* reset kernel of tensorflow_distributed_mnist_neo.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_distributed_mnist_neo_inf1.ipynb to conda_tensorflow2_p36
* reset kernel of sagemaker-neo-tf-unet.ipynb to conda_tensorflow2_p36
* reset kernel of sagemaker-spark-processing.ipynb to pysparkkernel
* reset kernel of feature_transformation_with_sagemaker_processing.ipynb to pysparkkernel
* reset hyperparams
* reset kernel of sagemaker-spark-processing.ipynb to pysparkkernel
* reset kernel of hpo_tensorflow2_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of hpo_bring_your_own_keras_container.ipynb to conda_tensorflow2_p36
* reset kernel of hpo_pytorch_mnist.ipynb to conda_pytorch_p36
* reset kernel of hpo_image_classification_warmstart.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of hpo_tensorflow2_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of hpo_pytorch_mnist.ipynb to conda_pytorch_p36
* reset kernel of HPO_Analyze_TuningJob_Results.ipynb to conda_mxnet_p36
* reset kernel of hpo_mxnet_mnist.ipynb to conda_mxnet_p36
* reset kernel of hpo_tensorflow_mnist.ipynb to conda_tensorflow2_p36
* reset hyperparams
* reset kernel of hpo_tensorflow2_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of hpo_pytorch_mnist.ipynb to conda_pytorch_p36
* reset kernel of tensorflow-serving-tfrecord.cli.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow-serving-tfrecord-python-sdk.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow-serving-jpg-python-sdk.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow-serving-jpg-cli.ipynb to conda_tensorflow2_p36
* reset kernel of working-with-tfrecords.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow-serving-cifar10-python-sdk.ipynb to conda_tensorflow2_p36
* reset kernel of 04c_pytorch_training.ipynb to conda_pytorch_p36
* reset hyperparams
* reset kernel of hpo_tensorflow2_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of 03c_pytorch_preprocessing.ipynb to conda_pytorch_p36
* reset kernel of 03a_builtin_preprocessing.ipynb to conda_mxnet_p36
* reset kernel of pt-resnet-profiling-multi-gpu-multi-node.ipynb to conda_pytorch_p36
* reset kernel of pt-resnet-profiling-single-gpu-single-node.ipynb to conda_pytorch_p36
* reset hyperparams
* reset kernel of pt-resnet-profiling-multi-gpu-single-node.ipynb to conda_pytorch_p36
* reset kernel of mxnet-spot-training-with-sagemakerdebugger.ipynb to conda_mxnet_p36
* reset kernel of tf2-keras-default-container.ipynb to conda_tensorflow2_p36
* reset kernel of tf2-keras-custom-container.ipynb to conda_tensorflow2_p36
* reset kernel of tf-mnist-builtin-rule.ipynb to conda_tensorflow2_p36
* reset hyperparams
* reset kernel of tf-resnet-profiling-single-gpu-single-node.ipynb to conda_tensorflow2_p36
* reset kernel of tf-resnet-profiling-multi-gpu-multi-node.ipynb to conda_tensorflow2_p36
* reset kernel of tf-resnet-profiling-multi-gpu-multi-node-boto3.ipynb to conda_tensorflow2_p36
* reset kernel of iterative_model_pruning_resnet.ipynb to conda_mxnet_p36
* reset kernel of iterative_model_pruning_alexnet.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of pytorch_byoc_smdebug.ipynb to conda_pytorch_p36
* reset kernel of tf-keras-custom-rule.ipynb to conda_tensorflow2_p36
* reset kernel of detect_stalled_training_job_and_actions.ipynb to conda_tensorflow2_p36
* reset kernel of tf-mnist-stop-training-job.ipynb to conda_tensorflow2_p36
* reset kernel of sagemaker_edge_example.ipynb to conda_tensorflow2_p36
* reset kernel of object_detection_birds.ipynb to conda_mxnet_p36
* reset kernel of Image-classification-fulltraining-highlevel.ipynb to conda_mxnet_p36
* reset kernel of tensorflow_serving_pretrained_model_elastic_inference.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_script_mode_quickstart.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_script_mode_training_and_serving.ipynb to conda_tensorflow2_p36
* reset hyperparams
* reset hyperparams
* reset kernel of mxnet_mnist_horovod.ipynb to conda_mxnet_p36
* reset kernel of tensorflow_keras_CIFAR10.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_serving_container.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_moving_from_framework_mode_to_script_mode.ipynb to conda_tensorflow2_p36
* reset kernel of Bring Your Own DL Framework to Amazon Sagemaker with Model Server for Apache MXNet's (MMS) BYO container.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of pytorch_local_mode_cifar10.ipynb to conda_pytorch_p36
* reset kernel of tf-eager-sm-scriptmode.ipynb to conda_tensorflow2_p36
* reset kernel of managed_spot_training_tensorflow_estimator.ipynb to conda_tensorflow2_p36
* reset kernel of pytorch_mnist_elastic_inference.ipynb to conda_pytorch_p36
* reset kernel of pytorch_mnist.ipynb to conda_pytorch_p36
* reset hyperparams
