feat: switched to local env variables

.env

@@ -1 +1,4 @@
-GITHUB_REGISTRY_ACCESS_TOKEN=<your_github_access_token>
+GHCR_PERSONAL_ACCESS_TOKEN=g<your_github_personal_access_token>
+AWS_ACCESS_KEY_ID=<aws_s3_user_access_key_id>
+AWS_SECRET_ACCESS_KEY=<aws_s3_user_access_key_secret>
+BUCKET_NAME=<s3_bucket_name>

Makefile

@@ -1,22 +1,20 @@
+include .env
+
 build_mlflow_ui_container:
 	@docker info > /dev/null 2>&1 || (echo "Docker is not running. Please start Docker." && exit 1)
 	@docker build -t mlflow_s3_sync_image -f mlflow/Dockerfile mlflow
 	@docker save -o mlflow/mlflow_s3_sync_image.tar mlflow_s3_sync_image
 	@docker rmi mlflow_s3_sync_image
 
+setup_mlflow_ui:
+	@docker info > /dev/null 2>&1 || (echo "Docker is not running. Please start Docker." && exit 1)
+	@echo "Logging in to GitHub Container Registry..."
+	@echo $(GHCR_PERSONAL_ACCESS_TOKEN) | docker login ghcr.io -u USERNAME --password-stdin
+	@echo "Grabbing the container package..."
+	@docker pull ghcr.io/bosch-devopsuplift/mlflow_ui_s3:main
+
 start_mlflow_ui:
 	@docker info > /dev/null 2>&1 || (echo "Docker is not running. Please start Docker." && exit 1)
-	@if ! docker images --format "{{.Repository}}" | grep -q "^mlflow_s3_sync_image$$"; then \
-		if [ -f mlflow/mlflow_s3_sync_image.tar ]; then \
-			echo "Image mlflow_s3_sync_image not found. Loading image from mlflow_s3_sync_image.tar ..."; \
-			sudo docker load -i mlflow/mlflow_s3_sync_image.tar; \
-		else \
-			echo "Image mlflow_s3_sync_image not found and mlflow_s3_sync_image.tar does not exist. Please build or export the image."; \
-			exit 1; \
-		fi \
-	else \
-		echo "Image mlflow_s3_sync_image exists."; \
-	fi
 	@if docker ps -a --format '{{.Names}}' | grep -w mlflow_ui > /dev/null; then \
 		if docker ps --format '{{.Names}}' | grep -w mlflow_ui > /dev/null; then \
 			echo "Container mlflow_ui is already running."; \
@@ -26,7 +24,7 @@ start_mlflow_ui:
 		fi \
 	else \
 		echo "Creating and starting a new container ..."; \
-		docker run -d -p 4444:5000 --name mlflow_ui mlflow_s3_sync_image; \
+		docker run -e AWS_ACCESS_KEY_ID=$(AWS_ACCESS_KEY_ID) -e AWS_SECRET_ACCESS_KEY=$(AWS_SECRET_ACCESS_KEY) -e BUCKET_NAME=$(NUCKET_NAME)  -d -p 4444:5000 --name mlflow_ui mlflow_ui_s3; \
 	fi
 	@echo "MLflow UI is running at http://localhost:4444"
 
@@ -38,4 +36,4 @@ remove_mlflow_ui:
 	@docker info > /dev/null 2>&1 || (echo "Docker is not running. Please start Docker." && exit 1)
 	@docker stop mlflow_ui || true
 	@docker rm mlflow_ui || true
-	@docker rmi mlflow_s3_sync_image || true
+	@docker rmi mlflow_ui_s3 || true

readme.md

@@ -0,0 +1,9 @@
+# How to start the mlfluw ui
+
+- Create a classic personal access token in your github developer settings (it only needs the read packages permission) and paste it to .env
+- Run 'make setiup_mlflow_ui' to setup the docker container
+- Add the aws key id and secrret and the bucket name to the .env file
+- Run 'make start_mlflow_ui' to start the docker container
+- Access the mlflow ui at localhost:4444
+- Run 'make stop_mlflow_ui' to stop the docker container
+- Run 'make 'remove_mlflow_ui' to delete the docker container and the image