Custom Deployment Containers
Last updated
Last updated
Gradient provides the ability to use any public or private Docker container. There are several parameters that can be used to maximize the flexibility of this managed service.
To run a custom container on a private registry, you need to specify the following fields:
Populate the Container Name, Image Server, Registry Username, Registry Password fields (plus any other optional fields) to pull a container from your private registry.
Parameter
Description
Container Name
The path to the container eg tensorflow/serving:latest-gpu
Registry Username
Username used to access docker image. This field is only required if your container originates from somewhere that is password-protected, e.g. Docker Hub.
Registry Password
Password used to access docker image. This field is only required if your container originates from somewhere that is password-protected, e.g. Docker Hub.
Image Server
Docker image server eg https://index.docker.io/v1
Container Model Path
Path to the model within the container
Method
Method
Port
Ports to open up eg 80:8080
Container URL Path
Docker image for model serving
Endpoint URL Path
Docker Arguments (CSV)
Environment Variables (JSON)