Alternatives to Clipper logo

Alternatives to Clipper

Python, TensorFlow, PyTorch, Keras, and scikit-learn are the most popular alternatives and competitors to Clipper.
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What is Clipper and what are its top alternatives?

It is a low-latency prediction serving system for machine learning. Clipper makes it simple to integrate machine learning into user-facing serving systems.
Clipper is a tool in the Machine Learning Tools category of a tech stack.
Clipper is an open source tool with 1.3K GitHub stars and 271 GitHub forks. Here’s a link to Clipper's open source repository on GitHub

Top Alternatives to Clipper

  • Python

    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • 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. ...

  • 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. ...

  • TensorFlow.js

    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 ...

Clipper alternatives & related posts

Python logo

Python

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A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
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PROS OF PYTHON
  • 1.1K
    Great libraries
  • 933
    Readable code
  • 824
    Beautiful code
  • 772
    Rapid development
  • 674
    Large community
  • 420
    Open source
  • 380
    Elegant
  • 270
    Great community
  • 262
    Object oriented
  • 209
    Dynamic typing
  • 71
    Great standard library
  • 53
    Very fast
  • 50
    Functional programming
  • 37
    Scientific computing
  • 36
    Easy to learn
  • 31
    Great documentation
  • 25
    Matlab alternative
  • 23
    Productivity
  • 23
    Easy to read
  • 20
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 15
    Free
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 11
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Ease of development
  • 8
    Unlimited power
  • 8
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    Print "life is short, use python"
  • 6
    It's lean and fun to code
  • 5
    Fast coding and good for competitions
  • 5
    Flat is better than nested
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    I love snakes
  • 5
    Although practicality beats purity
  • 5
    Great for tooling
  • 4
    Readability counts
  • 3
    Plotting
  • 3
    CG industry needs
  • 3
    Beautiful is better than ugly
  • 3
    Complex is better than complicated
  • 3
    Great for analytics
  • 3
    Multiple Inheritence
  • 3
    Now is better than never
  • 3
    Lists, tuples, dictionaries
  • 3
    Rapid Prototyping
  • 3
    Socially engaged community
  • 2
    List comprehensions
  • 2
    Web scraping
  • 2
    Many types of collections
  • 2
    Ys
  • 2
    Easy to setup and run smooth
  • 2
    Generators
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 1
    Better outcome
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Powerful language for AI
  • 1
    Should START with this but not STICK with This
  • 1
    Flexible and easy
  • 1
    Batteries included
  • 1
    Good
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Because of Netflix
  • 1
    Pip install everything
  • 0
    Powerful
  • 0
    Pro
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 29
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Dynamic typing
  • 12
    Hard to understand
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Indentations matter a lot
  • 7
    Explicit self parameter in methods
  • 6
    No anonymous functions
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    Requires C functions for dynamic modules
  • 5
    The "lisp style" whitespaces
  • 5
    Fake object-oriented programming
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 4
    Circular import
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Official documentation is unclear.
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.4M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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PROS OF TENSORFLOW
  • 25
    High Performance
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    Connect Research and Production
  • 13
    Deep Flexibility
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    True Portability
  • 9
    Auto-Differentiation
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 9
    Hard
  • 6
    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.

!

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PyTorch logo

PyTorch

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A deep learning framework that puts Python first
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PROS OF PYTORCH
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    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

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959
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Deep Learning library for Theano and TensorFlow
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+ 1
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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

related Keras posts

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.

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scikit-learn logo

scikit-learn

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942
36
Easy-to-use and general-purpose machine learning in Python
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942
+ 1
36
PROS OF SCIKIT-LEARN
  • 20
    Scientific computing
  • 16
    Easy
CONS OF SCIKIT-LEARN
  • 1
    Limited

related scikit-learn posts

CUDA logo

CUDA

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127
0
It provides everything you need to develop GPU-accelerated applications
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+ 1
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PROS OF CUDA
    Be the first to leave a pro
    CONS OF CUDA
      Be the first to leave a con

      related CUDA posts

      Kubeflow logo

      Kubeflow

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      468
      16
      Machine Learning Toolkit for Kubernetes
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      468
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      PROS OF KUBEFLOW
      • 8
        System designer
      • 3
        Customisation
      • 3
        Kfp dsl
      • 2
        Google backed
      CONS OF KUBEFLOW
        Be the first to leave a con

        related Kubeflow posts

        Biswajit Pathak
        Project Manager at Sony · | 5 upvotes · 26.3K 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
        TensorFlow.js logo

        TensorFlow.js

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        Machine Learning in JavaScript
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        PROS OF TENSORFLOW.JS
        • 4
          NodeJS Powered
        • 4
          Open Source
        • 1
          Deploy python ML model directly into javascript
        CONS OF TENSORFLOW.JS
          Be the first to leave a con

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          rashid munir
          freelancer at freelancer · | 3 upvotes · 20K views

          Need your kind suggestion if I should choose TensorFlow.js or TensorFlow with Python for ML models. As I don't want to go and have not gone too deep in JavaScript, I need your suggestion.

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          Neel Bavarva
          Student at NeelBavarva · | 2 upvotes · 23.3K views
          Shared insights
          on
          TensorFlow.jsTensorFlow.jsPythonPython

          I am highly confused about using Machine Learning in Web Development. Should I learn ML in Python or start with TensorFlow.js as I'm a MERN stack developer? If any good resource (course/youtube channel) for learning Tensorflow.js is out there, please share it.

          See more