Keras vs Streamlit

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

Streamlit

Stacks404

Followers407

Votes12

GitHub Stars42.1K

Forks3.9K

Keras vs Streamlit: What are the differences?

Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, Streamlit is detailed as "A Python app framework built specifically for Machine Learning and Data Science teams". It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

Keras and Streamlit belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Keras are:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

On the other hand, Streamlit provides the following key features:

Free and open source
Build apps in a dozen lines of Python with a simple API
No callbacks

Keras and Streamlit are both open source tools. It seems that Keras with 44.7K GitHub stars and 17K forks on GitHub has more adoption than Streamlit with 2.73K GitHub stars and 184 GitHub forks.

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Advice on Keras, Streamlit

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

Keras	Streamlit
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	Free and open source; Build apps in a dozen lines of Python with a simple API; No callbacks; No hidden state; Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more
Statistics
GitHub Stars -	GitHub Stars 42.1K
GitHub Forks -	GitHub Forks 3.9K
Stacks 1.1K	Stacks 404
Followers 1.1K	Followers 407
Votes 22	Votes 12
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	Pros 11 Fast development 1 Fast development and apprenticeship
Integrations
TensorFlow scikit-learn Python	Python Plotly.js PyTorch Pandas Bokeh NumPy Matplotlib TensorFlow Altair GraphQL

What are some alternatives to Keras, Streamlit?

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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Related Comparisons

Some of the features offered by Keras are:

neural networks API
Allows for easy and fast prototyping
Convolutional networks support

On the other hand, Streamlit provides the following key features:

Free and open source
Build apps in a dozen lines of Python with a simple API
No callbacks

Keras vs Streamlit