Welcome to the exciting world of Bodywork, a command line tool that simplifies deploying machine learning (ML) pipelines on Kubernetes. If you’ve ever dreamt of focusing solely on your ML models without getting bogged down by the intricacies of containers and orchestration, Bodywork is here to help. Let’s dive into how you can get started!
Who is Bodywork For?
Are you part of a team that aspires to run ML pipelines and deploy models on Kubernetes seamlessly? If so, Bodywork is your lightweight companion! It serves as a simplified alternative to platforms like Kubeflow, Apache Airflow, Argo Workflows, and Dagster, relieving you of the need to build a complex custom platform.
Understanding the Pipeline Structure
Think of a Bodywork pipeline like a fancy restaurant kitchen. Each chef in the kitchen (or stage in the pipeline) has two primary roles they can play:
- Run a batch job: Just as a chef might prepare ingredients, train models, or compute predictions, each stage can perform essential ML tasks.
- Start a long-running process: Similar to how a chef might continuously serve meals to guests, a stage can initiate a service such as a Flask app that serves model predictions over HTTP.
By combining these roles, you can craft pipelines that cater to a variety of ML use cases—from serving pre-trained models to setting up continuous training.
No Boilerplate Code Required
Building a stage is as straightforward as preparing a dish! Create an executable Python module or a Jupyter notebook for your desired task and commit it to your project’s Git repository. You’re free to shape the code as per your desire without having to learn a brand-new API.
Easy Configuration
Stages are connected into Directed Acyclic Graphs (DAGs), which illustrate your pipeline’s workflow. To make this work, all key configurations can be specified in a single bodywork.yaml file that you add to your project.
Simplified DevOps for ML
Bodywork takes the stress off DevOps by managing containers for you. It utilizes a custom container image that pulls required files from your Git repository, installs dependencies, and executes your designated Python module (or Jupyter notebook).
Getting Started with Bodywork
Ready to dive in? Follow these simple steps to get started:
- Install Bodywork from PyPI.
- Create and configure a bodywork.yaml file in your project’s Git repo.
- Use Bodywork commands from the terminal to deploy your project to Kubernetes with ease.
If you’re new to Kubernetes, check out our guide on Kubernetes for ML to have a full working setup in less than 10 minutes!
Troubleshooting Common Issues
Like any great culinary masterpiece, things can go wrong. Here are some troubleshooting tips you can follow:
- If the command line interface is unresponsive, ensure that your installation from PyPI was successful.
- If you encounter issues with pipeline execution, verify that your bodywork.yaml file is correctly configured.
- For dependency problems, check that all required libraries are specified correctly in your modules.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Documentation and Resources
Looking for more detailed instructions? The documentation for bodywork-core is your best friend! Additionally, check out deployment templates for common use cases:
- Batch scoring data
- Deploying a prediction service with a REST API
- Scheduling a continuous-training pipeline
Happy deploying!