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To Deploy in AWS ☁️

You need a Kubernetes cluster and kubectl set up to be able to access that cluster. On AWS, we use Amazon Elastic Kubernetes Service (Amazon EKS) for this. - Please refer to the Amazon EKS on how to set things up - Make sure you can AWS CLI installed and configured as well

Also, make sure Docker is installed and running in your environment

Set up Paradigm

In a terminal with the above kubectl access, follow the below steps.

  • (Recommended) Create a new Python environment with your preferred environment manager
  • Clone this repo
    • git clone https://github.com/ParadigmAI/paradigm.git
  • Go into the directory
    • cd paradigm
  • Make the installation script executable
    • chmod +x install-aws.sh
  • Run the intallation script
    • ./install-aws.sh
  • Validate if paradigm was properly installed
    • paradigm --help

Now let's move into your ML project folder

Your folder can contain one or more scripts/notebooks that you want to execute as steps in an ML pipeline.

From here we follow a basic example project just to make it easier to exaplin the commands. Please change the necessary parameters according to your project - The preferred directory structure should be as follows. In the below example, p1, p2 and p3 represent the names of the python scripts or notebooks you have. (Refer the examples/basic) - IMPORTANT - Note the requirements.<file name> files. You have to create a txt with that specific naming only for the scripts or notebooks that have additional dependencies. It becomes the requirements.txt for that step. We promise this is the only file addition before taking your ML code to prodution. - Example:

    - 📁 project_root
        - 📄 p1.py
        - 📄 p2.ipynb
        - 📄 p3.py
        - 📄 requirements.p1
        - 📄 requirements.p3
  • Now we are ready to let Paradigm get things ready before deploying to Kubernetes. Include the scripts/notebook you want as steps in the below command. This command basically containerizes your code.
paradigm launch --steps p1 p2 p3 --region_name us-east-1
  • As the final step, deploy the pipeline with the below command.
paradigm deploy --steps p1 p2 --dependencies "p2:p1,p3:p2|p1" --deployment p3 --deployment_port 8000 --deployment_memory 2Gi --output workflow.yaml --name pipe1 --region_name us-east-1

  • In the above command:

    • --steps should speicify all steps, except any step that should be run as a service, e.g., an API endpoint.
    • --dependencies "p2:p1,p3:p2|p1" defines the graph stucture (DAG) on how the steps should be run. In this example, we are stating that step p2 is dependent on p1 and step p3 is dependent on both p2 and p1.
    • --deployment p3 defines a service that needs to be run at the end of the pipeline. Hence, we don't mention is under --steps.
    • --deployment_port is defined if the above service is exposed via a specific port internally.
    • --deployment_memory is to specify the amount of memory required for the deployment step
    • --name can be any name that you want to give this particualr pipeline
    • --region_name is the aws region that you want to use

  • (OPTIONAL) You can use Argo UI to observe all pipelines and get logs. For that, first make it accessible via your browser by running the below command.

    • kubectl -n paradigm port-forward deployment/argo-server 2746:2746
    • Now I your local browser, go to http://localhost:2746

  • (OPTIONAL) In case you want to delete the running service and deployment, use the following commands. <deployment_step> is the make of the file that has the deolyment code.

    • kubectl delete deployment deploy-<deployment_step> -n paradigm
    • kubectl delete service deploy-<deployment_step> -n paradigm