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 Runtimes utilize to execute your code.
When launching a Runtime, 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 Machine Learning projects without unnecessary setup time.
Platform Environments come equipped with popular Machine Learning libraries such as PyTorch, HuggingFace, Langchain and more.
If you need a specific library that is not included in the Platform Environments, you can install it directly within your Runtime using the !pip install command.
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.
Stay tuned for updates on this exciting feature!