Overview

Torch

Stacks355

Followers61

Votes0

GitHub Stars9.1K

Forks2.4K

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

PyTorch vs Torch: What are the differences?

PyTorch and Torch are both popular deep learning frameworks. Let's explore the key differences between them.

Architecture and Development: The major difference between PyTorch and Torch lies in their architecture and development. PyTorch is based on Torch, but it has been re-engineered to provide a more dynamic and intuitive development experience. It includes features such as automatic differentiation, which allows developers to define and optimize computational graphs on the fly. In contrast, Torch uses a more static and declarative approach to building and optimizing computational graphs.
Pythonic Interface: PyTorch is designed to have a more pythonic interface compared to Torch. It leverages the power and simplicity of Python, making it easier for developers to write and debug deep learning models. Torch, on the other hand, provides a Lua interface, which may require additional effort for developers who are not familiar with the language.
Popularity and Community Support: PyTorch has gained significant popularity in recent years and has a large and active community. It has become the preferred choice for many researchers and practitioners in the deep learning community. Torch, while still widely used, may not have the same level of popularity and community support as PyTorch.
Development and Maintenance: PyTorch is actively developed and maintained by Facebook's AI Research (FAIR) group. This ensures that the framework is constantly updated with new features and bug fixes. Torch, on the other hand, is primarily developed and maintained by a smaller group of developers. While it is still actively maintained, the development pace may not be as fast as PyTorch.
Integration with Python Libraries: PyTorch seamlessly integrates with other popular Python libraries such as NumPy, SciPy, and scikit-learn. This allows developers to leverage the rich ecosystem of Python libraries and tools for data manipulation and analysis. Torch, being primarily Lua-based, may not have the same level of integration with Python libraries, although there are ways to bridge the two languages.

In summary, PyTorch offers a more dynamic and pythonic experience with a larger community, while Torch may be preferred by developers who are already familiar with Lua or have specific requirements.

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Advice on Torch, PyTorch

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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Comments

Detailed Comparison

Torch	PyTorch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.	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.
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	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars 9.1K	GitHub Stars 94.7K
GitHub Forks 2.4K	GitHub Forks 25.8K
Stacks 355	Stacks 1.6K
Followers 61	Followers 1.5K
Votes 0	Votes 43
Pros & Cons
No community feedback yet	Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop
Integrations
Python SQLFlow GraphPipe Flair Pythia Databricks Comet.ml	Python

What are some alternatives to Torch, PyTorch?

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.

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.

PredictionIO

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