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PyTorch pros and cons

Vendor: PyTorch
4.3 out of 5

Pros & Cons summary

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Prominent pros & cons

PROS

PyTorch follows a pythonic way, making it user-friendly and easy to use.
The framework is valuable and suitable for AIML project development.
PyTorch is gaining credibility in research, with increased availability of example papers.
It has good scalability when using multiple GPUs.
PyTorch's scalability and developer-friendly nature support continuous new project creation.

CONS

Documentation for some methods and parameters is lacking, making it hard to find necessary information.
Faster training of models and support for more frameworks in production are needed.
Beginners may find concepts in backward propagation complex despite clarity in defining loss functions and gradients.
Stability issues arise when handling large datasets and testing various modeling techniques.
Version incompatibility and lack of updates beyond version 12.3 are problematic.
 

PyTorch Pros review quotes

Rohan Sharma - PeerSpot reviewer
AI/ML Co-Lead at Developer Student Clubs - GGV
Feb 12, 2025
PyTorch is developer-friendly, allowing developers to continuously create new projects.
Praveen Kumar Tiwari - PeerSpot reviewer
Team Lead at Tech Mahindra Limited
May 28, 2024
It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install.
TS
Machine Learning Engineer at IIIT Kottayam
Nov 29, 2024
PyTorch allows me to build my projects from scratch.
Learn what your peers think about PyTorch. Get advice and tips from experienced pros sharing their opinions. Updated: April 2026.
893,221 professionals have used our research since 2012.
Karthikeyan Katkam - PeerSpot reviewer
AWS Engineer at Neurolov.ai
Nov 18, 2024
I like PyTorch's scalability.
reviewer2384079 - PeerSpot reviewer
Data Scientist. at a computer software company with 501-1,000 employees
Mar 27, 2024
It's been pretty scalable in terms of using multiple GPUs.
Jithin James - PeerSpot reviewer
Financial Analyst 4 (Supply Chain & Financial Analytics) at Juniper Networks
Mar 28, 2024
The tool is very user-friendly.
Arucy Lionel - PeerSpot reviewer
Co-Founder at Afriziki
Nov 27, 2023
yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily.
reviewer2514822 - PeerSpot reviewer
Associate Machine Learning Engineer at a tech services company with 501-1,000 employees
Jul 15, 2024
For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn.
Murali Mallikarjuna Perumalla - PeerSpot reviewer
Data Scientist at a tech services company with 201-500 employees
May 29, 2024
The product's initial setup phase is easy.
Swayan Jeet Mishra - PeerSpot reviewer
Lead Machine Learning Engineer at Schlumberger
Oct 31, 2022
The framework of the solution is valuable.
 

PyTorch Cons review quotes

Rohan Sharma - PeerSpot reviewer
AI/ML Co-Lead at Developer Student Clubs - GGV
Feb 12, 2025
PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow.
Praveen Kumar Tiwari - PeerSpot reviewer
Team Lead at Tech Mahindra Limited
May 28, 2024
I would like to see better learning documents.
TS
Machine Learning Engineer at IIIT Kottayam
Nov 29, 2024
I do not have any complaints.
Learn what your peers think about PyTorch. Get advice and tips from experienced pros sharing their opinions. Updated: April 2026.
893,221 professionals have used our research since 2012.
Karthikeyan Katkam - PeerSpot reviewer
AWS Engineer at Neurolov.ai
Nov 18, 2024
The analyzing and latency of compiling could be improved to provide enhanced results.
reviewer2384079 - PeerSpot reviewer
Data Scientist. at a computer software company with 501-1,000 employees
Mar 27, 2024
PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex.
Jithin James - PeerSpot reviewer
Financial Analyst 4 (Supply Chain & Financial Analytics) at Juniper Networks
Mar 28, 2024
I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques.
Arucy Lionel - PeerSpot reviewer
Co-Founder at Afriziki
Nov 27, 2023
On the production side of things, having more frameworks would be helpful.
reviewer2514822 - PeerSpot reviewer
Associate Machine Learning Engineer at a tech services company with 501-1,000 employees
Jul 15, 2024
The product has breakdowns when we change the versions a lot.
Murali Mallikarjuna Perumalla - PeerSpot reviewer
Data Scientist at a tech services company with 201-500 employees
May 29, 2024
The product has certain shortcomings in the automation of machine learning.
Swayan Jeet Mishra - PeerSpot reviewer
Lead Machine Learning Engineer at Schlumberger
Oct 31, 2022
The training of the models could be faster.