Pytorch in a nutshell
Rating: 10 out of 10
IncentivizedUse 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.
Vetted Review
5 years of experience