Overview

To get started with PureML, you’ll need to install the PureML Python SDK. This SDK will allow you to work with the PureML platform, from creating your account to deploying your models.

To install PureML, simply run the following command:

If you`re a large company looking for a proof-of-concept, or an engineer looking to use the open-source version in a funky non-production way, then you’re in the right place! Our Docker compose deployment let’s you spin up a fresh Pureml instance in minutes.

Want more reliability? The easiest way to get started with PureML is to use PureML Cloud.

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Requirements

• A subsystem with docker-engine installed. Installation guidelines

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Setting up the compose

1. In a new directory where you want to setup the containers, create a new file docker-compose.yml

2. Add the following content from our official docker-compose example file

   docker-compose.yml

   version: "3"
   
   services:
     backend:
       image: puremlhq/pureml_backend:local-base
       environment:
         - PURE_SITE_BASE_URL=http://localhost:3000
       ports:
         - 8080:8080
       volumes:
         - pureml-data:/pureml_backend/data
   
     frontend:
       image: puremlhq/pureml_frontend
       environment:
         - BACKEND_URL=http://backend:8080/api/
       ports:
         - 3000:3000
       links:
         - backend
   
   volumes:
     pureml-data:
   

3. Run the following command to start your containers

Make sure your docker engine is running for the docker command to work.

If all goes well, you should have a PureML local instance setup and running

• PureML UI at localhost:3000 with your
• PureML Backend hosted at localhost:8080/api.

You can even checkout the auto generated Open API swagger documentation at /api/swagger/index.html

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Update docker image

Make sure you are using latest docker image to use latest features of PureML. You can upgrade the package by using below command: