From 133b4d93bad44bc66256a0b455d948774cc623e0 Mon Sep 17 00:00:00 2001 From: EC2 Default User Date: Wed, 4 Apr 2018 01:53:09 +0000 Subject: [PATCH] Update eval frequency param for TensorFlow 1.6 image --- ...ensorflow_resnet_cifar10_with_tensorboard.ipynb | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb b/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb index c7188aa8b4..4763134c4d 100644 --- a/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb +++ b/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb @@ -51,6 +51,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": false }, "outputs": [], @@ -105,6 +106,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "collapsed": true, "scrolled": false }, "outputs": [], @@ -116,7 +118,7 @@ "estimator = TensorFlow(entry_point='resnet_cifar_10.py',\n", " source_dir=source_dir,\n", " role=role,\n", - " hyperparameters={'min_eval_frequency': 10},\n", + " hyperparameters={'throttle_secs': 30},\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", @@ -138,7 +140,7 @@ "\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.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)**." + "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 **```throttle_secs```** 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)." ] }, { @@ -155,7 +157,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')" @@ -173,7 +177,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import numpy as np\n",