Overview

Leaf

Stacks21

Followers42

Votes0

GitHub Stars5.5K

Forks269

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Leaf vs PyTorch: What are the differences?

Introduction

Computation Graphs: In PyTorch, computation graphs are defined dynamically, allowing for dynamic computational graphs to be created during runtime, which is beneficial for tasks that require flexibility and adaptability. On the other hand, in Leaf, computation graphs are defined statically, which means the computational graph needs to be defined before the actual computation, offering deterministic behavior but with less flexibility.
Automatic Differentiation: PyTorch offers automatic differentiation out of the box, allowing for easy calculation of gradients without the need for manual computation. However, in Leaf, automatic differentiation needs to be explicitly defined, which can be useful for certain tasks requiring more control over the differentiation process.
Ease of Use: PyTorch is known for its ease of use and beginner-friendly interface, with a vast community and extensive documentation available, making it easier for new users to get started with deep learning projects. On the other hand, Leaf may have a steeper learning curve due to its more advanced features and less extensive documentation, requiring a deeper understanding of the underlying concepts.
Deployment and Performance: PyTorch is widely adopted in research and production environments, with strong support for deployment on various platforms and high-performance computing tasks. In contrast, Leaf may lack some of the deployment capabilities and performance optimizations found in PyTorch, making it more suitable for experimental research or specific use cases.
Extensions and Libraries: PyTorch has a rich ecosystem of extensions and libraries, such as torchvision and torchtext, that provide additional functionality and tools for deep learning tasks. While Leaf also has some extensions and libraries available, the overall ecosystem may not be as extensive as PyTorch, limiting the range of tools and resources available for developers.
Integration with Other Frameworks: PyTorch has strong integration with other popular machine learning frameworks like TensorFlow, enabling users to combine the strengths of different frameworks in a single project. On the other hand, Leaf may not have as seamless integration with other frameworks, potentially limiting the interoperability with existing projects and tools.

In Summary, PyTorch offers dynamic computation graphs, automatic differentiation, ease of use, deployment capabilities, extensive libraries, and integration with other frameworks, making it a popular choice for deep learning projects, while Leaf provides static computation graphs, explicit differentiation, advanced features, and limited ecosystem, catering to users with specific requirements and preferences.

Advice on Leaf, 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.

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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.

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Comments

Detailed Comparison

Leaf	PyTorch
Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.	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.
-	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars 5.5K	GitHub Stars 94.7K
GitHub Forks 269	GitHub Forks 25.8K
Stacks 21	Stacks 1.6K
Followers 42	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
Rust	Python

What are some alternatives to Leaf, 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.