Supported Environments
When creating a project in the Data Science Lab (DSLab), users must select the execution environment in which their code and models will run. The environment defines the runtime libraries, frameworks, and configurations available within the project.
Currently, the following environments are supported:
Python TensorFlow – Provides a preconfigured environment with TensorFlow libraries for developing and training deep learning models.
Python PyTorch – Provides a preconfigured environment with PyTorch libraries for building and experimenting with neural networks and deep learning workflows.
PySpark – Enables distributed data processing and machine learning using Apache Spark APIs.
Python – A general-purpose Python environment suitable for traditional machine learning, data analysis, and scripting.
BaseDS – A lightweight base environment that provides core data science libraries and utilities for custom experimentation.