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TensorFlow VS pipenv

Compare TensorFlow VS pipenv and see what are their differences

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TensorFlow logo 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.

pipenv logo pipenv

Python Development Workflow for Humans. Contribute to pypa/pipenv development by creating an account on GitHub.
  • TensorFlow Landing page
    Landing page //
    2023-06-19
  • pipenv Landing page
    Landing page //
    2023-08-26

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

pipenv features and specs

  • Integrated Workflow
    Pipenv combines the functionalities of pip and virtualenv, providing a seamless environment for package installation and management, making the development workflow more efficient and organized.
  • Automatic Virtual Environment Management
    Automatically creates and manages a virtual environment for projects, ensuring that dependencies are maintained separately and do not interfere with the system Python or other projects.
  • Lock File Generation
    Generates a Pipfile.lock to ensure deterministic builds, making sure that installations are consistent across different environments or deployments.
  • User-Friendly Package Installation
    Simplifies package installation with a straightforward and intuitive interface. Pipenv handles both direct package specification and environment management in a unified manner.
  • Environment Consistency
    By using the Pipfile and Pipfile.lock, Pipenv ensures that all developers working on a project have a consistent set of dependencies, reducing 'it works on my machine' issues.
  • Dependency Resolution
    Pipenv uses an advanced dependency resolver, helping to avoid dependency conflicts that can occur with complex package requirements.

Possible disadvantages of pipenv

  • Performance Overhead
    The dependency resolution process can sometimes be slow, which might be noticeable in larger projects or when installing multiple packages at once.
  • Limited Flexibility
    Pipenv abstracts away some of pip and virtualenv’s flexibility, which might limit advanced configurations or setups required by more complex projects.
  • Complexity for Simple Projects
    May add unnecessary complexity for simple or small projects where virtualenv and pip would suffice without additional layers.
  • Slower Updates
    Pipenv may lag behind updates compared to pip and virtualenv due to its additional integration layer, meaning it might not always provide immediate support for the latest Python packaging developments.
  • Learning Curve
    Requires initial learning and adjustment for developers who are accustomed to using pip and virtualenv separately, potentially slowing down onboarding for new team members.

TensorFlow videos

+ Add

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

pipenv videos

+ Add

Pipenv Crash Course

More videos:

  • Tutorial - How to use Pipenv to Manage Python Dependencies (Tutorial)
  • Review - venv, pyenv, pypi, pip, pipenv, pyWTF?

Category Popularity

0-100% (relative to TensorFlow and pipenv)

TensorFlow

pipenv

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Front End Package Manager
0 0%
Front End Package Manager
100% 100
AI
100 100%
AI
0% 0
Package Manager
0 0%
Package Manager
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare TensorFlow and pipenv

TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

pipenv Reviews

We have no reviews of pipenv yet.
Be the first one to post

Social recommendations and mentions

TensorFlow might be a bit more popular than pipenv. We know about 7 links to it since March 2021 and only 6 links to pipenv. 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.

TensorFlow mentions (7)

  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 2 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
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pipenv mentions (6)

  • Generate pip requirements.txt file based on imports of any project
    https://github.com/pypa/pipenv Pipenv was last updated 10 hours ago. Looks like it's still an active project to me. - Source: Hacker News / 9 months ago
  • Adding Virtual Environments to Git Repo
    Pipenv solves this by having both kinds of requirement files: Pipfile lists package names and known constraints on which versions can be used, while Pipfile.lock gives specific package versions with hashes. Theoretically the Pipfile (and its lockfile) format were supposed to be a standard that many different tools could use, but I haven't seen it get adopted much outside of pipenv itself, so I'm not sure if it's... Source: about 2 years ago
  • Top 10 Python security best practices
    Alternatively, you can look into Pipenv, which has a lot more tools to develop secure applications with. - Source: dev.to / almost 3 years ago
  • Why and how to use conda?
    I’m partial to pipenv but it does depend on pyenv (which works on Windows albeit via WSL, no?). Source: about 3 years ago
  • How to make a Python package in 2021
    I think I went through the same progression — thinking pipenv was the official solution before deciding it isn’t. To add to the confusion, I just realized that pipenv [1] is currently owned by the Python Packaging Authority (PyPA) which also owns the official pip [2] and virtualenv [3]. [1]: https://github.com/pypa/pipenv [2]: https://github.com/pypa/pip [3]: https://github.com/pypa/virtualenv. - Source: Hacker News / about 4 years ago
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What are some alternatives?

When comparing TensorFlow and pipenv, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Python Poetry - Python packaging and dependency manager.

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

Conda - Binary package manager with support for environments.

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

pip - The PyPA recommended tool for installing Python packages.

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