# 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](https://github.com/ksonnet/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: ```bash 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: ```bash 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: ```bash 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`): ```bash 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: ```bash ks generate tf-cnn cnn --name=cnn ks apply gke -c cnn ``` Using GPU: ```bash 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](http://blog.kubernetes.io/2017/12/introducing-kubeflow-composable.html) * *** ## Tensorflow ### Kubeflow: Google's Kubernetes-Based Framework for Managing TensorFlow Tasks [Kubeflow](https://github.com/google/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](https://github.com/ksonnet/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: ```bash 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: ```bash 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: ```bash 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`): ```bash 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: ```bash ks generate tf-cnn cnn --name=cnn ks apply gke -c cnn ``` Tapping into GPU Power: ```bash 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](http://blog.kubernetes.io/2017/12/introducing-kubeflow-composable.html) * --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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