Package: bodywork
Bodywork is a command line tool that deploys machine learning pipelines toKubernetes. It takes care of everything to do with containers and orchestration, so that you don't have to.Who is this for?Bodywork is aimed at teams who want a solution for running ML pipelines and deploying models to Kubernetes. It is a lightweight and simpler alternative toKubeflow, or to building your own platform based around a workflow orchestration tool likeApache Airflow,Argo WorkflowsorDagster.Pipeline = Jobs + ServicesAny stage in a Bodywork pipeline can do one of two things:run a batch job, to prepare features, train models, compute batch predictions, etc.start a long-running process, like a Flask app that serves model predictions via HTTP.You can use these to compose pipelines for many common ML use-cases, from serving pre-trained models to running continuous training on a schedule.No Boilerplate Code RequiredDefining a stage is as simple as developing an executable Python module or Jupyter notebook that performs the required tasks, and then committing it to your project's Git repository. You are free to structure your codebase as you wish and there are no new APIs to learn.Easy to ConfigureStages are assembled intoDAGsthat define your pipeline's workflow. This and other keyconfigurationis contained within a singlebodywork.yaml file.Simplified DevOps for MLBodywork removes the need for you to build and manage container images for any stage of your pipeline. It works by running all stages using Bodywork'scustom container image. Each stage starts by pulling all the required files directly from your project's Git repository (e.g., from GitHub), pip-installing any required dependencies, and then running the stage's designated Python module (or Jupyter notebook).Get StartedBodywork is distributed as a Python package - install it fromPyPI:Add abodywork.yamlfile to your Python project’s Git repo. The contents of this file describe how your project will be deployed:Bodywork is used from the command-line to deploy projects to Kubernetes clusters. With a single command, you can start Bodywork containers (hosted by us on Docker Hub), that pull Python modules directly from your project’s Git repo, and run them:You don’t need to build Docker images or understand how to configure Kuberentes resources. Bodywork will fill the gap between executable Python modules and operational jobs and services on Kubernetes.If you’re new to Kubernetes then check out our guide toKubernetes for ML- we’ll have you up-and-running with a test cluster on your laptop, in under 10 minutes.DocumentationThe documentation for bodywork-core can be foundhere. This is the best place to start.Deployment TemplatesTo accelerate your project's journey to production, we providedeployment templatesfor common use-cases:batch scoring datadeploying a prediction service with a REST APIscheduling a continuous-training pipelineWe want your FeedbackIf Bodywork sounds like a useful tool, then please send us a signal with a GitHub ★Contacting UsIf you:Have a question that these pages haven't answered, or need help getting started with Kubernetes, then please use ourdiscussion board.Have found a bug, then pleaseopen an issue.Would like to contribute, then please talk to usfirstatinfo@bodyworkml.com.Would like to commission new functionality, then please contact us atinfo@bodyworkml.com.Bodywork is brought to you byBodywork Machine Learning.