Pytorch in a nutshell
August 26, 2022

Pytorch in a nutshell

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Pytorch

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

Do you think Pytorch delivers good value for the price?

Yes

Are you happy with Pytorch's feature set?

Yes

Did Pytorch live up to sales and marketing promises?

Yes

Did implementation of Pytorch go as expected?

Yes

Would you buy Pytorch again?

Yes

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.

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