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Pytorch

Pytorch

Overview

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.

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Learn from top reviewers

A great tool for developing your own DL flows

Rating: 9 out of 10
September 30, 2022
IncentivizedVetted ReviewVerified User
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/algorit...
Continue reading
Taapas Agrawal
Partner - Research & Development
Research Intern
RLAI Lab, University Of Alberta
Pytorch4 years of experience

Advanced and useful framework for Data Science.

Rating: 9 out of 10
August 31, 2022
IncentivizedVetted ReviewVerified User
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...
Continue reading
Verified user
Engineer
Pytorch6 years of experience

Pytorch: Best framework for building AI models

Rating: 10 out of 10
August 26, 2022
IncentivizedVetted ReviewVerified User
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 writi...
Continue reading
Verified user
Employee
Pytorch4 years of experience

Pytorch in a nutshell

Rating: 10 out of 10
August 26, 2022
IncentivizedVetted ReviewVerified User
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...
Continue reading
Verified user
Team Lead
Pytorch5 years of experience

Pytorch is better than the competition

Rating: 9 out of 10
August 26, 2022
IncentivizedVetted ReviewVerified User
Pytorch is used to build ML models for recommender systems. As pytorch was developed in Meta it is frequently used across the whole organization (instagram, facebook, whatsapp...
Continue reading
Verified user
Engineer
Pytorch4 years of experience
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Pytorch

Pytorch

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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.

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Product Demos

Video Demo with PixelLib Pytorch version using PointRend for instance segmentation.

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Deep learning for parameter discovery (CNN on Gaussian in PyTorch demo)

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Intro to PyTorch Tutorial: Building fashion recognizer

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Linear Regression using PyTorch C++ API (Libtorch) on CSV Files: Code Review and Demo Run!

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Demo - Face Recognition using pytorch (Arcface)

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An Overview of the PyTorch Mobile Demo Apps

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Product Details

What is Pytorch?

Pytorch Technical Details

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Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(14)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Easy to use: Users have consistently found PyTorch to be one of the easiest deep learning frameworks, with a simple model definition and easy hyperparameter setting. Many reviewers stated that they were able to quickly grasp the basics of PyTorch and start building their models without much difficulty.

Strong documentation and community support: The documentation and community around PyTorch are highly praised by users. Numerous reviewers have mentioned that they appreciate the comprehensive documentation provided, which has helped them troubleshoot issues and understand the framework better. Additionally, many users have reported quick resolution of their problems when seeking help from the active online community.

Versatile for research and development: PyTorch is considered an optimized and easy-to-use framework for beginners in the field of AI. It offers a wide range of data types and model architecture selections, making it suitable for both research experiments as well as production usage. Several reviewers specifically mentioned that they appreciate PyTorch's module writing style and seamless integration of various layers/architectures, which allows for versatile use cases in both research and development settings.

Inefficient Dataloaders: Some users have found that the dataloaders in PyTorch are inefficient and can cause bottlenecks in their training workflows.

Lack of Monitoring and Visualization Tools: PyTorch lacks good monitoring and visualization tools, unlike frameworks like TensorFlow which have tensorboard for visualization and creating plots during training. This has been a drawback for some users who rely on these tools for better insights into their models' performance.

Scalability Issues and Limited Platform Support: There are scalability issues with PyTorch, making it difficult to integrate into larger applications. Additionally, only a C++ API is provided, which makes deploying models on mobile platforms challenging. Some users have faced difficulties due to these limitations.

PyTorch is a highly recommended tool for beginners in the field of deep learning. It provides a user-friendly environment that makes it easy for newcomers to get started.

For experts in deep learning, PyTorch is also highly recommended. Its advanced features and flexibility make it a preferred choice among experienced users.

When comparing different deep learning libraries, many users highly recommend PyTorch. Its comprehensive ecosystem and support throughout the development process are valued by the community.

Depending on the specific use case, users suggest considering PyTorch or trying Keras as an alternative. This reflects an acknowledgment that different projects may have varying requirements and that exploring different frameworks can lead to better results based on individual needs.

