Create a notebook instance with pre-built container

Create a notebook instance with pre-built container

A pre-built notebook instance is a managed environment where you can easily run Jupyter notebooks with access to Greennode AI resources. These instances are pre-configured with the computational resources you choose and can be used for data analysis, machine learning, and much more. It might be sufficient when:

  1. You only need the standard tools and libraries provided by the platform.
  2. You don't require any specific customizations or optimizations.
  3. You prioritize ease of use and quick setup.
This guide will assist you in creating a notebook instance on the Greennode ML 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.

Step 1: Accessing the Notebook Instance

Step 2: Creating a Notebook Instance

1. Basic Configuration

  • Provide a suitable name for your instance, e.g., "MyMLInstance."

  • Choosing the location for this notebook instance.

2. Resource configuration
Specify the instance type for your notebook instance including CPU, GPU, and RAM configurations based on your workload. This selection will affect on your cost, please only select the instance type that is suitable for your demands.

3. Image
Select the option Pre-built container to use as a supported framework s like TensorFlow, PyTorch for your ML tasks and do not forget to specify the version of them. There are an available list of defined images compatible with your selection for instance type at step 2.2

4. Data mount (optional)
      4.1 Network volume (optional)
To read and write data from/to an network volume in the concept of notebook instance, follow these steps:
  1. 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.
      4.2 Git repository (optional)
To fetch source code or any related data stored on Git Repository, follow these steps:
      4.3 S3 bucket (optional)
If your related data is stored in an S3 bucket, follow these steps:

  • 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)

Import a Public key to later authenticate logins to this notebook instance.
  1. 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/.

NOTE

  • 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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