Sphinx vs TensorFlow: What are the differences?
What is Sphinx? Open source full text search server, designed from the ground up with performance, relevance (aka search quality), and integration simplicity in mind. Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server. A variety of text processing features enable fine-tuning Sphinx for your particular application requirements, and a number of relevance functions ensures you can tweak search quality as well.
What is TensorFlow? Open Source Software Library for Machine Intelligence. 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.
Sphinx belongs to "Search Engines" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".
"Fast" is the top reason why over 12 developers like Sphinx, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.
Uber Technologies, 9GAG, and Postmates are some of the popular companies that use TensorFlow, whereas Sphinx is used by Webedia, Grooveshark, and Ansible. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to Sphinx, which is listed in 38 company stacks and 14 developer stacks.
What is Sphinx?
What is TensorFlow?
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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—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s 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’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
We use Sphinx as the main search indexing system on our clients' websites. It's a more powerful system than we even scratch the surface of, and allows us to index data from a variety of sources.
Machine Learning in EECS 445