Reviews

(1-5 of 5)
Companies can't remove reviews or game the system. Here's why

A great tool for developing your own DL flows

Rating: 9 out of 10
Incentivized
September 30, 2022
TA
Taapas Agrawal
Research Intern
RLAI Lab, University Of Alberta (Computer Software, 501-1000 employees)
Vetted Review
Verified User
Pytorch
4 years of experience
View profile
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 and Cons
  • 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.
Likelihood to Recommend
Pytorch is a great tool for experimentation and testing/developing ML flows and for reproducing results from top conferences. The components it provides with helps create Deep neural networks and flows with great ease and a level of cleaness.
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
Other Software Used

Advanced and useful framework for Data Science.

Rating: 9 out of 10
Incentivized
August 31, 2022
Verified User
Vetted Review
Verified User
Pytorch
6 years of experience
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 and Cons
  • It's easy to write custom neural networks.
  • It optimises algebraic operation.
  • It has good support for computation on GPUs.
Likelihood to Recommend
They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
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
Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly less time to create valuable POCs as most of the things are inbuilt.
Other Software Used

Pytorch: Best framework for building AI models

Rating: 10 out of 10
Incentivized
August 26, 2022
Verified User
Vetted Review
Verified User
Pytorch
4 years of experience
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 and Cons
  • Dataloaders
  • Deep Learning Models support
  • Excellent documentation
  • Excellent community
  • Support for major loss functions
Likelihood to Recommend
Suitable for:
1. If you're working on some deep learning-related problem that requires some complex data loaders and augmentation strategies.
2. Gives you the support to use existing models and simply change the further layers, play with hyperparameters
3. Support for complex loss functions, optimisers, and schedulers which are required for handling complex training cases
4. Working on a big project that requires a lot of experimentation and model tweaking.

Not suitable for:
1. Playing around with simple ML models, use other libraries
2. Playing with small DL models with standard datasets like MNIST. Other libraries have very good support for them
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
Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems.

Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
Other Software Used

Pytorch in a nutshell

Rating: 10 out of 10
Incentivized
August 26, 2022
Verified User
Vetted Review
Verified User
Pytorch
5 years of experience
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 and Cons
  • Training of Deep Learning Models
  • Generation of clean code that is explainable
  • Use of the last version of Nvidia images
Likelihood to Recommend
Pytorch is very well suited to train Deep Learning Models in the Computer Vision field with the support of State of the Art models trained in that framework. There is a large number of pre-trained models and generated images to pick and start working. It can be less appropriate when the production part of the project is more important than the model itself; here, TensorFlow has some advantages.
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
As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks.

You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.
Other Software Used

Pytorch is better than the competition

Rating: 9 out of 10
Incentivized
August 26, 2022
Verified User
Vetted Review
Verified User
Pytorch
4 years of experience
Use Cases and Deployment Scope
Pytorch is used to build ML models for recommender systems. As pytorch was developed in Meta it is frequently used across the whole organization (instagram, facebook, whatsapp, reality labs). We use it for quicker iteration, better debugging, and better support than some of its competitors. I can't talk about exact details too much for the products it's used for, but it is widely used in massive models that are put into production.
Pros and Cons
  • debugging is better than other frameworks
  • iteration is easy
  • pythonic syntax
  • great documentation
Likelihood to Recommend
Pytorch is great for all deep learning models and is my go-to framework for this. It offers a great deal of flexibility which is a huge bonus when trying to get a new type of model to work or when you need to debug. The case where it isn't great right now is "on device" ML .
Most Important Features
  • documentation
  • pythonic syntax and programming style
  • ease of use
Return on Investment
  • I'd estimate I can build a model 50% faster on pytorch vs other frameworks
Alternatives Considered
The syntax of PyTorch is much better in my opinion, and the programming style is more pythonic and easier to use. I also think PyTorch is a lot easier to debug than the competitors I've listed (Caffe2 and TensorFlow). I do like some of the examples given on tensorflows website, but PyTorch has good examples too.
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