Tensorflow

Tensorflow

Kubeflow is a framework released by Google for deploying and managing tensorflow tasks in Kubernetes clusters. Its main features include:

• JupyterHub service for managing Jupyter notebooks

• Tensorflow Training Controller for managing training tasks

• TF Serving container for model services

Deployment

Before deploying, ensure that:

• A Kubernetes cluster or Minikube is set up, with the kubectl command-line tool configured

• ksonnet version 0.8.0 or higher is installed

For Kubernetes clusters with RBAC enabled, first create a cluster role binding for admins:

kubectl create clusterrolebinding tf-admin --clusterrole=cluster-admin --serviceaccount=default:tf-job-operator

Then run the following commands to deploy:

ks init my-kubeflow
cd my-kubeflow
ks registry add kubeflow github.com/google/kubeflow/tree/master/kubeflow
ks pkg install kubeflow/core
ks pkg install kubeflow/tf-serving
ks pkg install kubeflow/tf-job
ks generate core kubeflow-core --name=kubeflow-core
ks apply default -c kubeflow-core

If you have multiple Kubernetes clusters, you can switch to another cluster to deploy, for example:

kubectl config use-context gke
ks env add gke
ks apply gke -c kubeflow-core

After a while, you can see the public IP of the tf-hub-lb service, which is the access address for JupyterHub:

kubectl get svc tf-hub-lb

For clusters that do not support LoadBalancer Service, you can also access it through port forwarding (http://127.0.0.1:8100):

kubectl port-forward tf-hub-0 8100:8000

By default, JupyterHub can be logged in with any username and password. After logging in, you can use custom images to start the Notebook Server, such as:

• gcr.io/kubeflow/tensorflow-notebook-cpu

• gcr.io/kubeflow/tensorflow-notebook-gpu

Training Example

Using CPU:

ks generate tf-cnn cnn --name=cnn
ks apply gke -c cnn

Using GPU:

ks param set cnn num_gpus 1
ks param set cnn num_workers 1
ks apply default -c cnn

Model Deployment

MODEL_COMPONENT=serveInception
MODEL_NAME=inception
MODEL_PATH=gs://cloud-ml-dev_jlewi/tmp/inception
ks generate tf-serving ${MODEL_COMPONENT} --name=${MODEL_NAME} --namespace=default --model_path=${MODEL_PATH}

ks apply gke -c ${MODEL_COMPONENT}

Reference Documents

• Introducing Kubeflow - A Composable, Portable, Scalable ML Stack Built for Kubernetes

• https://github.com/google/kubeflow

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Tensorflow

Kubeflow: Google's Kubernetes-Based Framework for Managing TensorFlow Tasks

Kubeflow, crafted by Google, is an exceptional tool for deploying and overseeing TensorFlow processes within Kubernetes environments. It boasts a suite of impressive features, such as:

• JupyterHub services for the seamless running of Jupyter notebooks

• A dedicated Tensorflow Training Controller for orchestrating training operations

• A ready-to-serve TF Serving container aimed at model deployment

How to Deploy

Before ushering into the deployment phase, ensure the following prerequisites are met:

• An operational Kubernetes cluster or Minikube, along with the adeptly configured kubectl CLI

• Installation of ksonnet version 0.8.0 or higher is complete

In the case of Kubernetes clusters that are fortified with RBAC, kick off by assembling an admin-level cluster role binding:

kubectl create clusterrolebinding tf-admin --clusterrole=cluster-admin --serviceaccount=default:tf-job-operator

Subsequently, embark on the deployment journey with these commands:

ks init my-kubeflow
cd my-kubeflow
ks registry add kubeflow github.com/google/kubeflow/tree/master/kubeflow
ks pkg install kubeflow/core
ks pkg install kubeflow/tf-serving
ks pkg install kubeflow/tf-job
ks generate core kubeflow-core --name=kubeflow-core
ks apply default -c kubeflow-core

Got more than one Kubernetes cluster? No problem! Simply swap over to another and proceed with the deployment, take for instance:

kubectl config use-context gke
ks env add gke
ks apply gke -c kubeflow-core

Hang tight for a bit, and soon the tf-hub-lb service's public IP surfaces, serving as your gateway to JupyterHub:

kubectl get svc tf-hub-lb

In scenarios where the LoadBalancer Service isn't in the cards, reach your destination via port forwarding (http://127.0.0.1:8100):

kubectl port-forward tf-hub-0 8100:8000

JupyterHub's doors are open to any username and password by default. Once inside, spark up your Notebook Server using custom images like:

• gcr.io/kubeflow/tensorflow-notebook-cpu

• gcr.io/kubeflow/tensorflow-notebook-gpu

Training Showcase

Flexing CPU Muscles:

ks generate tf-cnn cnn --name=cnn
ks apply gke -c cnn

Tapping into GPU Power:

ks param set cnn num_gpus 1
ks param set cnn num_workers 1
ks apply default -c cnn

Model On the Move

MODEL_COMPONENT=serveInception
MODEL_NAME=inception
MODEL_PATH=gs://cloud-ml-dev_jlewi/tmp/inception
ks generate tf-serving ${MODEL_COMPONENT} --name=${MODEL_NAME} --namespace=default --model_path=${MODEL_PATH}

ks apply gke -c ${MODEL_COMPONENT}

Handy Guides

• Introducing Kubeflow - A Composable, Portable, Scalable ML Stack Built for Kubernetes

• https://github.com/google/kubeflow