Fixing outofdate readme.md for heterogeneous clusters feature (aws#3617)

* initial commit

* notebook fix and misspelling

* add link from root readme.md

* switching cifar-10 to artificial dataset for TF

* adding retries to fit()

* grammer fixes

* remove cifar references

* Removing local tf and pt execution exmaples

* Add security group info for private VPC use case

* Adding index.rst for heterogeneous clusters

* fix PT notebook heading for rst

* fix rst and notebook tables for rst

* Adding programmatic kernel restart

* removing programmatic kernel restart - breaks CI

* Remove tables that don't render in RST

* updating outofdate readme.md

training/heterogeneous-clusters/README.md

@@ -1,38 +1,30 @@
-# Heterogeneous Clusters
-SageMaker Training Heterogeneous Clusters allows you to run one training job 
-that includes instances of different types. For example a GPU instance like 
-ml.p4d.24xlarge and a CPU instance like c5.18xlarge. 
+# SageMaker Heterogeneous Clusters for Model Training
+In July 2022, we [launched](https://aws.amazon.com/about-aws/whats-new/2022/07/announcing-heterogeneous-clusters-amazon-sagemaker-model-training/) heterogeneous clusters for Amazon SageMaker
+model training, which enables you to launch training jobs that use different instance types and
+families in a single job. A primary use case is offloading data preprocessing to 
+compute-optimized instance types, whereas the deep neural network (DNN) process continues to
+run on GPU or ML accelerated instance types. 
 
-One primary use case is offloading CPU intensive tasks like image 
-pre-processing (data augmentation) from the GPU instance to a dedicate 
-CPU instance, so you can fully utilize the expensive GPUs, and arrive at 
-an improved time and cost to train.
-
-You'll find TensorFlow (tf.data.service) and PyTorch (a customer gRPC based distributed data loading) examples on how to utilize Heterogeneous clusters in your training jobs. You can reuse these examples when enabling your own training workload to use heterogeneous clusters.
+In this repository, you'll find TensorFlow (tf.data.service) and PyTorch (a custom gRPC based distributed data loading) examples which demonstrates how to use heterogeneous clusters in your SageMaker training jobs. You can use these examples with minimal code changes in your existing training scripts.
 
 ![Hetero job diagram](tf.data.service.sagemaker/images/basic-heterogeneous-job.png)
 
 ## Examples:
 
-### TensorFlow examples
-- [**TensorFlow's tf.data.service running locally**](tf.data.service.local/README.md):
-This example runs the tf.data.service locally on your machine (not on SageMaker). It's helpful in order to get familiar with tf.data.service and to run small scale quick experimentation.
+### Hello world example
+- [**Heterogeneous Clusters - a hello world example**](hello.world.sagemaker/helloworld-example.ipynb):
+This basic example runs a heterogeneous training job consisting of two instance groups. Each group includes a different instance_type. 
+Each instance prints its instance group information and exits. 
+Note: This example only shows how to orchestrate the training job with instance type. For actual code to help with a distributed data loader, see the TensorFlow or PyTorch examples below.
 
-- [**TensorFlow's tf.data.service with Amazon SageMaker Training Heterogeneous Clusters**](tf.data.service.sagemaker/hetero-tensorflow-restnet50.ipynb):
-This TensorFlow example runs a Homogenous trainign job and compares its results with a Heterogeneous Clusters SageMaker training job that runs with two instance groups:
-  - `data_group` - this group has two ml.c5.18xlarge instances to which data augmentation is offloaded.
-  - `dnn_group` - Running one ml.p4d.24xlarge instance (8GPUs) in a horovod/MPI distribution.
+### TensorFlow examples
+- [**TensorFlow's tf.data.service based Amazon SageMaker Heterogeneous Clusters**](tf.data.service.sagemaker/hetero-tensorflow-restnet50.ipynb):
+This TensorFlow example runs both Homogeneous and Heterogeneous clusters SageMaker training job, and compares their results. The heterogeneous cluster training job runs with two instance groups:
+  - `data_group` - this group has two ml.c5.18xlarge instances to which data preprocessing/augmentation is offloaded.
+  - `dnn_group` - this group has one ml.p4d.24xlarge instance (8GPUs) in a horovod/MPI distribution.
 
 ### PyTorch examples
-- [**PyTorch with gRPC distributed dataloader running locally**](pt.grpc.local/README.md):
-This Pytorch example runs a training job split into two processes locally on your machine (not on SageMaker). It's helpful in order to get familiar with the GRPC distributed data loader and to run small scale quick experimentation.
-
-- [**PyTorch with gRPC distributed dataloader Heterogeneous Clusters training job example**](pt.grpc.sagemaker/hetero-pytorch-mnist.ipynb):
-This PyTorch example runs a Hetero SageMaker training job that uses gRPC to offload data augmentation to a CPU based server.
-
-
-### Hello world example
-- [**Hetero Training Job - Hello world**](hello.world.sagemaker/README.md):
-This basic example run a heterogeneous training job consisting of two instance groups. Each group includes a different instance_type. 
-Each instance prints its instance group information and exits. 
-Note: This example only shows how to orchastrate the training job with instance type, for actual code to help with a distributed data loader, see the TF or PT examples below.
+- [**PyTorch and gRPC distributed dataloader based Amazon SageMaker Heterogeneous Clusters**](pt.grpc.sagemaker/hetero-pytorch-mnist.ipynb):
+This PyTorch example enables you to run both Homogeneous and Heterogeneous clusters SageMaker training job. We then compare their results, and understand price performance benefits. 
+  - `data_group` - this group has one ml.c5.9xlarge instance for offloading data preprocessing job.
+  - `dnn_group` - this group has one ml.p3.2xlarge instance