User Guide: Data Lab Environment Manager

Overview

This tool helps Data Lab users transition their Notebooks between different Python versions, manage projects, and handle Python packages installed across Python versions effectively. It features four main tabs: Projects Overview, Notebooks Overview, Packages, and Console Output.

Installation and Access

Installation

This tool is available on PyPI.

pip install seeq-data-lab-env-mgr

Accessing the Tool

After the installation, use the following code snippet in a Jupyter notebook to launch the tool:

from seeq.data_lab_env_mgr import DataLabEnvMgr
DataLabEnvMgr()

Features

1. Notebooks Overview

Purpose:
The Notebooks Overview section allows you to see a list of notebooks within the current project. Each Notebook shows its current kernel version.

Notebooks within the SPy Documentation folder are not included in this list.

How to Use:

• List of Notebooks: View all your notebooks.

• Current Kernel Information: See which kernel each notebook is currently using.

2. Projects Overview

Purpose:
The Projects Overview section helps you manage your projects by providing a list of projects you have access to, along with various filtering and sorting options.

How to Use:

• List of Projects: See all projects you have access to.

• Filters:

  • By Last Used: Filter projects based on when they were last accessed. Options include:

    • Last 1 month

    • Last 3 months

    • Last 6 months

    • Last 1 year

    • All time

  • By Scheduled Notebooks Status: Filter projects based on the status of scheduled notebooks:

    • All

    • Active

    • Stopped

    • None

  • By Project Type: Filter projects based on the type:

    • All: Displays all available projects without any filtering.

    • Data Lab: A standard Data Lab project.

    • Data Lab Functions: A Data Lab project specifically of type DATA_LAB_FUNCTIONS.

    • Data Lab Add-On: Data Lab projects that include Add-On Tools.

    • Data Lab Packaged Add-On: Data Lab Projects containing Add-On Tools installed using the Add-on Manager.

• Sort Projects: You can sort projects by the last updated date to quickly identify and update recently used projects.

• Project Access: Click on the project name to open the project in a new tab.

3. Packages

Purpose:
Manage Python package installations across different Python versions (locally, globally and system).

How to Use:

• Three Tabs for Package Management:

  1. Local Packages: Lists packages installed locally for each Python version.

  2. Global Packages: Lists packages installed globally, accessible by all users.

  3. System Packages: Lists pre-installed system packages, which cannot be modified.

• Table View:

  • Package Name Column: Displays the names of installed packages.

  • Python Version Columns: Shows the version of the package installed for each Python version or provides an "Install" button.

• Install Packages: If a package is installed in one Python version, you can install it in another version by clicking the "Install" button next to the respective Python version column.

• System Packages Placeholder: A placeholder "-" is displayed when a system package is missing.

• Install New Package:
  Located at the bottom of the Packages tab, this feature allows users to install new packages directly:

  • Enter the package name in the text box provided.

  • Choose the installation scope (local, global).

  • Select the Python version from the dropdown list.

  • Click the "Install" button to install the package.

Install Packages will always install the latest version of the package.

4. Console Output

Purpose:
Monitor the installation process of Python packages.

How to Use:

• When you click on an "Install" button for any package, the focus will switch to the Console Output tab.

• The installation log will be displayed here, showing the progress and any messages related to the package installation process.