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Open Source Software Library for Machine Intelligence
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What is 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.
TensorFlow is a tool in the Machine Learning Tools category of a tech stack.
TensorFlow is an open source tool with 147.3K GitHub stars and 82.4K GitHub forks. Here鈥檚 a link to TensorFlow's open source repository on GitHub

Who uses TensorFlow?

350 companies reportedly use TensorFlow in their tech stacks, including Uber, Delivery Hero, and Ruangguru.

1546 developers on StackShare have stated that they use TensorFlow.

TensorFlow Integrations

JavaScript, Jupyter, Keras, Kubeflow, and Databricks are some of the popular tools that integrate with TensorFlow. Here's a list of all 41 tools that integrate with TensorFlow.
Public Decisions about TensorFlow

Here are some stack decisions, common use cases and reviews by companies and developers who chose TensorFlow in their tech stack.

Tom Klein
Tom Klein

Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 973.5K 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:


(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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Deep learning jobs require a unique challenge versus other jobs that run across multiple GPUs: they need every node to stay up and running till the job is complete, which is why Uber uses gang scheduling.

Gang scheduling (an optimization algorithm) means that for a cluster computing job to run, all the nodes have to be ready to run at the same time. This is especially useful in deep learning training, which involves constant feedback exchanged between nodes. Uber implemented gang scheduling in an Open Source framework called Horovod, to run Google鈥檚 TensorFlow machine learning software across multiple nodes.

Because they needed GPUs in upstream releases as well, Uber鈥檚 engineers chose to use Mesos containers over Docker.

The engineers at Uber used Horovod (and the TensorFlow package compatible with it) because it was easier to learn the rules of the MPI library in Horovod, than learning an entirely new system.

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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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Shared insights

Hello All, I have concerns about which framework to use in my case. I'm working on a project that uses TensorFlow for implementing CNN and image processing, it also deals with a huge dataset. Shall I implement the rest APIs in Phalcon because of its speed and great performance or Falcon since I'm working with TensorFlow and doing image processing steps?

PS: APIs are to receive the image from the user, and call *.py files to execute image processing steps and CNN Thanks In Advance :D

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TensorFlow Alternatives & Comparisons

What are some alternatives to TensorFlow?
Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
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.
OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
See all alternatives

TensorFlow's Followers
1991 developers follow TensorFlow to keep up with related blogs and decisions.
Leon Wang
Ariel Brandes
Daniel Garcia Tello
Wh isere
Abhishek Pokala
Georgi Dimitrov
naveen garhwal
Ayush Kumar Shah