If not already open, click the 1001 data icon at the upper part of the page to open the Files subpanel. Scroll down to the third cell, and select the empty line in the middle of the cell. įrom the notebook page, make the following changes: NOTE: If you run into any issues completing the steps to execute the notebook, a completed notebook with output is available for reference at the following URL. If the notebook is not currently open, you can start it by clicking the Edit icon displayed next to the notebook in the Asset page for the project: This initiates the loading and running of the notebook within IBM Watson Studio. On the New Notebook page, configure the notebook as follows:Įnter the name for the notebook (for example, ‘customer-churn-kaggle’).Įnter the following URL for the notebook:Ĭlick Create. This value must be imported into your notebook.Ĭreate a Jupyter Notebook for predicting customer churn and change it to use the data set that you have uploaded to the project. After you reach a certain threshold, the banner switches to “IBM Cloud Pak for Data”.Ĭlick New Deployment Space + to create your deployment space.Įnsure that you assign your storage and machine learning services to your space.Īfter it’s created, click the Manage tab to view the Space GUID. NOTE: You might notice that the following screenshots have the banner “IBM Cloud Pak for Data” instead of “IBM Watson Studio.” The banner is dependent on the number of services you have created on your IBM Cloud account. To create a deployment space, select View all spaces from the Deployments menu in the Watson Studio menu. Create deployment spaceĪ deployment space is required when you deploy your model in the notebook. NOTE: Current regions include: au-syd, in-che, jp-osa, jp-tok, kr-seo, eu-de, eu-gb, ca-tor, us-south, us-east, and br-sao. In this case, the service is located in Dallas, which equates to the us-south region. One way to determine this is to click on your service from the resource list in the IBM Cloud dashboard. You also must determine the location of your Watson Machine Learning service. IMPORTANT: The generated API Key is temporary and will disappear after a few minutes, so it is important to copy and save the value for when you need to import it into your notebook. Enter a name for your key, and then click Create.Ĭopy the API key because it is required when you run the notebook. From the main dashboard, click the Manage menu option, and select Access (IAM).Ĭlick Create an IBM Cloud API key. To access your Watson Machine Learning service, create an API key from the IBM Cloud console. To run the following Jupyter Notebook, you must first create an API key to access your Watson Machine Learning service, and create a deployment space to deploy your model to. NOTE: The Watson Machine Learning service is required to run the notebook. If you have finished setting up your environment, continue with the next step, creating the notebook. You must complete these steps before continuing with the learning path.
#Creating a jupyter notebook tutorial how to
This tutorial explains how to set up and run Jupyter Notebooks from within IBM® Watson™ Studio.