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Self-Evolving AI/ML Workflow

Our system is managed by ArgoCD, all changes to conf/kube_conf will be picked up by Argo and can be synced manually or automatically to our cluster.

If running on a different VM, IPs are currently hardcoded for minio in the conf files and need to be changed. (files: conf/kube_conf/flyte/values/local-values.yaml,conf/kube_conf/mlflow/values.yaml ) Number of replicas and resources for Ray should be adjusted in conf/kube_conf/ray/ray-serve.yaml.

To recreate our system:

  • Use at least one node with Ubuntu installed (Tested with 22.04.1-Ubuntu).
  • Clone the repo.
  • Run install script ./scripts/system-install.sh which installs microk8s and argocd and automatically deploys all our apps on the cluster.
  • (Optional) Run install script scripts/rasp-install.sh on any raspberry pi node you want to join to the cluster.
  • Run config script ./scripts/env-prepare.sh on VM to connect to zenml instance and prepare VM env for running pipelines. This can be run from a different machine (Not tested).

Scripts should be run from root of repository. For scripts to work entire conf/ folder needs to be included If any of the scripts fail, they should be run line by line to debug the problem.

In short:

git clone https://github.com/copandrej/IJS-AI_orchestration.git && cd IJS-AI_orchestration/
./scripts/system-install.sh
./scripts/env-prepare.sh

Project

scripts/ Install & configure scripts for the system

flyte-projects/ Examples of full MLOps pipelines

conf/ Yaml files for kubernetes and software running on kubernetes. Single source of truth for ArgoCD

docs/ Explanation and procedures for creating and using our system. TBC.

docker/ Centralized dockerfiles and building scripts. TBC.