## Use Cases

1. Once you are logged into the Kubeflow dashboard, ensure you have the right namespace "kubeflow-user-example-com" on top of the drop down

<center><img src="img/1_kubeflow_dashboard.png" width="80%"/> </center><br/><br/>

2. Create Jupyter Notebook

2.1 Click on “Notebooks” on left navigation of the Kubeflow dashboard. Click on “New Notebook” button. Enter name as “aws-nb”, select “jupyter-pytorch:1.12.0-cpu-py38-ubuntu20.04-ec2-2022-09-20” as Jupyter Docker Image. Enter 1 for CPU and 5 as memory in Gi. Keep GPUs as None.


<center><img src="img/2.1_kubeflow_create_jupyter.png" width="80%"/> </center><br/><br/>

2.2 Do not change "Workspace Volume" and "Data Volumes" section. 

2.3 Select value "Allow access to Kubeflow Pipelines" in Configurations section and click LAUNCH . 


<center><img src="img/2.3_kubeflow_config_jupyter.png" width="80%"/> </center><br/><br/>

2.4 Verify if notebook is created successfully and CONNECT button is activated. It might takes couple of minutes to create a notebook.


<center><img src="img/2.4_notebook_success.png" width="80%"/> <center/><br/><br/>

2.5 Click on CONNECT button to log on to JupyterLab shown below. 


<center><img src="img/2.5_jupyterlab.png" width="80%"/> </center><br/><br/>


2.6 Clone the repo by entering `https://github.com/aws-samples/aws-deeplearning-labs.git` in "Clone a repo" field

<center><img src="img/2.6.1_git_clone1.png" width="80%"/> </center><br/><br/>


<center><img src="img/2.6.2_git_clone2.png" width="80%"/> </center><br/><br/>


2.6 Browse to "aws-deeplearning-labs/workshop/pytorch-distributed-training" folder


2.7 Run notebook “STEP1.0_create_pipeline_k8s_sagemaker.ipynb" to create a pipeline that runs and deploy models on Amazon SageMaker

2.8 Run notebook “STEP1.1_invoke_sagemaker_endpoint.ipynb” to invoke and test SageMaker model inference endpoint created in previous notebook

2.9 Run notebook “STEP2_simple_xgboost_training_pipeline.ipynb” to run simple XGBoost training pipline