Environments
In the context of Datalayer, an environment refers to the collection of Python libraries and hardware requirements that your Jupyter Kernels utilize to execute your code. We offer 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.
Predefined Environments
Our predefined environments come equipped with popular data science libraries such as PyTorch, HuggingFace, Langchain and more. You can explore the full list and details of these predefined environments under the "Environments" tab in Datalayer once you are logged in.
If you need a specific library that is not included in our predefined environments, you can always install it directly within your Jupyter Notebook using the !pip install command.
We are currently working on enabling custom 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!
Environments and Kernels
When launching a remote kernel (see Kernels for more details), you'll be prompted to select an environment.
