Install a Notebook Instance
This guide will assist you in creating a notebook instance on the Greennode AI Platform for your data science and machine learning projects, where data scientists and engineers can write, execute, and iterate on code. It usually includes features for data exploration, visualization, and collaborative work.
It outlines the step-by-step process of creating a notebook instance on the platform, specifying instance configurations, recommending S3 mounting for efficient data access, and providing user security and maintenance considerations.
Step 1: Accessing the Notebook Instance
Log in to your Greennode AI Platform account and navigate to the Notebook instance dashboard at: https://aiplatform.console.greennode.ai/notebooks
Find and click on the "Create a notebook instance" button.
Step 2: Creating a Notebook Instance
Provide a suitable name for your instance, e.g., "MyMLInstance."
Choosing the location for this notebook instance.
- Specify a network volume: Select a network volume that you want to access from the notebook instance. This network volume will be mounted at pre-defined folder.
Mount directory: The mount directory is where you mount the s3 bucket to the notebook instance, and once mounted, your source code will be placed in that directory. Input exactly the path to the folder containing your source code within the notebook instance. e.g., “/home/aiproject”.
Git repository URL: Leave the path to your git repository in 2 cases
Public repository: Input full path access to your git repository, e.g., https://github.com/miguelgfierro/ai_projects.git“
Private repository: When accessing a private Git repository via HTTPS, you'll need to provide your Git credentials (username and password) for authentication. The URL format for accessing a private Git repository via HTTPS is typically: https://username:password@github.com/owner/repository.git. For example https://yourusername:yourpassword@github.com/owner/repository.git.
Mount directory: The mount directory is where you mount the s3 bucket to the notebook instance, and once mounted, your source code will be placed in that directory. Input exactly the path to the folder containing your source code within the notebook instance. e.g., “/home/aiproject”.
S3 Folder URL: Enter the URL of your S3 bucket and specify the path to the data folder or directory. If your S3 bucket requires authentication, provide your AWS credentials by following these steps:
Click on the key icon to open the popup “S3 object information”.
Region: Input the region of the S3 object, e.g., “ap-southeast-1”.
Endpoint: Input the S3 endpoint URL, e.g., “https://s3.ap-southeast-1.amazonaws.com”.
S3 URI: As the path to your S3 bucket, e.g., “s3://tult4-ai-platform/notebook-training/”.
Access key & Secret key: Input your access key & secret key belongs to the AWS user account, which is permitted to access the S3 object.
Click the “Save” button to complete the process.
5. Public key (optional)
- Public key: Fill the public key to ssh to this notebook instance, e.g., ssh-rsa AAAAB3Nxxx/JQOYYSatkSvSQDfQNJSEDXBV30thcbV9S OFffFsTvVtuh4iXsUSSIOG/AYBdV VNGCLOUD
Step 3: Initializing the Instance
Click the "Create Instance" button to initialize your notebook instance with the specified configurations at the bottom right corner to complete the process.
Step 4: Accessing the Notebook Instance
Once created successfully, access your notebook instance with “Running” status from the Notebook Dashboard.
Click on the “Open Editor” option to use your selected Code Editor for coding. It enables the user to upload their data stored locally to their Notebook Instance Cloud Server. Understand more about our third-party code editor at https://jupyterlab.readthedocs.io/en/latest/.
Security Considerations: Ensure that your notebook instance and any connected resources are adequately secured. Use strong credentials and enable encryption where necessary.
Stopping or Deleting the Instance:
Once you're done using the instance, make sure to stop it to avoid unnecessary charges.
If not in use for an extended period, consider deleting the instance to save resources and costs.
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