Configuring and Deploying TensorFlow Jobs and Models with Kubeflow and Ksonnet

ks apply minikube -c cnn ``` On GKE, we’ll want to tweak the prototype to take advantage of the multiple nodes and GPUs. First, let’s list all the parameters available: ``` # To see a list of parameters ks prototype list tf-job ``` Now let’s adjust the parameters to take advantage of GPUs and access to multiple nodes. ``` ks param set --env=gke cnn num\_gpus 1 ks param set --env=gke cnn num\_workers 1 ks apply gke -c cnn ``` Note how we set those parameters so they are used only when you deploy to GKE. Your minikube parameters are unchanged! After training, you [export your model](https://www.tensorflow.org/serving/serving_basic) to a serving location. Kubeflow also includes a serving package as well. To deploy a the trained model for serving, execute the following: ``` ks generate tf-serving inception --name=inception ---namespace=default --model\_path=gs://$bucket_name/$model_loc ks apply gke -c inception ``` This highlights one more option in Kubeflow - the ability to pass in inputs based on your deployment. This command creates a tf-serving service on the GKE cluster, and makes it available to your application. For more information about of deploying and monitoring TensorFlow training jobs and TensorFlow models please refer to the [user guide](https://github.com/google/kubeflow/blob/master/user_guide.md). ## Kubeflow + ksonnet One choice we want to call out is the use of the ksonnet project. We think working with multiple environments (dev, test, prod) will be the norm for most Kubeflow users. By making environments a first class concept, ksonnet makes it easy for Kubeflow users to easily move their workloads between their different environments. Particularly now that [Helm is integrating ksonnet](https://blog.heptio.com/ksonnet-intro-43f6183a97a6) with the next version of their platform, we felt like it was the perfect choice for us. More information about ksonnet can be found in the ksonnet [docs](https://ksonnet.io/). We also want to thank the team at [Heptio](https://heptio.com/) for expediting features critical to Kubeflow's use of ksonnet.

This section details configuring and deploying TensorFlow jobs on different environments (GKE and minikube) using Kubeflow and Ksonnet. It covers adjusting parameters like the number of GPUs and workers for GKE deployments, while leaving minikube configurations unchanged. It then explains how to deploy trained models for serving using Kubeflow's serving package, emphasizing the ability to pass deployment-specific inputs. Finally, it highlights the use of Ksonnet for managing multiple environments and acknowledges Heptio's contributions to Ksonnet.