Alternatives to CUDA logo

Alternatives to CUDA

OpenCL, OpenGL, TensorFlow, PyTorch, and Keras are the most popular alternatives and competitors to CUDA.
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What is CUDA and what are its top alternatives?

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.
CUDA is a tool in the Machine Learning Tools category of a tech stack.

Top Alternatives to CUDA

  • OpenCL

    OpenCL

    It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing. ...

  • OpenGL

    OpenGL

    It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering. ...

  • 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. ...

  • 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 ...

  • 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. ...

CUDA alternatives & related posts

OpenCL logo

OpenCL

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The open standard for parallel programming of heterogeneous systems
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      OpenGL logo

      OpenGL

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      An environment for developing 2D and 3D graphics applications
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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
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            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
          • 8
            Hard
          • 5
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          • 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
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            Easy to use
          • 11
            Developer Friendly
          • 10
            Easy to debug
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            Sometimes faster than TensorFlow
          CONS OF PYTORCH
          • 3
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          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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          Deep Learning library for Theano and TensorFlow
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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

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          Easy-to-use and general-purpose machine learning in Python
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          PROS OF SCIKIT-LEARN
          • 20
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            Easy
          CONS OF SCIKIT-LEARN
          • 1
            Limited

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          TensorFlow.js logo

          TensorFlow.js

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          Machine Learning in JavaScript
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          PROS OF TENSORFLOW.JS
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            Deploy python ML model directly into javascript
          CONS OF TENSORFLOW.JS
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            rashid munir
            freelancer at freelancer · | 3 upvotes · 12K 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.

            See more
            Neel Bavarva
            Student at NeelBavarva · | 2 upvotes · 16.8K views
            Shared insights
            on
            TensorFlow.js
            Python

            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.

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            Kubeflow logo

            Kubeflow

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            Machine Learning Toolkit for Kubernetes
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            PROS OF KUBEFLOW
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              System designer
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              Customisation
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              Kfp dsl
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              Google backed
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              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)?

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