Kubeflow
tl; dr; A combinator governance component that provides Kubeflow, a pipelining tool, Jupyter host, and hyperparameter tuner.
Introduction
Kubeflow is an open-source MLOps platform that combines Jupyter hosting, ML pipelining, and hyperparameter tuning. It is packaged into a single UI to help data scientists train their ML models.
Kubeflow Pipelines (KFP) in particular, has emerged as one eminent ML pipelinging technology, mainly thanks to the managed hosting in various clouds.
Its opinionated ML-specific API helps data scientists and ML engineers develop robust, repeatable pipelines.
Kubeflow Version
This installation uses Kubeflow version 1.2, which is now out of date.
Status and Recommendations
For Testing Only
This installation method is not recommended for use. It required a lot of work-arounds that are not suitable for production use. Please refer to the official documentation for production installation instructions.
Test Drive
The fastest way to get started is to use the test drive functionality provided by TestFaster. Click on the "Launch Test Drive" button below (opens a new window).
Launch Test Drive Usage
Prerequisites
Start by preparing your Kubernetes cluster using one of the infrastructure components or use your own cluster.
Component Usage
module "kubeflow" {
source = "combinator-ml/kubeflow/k8s"
# Optional settings go here
}
See the full configuration options below.
Instructions
Kubeflow is big, so it can take some time to start. Once it does connect to the istio ingress gateway service.
Once you see the login screen, the username is admin@kubeflow.org and the password is 12341234.
Requirements
| Name | Version |
|---|---|
| terraform | >= 0.13 |
| helm | = 2.2.0 |
| k8s | = 0.9.1 |
| kubernetes | = 2.3.2 |
Providers
No provider.
Modules
| Name | Source | Version |
|---|---|---|
| kubeflow | ./terraform-module-kubeflow |
Resources
No resources.
Inputs
No input.
Outputs
No output.