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JupyterLite

JupyterLite is a responsive python notebook environment we provide for your learners' best experience.

Using JupyterLite​

Installing Packages​

Installing packages in JupyterLab is usually done with a package manager such as pip, conda or mamba like:

!pip install numpy pandas
!conda install -c conda-forge numpy pandas
!mamba install numpy pandas

In JupyterLite you simply use the %pip magic command:

%pip install numpy pandas

Downloading Data​

You may be accustomed to loading data the following way:

import pandas as pd

URL = 'https://www.url.to/my/dataset.csv'

df = pd.read_csv(URL)

In JupyterLite, you must use the following method instead:

import pandas as pd
import skillsnetwork

URL = 'https://www.url.to/my/dataset.csv'

await skillsnetwork.download_dataset(URL)
df = pd.read_csv('dataset.csv')

Should you use JupyterLite?​

For python-notebook labs, we recommend you use JupyterLite for your learners' best experience.

However, sometimes JupyterLite may not be an option for you - we lay out these cases in the next sections.

For more information on why JupyterLite is preferred over JupyterLab see JupyterLite vs JupyterLab.

Unsupported Libraries​

The following libraries are currently unsupported in JupyterLite If your lab requires them, you should use JupyterLab instead:

  • pytorch
  • tensorflow
  • requests
  • wordcloud
  • pyspark
  • sqlite3

Additional Caveats​

Even if your lab uses libraries supported by JupyterLite, there are specific scenarios where you may want to consider using JupyterLab:

  • Your lab has highly cpu-intensive code cells.
    • For example, sklearn.manifold.TSNE.fit_transform was found to be impractical in JupyterLite.
  • Large datasets (>100MB+) are being used.
    • Loading large datasets may be inconvenient for your learners on jupyterlite - they will load faster with JupyterLab.