How to use Jupyter Notebook via JupyterHub

In this tutorial, you will learn how to use Jupyter Notebook via JupyterHub, and run an example code.

 

Step 1: Install JupyterHub and open the Notebook server

  • JupyterHub can be installed from the QTS App Center.
  • Launch and log in to JupyterHub.
  • Click the switch from Off to “On” to start the Notebook server.
  • The interface will appear as following:
  1. “Running”: Check started instances
  2. “Upload”: Upload local files to the server
  3. “New”: Open a new Notebook, terminal or folder
  4. “Admin”: Switch to the admin page (administrator accounts only)
  5. Sign out of Jupyter Notebook
  • If a Notebook is running, click “Running” to view the following page. You can also click “Shutdown” to close it.
  • Administrators can enter the “”Admin” page and access a user’s Notebook.

 

Step 2: Run example code

  • Choose “Jupyter_example” on the list.
  • Open “example.ipynb”.
  • A Python example code will be opened on a new Notebook.
    This program can train a Convolutional Neural Network via Keras, which is a high-level neural networks API, for handwritten digit recognition in MNIST dataset.
    For more information, visit:
    Keras: https://keras.io/
    MNIST: http://yann.lecun.com/exdb/mnist/
  • The example code has been executed and saved. You can also run it again.
    • Click “Run” to execute a specific section or run it sequentially.
    • Click “Cell” and choose “Run All” to execute complete code.
    • For more Notebook tutorials, visit http://jupyter.org/documentation
  • The program does the following:
    • At the beginning, required libraries are imported.
      Import Keras libraries

      Import other Python libraries

    • Load MNIST dataset

      Randomly pick and check an image-label pair

    • Preprocess the training set
      Reshape and normalize training images

      One-hot encode training labels

    • Create a Sequential Model layer by layer
    • Use the Adam optimizer and choose categorical cross entropy as the objective function to train the model. The following part runs for a few seconds.
    • Evaluate the model using the test set. Although the accuracy on training set is higher than 99%, the accuracy on the test set may slightly decrease.
    • Finally the testing results are displayed.

Fore more tutorial, please refer to https://qiot.qnap.com/blog/en/category/learn/

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