Important: This project is no longer maintained.

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Soopervisor runs Ploomber pipelines for batch processing (large-scale training or batch serving) or online inference.

pip install soopervisor

Check out the documentation to learn more.

Compatible with Python 3.7 and higher.

• Batch serving and large-scale training:
  • Airflow
  • Argo/Kubernetes
  • AWS Batch
  • Kubeflow
  • SLURM
• Online inference:
  • AWS Lambda

We also have an example that shows how to use our ecosystem of tools to go from a monolithic notebook to a pipeline deployed in Kubernetes.

Say that you want to train multiple models in a Kubernetes cluster, you may create a new target environment to execute your pipeline using Argo Workflows:

soopervisor add training --backend argo-workflows

After filling in some basic configuration settings, export the pipeline with:

soopervisor export training

Depending on the selected backend (Argo, Airflow, AWS Batch, or AWS Lambda), configuration details will change, but the API remains the same: soopervisor add, then soopervisor export.

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