Name: Pytorch
Rating: 9.3 (15 reviews)
Author: Open Source

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### Deep Learning Frameworks
Users consistently highlight Pytorch's deep learning framework as a robust tool for building and training machine learning models. The framework's support for complex data loaders, pre-trained models, and various optimization strategies is widely appreciated by the community. Additionally, the ease of writing custom neural networks and the availability of extensive documentation contribute to Pytorch's reputation as a versatile and user-friendly deep learning framework. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-09-30-08-07-50), [2](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [3](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08), [4](https://www.trustradius.com/reviews/pytorch-2022-08-27-01-20-05), [5](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51))

### Neural Network Architectures
Users generally appreciate Pytorch's capabilities in neural network architectures. Reviewers highlight that Pytorch offers a user-friendly platform for constructing and training deep learning models, particularly in the field of computer vision. The framework's support for state-of-the-art models and pre-trained networks simplifies the process of model selection and implementation. While some users find Pytorch less suitable for on-device machine learning, overall, it is recognized for its flexibility and robustness in building and optimizing neural network architectures. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-08-27-01-20-05), [2](https://www.trustradius.com/reviews/pytorch-2022-09-30-08-07-50), [3](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51), [4](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [5](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08))

### Machine Learning Algorithms
Reviewers across various industries and projects have consistently praised Pytorch for its robust machine learning algorithms. Users appreciate its ability to handle complex data loaders, support for experimenting with different models, and flexibility in tweaking hyperparameters. Pytorch's machine learning algorithms have proven to be instrumental in tasks such as building computer vision models, developing recommender systems, and predicting biomarkers in clinical trials. The community support and Pythonic approach of Pytorch further enhance its appeal for those working on intricate machine learning projects. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-09-30-08-07-50), [2](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51), [3](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [4](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08))

### Code Writing and Debugging
Users across various industries and projects consistently praise Pytorch for its code writing and debugging capabilities. Reviewers highlight Pytorch's efficiency in writing custom neural networks and its robust debugging features. The framework's Pythonic syntax, extensive documentation, and support for computation on GPUs are frequently cited as strengths by users. Additionally, Pytorch's ability to optimize algebraic operations and provide pre-coded neural net components for streamlined development are noted as valuable assets for code production and debugging. Despite some minor complexities compared to other frameworks, Pytorch's overall performance in code writing and debugging is well-regarded by users. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08), [2](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [3](https://www.trustradius.com/reviews/pytorch-2022-08-27-01-20-05), [4](https://www.trustradius.com/reviews/pytorch-2022-09-30-08-07-50), [5](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51))

### Training and Tuning
Reviewers consistently praise Pytorch for its robust training and fine-tuning capabilities, highlighting the ease of model tweaking and experimentation it offers. While some users express a desire for more pretrained models and additional courses, the overall sentiment leans towards Pytorch being a valuable tool for refining and optimizing deep learning models. The community support and Pythonic approach to training and tuning further enhance the user experience, making Pytorch a preferred choice for those working on complex deep learning projects. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-08-27-01-20-05), [2](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51), [3](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [4](https://www.trustradius.com/reviews/pytorch-2022-09-30-08-07-50), [5](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08))

### Model Deployment and Production
Users generally appreciate Pytorch's model deployment and production capabilities. While some users find the distributed data parallel aspect to be complex, overall, Pytorch offers a Pythonic approach that simplifies putting models into production. The platform's support for training and deploying Deep Learning models in the Computer Vision field is well-received, especially with its array of pre-trained models and community support. However, there are mentions that Pytorch may be less suitable when the production phase outweighs the model development aspect. (Source Reviews: [1](https://www.trustradius.com/reviews/pytorch-2022-08-17-11-52-51), [2](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-35-55), [3](https://www.trustradius.com/reviews/pytorch-2022-08-17-10-12-08))

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.

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.

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 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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Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

Machine Learning

How to Run PyTorch Models in the Browser With ONNX.js

PyTorch implementation of DeepLabV3 | Semantic Segmentation for Autonomous Driving

ONNX is a open format to represent deep learning models that is supported by various frameworks and tools. This format makes it easier to interoperate between frameworks and to maximize the reach of your hardware optimization investments

In this episode, Seth Juarez (@sethjuarez) sits with Rich to show us how we can use the ONNX runtime inside of our .NET applications. He gives us a quick introduction to training a model with PyTorch, and also explains some foundational concepts around prediction accuracy.

[T01:01] - Brief overview of training a machine learning model
[T02:54] - PyTorch training in Python
[T07:43] - Understanding accuracy and loss
[T10:50] - What is ONNX ?
[T11:53] - Consuming model with ONNX in .NET
[T25:38] - ML .NET and ONNX
[T26:58] - Industry usage


Useful Links

ONNX on GitHub
https://github.com/microsoft/onnxruntime

ONNX NuGet
https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime?WT.mc_id=ondotnet-channel9-cephilli

ONNX Runtime is now open source
https://azure.microsoft.com/en-us/blog/onnx-runtime-is-now-open-source?WT.mc_id=ondotnet-channel9-cephilli

PyTorch
https://pytorch.org/

Seth’s Pytorch demo on GitHub
https://github.com/sethjuarez/DeepLearningWithPyTorch

Learning Machine Learning with .NET, PyTorch and the ONNX Runtime

This is an implementation of the paper: Show, Attend and Tell: https://arxiv.org/pdf/1502.03044.pdf

I packaged the model inside a small web app for demo purposes where you can see live image captioning with visual attention mechanism to highlight the areas where the model focuses when generating each word.

