The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. Contained in this repository are manifests for creating:
This document details the steps needed to run the kubeflow project in any environment in which Kubernetes runs.
Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at:
Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.
Ultimately, we want to have a set of simple manifests that give you an easy to use ML stack anywhere Kubernetes is already running and can self configure based on the cluster it deploys into.
This documentation assumes you have a Kubernetes cluster already available. For specific Kubernetes installations, additional configuration may be necessary.
Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day. The below steps apply to a minikube cluster - the latest version as of writing this documentation is 0.23.0. You must also have kubectl configured to access minikube.
Google Kubernetes Engine is a managed environment for deploying Kubernetes applications powered by Google Cloud. If you're using Google Kubernetes Engine, prior to creating the manifests, you must grant your own user the requisite RBAC role to create/edit other RBAC roles.
kubectl create clusterrolebinding default-admin --clusterrole=cluster-admin --email@example.com
In order to quickly set up all components of the stack, run:
kubectl apply -f components/ -R
The above command sets up JupyterHub, an API for training using Tensorflow, and a set of deployment files for serving. Used together, these serve as configuration that can help a user go from training to serving using Tensorflow with minimal effort in a portable fashion between different environments. You can refer to the instructions for using each of these components below.
This section describes the different components and the steps required to get started.
Once you create all the manifests needed for JupyterHub, a load balancer service is created. You can check its existence using the kubectl commandline.
kubectl get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 10.11.240.1 <none> 443/TCP 1h tf-hub-0 ClusterIP None <none> 8000/TCP 1m tf-hub-lb LoadBalancer 10.11.245.94 xx.yy.zz.ww 80:32481/TCP 1m
If you're using minikube, you can run the following to get the URL for the notebook.
minikube service tf-hub-lb --url http://xx.yy.zz.ww:31942
For some cloud deployments, the LoadBalancer service may take up to five minutes display an external IP address. Re-executing
kubectl get svc
repeatedly will eventually show the external IP field populated.
Once you have an external IP, you can proceed to visit that in your browser. The hub by default is configured to take any username/password combination. After entering the username and password, you can start a single-notebook server, request any resources (memory/CPU/GPU), and then proceed to perform single node training.
We also ship standard docker images that you can use for training Tensorflow models with Jupyter.
In the spawn window, when starting a new Jupyter instance, you can supply one of the above images to get started, depending on whether you want to run on CPUs or GPUs. The images include all the requisite plugins, including Tensorboard that you can use for rich visualizations and insights into your models. Note that GPU-based image is several gigabytes in size and may take a few minutes to localize.
Also, when running on Google Kubernetes Engine, the public IP address will be exposed to the internet and is an unsecured endpoint by default. For a production deployment with SSL and authentication, refer to the documentation .
The TFJob controller takes a YAML specification for a master, parameter servers, and workers to help run distributed tensorflow
. The quick start deploys a TFJob controller and installs a new
API type. You can create new Tensorflow Training deployments by submitting a specification to the aforementioned API.
An example specification looks like the following:
apiVersion: "tensorflow.org/v1alpha1" kind: "TfJob" metadata: name: "example-job" spec: replicaSpecs: - replicas: 1 tfReplicaType: MASTER template: spec: containers: - image: gcr.io/tf-on-k8s-dogfood/tf_sample:dc944ff name: tensorflow restartPolicy: OnFailure - replicas: 1 tfReplicaType: WORKER template: spec: containers: - image: gcr.io/tf-on-k8s-dogfood/tf_sample:dc944ff name: tensorflow restartPolicy: OnFailure - replicas: 2 tfReplicaType: PS
For runnable examples, look under the tf-controller-examples/ directory. Detailed documentation can be found in the tensorflow/k8s repository for more information on using the TfJob controller to run TensorFlow jobs on Kubernetes.
Refer to the instructions in components/k8s-model-server to set up model serving with the included Tensorflow serving deployment.