Kubeflow is an open-source platform that is designed to simplify and automate the process of deploying, managing, and scaling machine learning workloads in Kubernetes clusters. Kubeflow enables data scientists and machine learning engineers to build and deploy machine learning models without worrying about the underlying infrastructure.
Kubeflow Central Dashboard is an important component of the Kubeflow platform that provides a user interface for managing and monitoring machine learning workloads. In this article, we will explore how to use the Kubeflow Central Dashboard on GitHub to manage your machine learning workloads.
Getting Started with Kubeflow Central Dashboard
Before we start, ensure you have the following prerequisites:
- A running Kubernetes cluster.
- Kubeflow installed on your Kubernetes cluster.
- A GitHub account.
Step 1: Clone the Kubeflow Central Dashboard Repository
The first step is to clone the Kubeflow Central Dashboard repository from GitHub. You can use the following command to clone the repository:
git clone https://github.com/kubeflow-central/kubeflow-central-dashboard.git
Step 2: Install the Dependencies
The next step is to install the dependencies required by the Kubeflow Central Dashboard. You can use the following command to install the dependencies:
Step 3: Build and Deploy the Dashboard
Once you have installed the dependencies, you can build and deploy the Kubeflow Central Dashboard using the following command:
npm run build && npm run deploy
This command will build the dashboard and deploy it to your Kubernetes cluster.
Step 4: Access the Dashboard
After the dashboard has been deployed, you can access it using the following command:
kubectl port-forward -n kubeflow svc/centraldashboard 8080:80
This command will forward traffic from port 8080 on your local machine to port 80 on the Kubeflow Central Dashboard service.
Open your web browser and go to
http://localhost:8080 to access the dashboard.
Using the Kubeflow Central Dashboard
Once you have accessed the Kubeflow Central Dashboard, you can use it to manage your machine learning workloads. Here are some of the tasks you can perform using the dashboard:
- Create new Jupyter notebooks.
- Manage and monitor running Jupyter notebooks.
- Manage and monitor running TensorFlow jobs.
- Create and manage Kubeflow Pipelines.
In this article, we explored how to use the Kubeflow Central Dashboard on GitHub to manage your machine learning workloads. By following the steps outlined in this article, you should be able to deploy and use the Kubeflow Central Dashboard to manage your machine learning workloads.
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That's it for this post. Keep practicing and have fun. Leave your comments if any.
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