Deployment Wizard
Last updated
Last updated
The Deployment Wizard guides you through setting up and configuring new deployments.
The Overview button on the top-left of your screen will take you to the list of current deployments, where the deployment wizard can be opened by clicking on the Create new deployment button on the top-right.
In the Deployment Details page, you need to choose a Name and Namespace for your deployment. You also need to choose the deployment Type, which affects the following sections of this wizard.
Note: Deployments of type Seldon Deployment use Seldon Core v1, while those of type Seldon ML Pipeline use Seldon Core v2. See the feature comparison between Core v1 and Core v2 for details.
In the Default Predictor page, you will need to specify your model's Runtime and the Model URI where its artifacts are stored. The Runtime field is pre-filled with supported runtimes, and also includes options for Custom runtime configurations.
You can optionally provide a Storage Secret for private model artifacts. See Secrets Management for details.
You can also change the Model Project from the default project. Projects are a logical grouping of resources for authorization purposes.
In addition to pre-packaged runtimes, custom container images or custom inference artifacts can be configured in a similar manner.
For deployments of type Seldon Deployment, you can choose the Custom runtime and specify the URI for your Docker image instead of a model artifact URI.
For deployments of type Seldon ML Pipeline, you can select a custom inference server for your model by specifying Model Requirements as follows. Custom inference servers need to be set up manually, not through the Deployment Wizard.
See Servers in Seldon Core v2 documentation for how to configure servers and models.
In the following pages, you can set optional configurations such as resource requirements, auto-scaling, and version comments for GitOps.
For deployments of type Seldon Deployment, you can additionally configure environment variables, inference request logging, and input/output transformers.
Before creating the deployment, you can review its Kubernetes manifest in the Launch Deployment page. Click on the LAUNCH button to proceed.
