Tensorflow
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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
Before deploying, ensure that:
A Kubernetes cluster or Minikube is set up, with the kubectl command-line tool configured
version 0.8.0 or higher is installed
For Kubernetes clusters with RBAC enabled, first create a cluster role binding for admins:
Then run the following commands to deploy:
If you have multiple Kubernetes clusters, you can switch to another cluster to deploy, for example:
After a while, you can see the public IP of the tf-hub-lb
service, which is the access address for JupyterHub:
For clusters that do not support LoadBalancer Service, you can also access it through port forwarding (http://127.0.0.1:8100
):
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
Using CPU:
Using GPU:
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
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
In the case of Kubernetes clusters that are fortified with RBAC, kick off by assembling an admin-level cluster role binding:
Subsequently, embark on the deployment journey with these commands:
Got more than one Kubernetes cluster? No problem! Simply swap over to another and proceed with the deployment, take for instance:
Hang tight for a bit, and soon the tf-hub-lb
service's public IP surfaces, serving as your gateway to JupyterHub:
In scenarios where the LoadBalancer Service isn't in the cards, reach your destination via port forwarding (http://127.0.0.1:8100
):
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
Flexing CPU Muscles:
Tapping into GPU Power:
, 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:
Installation of version 0.8.0 or higher is complete