This documentation was written for Charmed Kubeflow based on Kubeflow 1.1 and Katib v1alpha3.
Using Kubeflow - Basic
Congratulations on installing Kubeflow! This page will run through setting up a typical workflow to familiarise you with how Kubeflow works.
These instructions assume that :
You have already installed Kubeflow on your cluster (full or lite version).
You have logged in to the Kubeflow dashboard.
You have access to the internet for downloading the example code (notebooks, pipelines) required.
You can run Python 3 code in a local terminal (required for building the pipeline).
This documentation will go through a typical, basic workflow so you can familiarise yourself with Kubeflow. Here you will find out how to:
Create a new Jupyter notebook
Add a Tensorflow example image
Create a pipeline
Start a run and examine the result
Get to know the Dashboard
The Kubeflow Dashboard combines some quick links to the UI for various components of your Kubeflow deploy (Notebooks, Pipelines, Katib) as well as shortcuts to recent actions and some handy links to the upstream Kubeflow documentation.
Creating a Kubeflow Notebook
This Dashboard will give you an overview of the Notebook Servers currently available on your Kubeflow installation. In a freshly installed Kubeflow there will be no Notebook Server.
You can create a new Notebook Server by clicking on
Notebooks in the left-side navigation and then clicking on the
New notebook button.
New Notebook section you will be able to specify several options for the notebook you are creating. In the image section choose an image of
tensorflow 2, it is required for our example notebook.
Once the Notebook Server is created you will be able to connect to it and access your Jupyter Notebook environment which will be opened in a new tab.
For testing the server we will upload the Tensorflow 2 quickstart for experts example.
Click on the link above and click on the
Download Notebook button just below the heading. This will download the file
advanced.ipynb into your usual Download location. This file will be used to create the example notebook.
Notebook Server page, click on the
Upload button and select the
Once uploaded, click on the notebook name to open a new tab with the notebook content.
You can read through the content for a better understanding of what this notebook does. Click on the Run button to execute each stage of the document, or click on the double-chevron (
>>) to execute the entire document.
Adding Kubeflow Pipelines
The official Kubeflow Documentation explains the recommended workflow for creating a pipeline. This documentation is well worth reading thoroughly to understand how pipelines are constructed. For this example run-through though, we can take a shortcut and use one of the Kubeflow testing pipelines.
If you wish to skip the step of building this pipeline yourself, you can instead download the compiled YAML file.
To install the pipeline compiler tools, you will need to first have Python 3 available, and whichever pip install tool is relevant for your OS. On Ubuntu 20.04 and similar systems:
sudo apt update sudo apt install python3-pip
pip to install the Kubeflow Pipeline package
pip3 install kfp
(depending on your operating system, you may need to use
pip instead of
pip3 here, but make sure the package is installed for Python3)
Next fetch the Kubeflow repository:
git clone https://github.com/juju-solutions/bundle-kubeflow.git
The example pipelines are Python files, but to be used through the dashboard, they need to be compiled into a YAML. The
dsl-compile command can be used for this usually, but for code which is part of a larger package, this is not always straightforward. A reliable way to compile such files is to execute them as a python module in interactive mode, then use the
kfp tools within Python to compile the file.
First, change to the right directory:
Then execute the pipelines/mnist.py file as a module:
python3 -i -m pipelines.mnist
With the terminal now in interactive mode, we can import the
… and execute the function to compile the YAML file:
In this case,
mnist_pipeline is the name of the main pipeline function in the code, and
mnist.yaml is the file we want to generate.
Once you have the compiled YAML file (or downloaded it from the link above) go to the Kubeflow Pipelines Dashboard and click on the
Upload Pipeline button.
In the upload section choose the “Upload a file” section and choose the mnist.yaml file. Then click “Create” to create the pipeline.
Once the pipeline is created we will be redirected to its Dashboard. Click on
Create Run to create your first Pipeline run!
For this test run select ‘One-off’ run and leave all the default parameters and options. Then click
Once the run is started, the browser will redirect to the
run dashboard, detailing all the stages of the run. After a few minutes there should be a checkpoint showing that the run has been executed successfully.
Using Kubeflow Kale
Kale is a project that makes it easy to create a pipeline from a notebook server. The recommended way of using Kale is to input a custom image when creating a notebook server that includes kale built-in. Here is an example custom image and how to use it:
The upstream default jupyterlab Dockerfile is available as:
After the notebook server starts up, you can open the Kale dashboard from the side menu:
Note that Kale is not currently up-to-date with the latest version of jupyterlab, so you cannot install it into a notebook server you have already created, you need to use a pre-built docker image that includes Kale.
Running experiments with Katib
If you are unfamiliar with Katib and hyperparameter tuning, plenty of information is available on the upstream Kubeflow documentation. In summary, Katib automates the tuning of machine learning
hyperparameters - those which control the way and rate at which the AI learns; as well as offering neural architecture search features to help you find the optimal architecture for your model. By running experiments, Katib can be used to get the most effective configuration for the current task.
Each experiment represents a single tuning operation and consists of an objective (what is to be optimised), a search space(the constraints used for the optimisation) and an algorithm(how to find the optimal values).
You can run Katib Experiments from the UI and from CLI.
For CLI execute the following commands:
curl https://raw.githubusercontent.com/kubeflow/katib/4559e16/examples/v1alpha3/grid-example.yaml > grid-example.yaml kubectl apply -f grid-example.yaml
If you are using a different namespace than
kubeflow make sure to change that in
grid-example.yaml before applying the manifest.
These commands will download an example which will create a katib experiment. We can inspect experiment progress using
kubectl by running the following command:
kubectl -n kubeflow get experiment grid-example -o yaml
We can also use the UI to run the same example. Go to
Experiments (AutoML), and select “New Experiment”.
Click the link labelled “Edit and submit YAML”, and paste the contents of this YAML file into the text field. Remember to change the namespace field in the metadata section to the namespace where you want to deploy your experiment. Afterwards we will click
Once the experiment has been submitted, go to the Katib Dashboard and select the experiment.
In the Experiment Details view, you can see how your experiment is progressing.
When the experiment completes, you will be able to see the recommended hyperparameters.
This has been a very brief run through of the major components of Kubeflow. To discover more about Kubeflow and its components, we recommend the following resources:
- The upstream Kubeflow Documentation.
Last updated 5 days ago.