4.xlarge** instances. These instances will write checkpoints to the s3 bucket **```sagemaker-{your aws account number}```**.\n",
     "\n",
     "If you don't have this bucket yet, **```sagemaker_session```** will create it for you. These checkpoints can be used for restoring the training job, and to analyze training job metrics using **TensorBoard**. \n",
     "\n",
@@ -164,9 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
@@ -176,8 +165,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deleting the endpoint\n",
-    "**Important** "
+    "# Cleaning up\n",
+    "To avoid incurring charges to your AWS account for the resources used in this tutorial you need to delete the **SageMaker Endpoint:**"
    ]
   },
   {