Yandex DataSphere is a machine learning (ML) development environment that combines the familiar Jupyter® Notebook interface, serverless computing technology, and seamless use of different computing resource configurations. Yandex DataSphere helps significantly reduce the cost of machine learning compared to computing on your own hardware or other cloud platforms.
If you never used Jupyter Notebook, try it: notebooks are convenient as they help you execute code sequentially and immediately visualize the results. Notebooks are also a convenient tool for drafting analytical reports and papers: you can add comments between cells with code using the Markdown language.
Advantages of the service
Ready-to-use development environment
You don't need to spend time creating and maintaining VMs: when you create a new project, computing resources are automatically allocated for it.
The VM comes ready with the JupyterLab development environment and pre-installed packages for data analysis and ML (such as TensorFlow, Keras, and NumPy), which you can start using immediately. The full list of pre-installed packages.
If you're missing a package, you can install it right from the notebook.
Automatic maintenance of computing resources
The service automatically manages resource allocation. If you don't perform any computations, no resources are allocated. If you use early access features, your vCPU and memory usage are shown directly in the notebook interface.
Saving states at shutdown
If you close a notebook tab, the interpreter state, all variables, and computation results are saved. You can continue working when you reopen your project.
Secure storage of private data
To handle private data (such as passwords and keys), the service has a special tool, referred to as a vault, that enables you to store and process data in encrypted form. Learn more about the vault.
Managing computing resources
Different computing resources are required for different tasks. For some of them, a regular processor is enough, but for others, you need a GPU.
DataSphere supports different computing resource configurations. By default, a project launches with the minimum
c1.4 configuration (32 GB of RAM and 4 vCPUs).
You can change the configuration at any time when working in the notebook. The state of the interpreter is maintained.
You can share your results
To share your results:
- Publish a notebook as a report link in HTML format.
- Export your project as a ZIP file.
- Export a checkpoint.
Current service limitations
For more information about service limits, see Quotas and limits in DataSphere.
Early access mode
Try out new DataSphere features before they are released in early access mode and tell us what you think about them.
Contacting support from the service
To contact technical support in the service:
Click in the lower-right corner of the notebook window or select Report a bug in the Help menu.
In the window that opens, describe your problem in the Bug and Give us more detail fields.
Click Report a bug.
You'll receive your request number by email.