Environments
Allows users to select the environment they want to work in. Currently, supported Python frameworks are Sklearn (default), TensorFlow, PyTorch and PySpark.
Last updated
Allows users to select the environment they want to work in. Currently, supported Python frameworks are Sklearn (default), TensorFlow, PyTorch and PySpark.
Last updated
The user can select only single environment for one particular project. The project will have all the dependencies of the particular environment selected for in the project level.
TensorFlow: Users can execute Sklearn commands by default in the notebook. If the users select the TensorFlow environment, they do not need to install packages like the TensorFlow and Keras explicitly in the notebook. These packages can simply be imported inside the notebook.
PyTorch: If the users select the PyTorch environment, they do not need to install packages like the Torch and Torchvision explicitly in the notebook. These packages can simply be imported inside the notebook.
PySpark: If the users select the PySpark environment, they do not need to install packages like the PySpark explicitly in the notebook. These packages can simply be imported inside the notebook.
Please Note: Refer the Data Science Lab Quick Start Flow page to get an overview of the Data Science Lab module in nutshell. Click here to get redirected to the quick start flow page.
