NVIDIA Run:ai workload assets are preconfigured building blocks that simplify the workload submission effort and remove the complexities of Kubernetes and networks for AI practitioners.
Workload assets enable organizations to:
Create and reuse preconfigured setup for code, data, storage and resources to be used by AI practitioners to simplify the process of submitting workloads
Share the preconfigured setup with a wide audience of AI practitioners with similar needs
Note
The creation of assets is possible only via API and the NVIDIA Run:ai UI.
The submission of workloads using assets, is possible only via the NVIDIA Run:ai UI.
There are four workload asset types used by the workload:
Environments The container image, tools and connections for the workload
Data sources The type of data, its origin and the target storage location such as PVCs or cloud storage buckets where datasets are stored
Compute resources The compute specification, including GPU and CPU compute and memory
Credentials The secrets to be used to access sensitive data, services, and applications such as docker registry or S3 buckets
When a workload asset is created, a scope is required. The scope defines who in the organization can view and/or use the asset.
Note
When an asset is created via API, the scope can be the entire account. This is currently an experimental feature.
Any subject (user, application, or SSO group) with a role that has permissions to Create an asset, can do so within their scope.
Assets are used when submitting workloads. Any subject (user, application or SSO group) with a role that has permissions to Create workloads, can also use assets.
Any subject (user, application, or SSO group) with a role that has permission to View an asset, can do so within their scope.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4