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pre-commit by Yelp VS PyTorch

Compare pre-commit by Yelp VS PyTorch and see what are their differences

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pre-commit by Yelp logo pre-commit by Yelp

A framework for managing and maintaining multi-language pre-commit hooks

PyTorch logo PyTorch

Open source deep learning platform that provides a seamless path from research prototyping to...
  • pre-commit by Yelp Landing page
    Landing page //
    2022-01-08
  • PyTorch Landing page
    Landing page //
    2023-07-15

pre-commit by Yelp features and specs

  • Comprehensive Hook Management
    Pre-commit provides a robust framework to manage and configure git hooks in a standardized way, simplifying the process of ensuring code quality.
  • Language Agnostic
    Supports hooks written in all kinds of languages including Python, Ruby, JavaScript, etc., making it versatile and adaptable to any codebase.
  • Ease of Setup
    Installing and configuring pre-commit hooks is straightforward, often just involving the addition of a simple configuration file to the repository.
  • Version Control
    Pre-commit ensures that the same versions of hooks are consistently run across developers' environments by locking the version of each hook.
  • Centralized Configuration
    Project-wide configuration means that all contributors use the same hooks and settings, fostering code consistency and quality.

Possible disadvantages of pre-commit by Yelp

  • Learning Curve
    New users might face a learning curve initially when setting up a configuration file and understanding how to integrate it with existing workflows.
  • Performance Overhead
    Running hooks can add a noticeable delay when committing code, especially in larger projects with many hooks.
  • Dependency Management
    Some hooks might introduce additional dependencies that need to be managed within the project's environment.
  • Complex Configuration for Advanced Use
    While simple setups are easy, more complex configurations can become intricate and harder to manage.
  • Limited to Pre-defined Hooks
    If a desired hook isn't available, users may have to create their own, which can require additional effort and maintenance.

PyTorch features and specs

  • Dynamic Computation Graph
    PyTorch uses a dynamic computation graph, which allows for interactive and flexible model building. This is particularly beneficial for researchers who need to modify the network architecture on-the-fly.
  • Pythonic Nature
    PyTorch is designed to be deeply integrated with Python, making it very intuitive for Python developers. The framework feels more 'native' to Python, which improves the ease of learning and use.
  • Strong Community Support
    PyTorch has a large, active, and growing community. This means abundant resources such as tutorials, forums, and third-party tools are available to help developers solve problems and share solutions.
  • Flexibility and Control
    PyTorch offers granular control over computations and provides extensive debugging capabilities. This level of control is beneficial for tasks that require precise tuning and custom implementations.
  • Support for GPU Acceleration
    PyTorch offers seamless integration with GPU hardware, which significantly accelerates the computation process. This makes it highly efficient for deep learning tasks.
  • Rich Ecosystem
    PyTorch has a rich ecosystem including libraries like torchvision, torchaudio, and torchtext, which are specialized for different data types and can significantly shorten development times.

Possible disadvantages of PyTorch

  • Limited Production Deployment Tools
    PyTorch is primarily designed for research rather than production. While deployment tools like TorchServe exist, they are not as mature or integrated as solutions offered by other frameworks like TensorFlow.
  • Lesser Adoption in Industry
    While PyTorch is popular among researchers, it has historically seen less adoption in industry compared to TensorFlow, which means there might be fewer resources for large-scale production deployments.
  • Inconsistent API Changes
    As PyTorch continues to evolve rapidly, occasionally there are breaking changes or inconsistent API updates. This can create maintenance challenges for existing codebases.
  • Steeper Learning Curve for Beginners
    Despite its Pythonic design, PyTorch's focus on flexibility and control can make it slightly harder for beginners to get started compared to some other high-level libraries and frameworks.
  • Less Mature Documentation
    Although the documentation is improving, it has been historically less comprehensive and mature compared to other frameworks like TensorFlow, which can make it difficult to find detailed, clear information.

pre-commit by Yelp videos

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PyTorch videos

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PyTorch in 5 Minutes

More videos:

