Overview

Swift AI

Stacks14

Followers52

Votes0

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

PyTorch vs Swift AI: What are the differences?

Introduction

Both PyTorch and Swift AI are widely used frameworks for developing artificial intelligence and machine learning models. Although they serve similar purposes, there are several key differences between the two.

Syntax and Language: PyTorch is primarily based on Python, a popular programming language in the data science community. On the other hand, Swift AI uses Swift, a powerful and modern language developed by Apple. While PyTorch benefits from Python's extensive library ecosystem, Swift AI excels in terms of performance due to its language design.
Usability and Flexibility: PyTorch has a mature ecosystem and is known for its ease of use, making it suitable for both beginners and experienced developers. It provides a high level of flexibility, allowing developers to define and execute custom operations easily. In contrast, Swift AI is relatively new and less mature, with a smaller community and ecosystem. However, it offers great flexibility for iOS and macOS developers who are already familiar with Swift.
Graph Execution: PyTorch adopts a dynamic computational graph approach, which means the graph is built and optimized during runtime. This allows for flexibility and easier debugging. In contrast, Swift AI utilizes a static computational graph approach, where the graph is built ahead of time, providing potential performance optimizations. However, this comes at the cost of reduced flexibility during runtime.
Model Deployment: PyTorch provides seamless integration with popular deployment platforms like ONNX and TorchServe, making it easier to deploy models at scale. In comparison, Swift AI has limited deployment options and requires additional effort and development to deploy models effectively.
Community and Documentation: PyTorch has a large and active community, making it easier to find resources, tutorials, and community support. It also offers extensive documentation and numerous examples, contributing to its popularity. Swift AI, being relatively new, has a smaller community and still evolving documentation, making it harder to find resources and support compared to PyTorch.
Platform Compatibility: PyTorch is known for its cross-platform compatibility, providing support for multiple operating systems, including Windows, Linux, and macOS. On the other hand, Swift AI focuses primarily on iOS and macOS platforms, limiting its usability for developers working on other platforms.

In summary, PyTorch and Swift AI differ in terms of language, usability, graph execution approach, model deployment options, community support, and platform compatibility. While PyTorch offers a well-established ecosystem and a broader range of capabilities, Swift AI offers high performance and is specifically designed for iOS and macOS developers.

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Advice on Swift AI, PyTorch

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

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!!

107k views107k

Comments

cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

72.8k views72.8k

Comments

Detailed Comparison

Swift AI	PyTorch
Swift AI is a high-performance AI and machine learning library written entirely in Swift. We currently support iOS and OS X, with support for more platforms coming soon!	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.
Feed-Forward Neural Network; Fast Matrix Library	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars -	GitHub Stars 94.7K
GitHub Forks -	GitHub Forks 25.8K
Stacks 14	Stacks 1.6K
Followers 52	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
Swift	Python

What are some alternatives to Swift AI, 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.