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Kubeflow VS PySyft

Compare Kubeflow VS PySyft and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Kubeflow logo Kubeflow

Kubeflow makes deployment of ML Workflows on Kubernetes straightforward and automated

PySyft logo PySyft

A Library for Private, Secure Deep Learning
  • Kubeflow Landing page
    Landing page //
    2023-10-11
  • PySyft Landing page
    Landing page //
    2023-08-30

Kubeflow features and specs

  • Scalability
    Kubeflow leverages Kubernetes, enabling it to scale machine learning workflows efficiently across distributed systems.
  • Portability
    As it's built on Kubernetes, Kubeflow can run on various cloud and on-premise environments without modification.
  • End-to-End Pipeline Management
    Kubeflow provides an integrated platform to design and deploy end-to-end machine learning pipelines, simplifying model training, serving, and monitoring.
  • Open Source Community
    Being an open-source project, Kubeflow benefits from a strong community contributing to feature development and support.
  • Interoperability
    Kubeflow supports various ML frameworks, ensuring compatibility and flexibility for developers using TensorFlow, PyTorch, and other libraries.

Possible disadvantages of Kubeflow

  • Complexity
    The learning curve for setting up and managing Kubeflow can be steep due to its reliance on a wide array of Kubernetes tools.
  • Resource Intensive
    Running Kubeflow can be resource-intensive, requiring significant computational resources for effective deployment and management.
  • Operational Overhead
    Managing a Kubeflow deployment involves handling Kubernetes clusters, which can introduce additional operational overhead.
  • Limited GUI
    Kubeflow's graphical user interface may be less intuitive than other platforms, making it challenging for users without command-line proficiency.
  • Rapid Evolution
    Kubeflow is constantly evolving, which can lead to potential instability or the need for frequent updates and adjustments.

PySyft features and specs

No features have been listed yet.

Kubeflow videos

+ Add

Kubeflow 0.6 Release Feature Review

More videos:

  • Review - Kubeflow @ApacheSpark Operator PR update with review feedback
  • Review - Sentiment Analysis using Kubernetes and Kubeflow

PySyft videos

+ Add

Introduction to PySyft source code

Category Popularity

0-100% (relative to Kubeflow and PySyft)

Kubeflow

PySyft

Data Science And Machine Learning
71 71%
Data Science And Machine Learning
29% 29
Machine Learning Tools
100 100%
Machine Learning Tools
0% 0
Data Science Tools
57 57%
Data Science Tools
43% 43
Python Tools
0 0%
Python Tools
100% 100

User comments

Share your experience with using Kubeflow and PySyft. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Kubeflow seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Kubeflow mentions (2)

  • The Bacalhau Vision – A Distributed Compute over Data Platform
    I'm David Aronchick - first non-founding PM on Kubernetes, co-founder of Kubeflow [1], and co-founder of the SAME project [2] - and we've spent the past year working on Bacalhau [3], an open source project to bring compute to data. We've recently opened up a public-hosted cluster (all runnable from colab in our docs [4]) and would love your feedback - you can see our vision at the attached blog post. Thanks!... - Source: Hacker News / about 2 years ago
  • An update on relationships between stocks - STATISTICS ROCKS! - Brought to you by the SuperstonkQuants 🦍🥼🔬🚀
    You have GitHub org and a Vue based website up and running already, so it seems like you have tech logistics covered. Just in case it's useful, I have experience with Kubernetes, which can help run computationally intense workloads (even if GPUs are needed) or provide a pool of compute for something like Kubeflow (kubeflow.org). Here if you want, feel free to ignore if you're all covered in this area - I'll be... Source: almost 4 years ago

PySyft mentions (0)

We have not tracked any mentions of PySyft yet. Tracking of PySyft recommendations started around Mar 2021.

What are some alternatives?

When comparing Kubeflow and PySyft, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

CUDA Toolkit - Select Target Platform Click on the green buttons that describe your target platform.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

TensorFlow.js - TensorFlow.js is a library for machine learning in JavaScript

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

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