AWS SageMaker Demo

This is a repo to store my implementation and steps using SageMaker (AWS). Objectives in the end:

• Create notebook instance;
• Save dataset to a default Amazon S3 bucket automatically paired with the SageMaker session;
• Submit a training job of an ML model to Amazon EC2;
• Deploy the trained model for prediction by hosting

Dataset

I'm using the sklearn.datasets.load_digits that consists of digits with 8×8 size, it means 64 features (pixels) and 1 target variable (the number).

SageMaker

Create notebook instance

1. Go to https://console.aws.amazon.com/sagemaker/ to create the jupyter instance
2. For the first time, we need to create a single user click on Set up for single user, and we have to wait for the platform to create our Domain.
3. Now, click on Notebook → Notebook instances → Create notebook instance
4. Type the Notebook instance name and select the cheapest Notebook instance type (ml.t2.medium)
5. In IAM role choose Create new role and Create role
6. In the end: Create notebook instance

Create a Jupyter Notebook

1. Wait until the Status is In-Service
2. Click on Open JupyterLab and will open the notebook in your browser
3. I'm using the kernel conda_python3
4. I download the Jupyter Notebook that I used in my AWS - SageMaker. You can find it here: SageMaker-Predict-Digit.ipynb. The superficial flow representation of the notebook is shown below:

graph TD;
    A["Download dataset"] --> B["Load dataset"];
    B --> C["Show samples & classes frequencies"];
    C --> D["Transform into Pandas DataFrame"];
    D --> E["Save on CSV in a bucket S3"];
    E --> F["Split dataset (train, val, test)"];
    F --> G["Train the model (XGBoost from images_uris)"];
    G --> H["Deploy the model (endpoint)"];
    H --> I["Evaluate the model"];

Clean up

To avoid incurring unnecessary charges, use the AWS Management Console to delete the endpoints and resources that you created while running. I deleted everything created as shown below:

1. Go to the SageMaker console:

   a. Under Inference, choose Endpoints and delete.

   

   b. Under Inference, choose Endpoint configurations and delete.

   

   c. Under Inference, choose Models and delete.

   

   d. Under Notebook, choose Notebook instances and delete.

   

2. Go to the S3 console and delete the bucket created (you need emptying before deleting)

3. Go to the CloudWatch console → Log Groups and delete everything starting with /aws/sagemaker/

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"PEOPLE'S DREAMS WILL NEVER END" - OP

_{Created by BrenoAV}