README.rst

Soopervisor

Tip: Deploy AI apps for free on Ploomber Cloud!

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.

Supported platforms

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

From notebook to a production pipeline

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.

Usage

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.

About Ploomber

Ploomber is a big community of data enthusiasts pushing the boundaries of Data Science and Machine Learning tooling.

Whatever your skillset is, you can contribute to our mission. So whether you're a beginner or an experienced professional, you're welcome to join us on this journey!

Click here to know how you can contribute to Ploomber.