Yandex Cloud
  • Services
  • Solutions
  • Why Yandex Cloud
  • Pricing
  • Documentation
  • Contact us
Get started
Language / Region
© 2022 Yandex.Cloud LLC
Yandex DataSphere
  • Getting started
  • Step-by-step instructions
    • All instructions
    • Project management
      • Creating a project
      • Choosing a Python version
      • Installing dependencies
      • Managing computing resources
      • Setting up consumption limits for a project
      • Setting up consumption limits for a folder
      • Resizing project storage
      • Changing a name or description
      • Deleting a notebook or project
    • Sharing a notebook
      • Publishing a notebook
      • Exporting a project
    • Working with a notebook
      • Running sample code in a notebook
      • Versioning. Working with checkpoints
      • Clearing the interpreter state
      • Working with Git
    • Managing Docker images
      • Docker image for a project
      • Docker image in a cell
    • Connecting to data sources
      • Connecting to a ClickHouse database
      • Connecting to a PostgreSQL database
      • Connecting to S3 storage
    • Setting up integration with Data Proc
    • Working with confidential data
      • Creating a secret
      • Referencing a secret
      • Updating a secret
      • Copying a secret
      • Destroying a secret
    • Launching distributed training
    • Deploying models
      • Creating a node from a Python code cell
      • Configuring the node environment
      • Queries to nodes
      • Deleting a node
  • Concepts
    • Overview
    • Project
    • List of pre-installed software
    • Available commands
    • #pragma service commands
    • Computing resource configurations
    • Integration with version and data control systems
    • Saving a state
    • Integration with Data Proc
    • Background operations
    • Datasets
    • Private data storage
    • Deploying models
    • Using TensorBoard in Yandex DataSphere
    • Distributed training
    • Cost management
    • Quotas and limits
  • Early access
    • Overview
    • Special background operations
  • Practical guidelines
    • All tutorials
    • Getting started with Yandex DataSphere
    • Voice biometrics
    • Evaluating the quality of STT models
    • Labeling audio files
    • Classification of images in video frames
    • Web analytics with funnels and cohorts calculated based on Yandex Metrica data
  • API reference
    • Authentication in the API
    • gRPC
      • Overview
      • AppTokenService
      • FolderBudgetService
      • NodeService
      • ProjectDataService
      • ProjectService
      • OperationService
    • REST
      • Overview
      • AppToken
        • Overview
        • validate
      • FolderBudget
        • Overview
        • get
        • set
      • Node
        • Overview
        • execute
      • Project
        • Overview
        • create
        • delete
        • execute
        • get
        • getCellOutputs
        • getNotebookMetadata
        • getStateVariables
        • getUnitBalance
        • list
        • open
        • setUnitBalance
        • update
  • Access management
  • Pricing policy
  • Public materials
  • Releases
  • Questions and answers
  1. Concepts
  2. Project

Project

Written by
Yandex Cloud

    A project is a JupyterLab development environment that runs on a Yandex Cloud VM. It includes the Jupyter Notebook editor and auxiliary tools.

    A notebook is an *.ipynb file that you are working with in the Jupyter Notebook editor. In this editor, you write code in cells and between them you can write explanations in Markdown. The code is run for each cell separately. Cells can be run in any order.

    Projects store the state of the interpreter, variables, installed packages, and much more. When you reopen your project, the notebook loads in the state you last saved it in.

    You can upload small amounts of data (up to 100 MB) to your DataSphere project over the interface. If you want to upload larger amounts of data, use your network storages or databases. For larger data volumes, it's also convenient to use datasets that are available in early access mode.

    DataSphere provides 10 GB of free storage for each project. If, when a cell is running, the amount of data exceeds the allocated storage, the cell is stopped. You can expand your primary storage. Additional space will have to be paid for separately. For more information about the main storage expansion costs, see Pricing for DataSphere.

    Note

    Some variables aren't serialized and therefore can't be saved. For example, a variable with a file open for writing: f = open("file.txt", "w").

    A warning is shown for these variables during the assignment: The following variables cannot be serialized:.

    The service automatically redirects you to the management console if you don't use the project for 20 minutes or close it. For more information about service limits, see Quotas and limits in DataSphere.

    See also

    • Step-by-step instructions
    • Quotas and limits in DataSphere

    Was the article helpful?

    Language / Region
    © 2022 Yandex.Cloud LLC