Volcano v1.8.2 Available Now
On January 9, 2024, UTC+8, Volcano version v1.8.2 was officially released. This version added the following new features:
On January 9, 2024, UTC+8, Volcano version v1.8.2 was officially released. This version added the following new features:
As artificial intelligence (AI) technologies advance and large language models (LLMs) grow more popular, the demand for AI compute has been booming. This has generated huge demand for high-performance scheduling for the AI and for hardware like AI chips.
Volcano 1.7.0 is now available with the following new features:
Volcano is the industry-first cloud native batch computing project. Open-sourced at KubeCon Shanghai in June 2019, it became an official CNCF project in April 2020. In April 2022, Volcano was promoted to a CNCF incubating project. By now, more than 490 global developers have committed code to the project. The community is seeing growing popularity among developers, partners, and users.
This article was firstly released at
Container Cubeon September 6th, 2021, refer to Volcano v1.4.0-Beta发布,支持NUMA-Aware等多个重要特性
Volcano, CNCF's first batch computing project, is now available with a new version, v1.4 (Beta). This version includes multiple important features, such as resource ratio-based partitions on GPU nodes, NUMA-aware, mixed deployment of multiple schedulers, and greatly improved stability.
The easiest way to deploy Volcano is using Helm charts.
Clone the repository to a local path:
# mkdir -p $GOPATH/src/volcano.sh/
# cd $GOPATH/src/volcano.sh/
# git clone https://github.com/volcano-sh/volcano.git
Official images are available on DockerHub. You can also build local images by running the following command:
cd $GOPATH/src/volcano.sh/volcano
make images
## Verify your images.
# docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
volcanosh/vk-admission latest a83338506638 8 seconds ago 41.4MB
volcanosh/vk-scheduler latest faa3c2a25ac3 9 seconds ago 49.6MB
volcanosh/vk-controllers latest 7b11606ebfb8 10 seconds ago 44.2MB
NOTE: Ensure that the images are correctly loaded to your Kubernetes cluster. For example, if you are using kind luster, run the kind load docker-image <image-name>:<tag> command for each image.