Pytorch Professional, Scientific, and Technical Services Reviews & Insights

Score9.3 out of 10

15 Reviews and Ratings

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.

Pytorch Reviews

2 Reviews

Professional, Scientific, and Technical ServicesInformation Technology & Services2

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 (Information Technology & Services company, 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 (Information Technology & Services company, 51-200 employees)

Vetted Review

4 years of experience

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