Create Project
This page explains project creation steps for a Data Science Lab Project.
What is a Project?
A Data Science Project created inside the Data Science Lab is like a Workspace inside which the user can create and store multiple data science experiments and their associated artifacts.
Creating a new Project
Check out the given illustration on how to create a DSL Project.
Pre-requisite: The users must have the following Admin-level settings configured to access and use the Repo Syncs Project functionality inside the DS Lab module.
Configuring the DS Lab Settings option is mandatory before beginning with the Data Science Project creation.
Also, select the Algorithms by using the Algorithms field from the DS Lab Settings section you wish to use for your DS Lab project.
The user must have the following Version Control settings done.
The token key has to be configured for the DS Lab module.
The repository and branch have to be specified to save the settings.
The user must complete the following Custom Field Settings:
Token key – bdbvcstoken
User id key - bdbvcsuserid
The user must do the following User-level configuration to create a Repo Sync DS Lab project.
Steps to create a new DSL Project
Navigate to the Home page of the Data Science Lab module.
Click the Create icon from the homepage.
The Create Project or Feature Store drawer opens.
Click the Create option provided for the Project.
The Create Project opens to provide the related information for a new Project.
Provide the following details for a new project:
Project Name: Give a name to the new project.
Project Description: Describe the project.
Select Algorithms: Select algorithms using the drop-down menu.
Environment: Allows users to select the environment they want to work in. Currently, supported Python frameworks are Sklearn (default), TensorFlow, and PyTorch (The user can execute Sklearn commands by default in the notebook).
Users who select the TensorFlow environment do not need to install packages like the TensorFlow and Keras explicitly in the notebook. These packages can be imported inside the notebook.
Users who select the PyTorch environment do not need to install packages like Torch and Torchvision in the notebook. These packages can be imported inside the notebook.
The users can select an option from the given choices: 1. Python Tensor Flow, 2. Python PyTorch
Resource Allocation: This allows the users to allocate CPU/ GPU and memory to be used by the Notebook container inside a given project. The currently supported Resource Allocation options are Low, Medium, and High.
Idle Shutdown: It allows the users to specify the idle time limit after which the notebook session will get disconnected, and the project will be deactivated. To use the notebook again, the project should be activated. The supported Idle Shutdown options are 30m, 1h, and 2h.
External Libraries: Mention the names of external libraries (if a specific version is required then mention the library name with the version number) that must be installed in your DSL project /notebook. The names of the external libraries should be separated only by commas (without space) for this field. This is an optional field.
After you fill in the mandatory fields the following modifiable fields appear with pre-selected values:
Image Name
Image Version
Limit
Memory
Request (CPU)
Memory
Git Project: Select a project from the drop-down menu.
Git Branch: Select a branch option from the drop-down menu (The supported branches are main, migration, and version).
GPU Type: Select GPU type from the drop-down menu (Currently we support Nvidia as the GPU Type).
GPU Limit: Set the GPU limit using this field (This field appears only after the GPU Type option is selected).
Sync git repo at project creation: Put a checkmark in the given checkbox to avail of sync git repo while creating a DS Lab project.
Please Note:
Click the Save option.
The confirmation message appears.
The newly created project gets saved, and it appears on the screen.
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