Platform For AI:Notebook Lab

Step 1: Open a notebook

1. Log on to the PAI console and select the target Region in the upper-left corner.

2. In the left-side navigation pane, click Workspaces. Click the name of the target workspace.

3. In the left-side navigation pane, choose Model Training > Data Science Workshop (DSW), and then click the Notebook tab.

Step 2: Select a code directory

Click Select Code Directory. Select an OSS bucket, create a new directory such as notebook_test, and select it. This directory stores your notebook files. After the code directory is configured, the notebook list scans this directory and displays all notebook files (*.ipynb) found at the OSS path.

Step 3: Create a notebook and develop code

1. Click Create Instance. This opens the notebook development environment and creates a new notebook file.

2. Edit the code. Click +Python to add a Python code cell to develop your code.

   

   The following is sample code:

   Sample code

   # Because Notebook files are decoupled from DSW instances, include the logic for downloading and installing environment dependencies in the Notebook file. This ensures that your code runs correctly when you switch between different DSW instances.
   !pip install pandas
   
   # 1. Import the pandas library and use pd as its alias.
   import pandas as pd
   
   # 2. Create the raw data.
   # Use a Python dictionary to create the data. The keys of the dictionary are column names, and the values are data lists.
   data = {
       'Fruit': ['Apple', 'Banana', 'Orange', 'Strawberry', 'Blueberry'],
       'Sales(kg)': [120, 200, 150, 80, 50],
       'Price(CNY/kg)': [8.5, 3.2, 5.0, 15.0, 25.0]
   }
   
   # 3. Create a DataFrame.
   # A DataFrame is the core data structure in Pandas. You can think of it as a two-dimensional table.
   df = pd.DataFrame(data)
   
   # 4. Display the data.
   df.show()

   Note

   • Install dependencies first: Because Notebook files and DSW instances are separate, include the logic for downloading and installing environment dependencies in the Notebook file. This ensures that your code runs correctly when you switch between different DSW instances.

   • Save your code: Notebook Lab does not automatically save your code by default. Save your code promptly after making changes.

Step 4: Associate a DSW instance and run the code

1. Click Select a DSW instance and choose a running instance. To improve resource utilization, multiple notebooks can be associated with the same DSW instance.

2. Click the icon to run the code.

   

   If the kernel fails to load in the Select Kernel section in the upper-right corner of the Notebook, refresh your browser to reload the environment.

   Sample code output: