Overview
What is Pytorch?
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.
A great tool for developing your own DL flows
Advanced and useful framework for Data Science.
Pytorch: Best framework for building AI models
Pytorch in a nutshell
Pytorch is better than the competition
Pricing
What is Pytorch?
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.
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Product Demos
Video Demo with PixelLib Pytorch version using PointRend for instance segmentation.
Deep learning for parameter discovery (CNN on Gaussian in PyTorch demo)
Intro to PyTorch Tutorial: Building fashion recognizer
Linear Regression using PyTorch C++ API (Libtorch) on CSV Files: Code Review and Demo Run!
Demo - Face Recognition using pytorch (Arcface)
An Overview of the PyTorch Mobile Demo Apps
Product Details
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- Tech Details
What is Pytorch?
Pytorch Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
Comparisons
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Reviews and Ratings
(14)Community Insights
- Pros
- Cons
- Recommendations
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.
Inefficient Dataloaders: Some users have found that the dataloaders in PyTorch are inefficient and can cause bottlenecks in their training workflows.
Lack of Monitoring and Visualization Tools: PyTorch lacks good monitoring and visualization tools, unlike frameworks like TensorFlow which have tensorboard for visualization and creating plots during training. This has been a drawback for some users who rely on these tools for better insights into their models' performance.
Scalability Issues and Limited Platform Support: There are scalability issues with PyTorch, making it difficult to integrate into larger applications. Additionally, only a C++ API is provided, which makes deploying models on mobile platforms challenging. Some users have faced difficulties due to these limitations.
PyTorch is a highly recommended tool for beginners in the field of deep learning. It provides a user-friendly environment that makes it easy for newcomers to get started.
For experts in deep learning, PyTorch is also highly recommended. Its advanced features and flexibility make it a preferred choice among experienced users.
When comparing different deep learning libraries, many users highly recommend PyTorch. Its comprehensive ecosystem and support throughout the development process are valued by the community.
Depending on the specific use case, users suggest considering PyTorch or trying Keras as an alternative. This reflects an acknowledgment that different projects may have varying requirements and that exploring different frameworks can lead to better results based on individual needs.
Reviews
(1-4 of 4)A great tool for developing your own DL flows
- 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.
- Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to TensorFlow
- The ability to use it to replicate historic algorithms
- Derive and test new DL flows
- It has a positive aspect as the ease of development results in publishing more papers for the community
Advanced and useful framework for Data Science.
- It's easy to write custom neural networks.
- It optimises algebraic operation.
- It has good support for computation on GPUs.
- 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.
- Most popular datasets like mnist, etc are integrated.
- Fine-tuning models is easy.
- Community support is good.
- It helped us creating quick POCs for customers.
- We can do customisation as we need.
- There is a learning curve so people need to spend some time for getting used to it.
- TensorFlow and Keras
Pytorch: Best framework for building AI models
- Dataloaders
- Deep Learning Models support
- Excellent documentation
- Excellent community
- Support for major loss functions
- Distributed data parallel still seems to be complicated
- Support for easy deployment to servers
- Torchvision to have support for latest models with pertained weights
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
- Loss functions
- Base dataloaders
- Torchvision models
- Neural Network module
- Inbuilt optimisers, initialisers
- Less time wasted on handling the library version issues
- Small learning curve as very similar to Python
- Compatibility with other popular Python libraries makes it easy to build a lot of things on it
- TensorFlow and Keras
Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
Pytorch in a nutshell
- Training of Deep Learning Models
- Generation of clean code that is explainable
- Use of the last version of Nvidia images
- 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
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.