Docs: Configure Kubeflow Fairing with Access to GCP

Mon, 01 Jan 0001 00:00:00 +0000

This guide describes how to configure your development environment with access to Google Cloud Platform (GCP), so you can use Kubeflow Fairing to train or deploy a model on Kubeflow on Google Kubernetes Engine (GKE).

If you have not installed Kubeflow Fairing, follow the guide to installing Kubeflow Fairing before continuing.

Using Kubeflow Fairing with Kubeflow notebooks

The standard Kubeflow notebook images include Kubeflow Fairing and come preconfigured to run training jobs on your Kubeflow cluster. No additional configuration is required.

If your Kubeflow notebook server was built from a custom Jupyter Docker image, follow the instruction in this guide to configure your notebooks environment with access to your Kubeflow environment.

Install and configure the Google Cloud SDK

Follow these instructions to set up the Google Cloud SDK in your local development environment.

To check if you have the Google Cloud SDK installed, run the following command:
```
which gcloud
```
The response should be something like this:
```
/usr/bin/gcloud
```
If you do not have the Google Cloud SDK installed, follow the guide to installing the Google Cloud SDK.

Use gcloud to set a default project.

export PROJECT_ID=<your-project-id>
gcloud config set project $PROJECT_ID

Kubeflow Fairing needs a service account to make API calls to GCP. The recommended way to provide Fairing with access to this service account is to set the GOOGLE_APPLICATION_CREDENTIALS environment variable. To check for the GOOGLE_APPLICATION_CREDENTIALS environment variable, run the following command:
```
ls "$GOOGLE_APPLICATION_CREDENTIALS"
```
The response should be something like this:
```
/.../.../key.json
```
If you do not have a service account, then create one and grant it access to the required roles.
```
export SA_NAME=<your-sa-name>
gcloud iam service-accounts create $SA_NAME
gcloud projects add-iam-policy-binding $PROJECT_ID \ 
    --member serviceAccount:$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com \
    --role 'roles/editor'
```
Create a key for your service account.
```
gcloud iam service-accounts keys create ~/key.json \
    --iam-account $SA_NAME@$PROJECT_ID.iam.gserviceaccount.com
```
Create the GOOGLE_APPLICATION_CREDENTIALS environment variable.
```
export GOOGLE_APPLICATION_CREDENTIALS=~/key.json
```

Configure Docker with access to Container Registry

Authorize Docker to access your GCP Container Registry.

gcloud auth configure-docker

Configure access to your Kubeflow cluster

Use the following instructions to update your kubeconfig with credentials and endpoint information for your Kubeflow cluster. If you do not have a Kubeflow cluster, follow the guide to deploying Kubeflow on GKE to set one up.

To find your cluster’s name, run the following command to list the clusters in your project:
```
gcloud container clusters list
```
Update the following command with your cluster’s name and GCP zone. Then, run the command to update your kubeconfig to provide it with credentials to access this cluster.
```
export CLUSTER_NAME=kubeflow
export ZONE=us-central1-a
gcloud container clusters get-credentials $CLUSTER_NAME --region $ZONE
```

Next steps

Follow the GCP samples and tutorials to learn more about how to run training jobs remotely on GCP with Kubeflow Fairing.

Docs: GCP Samples and Tutorials