Alternatives to Numba logo

Alternatives to Numba

Julia, CUDA, NumPy, PyPy, and Pandas are the most popular alternatives and competitors to Numba.
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What is Numba and what are its top alternatives?

It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
Numba is a tool in the Machine Learning Tools category of a tech stack.

Top Alternatives to Numba

  • Julia

    Julia

    Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. ...

  • 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. ...

  • NumPy

    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • PyPy

    PyPy

    It is a very compliant implementation of the Python language, featuring a JIT compiler. It runs code about 7 times faster than CPython. ...

  • Pandas

    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • CuPy

    CuPy

    It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. ...

  • 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. ...

  • 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. ...

Numba alternatives & related posts

Julia logo

Julia

387
504
118
A high-level, high-performance dynamic programming language for technical computing
387
504
+ 1
118
PROS OF JULIA
  • 18
    Designed for parallelism and distributed computation
  • 18
    Fast Performance and Easy Experimentation
  • 14
    Free and Open Source
  • 13
    Multiple Dispatch
  • 12
    Calling C functions directly
  • 12
    Lisp-like Macros
  • 11
    Dynamic Type System
  • 8
    Powerful Shell-like Capabilities
  • 4
    REPL
  • 4
    Jupyter notebook integration
  • 2
    Emojis as variable names
  • 2
    String handling
CONS OF JULIA
  • 5
    Immature library management system
  • 3
    Slow program start
  • 3
    Poor backwards compatibility
  • 2
    JIT compiler is very slow
  • 2
    Bad tooling
  • 2
    No static compilation

related Julia posts

CUDA logo

CUDA

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129
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It provides everything you need to develop GPU-accelerated applications
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+ 1
0
PROS OF CUDA
    Be the first to leave a pro
    CONS OF CUDA
      Be the first to leave a con

      related CUDA posts

      NumPy logo

      NumPy

      965
      627
      7
      Fundamental package for scientific computing with Python
      965
      627
      + 1
      7
      PROS OF NUMPY
      • 6
        Great for data analysis
      • 1
        Faster than list
      CONS OF NUMPY
        Be the first to leave a con

        related NumPy 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
        PyPy logo

        PyPy

        9
        23
        0
        A fast, JIT-compiled Python implementation
        9
        23
        + 1
        0
        PROS OF PYPY
          Be the first to leave a pro
          CONS OF PYPY
            Be the first to leave a con

            related PyPy posts

            Pandas logo

            Pandas

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            1K
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            High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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            1K
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            20
            PROS OF PANDAS
            • 19
              Easy data frame management
            • 1
              Extensive file format compatibility
            CONS OF PANDAS
              Be the first to leave a con

              related Pandas 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
              Guillaume Simler

              Jupyter Anaconda Pandas IPython

              A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

              The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

              See more
              CuPy logo

              CuPy

              0
              8
              0
              A NumPy-compatible matrix library accelerated by CUDA
              0
              8
              + 1
              0
              PROS OF CUPY
                Be the first to leave a pro
                CONS OF CUPY
                  Be the first to leave a con

                  related CuPy posts

                  PyTorch logo

                  PyTorch

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                  1.1K
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                  A deep learning framework that puts Python first
                  1K
                  1.1K
                  + 1
                  42
                  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
                  TensorFlow logo

                  TensorFlow

                  2.7K
                  2.9K
                  77
                  Open Source Software Library for Machine Intelligence
                  2.7K
                  2.9K
                  + 1
                  77
                  PROS OF TENSORFLOW
                  • 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
                  CONS OF TENSORFLOW
                  • 9
                    Hard
                  • 6
                    Hard to debug
                  • 1
                    Documentation not very helpful

                  related TensorFlow 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

                  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