You can quickly build an elastic and available cloud platform on which the Kubernetes container runs by two simple steps of creating clusters and creating services.
Flexible Cluster Management
Container Lifecycle Management
In-depth Integration of Cloud Services
Integrate with VPC to provide a safe and high-performance deployment scheme. Integrate with BLB to provide the container access ability. Integrate with the cloud storage to provide persistent storage ability.
High-performance Auto Scaling
Cluster scaling is officially supported by CA component of Kubernetes. It supports multiple scaling groups and node templates, and it is more flexible and conforms to the open-source native application mode.
Our many years’ experience in massive container cluster supports many core businesses, including driverless technology, finance and advertisement.
The container cluster provides the ability to cross the availability zone, guaranteeing that the cluster is not affected by the single physical data center failure. Furthermore, it monitors the cluster status, and automatically scales up or down the containers, ensuring the availability of the cluster.
It provides quick resource and service creation, and seamlessly links other required Baidu AI Cloud resources. Users only need to focus on development at the business level.
It supports the containerization deployment of deep learning frameworks, such as PaddlePaddle and Tensorflow, and provides the platform service the rapid deployment and start-up, resource isolation, dynamic schedule, high availability, development, and training test environment consistency for AI applications.
PaddlePaddle Cloud Deep Learning CCE integrates with PaddlePaddle Cloud to provide the quick distributed training solution in the cloud based on Kubernetes, Docker, and paddlecloud.