Alternatives to TensorFlow logo

Alternatives to TensorFlow

Theano, PyTorch, Keras, OpenCV, and Apache Spark are the most popular alternatives and competitors to TensorFlow.
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What is TensorFlow and what are its top alternatives?

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.
TensorFlow is a tool in the Machine Learning Tools category of a tech stack.
TensorFlow is an open source tool with 146.2K GitHub stars and 81.9K GitHub forks. Here鈥檚 a link to TensorFlow's open source repository on GitHub

Top Alternatives to TensorFlow

TensorFlow alternatives & related posts

Theano logo

Theano

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Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently
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      PyTorch logo

      PyTorch

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      A deep learning framework that puts Python first
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      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 952.3K 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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      Keras logo

      Keras

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      Deep Learning library for Theano and TensorFlow
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      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 952.3K 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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      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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      OpenCV logo

      OpenCV

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      Open Source Computer Vision Library
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      Apache Spark logo

      Apache Spark

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      Fast and general engine for large-scale data processing
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      Eric Colson
      Eric Colson
      Chief Algorithms Officer at Stitch Fix | 19 upvotes 1.3M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

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      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 635.6K views

      Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

      Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

      https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

      (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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      MXNet logo

      MXNet

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      A flexible and efficient library for deep learning
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          Caffe2 logo

          Caffe2

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          Open Source Cross-Platform Machine Learning Tools (by Facebook)
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            Microsoft Cognitive Toolkit logo

            Microsoft Cognitive Toolkit

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            An open-source toolkit for deep learning
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