Kubeflow vs Kubernetes

Overview

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

Kubeflow

Stacks205

Followers585

Votes18

Kubeflow vs Kubernetes: What are the differences?

Introduction

In the world of containerized applications, Kubernetes has emerged as a leading open-source platform for managing containerized workloads and services. However, as the complexity of managing machine learning and data science workflows on Kubernetes increased, Kubeflow was developed to address these specific challenges. While both Kubeflow and Kubernetes play significant roles in deploying and managing containerized applications, there are several key differences between the two.

Abstraction Level: Kubernetes provides a lower-level infrastructure for managing containers, offering fine-grained control over resources and orchestration. On the other hand, Kubeflow is a higher-level platform that builds on top of Kubernetes and provides a more user-friendly and specialized environment for machine learning workloads. It abstracts away the complexities of managing Kubernetes resources and offers specific features tailored to the needs of data scientists and ML practitioners.
Components: Kubernetes primarily consists of a cluster manager and a set of core resources like pods, containers, and services. It provides a declarative way to define and manage these resources. Kubeflow, however, is an ecosystem of tools and components that leverage Kubernetes to provide a complete machine learning workflow. It includes components like Jupyter notebooks, distributed training frameworks, serving infrastructure, and model versioning tools, among others.
Workflow Management: Kubernetes focuses on managing individual containers and their lifecycle. It is not inherently designed for managing complex workflows involving multiple interconnected components. Kubeflow, on the other hand, provides tools and abstractions specifically built for orchestrating machine learning workflows. It allows for defining complex pipelines that automate the execution of multiple steps, data dependencies, and parallelization.
Custom Resource Definitions (CRDs): Kubernetes provides a way to extend its core API by defining custom resource definitions (CRDs). These CRDs enable users to define and manage custom resources, such as specialized GPUs or specific ML job types. Kubeflow heavily utilizes CRDs to define higher-level abstractions for machine learning concepts like training jobs, serving deployments, and data pre-processing steps. This allows for a more intuitive and specialized management of ML-specific concepts.
Monitoring and Observability: Kubernetes provides basic monitoring and observability capabilities through integration with tools like Prometheus and Grafana. While these tools offer insights into the underlying infrastructure, they do not provide native support for monitoring the internals of individual ML workloads. Kubeflow, on the other hand, includes specialized components for monitoring and observability. It offers features like distributed tracing, metric collection for ML-specific parameters, and visualizations of training progress.
Community and Ecosystem: Kubernetes has a vast and mature ecosystem with a strong community backing, making it the go-to choice for managing containerized workloads. Kubeflow is a relatively newer project but is rapidly growing and gaining popularity within the data science and ML communities. It has its own growing ecosystem of tools, libraries, and community support. As a result, Kubeflow users can benefit from both the broader Kubernetes ecosystem and the specialized tools developed specifically for ML workloads.

In summary, while Kubernetes provides a robust infrastructure for managing containerized applications, Kubeflow builds on top of Kubernetes to provide a specialized platform for managing machine learning workflows. Kubeflow abstracts away the complexities of Kubernetes and offers ML-specific components and abstractions, making it easier for data scientists and ML practitioners to develop, deploy, and scale their models.

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Advice on Kubernetes, Kubeflow

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

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Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

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Detailed Comparison

Kubernetes	Kubeflow
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.	The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable	-
Statistics
Stacks 61.2K	Stacks 205
Followers 52.8K	Followers 585
Votes 685	Votes 18
Pros & Cons
Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems	Pros 9 System designer 3 Google backed 3 Customisation 3 Kfp dsl 0 Azure
Integrations
Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine	Jupyter TensorFlow

What are some alternatives to Kubernetes, Kubeflow?

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Related Comparisons

Kubeflow vs Kubernetes: What are the differences?

Introduction

Abstraction Level: Kubernetes provides a lower-level infrastructure for managing containers, offering fine-grained control over resources and orchestration. On the other hand, Kubeflow is a higher-level platform that builds on top of Kubernetes and provides a more user-friendly and specialized environment for machine learning workloads. It abstracts away the complexities of managing Kubernetes resources and offers specific features tailored to the needs of data scientists and ML practitioners.
Components: Kubernetes primarily consists of a cluster manager and a set of core resources like pods, containers, and services. It provides a declarative way to define and manage these resources. Kubeflow, however, is an ecosystem of tools and components that leverage Kubernetes to provide a complete machine learning workflow. It includes components like Jupyter notebooks, distributed training frameworks, serving infrastructure, and model versioning tools, among others.
Workflow Management: Kubernetes focuses on managing individual containers and their lifecycle. It is not inherently designed for managing complex workflows involving multiple interconnected components. Kubeflow, on the other hand, provides tools and abstractions specifically built for orchestrating machine learning workflows. It allows for defining complex pipelines that automate the execution of multiple steps, data dependencies, and parallelization.
Custom Resource Definitions (CRDs): Kubernetes provides a way to extend its core API by defining custom resource definitions (CRDs). These CRDs enable users to define and manage custom resources, such as specialized GPUs or specific ML job types. Kubeflow heavily utilizes CRDs to define higher-level abstractions for machine learning concepts like training jobs, serving deployments, and data pre-processing steps. This allows for a more intuitive and specialized management of ML-specific concepts.
Monitoring and Observability: Kubernetes provides basic monitoring and observability capabilities through integration with tools like Prometheus and Grafana. While these tools offer insights into the underlying infrastructure, they do not provide native support for monitoring the internals of individual ML workloads. Kubeflow, on the other hand, includes specialized components for monitoring and observability. It offers features like distributed tracing, metric collection for ML-specific parameters, and visualizations of training progress.
Community and Ecosystem: Kubernetes has a vast and mature ecosystem with a strong community backing, making it the go-to choice for managing containerized workloads. Kubeflow is a relatively newer project but is rapidly growing and gaining popularity within the data science and ML communities. It has its own growing ecosystem of tools, libraries, and community support. As a result, Kubeflow users can benefit from both the broader Kubernetes ecosystem and the specialized tools developed specifically for ML workloads.

Kubeflow vs Kubernetes

Overview

Kubeflow vs Kubernetes: What are the differences?