Alternatives to Polyaxon logo

Alternatives to Polyaxon

Kubeflow, MLflow, Pachyderm, TensorFlow, and PyTorch are the most popular alternatives and competitors to Polyaxon.
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What is Polyaxon and what are its top alternatives?

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Polyaxon is a tool in the Machine Learning Tools category of a tech stack.
Polyaxon is an open source tool with 2.9K GitHub stars and 284 GitHub forks. Here’s a link to Polyaxon's open source repository on GitHub

Top Alternatives to Polyaxon

  • Kubeflow

    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. ...

  • MLflow

    MLflow

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

  • Pachyderm

    Pachyderm

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations. ...

  • TensorFlow

    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. ...

  • PyTorch

    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

    Keras

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

  • scikit-learn

    scikit-learn

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

  • CUDA

    CUDA

    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation. ...

Polyaxon alternatives & related posts

Kubeflow logo

Kubeflow

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458
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Machine Learning Toolkit for Kubernetes
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458
+ 1
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PROS OF KUBEFLOW
  • 8
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
CONS OF KUBEFLOW
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    Biswajit Pathak
    Project Manager at Sony · | 4 upvotes · 13.6K views

    Can you please advise which one to choose FastText Or Gensim, in terms of:

    1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
    2. Performance
    3. Customization of Intermediate steps
    4. FastText and Gensim both have the same underlying libraries
    5. Use cases each one tries to solve
    6. Unsupervised Vs Supervised dimensions
    7. Ease of Use.

    Please mention any other points that I may have missed here.

    See more

    Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

    See more
    MLflow logo

    MLflow

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    376
    8
    An open source machine learning platform
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    PROS OF MLFLOW
    • 4
      Simplified Logging
    • 4
      Code First
    CONS OF MLFLOW
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      Shared insights
      on
      MLflow
      DVC

      I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?

      See more
      Biswajit Pathak
      Project Manager at Sony · | 4 upvotes · 13.6K views

      Can you please advise which one to choose FastText Or Gensim, in terms of:

      1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
      2. Performance
      3. Customization of Intermediate steps
      4. FastText and Gensim both have the same underlying libraries
      5. Use cases each one tries to solve
      6. Unsupervised Vs Supervised dimensions
      7. Ease of Use.

      Please mention any other points that I may have missed here.

      See more
      Pachyderm logo

      Pachyderm

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      58
      5
      MapReduce without Hadoop. Analyze massive datasets with Docker.
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      5
      PROS OF PACHYDERM
      • 3
        Containers
      • 1
        Versioning
      • 1
        Can run on GCP or AWS
      CONS OF PACHYDERM
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        TensorFlow logo

        TensorFlow

        2.6K
        2.8K
        77
        Open Source Software Library for Machine Intelligence
        2.6K
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        PROS OF TENSORFLOW
        • 25
          High Performance
        • 16
          Connect Research and Production
        • 13
          Deep Flexibility
        • 9
          Auto-Differentiation
        • 9
          True Portability
        • 2
          Easy to use
        • 2
          High level abstraction
        • 1
          Powerful
        CONS OF TENSORFLOW
        • 9
          Hard
        • 5
          Hard to debug
        • 1
          Documentation not very helpful

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more

        In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

        Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

        !

        See more
        PyTorch logo

        PyTorch

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        A deep learning framework that puts Python first
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        PROS OF PYTORCH
        • 14
          Easy to use
        • 11
          Developer Friendly
        • 10
          Easy to debug
        • 7
          Sometimes faster than TensorFlow
        CONS OF PYTORCH
        • 3
          Lots of code

        related PyTorch posts

        Server side

        We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

        • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

        • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

        • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

        Client side

        • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

        • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

        • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

        Cache

        • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

        Database

        • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

        Infrastructure

        • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

        Other Tools

        • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

        • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more
        Keras logo

        Keras

        910
        950
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        Deep Learning library for Theano and TensorFlow
        910
        950
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        PROS OF KERAS
        • 5
          Easy and fast NN prototyping
        • 5
          Quality Documentation
        • 4
          Supports Tensorflow and Theano backends
        CONS OF KERAS
        • 3
          Hard to debug

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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more

        I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

        See more
        scikit-learn logo

        scikit-learn

        872
        925
        36
        Easy-to-use and general-purpose machine learning in Python
        872
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        PROS OF SCIKIT-LEARN
        • 20
          Scientific computing
        • 16
          Easy
        CONS OF SCIKIT-LEARN
        • 1
          Limited

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        CUDA logo

        CUDA

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        It provides everything you need to develop GPU-accelerated applications
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