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Pytorch

Score9.3 out of 10

15 Reviews and Ratings

What is Pytorch?

Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.

Categories & Use Cases

Most Frequent Users

Top 3 industries using Pytorch
Based on HG Insights installation data
View all Reviews
#1 most frequent

Professional, Scientific, and Technical Services

26.4%

6,344 installations of 24,072

#2 most frequent

Information

15.9%

3,817 installations of 24,072

#3 most frequent

Manufacturing

11.5%

2,763 installations of 24,072

Reviews

What users are saying about Pytorch.
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Great tool, easy to learn and use.

Incentivized
Pierre TasselView profile
Data Scientist Engineer/Researcher in Research & Development at Prewave (51-200 employees employees)

Use Cases and Deployment Scope

I use PyTorch to teach deep learning to my university students and professionally train and deploy models. Sometimes, it is done directly or through other libraries like Transformers from Hugging Face. PyTorch is very flexible and easy to write with a battery included. It offers a nice tradeoff between helpfulness and flexibility.

Pros

  • flexibility
  • Clean code, close to the algorithm.
  • Fast
  • Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
  • Versatile, can work efficiently on text/audio/image/tabular datasets.

Cons

  • Not easy to handle TPUs.
  • Surprisingly, some industry-standard building blocks are not there (e.g., cosine lr scheduler with warmup).
  • Deployment requires additional things not there, for example, dynamic batching.

Return on Investment

  • Fast prototyping.
  • It's not platform-dependent.
  • The pre-processing function is already present.

Usability

Alternatives Considered

TensorFlow, JAX and Keras

A great tool for developing your own DL flows

Incentivized
Taapas AgrawalView profile
Research Intern in Research & Development at RLAI Lab, University Of Alberta (501-1000 employees employees)

Use Cases and Deployment Scope

Pytorch is an awesome way of coding Deep Learning and Reinforcement Learning Algorithms with great ease. Since it is mostly pythonic, converting your derived equations/algorithms and using your favourite optimizers to test is so great. Further, it has great extensions to use like weights and biases where you can see how weights change in your neural network. It is an ideal tool for experimentation in Deep learning domain.

Pros

  • Provides Benchmark datasets to test your custom algorithm
  • Provides with a lot of pre-coded neural net components to use for your flow
  • Gives a framework to write really abstract code.

Cons

  • Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

Most Important Features

  • The ability to use it to replicate historic algorithms
  • Derive and test new DL flows

Return on Investment

  • It has a positive aspect as the ease of development results in publishing more papers for the community

Alternatives Considered

TensorFlow

Other Software Used

TensorFlow, Google Cloud AI, Slack

Advanced and useful framework for Data Science.

Incentivized
Verified User
Engineer in Research & Development (10,001+ employees employees)

Use Cases and Deployment Scope

We use Pytorch for Data Science related projects; it is a very advanced framework for doing Machine/Deep Learning for people who are already familiar with python. It has a lot of datasets and models integrated that can be used just with a few lines of code to create a quick POC. It's very easy to write our neural networks with Pytorch.

Pros

  • It's easy to write custom neural networks.
  • It optimises algebraic operation.
  • It has good support for computation on GPUs.

Cons

  • It should have support for Java also as Java is one of the most popular language.
  • They should make things more easy if we want to use GPUs for computation.
  • They should keep adding the latest models so that we can easily load them for use for further fine-tuning.

Most Important Features

  • Most popular datasets like mnist, etc are integrated.
  • Fine-tuning models is easy.
  • Community support is good.

Return on Investment

  • It helped us creating quick POCs for customers.
  • We can do customisation as we need.
  • There is a learning curve so people need to spend some time for getting used to it.

Alternatives Considered

TensorFlow and Keras

Other Software Used

TensorFlow, Keras, Python IDLE, PyCharm, Jupyter Notebook, Microsoft Visual Studio Code, Visual Studio IDE

Pytorch: Best framework for building AI models

Incentivized
Verified User
Employee in Research & Development (51-200 employees employees)

Use Cases and Deployment Scope

We use Pytorch as the main framework for building ML models and writing data loaders. Being an AI company, we have to train a lot of deep learning models, which involves writing data loaders for our dataset, making networks, or using the existing networks from the torchvision library. Being an AI-first company, ML Scientists are supposed to experiment with the models, and that requires writing very robust and modular code.

Pros

  • Dataloaders
  • Deep Learning Models support
  • Excellent documentation
  • Excellent community
  • Support for major loss functions

Cons

  • Distributed data parallel still seems to be complicated
  • Support for easy deployment to servers
  • Torchvision to have support for latest models with pertained weights

Most Important Features

  • Loss functions
  • Base dataloaders
  • Torchvision models
  • Neural Network module
  • Inbuilt optimisers, initialisers

Return on Investment

  • Less time wasted on handling the library version issues
  • Small learning curve as very similar to Python
  • Compatibility with other popular Python libraries makes it easy to build a lot of things on it

Alternatives Considered

TensorFlow and Keras

Other Software Used

Slack, Brave, Calendly, Google Calendar

Pytorch in a nutshell

Incentivized
Verified User
Team Lead in Information Technology (1001-5000 employees employees)

Use Cases and Deployment Scope

We are using Pytorch to construct computer vision Deep Learning models for a battery of projects in the Data Platform project pipeline. Pytorch delivers a very Pythonic way of dealing with Deep Learning models that, from our point of view, make it easier for us to put the code in production, work in teams and be able to improve those different models in an iterative way. The business problems that we are solving are the generation of models to predict different biomarkers in both 2D and 3D images to improve the selection of patients in clinical trials. Both the training and the prediction models in Pytorch are very friendly and with a lot of support from the community.

Pros

  • Training of Deep Learning Models
  • Generation of clean code that is explainable
  • Use of the last version of Nvidia images

Cons

  • Creating an environment to watch model training like Tensorboard
  • More pretrained models
  • More courses

Most Important Features

  • Clean code
  • Dynamic Graphic memory
  • Pre generated docker images for cloud environments

Return on Investment

  • The ability to make models as never before
  • Being able to control the bias of models was not done before the arrival of Pytorch in our company

Alternatives Considered

TensorFlow

Other Software Used

TensorFlow, Google Cloud AI, Vertex