Since Facebook (now Meta) open-sourced PyTorch in 2017, there’s been no looking back for the AI ​​ecosystem. Today, most tech companies, including OpenAI, Tesla, Microsoft, use PyTorch because of the dynamic nature of its computational graph, which allows rapid prototyping and experimentation.

Researchers can easily test new ideas and iterate on their models without the need for extensive reconfiguration. This flexibility is a key advantage in the fast-paced world of AI research, where innovation and quick adaptation are key.

Rise of the Pi Torch

The use of PyTorch has continued to grow in the research community. For example, about 70% of recent machine learning papers use PyTorch, compared to only 4% that use TensorFlow.

Number of repositories for PyTorch
(Source: GitHub)

Furthermore, in 2022 alone, Added 45,000 unique PyTorch models. On Hugging Face, while only 4,000 new TensorFlow exclusives were added. This resulted in 92% exclusive models on Hugging Face for PyTorch.

Surprisingly, all 30 are the most popular models of Hugging Face. Available in PyTorch.and none are specific to TensorFlow, which points to PyTorch’s popularity and higher adoption rate than any other framework.

Top 30 AI models are exclusive to PyTorch.
(Source: GitHub)

Needless to say, PyTorch has become Top choice in the research community due to its less complex nature and features such as dynamic computational graphs and Pythonic nature.

PyTorch Fuels Tesla

Tesla uses a HydraNet architecture, which is a large neural network that handles multiple tasks simultaneously. This is architecture. Trained using PyTorchLeveraging its dynamic computation graph and distributed training capabilities to manage the complexity of tasks such as lane detection, pedestrian tracking, and traffic signal recognition.

Tesla’s DOJO supercomputer Uses PyTorch. To train specific parts of their neural network architecture, to optimize the training process and improve the performance of their self-driving models.

Tesla collects data from its fleet of vehicles and uses PyTorch to train models on this real-world data. This continuous loop of data collection, labeling and training helps Tesla improve its AI systems in real time.

Tesla’s reliance on P-Torch is so high that even Elon Musk said he hates Facebook, but still uses tech developed by Facebook (P-Torch).

Meta has moved all of its AI systems to PyTorch, which is used for applications such as content recommendation, image recognition, and natural language processing.NLP).

By standardizing on PyTorch, Meta has unified its research and production pipelines, allowing a seamless transition from experimentation to deployment. This integration has led to the development of more efficient and scalable AI models.

“We didn’t have a grand vision for what the Pi Torch could be in the world. We built it for ourselves and it ended up being something that was useful to other people. At the same time, the identifiable market has spread so much, and it’s made it a bigger deal than we ever thought.” Someth Chantla.said a Meta researcher, expressing how PyTorch unexpectedly took off.

Meta works. 1,700 PyTorch-based inference models In full production, performing trillions of inference operations daily. This widespread use illustrates the reliability and scalability of PyTorch for large-scale AI applications.

Airbnb and PyTorch

Airbnb uses PyTorch to power its customer service dialog assistant, which enhances the customer experience with smart responses.

Airbnb treats the Smart Answers recommendation model as a The problem of machine translationusing PyTorch’s sequence-based models to translate customer input messages into agent responses.This approach leverages PyTorch’s advanced attention mechanisms and beam-finding capabilities.

Additionally, by integrating PyTorch into its dialog assistant, Airbnb has improved the accuracy and relevance of automated responses, leading to a better customer service experience.

Blue River Technology farms AI by PyTorch

The flexibility of PyTorch allows Blue River Technologies to quickly adapt its models to the ever-changing conditions in the agricultural sector. This adaptability is essential to deal with the new challenges that arise every day.

“We chose PyTorch because it’s very flexible and easy to debug. New team members can get up to speed quickly, and the documentation is complete. The framework allows us to simultaneously support production model workflows and research workflows. provides the ability to do,” said. Chris PaddockDirector of Computer Vision Machine Learning at Blue River.

Blue River technology uses weights and biases for experimental tracking and model visualization, seamlessly integrated with PyTorch. This integration provides real-time insight into model performance and helps debug and optimize models.

PyTorch enables rapid experimentation and production of models, ensuring that Blue River Technology’s field machines operate with high accuracy and speed.

PyTorch illuminates Genentech’s research.

PyTorch’s dynamic computation graphs and ease of use facilitate the development of complex models to predict molecular properties, accelerating the drug discovery process.

“At Genentech, PyTorch is being used for personalized development. Cancer medicineas well as for drug discovery and cancer treatment,” said Daniel Buzinoff, Head of AI – Early Clinical Development Informatics, Genentech.

Genentech uses PyTorch to develop models that can identify cancer-specific molecules, enabling personalized cancer treatments. This application leverages PyTorch’s flexibility and control structure for precise model tuning.

PyTorch’s ability to transition seamlessly from research to production allows Genentech to rapidly deploy new models and integrate them into its therapeutic workflow.

Microsoft and PiTorch

“We take things to another level with Azure Machine Learning because we can essentially create the infrastructure to train our models. We’ve found PieTorch and Azure Machine Learning to be very powerful together. “said Jeremy Gensari, senior research scientist at Microsoft.

Microsoft uses PyTorch for its language modeling service, which includes state-of-the-art language models for various applications.

Microsoft also built an internal language modeling toolkit on top of PyTorch, which improved the onboarding of new users and facilitated the development of advanced/custom functions and architectures.

Additionally, PyTorch’s native scalability allowed Microsoft to scale its language modeling features to billions of words, increasing the performance and accuracy of its language models.

Why not TensorFlow?

While TensorFlow remains a powerful and mature framework with strong productivity capabilities, it struggles to compete with PyTorch in several key areas. PyTorch’s ease of use, flexibility, and dynamic computation graphs make it the preferred choice for researchers and developers who prioritize rapid prototyping and experimentation.

Its intuitive design and deep integration with Python has fostered a vibrant community that continues to drive innovation in AI. On the other hand, TensorFlow’s strengths lie in its scalability and robust performance, making it ideal for large-scale production environments.

Its extensive ecosystem and support for distributed training ensure that it is a valuable tool for deploying high-performance AI models.

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