Environments

In the context of Datalayer, an Environment refers to the collection of Python libraries, resources (CPU, GPU, Memory...) and storage requirements that your Jupyter Kernels utilize to execute your code.

When launching a Kernel, you'll be prompted to select an Environment.

📄️ Platform Environments

Datalayer supports a range of predefined environments to help you quickly leverage powerful GPU and CPU capabilities, enabling you to focus on your Data Science projects without unnecessary setup time.

📄️ User Environments

This upcoming feature will allow users to define and manage their own sets of libraries, data and configurations, providing the flexibility to create tailored environments that meet the unique requirements of your projects.