release process (#10)

.github/workflows/release.yml

@@ -0,0 +1,28 @@
+name: release
+on:
+  release:
+    types:
+      - published
+jobs:
+  release:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: '0' # ensure tag lookup works
+      - run: |
+          #!/usr/bin/env bash
+          set -e -u -x -o pipefail
+          export VERSION=${GITHUB_REF:11}
+          make helm-repo-login
+          make push-chart
+        env:
+          GHCR_PAT: ${{ secrets.RELEASE_PAT }}
+          USERNAME: alex-treebeard
+      - uses: EndBug/[email protected]
+        with:
+            author_name: github-actions
+            author_email: 41898282+github-actions[bot]@users.noreply.github.com
+            add: helm/kubeflow/values.yaml
+            branch: main
+            message: Published new version

Makefile

@@ -52,4 +52,4 @@ push-chart: build-chart
 	helm push helm/kubeflow-$(VERSION).tgz oci://ghcr.io/treebeardtech/helm
 
 helm-repo-login:
-	echo $(GHCR_PAT) | docker login ghcr.io -u alex-treebeard --password-stdin
+	echo $(GHCR_PAT) | docker login ghcr.io -u $(USERNAME) --password-stdin

README.md

@@ -24,7 +24,6 @@
 
 ## About this project
 
-
 This project simplifies MLOps in Kubernetes by providing Kubeflow in Helm and Terraform package formats. This allows scaling Kubeflow usage with the rest of your production systems.
 
 Kubeflow provides a cloud-native AI platform which can be used to deploy applications in
@@ -61,7 +60,7 @@ The default configuration of Kubeflow provided is designed to run on a cluster w
 
 ## Getting Started
 
-Note that as all of our examples are implemented in Terraform, we recommend using the Terraform module to start off. The [helm super-chart](https://github.com/treebeardtech/kubeflow/tree/main/helm/treebeard-kubeflow) is likely to be a more viable interface as you move into production.
+Note that as all of our examples are implemented in Terraform, we recommend using the Terraform module to start off. The [helm charts](helm) are likely to be a more viable interface as you move into production.
 
 We recommend trying out this module in a development environment first.
 
@@ -78,7 +77,12 @@ To do so, follow the [k3s tutorial](examples/k3s).
 ### Install via helm CLI
 
 ```sh
-helm install kf oci://ghcr.io/treebeardtech/helm/treebeard-kubeflow --version x.y.z
+# install argo (necessary for orchestration)
+helm repo add argo-cd https://argoproj.github.io/argo-helm
+helm install -n argocd --create-namespace argo-cd argo-cd/argo-cd
+
+# install kubeflow
+helm install kubeflow -n argocd oci://ghcr.io/treebeardtech/helm/kubeflow --version x.y.z
 ```
 
 In order to integrate Kubeflow with your production systems there are some changes you 
@@ -154,7 +158,7 @@ See the [gitops example](examples/k3s-gitops) for details.
 
 ## Troubleshooting
 
-## Reconfiguration challenges
+### Reconfiguration challenges
 
 Moving the deployment between different states of configuration can be challenging
 due to the dependencies between components in the cluster.

helm/kubeflow/values.yaml

@@ -6,7 +6,7 @@ finalizers:
   - resources-finalizer.argocd.argoproj.io
 project: default
 repoURL: ghcr.io/treebeardtech/helm
-targetRevision: "0.3.1"
+targetRevision: "0.4.0-dev9"
 chart: kubeflow-core
 destination:
   server: https://kubernetes.default.svc