Deploy Preset Models

Last updated 10th January, 2020.

Objective

Deploying models is the main feature of the ML Serving. This guide provides step by step instructions on how to deploy a preset model.

Requirements

• Having previously created a ML Serving namespace. Steps are described in section Initializing namespace

Instructions

Step 1 - Reach model page

Inside your ML Serving namespace page there is a dedicated tab for managing models : Models.

You can start the deployment of a new model by clicking the Deploy Model button.

Step 2 - Select the preset model

In this guide, we explain how to deploy a preset model. Just select preset model and click Next.

Then you have to choose which preset model to deploy.

Just select the chosen one and click Next.

Step 3 - Choose a name for your model

That name identifies your model among others on your namespace.

Once you chose a name, click the Next button.

Step 4 - Choose your auto-scaling options

A model is composed of one or several running instance(s). These instances are automaticaly scaled by the ML Serving depending on the input load of your model. That step allows you to configure the minimum and maximum number of instances that you want to run.

Step 5 - Choose your instances configuration

Each model instance is related to a CPU & RAM flavor. You can choose the wanted flavor among a list of existing ones.

Step 6 - Wait for model deployment

The ML Serving sequentially performs the following tasks :

• The building of the model : you should see the build status as Building.
• The deployment task : you should see the API Status asPending.

When everything is up and running you see the build status as Deployed and the api status as Running. The URL where to reach your model is also displayed so you can start requesting it.

Going Further

• You can check the official documentation of the open source product ML Serving about Models

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