Yandex DataSphere release notes
- Release on 27/03/2024
- Release on 01/03/2024
- Release on 27/02/2024
- Release on 29/01/2024
- Release on 15/01/2024
- Release on 20/12/2023
- Release on 10/10/2023
- Release on 25/09/2023
- Release on 18/09/2023
- Release on 21/07/2023
- Release on 20/06/2023
- Release on 23/05/2023
- Release on 29/03/2023
- Release on 24/03/2023
- Release on 02/03/2023
- Release on 19/01/2023
- Release on 20/10/2022
- Release on 23/09/2022
- Release 11.01.2022
- Release 18.11.2021
- Release 24.09.2021
- Release 16.02.2021
- Release 10.02.2021
- Release 24.12.2020
- Release 08.12.2020
- Release 23.11.2020
- Release 11.11.2020
- Release 01.10.2020
This section shows what changed in Yandex DataSphere.
Tip
To keep up to date with the latest changes and updates, subscribe to our Yandex DataSphere Community
Release on 27/03/2024
Model tuning in DataSphere now works based on the new YandexGPT Pro model.
Release on 01/03/2024
The Serverless mode is no longer supported.
Release on 27/02/2024
- Added the option to run a notebook in Dedicated mode to the API.
- Improved logs and metrics for nodes.
- Fixed bugs and added minor improvements in platform performance.
Release on 29/01/2024
- Updated the NVIDIA driver to version 535.
- Added support for multi-login to multiple organizations in various federations.
- Added the option to pause and resume a running node.
- Fixed bugs and added minor improvements in platform performance.
Release on 15/01/2024
- Added self-service problem-solving tools to the project page.
- In DataSphere Jobs, now you can use your project resources: secrets, S3 connectors, environment dockers, datasets, and project disk.
- Fixed bugs and added minor improvements in platform performance.
Release on 20/12/2023
- Added new configuration, gt4.1 (1 GPU NVIDIA T4).
- The g2.mig configuration (1 GPU MIG NVIDIA Ampere A100) is obsolete.
- A new node type from the model resource is available.
- Selecting a configuration in Dedicated mode will display its current availability.
- Fixed bugs and added minor improvements in platform performance.
Release on 10/10/2023
- You can test fine-tuned YandexGPT models right in DataSphere. YandexGPT DataSphere Playground is available after fine-tuning to users with access to YandexGPT.
- You can now connect your DataSphere project to JupyterLab from a local IDE.
- Fixed bugs and added minor improvements in platform performance.
Release on 25/09/2023
- With DataSphere Jobs, cloud computing resources in DataSphere can now be used from a user's local environment.
- DataSphere projects now have a new type of resources: Models.
- Optimized JupyterLab 3 (available in dedicated mode) by adding new extensions.
- YandexGPT model tuning is now available at the Preview stage.
- Fixed bugs and added minor improvements in platform performance.
Release on 18/09/2023
- A new DS Default (Python 3.10) system image is used by default.
- Community administrators can now manage permissions to use the functionality.
- Improved working with community and project lists.
- Fixed bugs and added minor improvements in platform performance.
Release on 21/07/2023
- Updated the JupyterLab extension for working with GIT.
- Community administrators can now manage permissions to use computing resources.
- Community and project members can now be added before they accept an invitation to join an organization.
- Improved the Docker image build editor.
- Added an example of operations with YandexGPT API to initial notebooks.
- The process of starting a project is now more obvious and transparent.
- Fixed bugs and added minor improvements in platform performance.
Release on 20/06/2023
- Added a page with a [list of all https://datasphere.yandex.cloud projects] of a user.
- Updated initial notebooks.
- Fixed bugs and added minor improvements in platform performance.
Release on 23/05/2023
- DataSphere now supports a new Dedicated operation mode.
- In the Dedicated mode, the IDE version is updated to JupyterLab 3.5.3.
- You can now select an organization in an optimized way.
- Operations with community and project members are now easier to perform.
- Fixed bugs and added minor improvements in platform performance.
Release on 29/03/2023
- You can now work with labels to label resources.
