Community insights

TrustRadius Insights for Pytorch are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

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.

Reviews

6 Reviews

Great tool, easy to learn and use.

Rating: 9 out of 10

Incentivized

January 24, 2025

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.

Likelihood to Recommend

Everything deep learning related if not on TPU (in such case, JAX would be better suited). For LLM deployment, libraries such as vLLM would be better suited, too; otherwise, wrapping the PyTorch model with Ray is a good option.

Pierre Tassel

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

Vetted Review

5 years of experience

Verified on LinkedIn

A great tool for developing your own DL flows

Rating: 9 out of 10

Incentivized

September 30, 2022

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

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.

Taapas Agrawal

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

Vetted Review

4 years of experience

View profile

Advanced and useful framework for Data Science.

Rating: 9 out of 10

Incentivized

August 31, 2022

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.

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.

Verified User

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

Vetted Review

6 years of experience

Pytorch: Best framework for building AI models

Rating: 10 out of 10

Incentivized

August 26, 2022

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

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

Verified User

Employee in Research & Development (51-200 employees)

Vetted Review

4 years of experience

Pytorch in a nutshell

Rating: 10 out of 10

Incentivized

August 26, 2022

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

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.

Verified User

Team Lead in Information Technology (1001-5000 employees)

Vetted Review

5 years of experience

Pytorch is better than the competition

Rating: 9 out of 10

Incentivized

August 26, 2022

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

debugging is better than other frameworks
iteration is easy
pythonic syntax
great documentation

Cons

Would like more examples online of certain models

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 .

Verified User

Engineer in Engineering (1001-5000 employees)

Vetted Review

4 years of experience

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Reviews and Ratings

Pytorch: Best framework for building AI models

Rating: 10 out of 10

Incentivized

August 26, 2022

Use Cases and Deployment Scope

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

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

Verified User

Employee in Research & Development (51-200 employees)

Vetted Review

4 years of experience