The model is implemented in PyTorch here: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning - you can reuse it. 

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Image Captioning with Deep Learning and Attention Mechanism in PyTorch

MMOCR - Text Detection, Text Recognition PyTorch toolbox demo
In this video I explore MMOCR. MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the OpenMMLab project.

Important links:
MMOCR : https://github.com/open-mmlab/mmocr
Code :  https://colab.research.google.com/drive/1wd8eId_OpZtos3PZQMb3F-3lVdXMBhFj?usp=sharing
OCR : https://en.wikipedia.org/wiki/Optical_character_recognition 

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MMOCR - Text Detection, Text Recognition PyTorch toolbox demo #computervision #ocr

In this video, I introduce TorchServe, an open-source model serving library for PyTorch jointly developed by AWS and Facebook. I provide a quick look at what it is, how it works, it's key capabilities and a live demo on how use TorchServe. The demo covers how to start torchserve, how to register and unregister models, query models, how to scale up and down workers and submit inference requests.

Additional resources:
Blog post: https://aws.amazon.com/blogs/machine-learning/deploying-pytorch-models-for-inference-at-scale-using-torchserve/
Deploying TorchServe with Amazon SageMaker: https://github.com/shashankprasanna/torchserve-examples
TorchServe: https://github.com/pytorch/serve

Introduction to TorchServe, an open-source model serving library for PyTorch

Download the libraries here:
https://github.com/Embarcadero/P4D-Data-Sciences

PyTorch demo with the Python for Delphi Data Sciences libraries

PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.

In this video, we'll dive deep into PyTorch Video for iOS and Android. We'll look at the following sections:

1. Downloading a pre-trained model (or training your own)
2. Pre-processing input data for PyTorch Mobile
3. Making an inference
4. Post-processing inference from PyTorch Mobile

An Overview of the PyTorch Mobile Demo Apps

Demo - Face Recognition using pytorch (Arcface)

Linear Regression using PyTorch C++ API (Libtorch) on CSV Files: Code Review and Demo Run!

PyTorch is one of the most popular open source machine learning framework that accelerates the path from research to production deployment. In this tutorial, Dmytro Dzhulgakov, core contributor for PyTorch, will go through an introductory level hands-on tutorial for building fashion recognizer. 

Jump To: 
[01:17] PyTorch Core functionality introduction
[05:08] Demo: classification problem
[10:38] Demo: Define network architecture
[13:13] Demo: Define loss function & optimizer
[16:04] Demo: Setting up TensorBoard
[16:45] Demo: Training Loop

More Information: 
PyTorch Tutorials  https://aka.ms/AIShow/PyTorchTutorials 
TorchVision https://aka.ms/AIShow/TorchVision
PyTorch GitHub https://aka.ms/AIShow/PyTorchGitHub
Tensorboard https://aka.ms/AIShow/Tensorboard
Follow Dmytro  https://twitter.com/dzhulgakov
Follow PyTorch https://twitter.com/pytorch

Create a Free account (Azure) https://aka.ms/aishow-seth-azurefree
Deep Learning vs. Machine Learning   https://aka.ms/AIShow/DLvML
Get Started with Machine Learning  https://aka.ms/AIShow/StartML
Follow Seth https://twitter.com/sethjuarez

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Intro to PyTorch Tutorial: Building fashion recognizer

This is a demo of using a convolutional neural network in PyTorch to discover parameters that govern 2D Gaussian blurs.

You can download the code and follow along! Instructions are in the video, or use this direct link: https://github.com/mikexcohen/DeepLearningDemos

This video demo is part of a full-length course on deep learning. Follow this link to check out the whole course:
https://www.udemy.com/course/deeplearning_x/?couponCode=202108

Deep learning for parameter discovery (CNN on Gaussian in PyTorch demo)

Video Demo with PixelLib Pytorch version using PointRend for instance segmentation.

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Pytorch 2025-01-13 10:00:00

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Pytorch 2022-09-30 08:07:50

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Pytorch 2022-08-17 11:52:51

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Pytorch 2022-08-17 10:35:55

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I use PyTorch to teach deep learning to my university students and professionally train and deploy models. Sometimes, it is done directly or through other libraries like Transformers from Hugging Face. PyTorch is very flexible and easy to write with a battery included. It offers a nice tradeoff between helpfulness and flexibility.

flexibility
Clean code, close to the algorithm.
Fast
Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
Versatile, can work efficiently on text/audio/image/tabular datasets.

Not easy to handle TPUs.
Surprisingly, some industry-standard building blocks are not there (e.g., cosine lr scheduler with warmup).
Deployment requires additional things not there, for example, dynamic batching.

Fast prototyping.
It's not platform-dependent.
The pre-processing function is already present.

Pytorch is an awesome way of coding Deep Learning and Reinforcement Learning Algorithms with great ease. Since it is mostly pythonic, converting your derived equations/algorithms and using your favourite optimizers to test is so great. Further, it has great extensions to use like weights and biases where you can see how weights change in your neural network. It is an ideal tool for experimentation in Deep learning domain.

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

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.

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, Keras, Python IDLE, PyCharm, Jupyter Notebook, Microsoft Visual Studio Code, Visual Studio IDE

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.

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

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

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.

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