- Fixed bugs and added minor improvements.
Release on 24/03/2023
- Added a tool to migrate projects to the new DataSphere version.
- Fixed bugs and added minor improvements.
Release on 02/03/2023
- You can now use the new DataSphere version via the API.
- Fixed bugs and added minor improvements.
Release on 19/01/2023
- The service now displays inherited roles of community and project members.
- Optimized the advanced settings for projects.
- Updated the snippets for working with S3, Yandex Disk, and Google Drive.
- Fixed bugs and added minor improvements.
Release on 20/10/2022
Greatly improved the Apache Spark™ cluster functionality:
- DataSphere now has a new type of resources: Data Proc templates.
- You can now configure a livy session when using Data Proc clusters.
- DataSphere now supports the Spark SQL library.
Release on 23/09/2022
Meet our large DataSphere update: new interface, communities, resources, and many other features for ML development.
Release 11.01.2022
- Added new computing resource configurations:
- g2.mig (1 MIG NVIDIA Ampere A100)
- g2.2 (2 GPU NVIDIA Ampere A100)
- g2.4 (4 GPU NVIDIA Ampere A100)
- Updated the introductory
Welcome
notebooks in Russian and English. - Fixed bugs, added minor improvements.
Release 18.11.2021
- You can now resize project storage.
- You can now set usage limits on individual folders and projects to manage costs.
- Projects take less time to open.
- Fixed bugs, added minor improvements.
Release 24.09.2021
- You can now connect to S3 object storage from the interface.
- Added rapid model deployment from Python code cells.
Release 16.02.2021
- Added a new state saving mode: Autosave Commit Mode.
Release 10.02.2021
- The approach to state serialization has been revised. The old mode has changed and become a bit more intuitive, also now you can enable autosave mode.
- Added GPU usage indicator.
- Added the option to contact support from the service.
Release 24.12.2020
-
Introduced Early Access Version: a new mode of running DataSphere.
Early Access Version is a pre-release version of the system where all the key new features will be announced.
Note
How do I use it?
You can select the Early Access Version operating mode for your project in its entirety. To activate this mode, from the project menu, select File and then Enable Early Access Version.
You can revert to the regular mode anytime: click File in your project menu and select Disable Early Access Version.Where to view it:
To find out what's new in the release and how you can use it, see our new notebook: What's new in Early Access?.
-
Memory and CPU usage indicators added in DataSphere: CPU core and memory usage is now shown directly in the notebook interface.
-
TensorBoard support added.
-
Implemented asynchronous background execution of operations in specially designated cells.
Release 08.12.2020
- An introductory Welcome notebook has been released (in Russian). It explains how the service works and how you can quickly get started.
Release 23.11.2020
- Code completion fixed.
- Added support for
widgets
.
Release 11.11.2020
- Implemented support for TensorFlow version 2.x.
- Implemented support for updating pre-installed libraries, such as TensorFlow.
- Improved the algorithm for detecting changed variables: now only the actually changed variables are included in commits, which decreases the time needed to save the state.
- Added a snippet to work with the SPARK cluster.
Release 01.10.2020
-
Yandex DataSphere has become a paid generally available (GA) feature.
Per-second billing is used, so you pay only for the computing time (you are not charged for using the notebooks).
The minimum charge is one billing unit. One billing unit represents the cost of using one CPU core for one second.The number of units and the cost depend on the computing resource configuration.
-
Added the option to use the bash command.
The
%%bash
command is still available directly, but you can use its functionality as follows:- Specify
#!S:bash
in the cell headers (S
indicates the type of VM instance to run bash on).
Limitations:
- Background jobs are not supported, for example,
sshd
. - Launching
pip
is not supported. Continue to usemagic
for pip.
- Specify
-
Full-featured integration with Apache Spark™ is now available. You can compute on the existing Data Proc clusters or even create temporary Data Proc clusters from DataSphere directly.
-
Added versioning and support of checkpoints.
-
Added new configuration types:
M
(8 cores, 0 gpu) andXL
(32 cores, 4 gpu Nvidia v100).