Kubeflow vs Kubernetes

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Kubeflow

123
410
+ 1
13
Kubernetes

30.3K
24.9K
+ 1
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Decisions about Kubeflow and Kubernetes
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 2.2M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis 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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Pros of Kubeflow
Pros of Kubernetes
  • 5
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
  • 151
    Leading docker container management solution
  • 121
    Simple and powerful
  • 95
    Open source
  • 70
    Backed by google
  • 55
    The right abstractions
  • 24
    Scale services
  • 16
    Replication controller
  • 9
    Permission managment
  • 6
    Simple
  • 5
    Supports autoscaling
  • 5
    Cheap
  • 3
    Promotes modern/good infrascture practice
  • 3
    No cloud platform lock-in
  • 3
    Self-healing
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Reliable
  • 2
    A self healing environment with rich metadata
  • 2
    Captain of Container Ship
  • 2
    Quick cloud setup
  • 1
    Custom and extensibility
  • 1
    Expandable
  • 1
    Easy setup
  • 1
    Gke
  • 1
    Golang
  • 1
    Backed by Red Hat
  • 1
    Everything of CaaS
  • 1
    Runs on azure
  • 1
    Cloud Agnostic
  • 1
    Sfg

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Cons of Kubeflow
Cons of Kubernetes
    Be the first to leave a con
    • 13
      Poor workflow for development
    • 10
      Steep learning curve
    • 4
      Orchestrates only infrastructure
    • 2
      High resource requirements for on-prem clusters

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    What is Kubeflow?

    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.

    What is Kubernetes?

    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.

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    What companies use Kubeflow?
    What companies use Kubernetes?

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    What tools integrate with Kubeflow?
    What tools integrate with Kubernetes?

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    What are some alternatives to Kubeflow and Kubernetes?
    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.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    MLflow
    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
    Airflow
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    Polyaxon
    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
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