Fast-Kubeflow VS pipelines

Compare Fast-Kubeflow vs pipelines and see what are their differences.

Fast-Kubeflow

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc. (by omerbsezer)
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Fast-Kubeflow pipelines
7 2
69 3,442
- 1.7%
3.6 9.8
2 months ago 2 days ago
Python Python
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Fast-Kubeflow

Posts with mentions or reviews of Fast-Kubeflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-04.

pipelines

Posts with mentions or reviews of pipelines. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-31.
  • Putting an ML model into production using Feast and Kubeflow on Azure (Part I)
    2 projects | dev.to | 31 Mar 2021
    Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.
  • Machine Learning Orchestration on Kubernetes using Kubeflow
    5 projects | dev.to | 23 Mar 2021
    You can run the notebook from the dashboard and create the pipeline. Please note, in Kubeflow v1.2, there is an issue causing RBAC: permission denied error while connecting to the pipeline. This will be fixed in v1.3 and you can read more about the issue here. As a workaround, you need to create Istio ServiceRoleBinding and EnvoyFilter to add an identity in the header. Refer this gist for the patch.

What are some alternatives?

When comparing Fast-Kubeflow and pipelines you can also consider the following projects:

Fast-Docker - This repo covers containerization and Docker Environment: Docker File, Image, Container, Commands, Volumes, Networks, Swarm, Stack, Service, possible scenarios.

kubeflow - Machine Learning Toolkit for Kubernetes

Fast-Kubernetes - This repo covers Kubernetes with LABs: Kubectl, Pod, Deployment, Service, PV, PVC, Rollout, Multicontainer, Daemonset, Taint-Toleration, Job, Ingress, Kubeadm, Helm, etc.

deployKF - deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.

fashion-mnist-kfp-lab - A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:

soopervisor - ☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.

community - Information about the Kubeflow community including proposals and governance information.

bodywork - ML pipeline orchestration and model deployments on Kubernetes.