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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Gradio vs MNN

Gradio vs MNN

OverviewComparisonAlternatives

Overview

MNN
MNN
Stacks1
Followers6
Votes0
GitHub Stars13.4K
Forks2.1K
Gradio
Gradio
Stacks37
Followers24
Votes0
GitHub Stars40.4K
Forks3.1K

Gradio vs MNN: What are the differences?

  1. Deployment Flexibility: Gradio provides a user-friendly interface for deployment, while MNN requires more technical knowledge and effort to deploy.
  2. Model Support: Gradio supports a wide range of pre-trained models and allows users to easily integrate custom models, whereas MNN has limited model support and requires manual setup for custom models.
  3. Input/Output Handling: Gradio offers flexible input and output handling options, including support for multiple data types, text inputs, and audio inputs, while MNN has more rigid input/output requirements and may need data preprocessing.
  4. Ease of Use: Gradio simplifies the process of building and deploying machine learning models with its drag-and-drop interface, whereas MNN is more suited for developers with a deeper understanding of model optimization and deployment.
  5. Community Support: Gradio has a larger community of users and developers, providing more resources and examples for beginners, while MNN's community is smaller and may have limited documentation and support available.
  6. Performance Optimization: Gradio focuses on user-friendly features and ease of deployment, potentially sacrificing some performance optimizations, whereas MNN prioritizes model efficiency and performance, suitable for applications requiring high-speed inference.

In Summary, Gradio offers user-friendly deployment with model support and input/output flexibility, while MNN caters to developers seeking performance optimization and customization.

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Detailed Comparison

MNN
MNN
Gradio
Gradio

It is a lightweight deep neural network inference engine. It loads models and do inference on devices. At present, it has been integrated in more than 20 apps of Alibaba-inc, such as Taobao, Tmall, Youku and etc., covering live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control and other scenarios. In addition, it is also used on embedded devices, such as IoT.

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.

Optimized for devices, no dependencies, can be easily deployed to mobile devices and a variety of embedded devices; Supports Tensorflow, Caffe, ONNX, and supports common neural networks such as CNN, RNN, GAN; High performance; Easy to use
Customizable Components; Multiple Inputs and Outputs; Sharing Interfaces Publicly & Privacy
Statistics
GitHub Stars
13.4K
GitHub Stars
40.4K
GitHub Forks
2.1K
GitHub Forks
3.1K
Stacks
1
Stacks
37
Followers
6
Followers
24
Votes
0
Votes
0
Integrations
No integrations available
Jupyter
Jupyter
TensorFlow
TensorFlow
PyTorch
PyTorch
Matplotlib
Matplotlib
scikit-learn
scikit-learn

What are some alternatives to MNN, Gradio?

TensorFlow

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

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

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

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

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

H2O

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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