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# 📜 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
```commandline
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
```commandline
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
```commandline
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
```commandline
zenml stack set sagemaker-stack
python run.py --training-pipeline
```
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