MNN vs Torch

Overview

Torch

Stacks355

Followers61

Votes0

GitHub Stars9.1K

Forks2.4K

MNN

Stacks1

Followers6

Votes0

GitHub Stars13.4K

Forks2.1K

MNN vs Torch: What are the differences?

Introduction: In the realm of machine learning frameworks, it is important to distinguish between MNN (Mobile Neural Network) and Torch. These frameworks have distinct differences that make each suitable for specific tasks and applications.

Architecture: MNN is specifically designed for mobile devices, offering optimized performance and efficiency for running neural networks on mobile platforms. Torch, on the other hand, is a more generic framework that is widely used for research and development in deep learning.
Language Support: MNN primarily supports C++ and Java for its development, making it well-suited for mobile application development. Torch, on the other hand, supports multiple languages including Lua, which was the original language of Torch, and Python.
Community and Ecosystem: Torch has a larger and more established community compared to MNN, which translates to better support, documentation, and a variety of pre-built models and tools for deep learning tasks. MNN, being more specialized, has a more focused ecosystem tailored towards mobile applications.
Performance Optimization: MNN places a stronger emphasis on optimizing performance for mobile devices, including tools for model quantization and compression, to ensure efficient execution on resource-constrained devices. Torch, being a more general-purpose framework, may not offer the same level of performance optimization for mobile platforms.
Deployment Flexibility: Torch is known for its flexibility in deployment, allowing models to be easily deployed on various platforms and devices. MNN, as a framework specifically designed for mobile devices, may have limitations in terms of deployment to other platforms or devices.
Ease of Use for Beginners: Torch is often considered more beginner-friendly with its intuitive API and extensive documentation, while MNN, being more specialized and focused on mobile applications, may have a steeper learning curve for beginners in the field of deep learning.

In Summary, MNN and Torch differ in their architecture, language support, community, performance optimization, deployment flexibility, and ease of use for beginners, catering to specific needs in the realm of machine learning frameworks.

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CLI (Node.js)

Manual

Detailed Comparison

Torch	MNN
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.	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.
A powerful N-dimensional array; Lots of routines for indexing, slicing, transposing; Amazing interface to C, via LuaJIT; Linear algebra routines; Neural network, and energy-based models; Numeric optimization routines; Fast and efficient GPU support; Embeddable, with ports to iOS and Android backends	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
Statistics
GitHub Stars 9.1K	GitHub Stars 13.4K
GitHub Forks 2.4K	GitHub Forks 2.1K
Stacks 355	Stacks 1
Followers 61	Followers 6
Votes 0	Votes 0
Integrations
Python SQLFlow GraphPipe Flair Pythia Databricks Comet.ml	No integrations available

What are some alternatives to Torch, MNN?

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.

Related Comparisons

MNN vs Torch: What are the differences?

Architecture: MNN is specifically designed for mobile devices, offering optimized performance and efficiency for running neural networks on mobile platforms. Torch, on the other hand, is a more generic framework that is widely used for research and development in deep learning.
Language Support: MNN primarily supports C++ and Java for its development, making it well-suited for mobile application development. Torch, on the other hand, supports multiple languages including Lua, which was the original language of Torch, and Python.
Community and Ecosystem: Torch has a larger and more established community compared to MNN, which translates to better support, documentation, and a variety of pre-built models and tools for deep learning tasks. MNN, being more specialized, has a more focused ecosystem tailored towards mobile applications.
Performance Optimization: MNN places a stronger emphasis on optimizing performance for mobile devices, including tools for model quantization and compression, to ensure efficient execution on resource-constrained devices. Torch, being a more general-purpose framework, may not offer the same level of performance optimization for mobile platforms.
Deployment Flexibility: Torch is known for its flexibility in deployment, allowing models to be easily deployed on various platforms and devices. MNN, as a framework specifically designed for mobile devices, may have limitations in terms of deployment to other platforms or devices.
Ease of Use for Beginners: Torch is often considered more beginner-friendly with its intuitive API and extensive documentation, while MNN, being more specialized and focused on mobile applications, may have a steeper learning curve for beginners in the field of deep learning.

MNN vs Torch

Overview