  • Review - Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast Clips
  • Review - PyTorch at Tesla - Andrej Karpathy, Tesla

Category Popularity

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PyTorch

Git
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Git
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Data Science And Machine Learning
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Data Science And Machine Learning
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Code Collaboration
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Code Collaboration
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Data Science Tools
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Data Science Tools
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Reviews

These are some of the external sources and on-site user reviews we've used to compare pre-commit by Yelp and PyTorch

pre-commit by Yelp Reviews

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PyTorch Reviews

10 Python Libraries for Computer Vision
Similar to TensorFlow and Keras, PyTorch and torchvision offer powerful tools for computer vision tasks. PyTorch’s dynamic computation graph and torchvision’s datasets and pre-trained models make it easy to implement tasks such as image classification, object detection, and style transfer.
Source: clouddevs.com
25 Python Frameworks to Master
Along with TensorFlow, PyTorch (developed by Facebook’s AI research group) is one of the most used tools for building deep learning models. It can be used for a variety of tasks such as computer vision, natural language processing, and generative models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
PyTorch is another open-source machine learning framework that is widely used in academia and industry. PyTorch provides excellent support for building deep learning models, and it has several pre-trained models for computer vision tasks, making it the ideal tool for several computer vision applications. PyTorch offers a user-friendly interface that makes it easier for...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
When we compare HuggingFace model availability for PyTorch vs TensorFlow, the results are staggering. Below we see a chart of the total number of models available on HuggingFace that are either PyTorch or TensorFlow exclusive, or available for both frameworks. As we can see, the number of models available for use exclusively in PyTorch absolutely blows the competition out of...
Source: www.assemblyai.com
15 data science tools to consider using in 2021
First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

pre-commit by Yelp might be a bit more popular than PyTorch. We know about 150 links to it since March 2021 and only 132 links to PyTorch. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

pre-commit by Yelp mentions (150)

  • Scalable Python backend: Building a containerized FastAPI Application with uv, Docker, and pre-commit: a step-by-step guide
    Pre-commit is a framework for managing and maintaining multi-language pre-commit hooks, ensuring consistency and quality in your codebase by running checks before a commit is finalized. - Source: dev.to / 4 months ago
  • Crafting a Custom SAM Template for Your AWS Lambda Function, Resource, and Operations
    Just give you an idea of how to implement a template for serverless in your organization; you can create multiple cases and embed the practice of your organization to the template like pre-commit, cicd, lambda-layer-secret, lambda-layer-powertools and more. - Source: dev.to / 5 months ago
  • 12 Steps to Organize and Maintain Your Python Codebase for Beginners
    Instead of running these tools manually every time you make changes, you can automate the process with pre-commit hooks. Pre-commit hooks run automatically before each commit, blocking the commit if any tool fails. - Source: dev.to / 6 months ago
  • How I use git
    Our team is small and we use: 
      git hooks from https://pre-commit.com.
    - Source: Hacker News / 7 months ago
  • How to Estimate Cloud Costs with Terraform and InfraCost
    You can also add InfraCost as part of the pre-commit. With pre-commit, you can define some hooks that you can easily run before you push your code. There are multiple ways to install pre-commit, and you can find examples here. - Source: dev.to / 8 months ago
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PyTorch mentions (132)

  • Top Programming Languages for AI Development in 2025
    With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / 4 days ago
  • Fine-tuning LLMs locally: A step-by-step guide
    Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / 24 days ago
  • 10 Must-Have AI Tools to Supercharge Your Software Development
    8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 3 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
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What are some alternatives?

When comparing pre-commit by Yelp and PyTorch, you can also consider the following products

Python Poetry - Python packaging and dependency manager.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

EditorConfig - EditorConfig is a file format and collection of text editor plugins for maintaining consistent coding styles between different editors and IDEs.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

mypy - Mypy is an experimental optional static type checker for Python that aims to combine the benefits of dynamic (or "duck") typing and static typing.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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