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Kubeflow

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Kubeflow vs TensorFlow: What are the differences?

What is Kubeflow? Machine Learning Toolkit for Kubernetes. 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 TensorFlow? Open Source Software Library for Machine Intelligence. 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.

Kubeflow and TensorFlow can be categorized as "Machine Learning" tools.

Kubeflow is an open source tool with 6.93K GitHub stars and 1K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

Decisions about Kubeflow and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 56.4K views

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.

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Pros of Kubeflow
Pros of TensorFlow
  • 8
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
  • 25
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    True Portability
  • 9
    Auto-Differentiation
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful

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Cons of Kubeflow
Cons of TensorFlow
    Be the first to leave a con
    • 9
      Hard
    • 6
      Hard to debug
    • 1
      Documentation not very helpful

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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 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.

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    What companies use Kubeflow?
    What companies use TensorFlow?
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    What tools integrate with Kubeflow?
    What tools integrate with TensorFlow?

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    What are some alternatives to Kubeflow and TensorFlow?
    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.
    Argo
    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).
    See all alternatives