Custom Deployment Containers

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.

Parameters

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)

Running Custom Container from Private Registry

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.

Example of running a flask server using the deployments CLI

gradient deployments create 
--deploymentType Custom 
-name "flaskserver" 
--machineType c5.xlarge 
--imageUrl tiangolo/uwsgi-nginx-flask 
--instanceCount 1 
--clusterId <someCluster>
--port 80:8080

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