* reset kernel of tensorflow_script_mode_using_shell_commands.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_script_mode_pipe_mode.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_script_mode_horovod.ipynb to conda_tensorflow2_p36
* reset kernel of sparkml_serving_emr_mleap_abalone.ipynb to pysparkkernel
* reset kernel of tensorflow_bring_your_own.ipynb to conda_tensorflow2_p36
* reset hyperparams
* reset kernel of mask-rcnn-experiment-trials.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-inference.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-fsx.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-scriptmode-efs.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-s3.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-efs.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-scriptmode-experiment-trials.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-scriptmode-s3.ipynb to conda_tensorflow2_p36
* reset kernel of mask-rcnn-scriptmode-fsx.ipynb to conda_tensorflow2_p36
* reset kernel of unity_mlagents_learn.ipynb to conda_tensorflow2_p36
* reset kernel of pytorch_multi_model_endpoint.ipynb to conda_pytorch_p36
* reset kernel of tensorflow_BYOM_iris.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow_smmodelparallel_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow2_smdataparallel_maskrcnn_demo.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow2_smdataparallel_bert_demo.ipynb to conda_tensorflow2_p36
* reset kernel of infer_tensorflow.ipynb to conda_tensorflow2_p36
* reset kernel of tensorflow2_smdataparallel_mnist_demo.ipynb to conda_tensorflow2_p36
* reset kernel of smp_bert_tutorial.ipynb to conda_pytorch_p36
* reset kernel of pytorch_smmodelparallel_mnist.ipynb to conda_pytorch_p36
* reset kernel of pytorch_smdataparallel_maskrcnn_demo.ipynb to conda_pytorch_p36
* reset kernel of tensorflow_bring_your_own.ipynb to conda_tensorflow2_p36
* reset kernel of mxnet_onnx_ei.ipynb to conda_mxnet_p36
* reset kernel of pytorch_cnn_cifar10.ipynb to conda_pytorch_p36
* reset kernel of mxnet_sentiment_analysis_with_gluon.ipynb to conda_mxnet_p36
* reset kernel of mxnet_mnist_with_batch_transform.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of tensorflow_mnist.ipynb to conda_tensorflow2_p36
* reset kernel of keras_pipe_mode_horovod_cifar10.ipynb to conda_tensorflow2_p36
* reset kernel of gluoncv_ssd_mobilenet_neo_studio.ipynb to conda_mxnet_p36
* reset kernel of tensorflow_distributed_mnist_neo_studio.ipynb to conda_tensorflow2_p36
* reset kernel of Image-classification-fulltraining-highlevel-neo-studio.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of pytorch_torchvision_neo_studio.ipynb to conda_pytorch_p36
* reset kernel of tensorflow_distributed_mnist_neo_inf1_studio.ipynb to conda_tensorflow2_p36
* reset kernel of pytorch-vgg19-bn-studio.ipynb to conda_pytorch_p36
* reset kernel of sagemaker-neo-tf-unet.ipynb to conda_tensorflow2_p36
* reset kernel of Image-classification-transfer-learning-highlevel.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of Image-classification-lst-format-highlevel.ipynb to conda_mxnet_p36
* reset kernel of Image-classification-lst-format.ipynb to conda_mxnet_p36
* reset kernel of Image-classification-transfer-learning.ipynb to conda_mxnet_p36
* reset kernel of Image-classification-fulltraining-highlevel.ipynb to conda_mxnet_p36
* reset kernel of Image-classification-fulltraining.ipynb to conda_mxnet_p36
* reset hyperparams
* reset kernel of tf-mnist-builtin-rule.ipynb to conda_tensorflow2_p36
* reset kernel of mxnet-realtime-analysis.ipynb to conda_mxnet_p36
* reset kernel of mnist-tensor-plot.ipynb to conda_mxnet_p36
* reset kernel of mnist_tensor_analysis.ipynb to conda_mxnet_p36
* reset hyperparams
* update notebooks to TensorFlow 1.14
* update notebooks to TensorFlow 1.14
* update notebooks to TensorFlow 1.14
* change: split training and serving logic
This reverts commit bb0a5fcafa.
The previous change may have a conflict with a SageMaker python SDK older than
1.30.0.
The revert was verified locally to pass the existing notebook tests.
* modified tensorflow quickstart notebook example
* change the branch name used
* add a missing quote
* delete 2 extra ','
* add git_config when creating the second estimator
* fix some typos
* change url link to the specific branch
* addressed comments
* This change checks if current user has sudo access or not. If the
current user has sudo access then script is run as before and if
the current user doesn't have sudo access then skip the script.
* Run setup.sh file from Notebook with the change with and without root access
* With root access
```
The user has root access.
SageMaker instance route table setup is ok. We are good to go.
SageMaker instance routing for Docker is ok. We are good to go!
```
* Without root access
```
sudo: a password is required
The user does not have root access. Everything required to run the notebook is already installed and setup. We are good to go!
```
atqy