Metadata-Version: 2.1
Name: jupyterflow
Version: 0.0.3
Summary: Run your workflow on JupyterHub
Home-page: https://github.com/hongkunyoo/jupyterflow
Author: hongkunyoo
Author-email: hongkunyoo@gmail.com
License: BSD 3-Clause
Description: # JupyterFlow
        
        Run your workflow on JupyterHub!
        
        ## What is JupyterFlow?
        
        Run [Argo Workflow](https://argoproj.github.io/argo) on [JupyterHub](https://jupyter.org/hub) with single command.
        
        - **No Kubernetes knowledge (YAML) needed to run.**
        - **No container image build & push or deploy.**
        - Just simply run your workflow with single command `jupyterflow`.
        
        `jupyterflow` is a command that helps user utilize Argo workflow engine without making any YAML files or building containers on JupyterHub.
        
        This project only works on Kubernetes.
        
        - [JupyterHub for Kubernetes](https://zero-to-jupyterhub.readthedocs.io/en/latest)
        - [Kubeflow](https://www.kubeflow.org)
        
        The following `jupyterflow` command in jupyter notebook will make sequence workflow. That's it!
        
        ```bash
        jupyterflow run -c "python hello.py >> python world.py"
        ```
        
        ![](https://raw.githubusercontent.com/hongkunyoo/jupyterflow/main/docs/images/intro.png)
        
        To make parallel workflow, write your own [`workflow.yaml`](https://hongkunyoo.github.io/jupyterflow/configuration/) file.
        
        ![](https://raw.githubusercontent.com/hongkunyoo/jupyterflow/main/docs/images/dag.png)
        
        ## Problem to solve
        
        - I wanted to train multiple ML models efficiently.
        - Using Kubernetes was a good idea, since
            - it is easy to make distributed jobs.
            - it is easy to schedule ML jobs on multiple training server.
            - it has native resource management mechanism.
            - it has good monitoring system.
        - But there were some drawbacks.
            - I needed to re-build & re-push image everytime I updated my model. This was painful.
            - People who were not familiar with k8s had a hard time using this method.
        
        `jupyterflow` aims to solve this problem. Run your workflow  on JupyterHub with single command without Kubernetes & container troublesome task.
        
        ## Getting Started
        
        To set up `jupyterflow` and start running your first workflow, follow the [Getting Started](https://hongkunyoo.github.io/jupyterflow/get-started) guide.
        
        ## How does it work
        
        To learn how it works, go to [How it works](https://hongkunyoo.github.io/jupyterflow/how-it-works) guide.
        
        ## Examples
        
        For examples how to use, please see [Examples](https://hongkunyoo.github.io/jupyterflow/examples) page.
        
        ## Configuration
        
        To find out more configuration, take a look at [Configuration](https://hongkunyoo.github.io/jupyterflow/configuration) page.
        
        ## CLI Reference
        
        For more detail usage of `jupyterflow` command line interface, find out more at [CLI Reference](https://hongkunyoo.github.io/jupyterflow/cli-ref) page.
        
Keywords: ctl,jupyterhub,pipeline,ML
Platform: UNKNOWN
Description-Content-Type: text/markdown
