Alternatives to Leaf logo

Alternatives to Leaf

Leaflet, Volt, TensorFlow, Keras, and scikit-learn are the most popular alternatives and competitors to Leaf.
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What is Leaf and what are its top alternatives?

Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.
Leaf is a tool in the Machine Learning Tools category of a tech stack.
Leaf is an open source tool with 5.4K GitHub stars and 274 GitHub forks. Here鈥檚 a link to Leaf's open source repository on GitHub

Top Alternatives to Leaf

Leaf alternatives & related posts

Leaflet logo

Leaflet

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JavaScript library for mobile-friendly interactive maps
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Which will give a better map (better view, markers options, info window) in an Android OS app?

Leaflet with Mapbox or Leaflet with OpenStreetMap?

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Volt logo

Volt

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A ruby web framework where your ruby runs on both server and client
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TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.1M 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)

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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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Keras logo

Keras

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Deep Learning library for Theano and TensorFlow
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related Keras posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.1M 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.

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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
CONS OF SCIKIT-LEARN

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PyTorch logo

PyTorch

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A deep learning framework that puts Python first
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related PyTorch posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.1M 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
CUDA logo

CUDA

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It provides everything you need to develop GPU-accelerated applications
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PROS OF CUDA
    No pros available
    CONS OF CUDA
      No cons available

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      ML Kit logo

      ML Kit

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      Machine learning for mobile developers (by Google)
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      PROS OF ML KIT
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        CONS OF ML KIT
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