Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Kubeflow

Stacks205

Followers585

Votes18

Kubeflow vs TensorFlow: What are the differences?

Introduction

When comparing Kubeflow and TensorFlow, it is important to understand the key differences between these two popular platforms used for machine learning and deep learning tasks.

Deployment Environment: Kubeflow is designed to be used in Kubernetes environments, providing a platform to deploy, monitor, and manage machine learning models at scale. On the other hand, TensorFlow is a deep learning framework developed by Google that can be run in various environments, including Kubernetes, but not limited to it. This means that Kubeflow is specifically tailored for Kubernetes orchestration, whereas TensorFlow can be used in a more diverse set of deployment environments.
Integrated Tools and Libraries: Kubeflow provides a comprehensive set of tools, libraries, and resources specifically designed for machine learning workflows. It includes components such as Jupyter notebooks, TensorFlow Extended (TFX), and other machine learning tools to streamline the development and deployment process. TensorFlow, on the other hand, is primarily a deep learning library that offers a wide range of functions and operations for building neural networks. While TensorFlow can be integrated with other tools and libraries, it may require additional setup and configuration compared to Kubeflow's integrated approach.
Workflow Orchestration: Kubeflow offers built-in support for end-to-end machine learning pipeline orchestration, allowing users to define, run, and monitor complex workflows involving data preprocessing, model training, evaluation, and serving. In contrast, TensorFlow focuses more on the development and training of deep learning models rather than the orchestration of complete machine learning pipelines. Users may need to leverage external tools or frameworks to achieve a similar level of workflow orchestration as Kubeflow provides.
Community and Support: TensorFlow has a larger and more established community of users, developers, and contributors compared to Kubeflow. This means that TensorFlow users have access to a wealth of resources, tutorials, and community-driven support for troubleshooting issues and sharing best practices. While Kubeflow's community is growing rapidly, it may not offer the same depth and breadth of resources as TensorFlow's well-established ecosystem.
Abstraction Level: Kubeflow operates at a higher level of abstraction compared to TensorFlow. It enables users to interact with machine learning tools and resources through a unified interface, simplifying the process of building, training, and deploying models. TensorFlow, on the other hand, requires users to have a deeper understanding of the underlying operations and functionalities of deep learning, providing more flexibility and control but potentially requiring more expertise to leverage effectively.
Scalability and Resource Management: Kubeflow is designed to leverage the scalability and resource management capabilities of Kubernetes, enabling users to scale their machine learning workloads dynamically based on demand. TensorFlow, while capable of running on distributed systems for parallel processing, may require additional configuration and management to achieve the same level of scalability and resource optimization as Kubeflow offers out of the box.

In Summary, the key differences between Kubeflow and TensorFlow lie in their deployment environments, integrated tools and libraries, workflow orchestration capabilities, community and support, abstraction levels, and scalability/resource management approaches.

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Advice on TensorFlow, Kubeflow

Xi

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.64k views4.64k

Comments

Detailed Comparison

TensorFlow	Kubeflow
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.	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.
Statistics
GitHub Stars 192.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks -
Stacks 3.9K	Stacks 205
Followers 3.5K	Followers 585
Votes 106	Votes 18
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 9 System designer 3 Google backed 3 Kfp dsl 3 Customisation 0 Azure
Integrations
JavaScript	Kubernetes Jupyter

What are some alternatives to TensorFlow, Kubeflow?

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/

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.