Platform For AI:Notebook Lab

Step 1: Access Notebook Lab

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

2. In the navigation pane on the left, click Workspaces, then click the name of your target workspace.

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

Step 2: Select a code directory

Click Select Code Directory. Select an OSS bucket and create a directory such as notebook_test to store your notebook files. The notebook list displays all *.ipynb files in this directory.

Step 3: Create a notebook and write code

1. Click Create Instance to open the development environment and create a notebook file.

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

   Example:

   Sample code

   # Since notebook files are decoupled from DSW instances, we recommend including dependency installation logic in your notebook file.
   # This ensures your code runs correctly when you switch between different DSW instances.
   !pip install pandas
   # 1. Import the pandas library and alias it as pd.
   import pandas as pd
   # 2. Create the source data.
   # Use a Python dictionary to create data, where keys are column names and 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 and can be thought of as a two-dimensional table.
   df = pd.DataFrame(data)
   # 4. Display the data.
   df

   Note

   • Install dependencies first: Include dependency installation commands in each notebook so your code runs correctly when switching between instances.

   • Save your code: Notebook Lab does not auto-save. Save your changes manually.

Step 4: Connect an instance and run code

1. Click Associate DSW instance, then select a Running instance.

2. Click the icon to run the code.

   # Since notebook files are decoupled from DSW instances, we recommend including dependency installation logic in your notebook file.
   # This ensures your code runs correctly when you switch between different DSW instances.
   !pip install pandas
   # 1. Import the pandas library and alias it as pd.
   import pandas as pd

   If the kernel fails to load from the Select Kernel menu, refresh your browser.

   Output:

   Pandas Version: 2.3.3
      Fruit  Sales (kg)  Price (CNY/kg)
   0  Apple      120       8.5
   1  Banana      200       3.2
   2  Orange      150       5.0
   3  Strawberry       80      15.0
   4  Blueberry       50      25.0