What Are the Differences Between Public Resource Pools and Dedicated Resource Pools in ModelArts?

• Dedicated resource pool: It delivers more controllable resources and cannot be shared with other users. Create a dedicated resource pool and select it during AI development.
• Public resource pool: It provides large-scale public computing clusters, which are allocated based on job parameter settings. Resources are isolated by job. You can use ModelArts public resource pools to deliver training jobs, deploy models, or run DevEnviron instances and will be billed on a pay-per-use basis.

Differences between dedicated resource pools and public resource pools:

• Dedicated resource pools provide dedicated computing clusters and network resources for users. The dedicated resource pools of different users are physically isolated, while public resource pools are only logically isolated. Compared with public resource pools, dedicated resource pools feature better performance in isolation and security.
• When a dedicated resource pool is used for creating jobs and the resources are sufficient, the jobs will not be queued. When a public resource pool is used for creating jobs, there is a high probability that the jobs will be queued.
• A dedicated resource pool is accessible to your network. All running jobs in the pool can access storage and resources in your network. For example, if you select a dedicated resource pool with an accessible network when creating a training job, you can access SFS data after the training job is created.
• Dedicated resource pools allow you to customize the runtime environment of physical nodes, for example, you can upgrade GPU or Ascend drivers. This function is not supported by public resource pools.