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removed deprecated configs
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README.md

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# Self-Evolving AI/ML Workflow
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**TODO**
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If running on a different VM, IPs are currently hardcoded in example flyte workflow and need to be changed: `flyte-projects/mnist/workflows/*`
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### Minimal requirements:
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12 CPUs
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32GB RAM
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### To recreate our system:
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- Use at least one node with Ubuntu installed (Tested with 22.04.1-Ubuntu).
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- Clone the repo.
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- Run install script `./scripts/system-install.sh` which installs microk8s and argocd and automatically deploys all our apps on the cluster.
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### Deployment:
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- Run install script `./scripts/system-install.sh` which installs microk8s and addons.
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- (Optional) Run install script `scripts/rasp-install.sh` on any raspberry pi node you want to join to the cluster.
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- Run config script `./scripts/env-prepare.sh` on VM to install requirements and connect flytectl to the cluster for running pipelines. This can be run from a different machine (Not tested).
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> Scripts should be run from root of repository.
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> For scripts to work entire conf/ folder needs to be included
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> If any of the scripts fail, they should be run line by line to debug the problem.
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- Adjust ray resources and replicas in `conf/kube_conf/ray/ray-serve.yaml` if needed and apply it to the cluster using kubectl.
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- Adjust IPs in Flyte workflow files.
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- Deploy with helm, adjust configs in `values_example.yaml`:
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### In short:
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```bash
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git clone https://github.com/copandrej/IJS-AI_orchestration.git && cd IJS-AI_orchestration/
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./scripts/system-install.sh
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./scripts/env-prepare.sh
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helm add repo semr_charts https://copandrej.github.io/SEMR_charts/
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helm install semr semr_charts/SEMR --values values_example.yaml
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```
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- Run config script `./scripts/env-prepare.sh` on VM to install requirements and connect flytectl to the cluster for running AI/ML workflows.
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### Usage:
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## Project
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conf/kube_conf/argocd/apps.yaml

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conf/kube_conf/flyte/local-flyte-resources.yaml

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conf/kube_conf/flyte/nodeports.yaml

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