kubeflow - Machine Learning Toolkit for Kubernetes

  •        51

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

https://github.com/kubeflow/kubeflow

Tags
Implementation
License
Platform

   




Related Projects

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Python

The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to train, test, and deploy best-of-breed open-source predictive models to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run KubeFlow.This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs.

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

  •    Python

Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

kubespawner - Kubernetes spawner for JupyterHub

  •    Python

The kubespawner (also known as JupyterHub Kubernetes Spawner) enables JupyterHub to spawn single-user notebook servers on a Kubernetes cluster. You can read a list of all the spawner options available on ReadTheDocs.

zero-to-jupyterhub-k8s - Resources for deploying JupyterHub to a Kubernetes Cluster

  •    Python

This is under active development and subject to change. This repo contains resources, such as Helm charts and the Zero to JupyterHub Guide, which help you to deploy JupyterHub on Kubernetes.


handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

  •    Jupyter

First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

minikube - Run Kubernetes locally

  •    Go

Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day.We also released a Debian package and Windows installer on our releases page If you maintain a minikube package, please feel free to add it here.

awesome-kubernetes - A curated list for awesome kubernetes sources :ship::tada:

  •    Makefile

Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery.

node-tensorflow - Node.js + TensorFlow

  •    Javascript

TensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

kube-solo-osx - Local development Kubernetes Solo Cluster for macOS made very simple

  •    Shell

Kube-Solo for macOS is a status bar App which allows in an easy way to bootstrap and control Kubernetes cluster on a standalone CoreOS VM machine. VM can also be controlled via ksolo cli. Also VM's docker API is exposed to macOS, so you can build your docker images with the same app and use them with Kubernetes. Kube-Solo for macOS is a similar app to minikube, just has more functionality and is an older project. You can run both Apps on your Mac even in parallel.

seldon-core - Machine Learning Deployment for Kubernetes

  •    Java

Seldon Core is an open source platform for deploying machine learning models on Kubernetes. A Kubernetes Cluster. Kubernetes can be deployed into many environments, both on cloud and on-premise.

tf-operator - Tools for ML/Tensorflow on Kubernetes.

  •    Go

TFJob provides a Kubernetes custom resource that makes it easy to run distributed or non-distributed TensorFlow jobs on Kubernetes. Please refer to the user guide for more information.

kubernetes-the-hard-way - Bootstrap Kubernetes the hard way on Google Cloud Platform. No scripts.

  •    

This tutorial walks you through setting up Kubernetes the hard way. This guide is not for people looking for a fully automated command to bring up a Kubernetes cluster. If that's you then check out Google Container Engine, or the Getting Started Guides.Kubernetes The Hard Way is optimized for learning, which means taking the long route to ensure you understand each task required to bootstrap a Kubernetes cluster.

devspace - Cloud Native Software Development with Kubernetes and Docker - simply run "devspace up" in any of your projects and start coding directly on top of Kubernetes (works with minikube, self-hosted and cloud-based clusters)

  •    Go

With a DevSpace, you can build, test and run code directly inside any Kubernetes cluster. You can run devspace up in any of your projects and the client-only DevSpace CLI will start a DevSpace within your Kubernetes cluster. Keep coding as usual and the DevSpace CLI will sync any code change directly into the containers of your DevSpace. No more waiting for re-building images, re-deploying containers and restarting applications on every source code change. Simply edit your code with any IDE and run your code instantly inside your DevSpace.

feast - Feature Store for Machine Learning

  •    Java

Feast (Feature Store) is a tool to manage storage and access of machine learning features. Access to features in serving: Machine learning models typically require access to features created in both batch pipelines, and real time streams. Feast provides a means for accessing these features in a serving environment, at low latency and high load.

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

  •    Jupyter

This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

ml-agents - Unity Machine Learning Agents

  •    CSharp

Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

pipeline - PipelineAI: Real-Time Enterprise AI Platform

  •    HTML

Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction. Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.

colossus - Colossus — An example microservice architecture for Kubernetes using Bazel, Go, Java, Docker, Kubernetes, Minikube, Gazelle, gRPC, Prometheus, Grafana, and more

  •    Python

Wait a second, these services don't do anything meaningful! Nope, they sure don't. But that's okay because the point of this project is to show you how to get the basic (yet not-at-all-trivial) plumbing to work. Colossus is a boilerplate project that's meant as a springboard to more complex and meaningful projects. Getting all of these technologies to work together was a real challenge. I had to dig through countless GitHub issues and dozens of example projects to make all these things work together. I'm offering this repo as a starter pack for other people with a Bazel monorepo targeting Kubernetes.





We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.