To install Kubeflow, please follow these steps:
-
Visit the kubeflow/manifests repository and Install Kubeflow using the provided manifests.
-
After installation, configure Kubeflow by following the instructions below:
Open the kubeflow/dex-config.yaml
file and make the following edits:
...
staticPasswords:
- email: <your email>
hash: <password hash>
...
Then apply the changes using the following command:
kubectl apply -f kubeflow/dex-config.yaml
To generate a password hash, you can use the following Python script:
python3 -c 'from passlib.hash import bcrypt; import getpass; print(bcrypt.using(rounds=12, ident="2y").hash(getpass.getpass()))'
Open the kubeflow/profile.yaml
file and configure a new profile by providing a name and owner's email (same as in kubeflow/dex-config.yaml
):
apiVersion: kubeflow.org/v1
kind: Profile
metadata:
name: <name>
spec:
owner:
kind: User
name: <email same as kubeflow/dex-config.yaml>
Apply the changes using the following command:
kubectl apply -f kubeflow/profile.yaml
Patch the Istio Ingressgateway service to use LoadBalancer type:
kubectl patch svc istio-ingressgateway -n istio-system -p '{"spec":{"type":"LoadBalancer"}}'
Open the kubeflow/access.yaml
file and grant access to Kubeflow pipelines by specifying the namespace (same as your profile name) in the metadata section:
apiVersion: kubeflow.org/v1alpha1
kind: PodDefault
metadata:
name: access-ml-pipeline
namespace: <same as your profile name>
....
Apply the changes using the following command:
kubectl apply -f kubeflow/access.yaml
To begin with, follow the steps below to set up your notebook server:
-
Make sure to click on the 'Allow access to Kubeflow Pipelines' option.
-
Connect to the notebook server and upload all the notebooks from the project directory.
Please follow these steps to run the project:
NOTE: You may need to install additional dependencies like TensorFlow for the project to work properly.
-
Open the
project/model.ipynb
notebook and run it. This will build the model and save it to the filesystem. -
NOTE: The storage options may need to be configured separately since I am using GCS instead of MinIO
Open the
project/pipeline.ipynb
notebook and run it. After a successful run, you will find a pipeline.yaml file in the same directory. Download this file and proceed to the next steps. -
Upload the pipeline using the Kubeflow dashboard. Create an experiment and run the pipeline as shown in the screenshots below:
After a successful run, you will have model endpoints available in the Endpoints section as shown in the screenshots below:
-
You can now test the model using the
project/test_model.ipynb
notebook.