[Examples] Revise GS-GB-SM example (#1171)

*Issue #, if available:*

*Description of changes:*
Revising readme, changing image names, fix typo.

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examples/sagemaker-pipelines-graphbolt/README.md

@@ -148,6 +148,8 @@ This will create the tabular graph data on S3 which you can verify by running
 
 ```bash
 aws s3 ls s3://$BUCKET_NAME/ogb-arxiv-input/
+```
+```
                            PRE edges/
                            PRE nodes/
                            PRE splits/
@@ -254,11 +256,11 @@ docker -v
 
 cd ~/graphstorm
 
-bash ./docker/build_graphstorm_image.sh --environment sagemaker --device cpu
+bash ./docker/build_graphstorm_image.sh --environment sagemaker --device cpu --image graphstorm-example-sagemaker-pipeline
 
-bash docker/push_graphstorm_image.sh -e sagemaker -r $REGION -a $ACCOUNT_ID -d cpu
+bash docker/push_graphstorm_image.sh -e sagemaker -r $REGION -a $ACCOUNT_ID -d cpu -i graphstorm-example-sagemaker-pipeline
 # This will push an image to
-# ${ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/graphstorm:sagemaker-cpu
+# ${ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-cpu
 ```
 
 Next, you will create a SageMaker Pipeline to run the jobs that are necessary to train GNN models with GraphStorm.
@@ -274,7 +276,7 @@ In this section, you will create a [Sagemaker Pipeline](https://docs.aws.amazon.
 ```bash
 PIPELINE_NAME="ogbn-arxiv-gs-pipeline"
 
-bash deploy_papers100M_pipeline.sh \
+bash deploy_arxiv_pipeline.sh \
     --account $ACCOUNT_ID \
     --bucket-name $BUCKET_NAME --role $SAGEMAKER_EXECUTION_ROLE_ARN \
     --pipeline-name $PIPELINE_NAME \
@@ -321,7 +323,7 @@ Every pipeline execution that shares the same input arguments will be under a ra
 Note that the particular execution subpath might be different in your case.
 
 ```bash
-aws s3 ls  s3://$BUCKET_NAME/pipelines-output/ogbn-arxiv-gs-pipeline/
+aws s3 ls s3://$BUCKET_NAME/pipelines-output/ogbn-arxiv-gs-pipeline/
 
 # 761a4ff194198d49469a3bb223d5f26e
 
@@ -439,9 +441,11 @@ For this job you will use large GPU instances, so you will build and push the GP
 ```bash
 cd ~/graphstorm
 
-bash ./docker/build_graphstorm_image.sh --environment sagemaker --device gpu
+bash ./docker/build_graphstorm_image.sh --environment sagemaker --device gpu --image graphstorm-example-sagemaker-pipeline
 
-bash docker/push_graphstorm_image.sh -e sagemaker -r $REGION -a $ACCOUNT_ID -d gpu
+bash docker/push_graphstorm_image.sh -e sagemaker -r $REGION -a $ACCOUNT_ID -d gpu -i graphstorm-example-sagemaker-pipeline
+# This will push an image to
+# ${ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-gpu
 ```
 
 ### Deploy and execute pipelines for papers-100M

examples/sagemaker-pipelines-graphbolt/deploy_arxiv_pipeline.sh

@@ -88,8 +88,8 @@ TASK_TYPE="node_classification"
 INFERENCE_MODEL_SNAPSHOT="epoch-9"
 
 JOBS_TO_RUN="gconstruct train inference"
-GSF_CPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm:sagemaker-cpu
-GSF_GPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm:sagemaker-gpu
+GSF_CPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-cpu
+GSF_GPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-gpu
 VOLUME_SIZE=50
 
 if [[ -z "${PIPELINE_NAME-}" ]]; then

examples/sagemaker-pipelines-graphbolt/deploy_papers100M_pipeline.sh

@@ -88,8 +88,8 @@ TRAIN_GPU_INSTANCE="ml.g5.48xlarge"
 GCONSTRUCT_INSTANCE="ml.r5.24xlarge"
 NUM_TRAINERS=8
 
-GSF_CPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm:sagemaker-cpu
-GSF_GPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm:sagemaker-gpu
+GSF_CPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-cpu
+GSF_GPU_IMAGE_URI=${ACCOUNT}.dkr.ecr.$REGION.amazonaws.com/graphstorm-example-sagemaker-pipeline:sagemaker-gpu
 
 GCONSTRUCT_CONFIG="gconstruct_config_papers100m.json"
 GRAPH_CONSTRUCTION_ARGS="--num-processes 16"