AI Lab Release Notes¶

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

2025-02-28¶

v0.14.1¶

• Added support for enabling RDMA configuration in training tasks.
• Added support for adding the HF_ENDPOINT environment variable to datasets.
• Added a time range selector in the monitoring dashboard.
• Updated the vLLM image to version 0.7.1 (with DeepSeek support).
• Updated Kueue to version 0.10.1.
• Fixed an issue where legend colors in the monitoring dashboard did not match expectations.
• Fixed an issue where GPU resource charts displayed incorrectly on the operations management overview page.

2025-01-31¶

v0.13.0¶

• Updated the default vLLM image to version 0.6.6 to improve compatibility for training and inference tasks.
• Fixed an issue where training tasks configured for checkpoint resume still showed as disabled in task details.
• Fixed an issue where GPU usage metrics in training task monitoring always showed as "no data."
• Fixed an issue where UI operations could not proceed when no default resource pool was present.

2024-12-31¶

v0.12.0¶

• Added support for custom resource pools in queues.
• Added a Muxi GPU monitoring dashboard with enhanced GPU observability metrics.
• Fixed vulnerabilities CVE-2024-45337 and CVE-2024-45338.
• Fixed an issue preventing proper dataset creation.

2024-11-30¶

v0.11.0¶

• Added status detail display for Notebooks, Datasets, Training Jobs, and Inference Service to improve exception handling efficiency.
• Added queue management in the operator console, allowing viewing of all resources that have used the queue on the queue details page.
• Improved interaction for dataset updates.

2024-10-31¶

v0.10.0¶

Features¶

• Added the feature to specify the type of GPU to use when configuring vGPU resources in the Training Job.
• Added support for Huggingface data sources in the Dataset, allowing you to download a vast array of models and datasets.
• Added support for Modelscope data sources in the Dataset, allowing you to download a vast array of models and datasets.
• Added the feature for cross-namespace references in Dataset.
• Added support for specifying the type of card to use when configuring vGPU resources in the Inference Service.
• Added a GPU management module in the Operations Console, which supports viewing card-level monitoring and metrics information.
• Added compatibility for Moxi GPUs.

Improvements¶

• Improved the dataset update interface to provide more configuration options.
• Improved the entry position of the Notebook to enhance accessibility.

2024-09-30¶

v0.9.0¶

• Added a new data management sub-module, “Data Labeling,” for managing data labeling features across main data categories.
• Added a new model management sub-module, “Model List,” which supports to quickly import data.
• Added the feature to specify PVC storage size when creating a “Dataset.”
• Added support for one-click restarts of training jobs.
• Added the option to specify the GPU type when using vGPU resources.
• Added an upgrade of the base image for “baize-notebook” to v0.9.0.
• Improved global alerts support to ensure data availability during cluster exceptions.

2024-08-31¶

v0.8.0¶

• Added [Beta] support for manually saving a Notebook as an image while it is running (depending on the Container Registry module).
• Added [Beta] support for automatically saving a Notebook as an image when it is closed (depending on the Container Registry module).
• Added an feature to select private images from the Container Registry via a form for Notebook images.
• Added support for configuring data input and data output for Notebook, directly associating them with datasets.
• Added support for configuring Notebook to start as Root.
• Added support for configuring data input and data output for training jobs, which can be directly linked to datasets.
• Added [Beta] support for configuring breakpoint continuation for training jobs, with automatic detection and repair of job failures.
• Added an feature to select private images from the Container Registry via a form for training job images.
• Added the display of job parameters in the detail page of training jobs.
• Added an feature in environment management to query preloading progress and provide a quick debugging entry.
• Added support for service invocation monitoring in the detail page of inference jobs.
• Added an upgrade of the base image for baize-notebook to v0.8.0.

2024-07-31¶

v0.7.0¶

• Added support for Datasets to query preloading progress after dataset creation, along with a quick debug entry.
• Added support for training jobs to create both single-machine and distributed tasks with MxNet.
• Added support for training jobs to create MPI distributed tasks.
• Added support for training jobs to use a default image, standardizing the use of base images.
• Added support for training jobs to configure the startup command directly with a startup script.
• Added support for training jobs 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¶

• 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.

Improvement¶

• Improved 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 Improvements 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 jobs can now configure SHM size through the UI.
• Added one-click restart function for training jobs.
• Added model training jobs support custom cluster scheduler specification.
• Added training job analysis tool Tensorboard support, can be launched with one click in Notebook and training jobs.
• Added when editing queue quotas, hints are provided for the shared resource configuration of the current workspace.
• Added upgrade and adapt Kueue 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¶

• 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 preloading.

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