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Intelligent Engine Release Notes

This page lists the Release Notes for Intelligent Engine, so that you can learn its evolution path and feature changes.

2024-07-31

v0.7.0

Features

  • Added support for Datasets to query preheating progress after dataset creation, along with a quick debug entry.
  • Added support for Training Tasks to create both single-machine and distributed tasks with MxNet.
  • Added support for Training Tasks to create MPI distributed tasks.
  • Added support for Training Tasks to use a default image, standardizing the use of base images.
  • Added support for Training Tasks to configure the startup command directly with a startup script.
  • Added support for Training Tasks to specify the working directory location for run parameters.
  • Added support for Inference Tasks to display example documentation for API calls in the details.
  • Improved the Env Management list to show the package managers and Python versions available in the environment.

2024-07-10

v0.6.1

Fixes

  • Fixed an issue where Inference create services using the Triton framework lacked the vLLM option.

2024-06-30

v0.6.0

Features

  • Added support for creating Code type Notebook, providing a native VS Code development experience.
  • Added support for quickly copying Notebook.
  • Added when selecting a worker cluster, display the cluster's status information, making it unselectable if it is disconnected or offline.
  • Added support for using vLLM as the inference engine, exposing native vLLM capabilities when creating inference services.
  • Added vLLM supports configuring Lora inference parameters when creating inference services.

Optimization

  • Optimized the default queue priority to High when creating a Notebook.

Fixes

  • Fixed an issue with minimum resource limits for Tensorboard to prevent startup failures due to insufficient resources.
  • Fixed an issue with the Chinese descriptions of task statuses to avoid misunderstandings caused by unclear status descriptions.

2024-05-30

v0.5.0

Features

  • Added support for adding Tensorboard analysis dashboard when creating tasks with baizectl.
  • Added support for binding Job to custom environments created in Environment Management.
  • Added optimizations for custom environment configuration updates and improvements to the Python version selector in Environment Management.
  • Added support for viewing resource monitoring dashboards in the details of Inference Service.
  • Added support for binding Inference Service to custom environments created in Environment Management.

Fixes

  • Fixed an issue where Python version prompts permission problems in certain cases within environment management.
  • Fixed an issue where the inference service does not support stopping during exceptions.

2024-04-30

v0.4.0

Features

  • Added Notebook now supports local SSH access, compatible with various development tools such as Pycharm, and VS Code.
  • Added upgrade Notebook image to support the built-in CLI tool baizectl, for command-line task submission and management.
  • Added Notebook adds affinity scheduling policy configuration.
  • Added distributed training tasks can now configure SHM size through the UI.
  • Added one-click restart function for training tasks.
  • Added model training tasks support custom cluster scheduler specification.
  • Added training task analysis tool Tensorboard support, can be launched with one click in Notebook and training tasks.
  • Added when editing queue quotas, hints are provided for the shared resource configuration of the current workspace.
  • Added upgrade and adapt Kueue version to v0.6.2.

Fixes

  • Fixed an occasional sync anomaly issue with Notebook CRD.
  • Fixed an issue where the query interface for Notebook affinity configuration parameters did not return.

2024-04-01

v0.3.0

Features

  • Added the Notebooks module, supporting development tools like Jupyter Notebook.
  • Added the Job Center module, supporting the training of jobs with various mainstream development frameworks such as Pytorch, Tensorflow, and Paddle.
  • Added the Model Inference module, supporting rapid deployment of Model Serving, compatible with any model algorithm and large language models.
  • Added the Data Management module, supporting the integration of mainstream data sources such as S3, NFS, HTTP, and Git, with support for automatic data preheating.

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