Alternatives to Paperspace logo

Alternatives to Paperspace

FloydHub, TensorFlow, scikit-learn, Keras, and PyTorch 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 Tools category of a tech stack.

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    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‚ÄĒfor instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA‚Äôs 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‚Äôs deep learning toolkit which makes it easier to start‚ÄĒand speed up‚ÄĒdistributed deep learning projects with TensorFlow:

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

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    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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