Data Science Lab
  • What is Data Science Lab?
  • Accessing the Data Science Lab Module
  • Data Science Lab Quick Start Flow
  • Project
    • Environments
    • Creating a Project
    • Project List
      • Keep Multiple Versions of a Project
      • Sharing a Project
      • Editing a Project
      • Activating a Project
      • Deactivating a Project
      • Deleting a Project
    • Tabs for a Data Science Lab Project
      • Tabs for TensorFlow and PyTorch Environment
        • Notebook
          • Ways to Access Notebook
            • Creating a Notebook
            • Uploading a Notebook
          • Notebook Page
            • Preview Notebook
            • Notebook Cells
              • Using a Code Cell
              • Using a Markdown Cell
            • Modifying a Notebook
            • Resource Utilization Graph
            • Notebook Taskbar
            • Notebook Operations
              • Datasets
                • Copy Path (for Sandbox files)
              • Secrets
              • Algorithms
              • Transforms
              • Models
                • Model Explainer
                • Registering & Unregistering a Model
                • Applying Filter
              • Predict
              • Artifacts
              • Variable Explorer
              • Find and Replace
          • Notebook List Page
            • Export
              • Export to Pipeline
              • Export to GIT
            • Notebook Version Control
            • Sharing a Notebook
            • Editing a Notebook
            • Deleting a Notebook
        • Dataset
          • Adding Data Sets
            • Data Sets
            • Data Sandbox
          • Dataset List Page
            • Preview
            • Data Profile
            • Create Experiment
            • Data Preparation
            • Delete
        • Utility
        • Model
          • Model Explainer
          • Import Model
          • Export to GIT
          • Register a Model
          • Unregister A Model
          • Register a Model as an API Service
            • Register a Model as an API
            • Register an API Client
            • Pass Model Values in Postman
          • AutoML Models
        • Auto ML
          • Creating Experiments
            • Accessing the Create Experiment Option
              • Configure
              • Select Experiment Type
          • AutoML List Page
            • View Report
              • Details
              • Models
                • View Explanation
                  • Model Summary
                  • Model Interpretation
                    • Classification Model Explainer
                    • Regression Model Explainer
                  • Dataset Explainer
            • Delete
      • Tabs for PySpark Environment
        • Notebook
          • Ways to Access Notebook
            • Creating a Notebook
            • Uploading a Notebook
          • Notebook Page
            • Preview Notebook
            • Notebook Cells
              • Using a Code Cell
              • Using a Markdown Cell
            • Modifying a Notebook
            • Resource Utilization Graph
            • Notebook Taskbar
            • Notebook Operations
              • Datasets
                • Copy Path (for Sandbox files)
              • Secrets
              • Variable Explorer
              • Find and Replace
          • Notebook List Page
            • Export
              • Export to Pipeline
              • Export to GIT
            • Notebook Version Control
            • Sharing a Notebook
            • Editing a Notebook
            • Deleting a Notebook
        • Dataset
          • Adding Data Sets
          • Dataset List Page
            • Preview
            • Data Profile
            • Data Preparation
            • Delete
        • Utility
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  1. Project

Environments

Allows users to select the environment they want to work in. Currently, supported Python frameworks are Sklearn (default), TensorFlow, PyTorch and PySpark.

PreviousProjectNextCreating a Project

Last updated 2 years ago

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.

Supported environments

  • 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. to get redirected to the quick start flow page.

Click here