This guide will walk through the creation of a new SageMaker Notebook Instance, the configuration of IAM policies, an S3 bucket and your first project with Amazon Forecast. The Notebook Instance can then be used again for additional exploratory work with Amazon Forecast.

First you will need to create a new Notebook Instance, to do that begin by logging into the AWS Console.

Next, ensure you are in the us-east-1 region, do do that look in the top left corner, if it says N.Virginia next to support that is correct, otherwise select N.Virginia from the drop-down.

Under Find services in the main body of the page, enter SageMaker, then select it from the drop-down.

To the left, will see a category titled Notebook inside that, click Notebook instances.

Click the orange Create notebook instance button.

Give the instance a name unique in the account you are using. If a shared account, place your name first like FirstNameLastNameForecastDemo. The default Instance type is fine.

The Next component to change is the IAM role. Under the drop-down click Create a new role. Then for S3, select Any S3 Bucket, finally click Create role. Note that the role itself has become a link. Open that link in a new tab.

Here you will update the policies of your instance to allow it to work with Forecast. Click the Attach policies button. Search and check the box next to the following policies: -IAMFullAccess -AmazonForecastFullAccess

Finally click the Attach policy button on the bottom right corner.

Now click on Trust relationship tab > click on Edit trust relationships button > update the json file with the following: "Service": [ "forecast.amazonaws.com", "sagemaker.amazonaws.com" ]

Next click the Create policy button at the top. In the new page, click the JSON tab.

Erase all of the content that is in the editor and paste the content in IAM_Policy.json.

After pasting, click the Review policy button. Give the policy again a personalize name like FirstNameLastNameForecastIAMPolicy.

For the description, enter in something about it being used to demo Forecast. Finally click Create policy. Close this tab or window.

Once closed you should see the tab for adding permissions to your SageMaker role. Click the Filter Policies link, then select Customer managed. After that, you should see the policy you just created, if the list is long, just paste the name in the search bar to reduce the number of items. If you do not see it still, click the refresh icon in the top right of the page.

After clicking the checkbox next to the policy, click Attach policy at the bottom of the page. Then close this window.

Back at the SageMaker Notebook Instance creation page, now click Create notebook instance at the bottom of the page. This process will take 5-10 minutes to complete. Once the status says InService you are ready to continue to the next session.

To begin, click Open Jupyter, this will take you to the default interface for the Notebook Instance.

Click New then click Terminal, this will open a BASH shell for you on this instance.

Enter the following commands to clone this repository onto this instance:

cd SageMaker
git clone https://github.com/pedrojpaez/forecastworkshop.git

After that close your Terminal tab and go back to the main Notebook interface.

A new folder titled amazon-forecast-samples should be visible, click it, click notebooks, then click ForecastDeepAR.ipynb this will open the notebook.

If prompted for a kernel, select conda_python3.

From here you will follow the instructions outlined in the notebook.

Read Every Cell FULLY Before Executing It