Gradio vs Neptune

Overview

Neptune

Stacks16

Followers38

Votes2

Gradio

Stacks37

Followers24

Votes0

GitHub Stars40.4K

Forks3.1K

Neptune vs Gradio: What are the differences?

Developers describe Neptune as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools. On the other hand, Gradio is detailed as "*GUIs for Faster ML Prototyping and Sharing *". It allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs.

Neptune and Gradio belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Neptune are:

Experiment tracking
Experiment versioning
Experiment comparison

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

Customizable Components
Multiple Inputs and Outputs
Sharing Interfaces Publicly & Privacy

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CLI (Node.js)

Manual

Detailed Comparison

Neptune	Gradio
It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.	It allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs.
Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning	Customizable Components; Multiple Inputs and Outputs; Sharing Interfaces Publicly & Privacy
Statistics
GitHub Stars -	GitHub Stars 40.4K
GitHub Forks -	GitHub Forks 3.1K
Stacks 16	Stacks 37
Followers 38	Followers 24
Votes 2	Votes 0
Pros & Cons
Pros 1 Supports both gremlin and openCypher query languages 1 Aws managed services Cons 1 Doesn't have much community support 1 Doesn't have proper clients for different lanuages 1 Doesn't have much support for openCypher clients	No community feedback yet
Integrations
PyTorch Keras R Language MLflow Matplotlib	Jupyter TensorFlow PyTorch Matplotlib scikit-learn

What are some alternatives to Neptune, Gradio?

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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.

Streamlit

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.

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.

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