README.md

📜 ZenML Stack Show Case

This project aims to demonstrate the power of stacks. The code in this project assumes that you have quite a few stacks registered already.

default

• default Orchestrator
• default Artifact Store

zenml stack set default
python run.py --training-pipeline

local-sagemaker-step-operator-stack

• default Orchestrator
• s3 Artifact Store
• local Image Builder
• aws Container Registry
• Sagemaker Step Operator

zenml stack set local-sagemaker-step-operator-stack
zenml integration install aws -y
python run.py --training-pipeline

sagemaker-airflow-stack

• Airflow Orchestrator
• s3 Artifact Store
• local Image Builder
• aws Container Registry
• Sagemaker Step Operator

zenml stack set sagemaker-airflow-stack
zenml integration install airflow -y
pip install apache-airflow-providers-docker apache-airflow~=2.5.0
zenml stack up
python run.py --training-pipeline

sagemaker-stack

• Sagemaker Orchestrator
• s3 Artifact Store
• local Image Builder
• aws Container Registry
• Sagemaker Step Operator

zenml stack set sagemaker-stack
python run.py --training-pipeline