tl; dr; A combinator governance component that provides Kubeflow, a pipelining tool, Jupyter host, and hyperparameter tuner.
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.
This installation uses Kubeflow version 1.2, which is now out of date.
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.
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).
Start by preparing your Kubernetes cluster using one of the infrastructure components or use your own cluster.
module "kubeflow" {
source = "combinator-ml/kubeflow/k8s"
# Optional settings go here
}See the full configuration options below.
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 [email protected] and the password is 12341234.
| Name | Version |
|---|---|
| terraform | >= 0.13 |
| helm | = 2.2.0 |
| k8s | = 0.9.1 |
| kubernetes | = 2.3.2 |
No provider.
| Name | Source | Version |
|---|---|---|
| kubeflow | ./terraform-module-kubeflow |
No resources.
No input.
No output.