Alternatives to Paperspace logo

Alternatives to Paperspace

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

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
Paperspace is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to Paperspace

  • FloydHub

    FloydHub

    Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem. ...

  • 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. ...

  • Azure Machine Learning

    Azure Machine Learning

    Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning. ...

  • Amazon SageMaker

    Amazon SageMaker

    A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. ...

  • 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. ...

Paperspace alternatives & related posts

FloydHub logo

FloydHub

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15
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Heroku for Deep Learning
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15
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      TensorFlow logo

      TensorFlow

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      PROS OF TENSORFLOW
      • 24
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      • 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
        Hard to debug
      • 1
        Documentation not very helpful

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M 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鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 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鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed 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

      893
      981
      37
      A deep learning framework that puts Python first
      893
      981
      + 1
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      PROS OF PYTORCH
      • 12
        Easy to use
      • 10
        Developer Friendly
      • 9
        Easy to debug
      • 6
        Sometimes faster than TensorFlow
      CONS OF PYTORCH
      • 3
        Lots of code

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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 路 1.2M 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鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 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鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

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

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

      See more
      Keras logo

      Keras

      881
      913
      14
      Deep Learning library for Theano and TensorFlow
      881
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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 路 1.2M 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鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 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鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed 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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      892
      35
      Easy-to-use and general-purpose machine learning in Python
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      PROS OF SCIKIT-LEARN
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        Scientific computing
      • 15
        Easy
      CONS OF SCIKIT-LEARN
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      Azure Machine Learning logo

      Azure Machine Learning

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      A fully-managed cloud service for predictive analytics
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      PROS OF AZURE MACHINE LEARNING
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        CONS OF AZURE MACHINE LEARNING
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          Amazon SageMaker logo

          Amazon SageMaker

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          Accelerated Machine Learning
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          PROS OF AMAZON SAGEMAKER
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            CONS OF AMAZON SAGEMAKER
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              Julien DeFrance
              Principal Software Engineer at Tophatter | 2 upvotes 路 46.9K views

              Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

              Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

              Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

              Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

              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
              CUDA logo

              CUDA

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              It provides everything you need to develop GPU-accelerated applications
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