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It makes coding complex neural networks simple. Spend more time on research, less on engineering. It is fully flexible to fit any use case and built on pure PyTorch so there is no need to learn a new language.
44
entreprises
Nous disposons de données sur 44 entreprises qui utilisent Pytorch Lightning. Notre liste de clients Pytorch Lightning est disponible en téléchargement et est enrichie de spécificités essentielles de l'entreprise, y compris la classification de l'industrie, la taille de l'organisation, la localisation géographique, les tours de financement et les chiffres d'affaires, entre autres.
Entreprise | Pays | Industrie | Employés | Chiffre d'affaires |
---|---|---|---|---|
Scale AI | États-Unis | Software Development | 3.8K | $100M |
Matterport | États-Unis | Software Development | 710 | $111M |
Weights & Biases | États-Unis | Software Development | 260 | |
Walmart | États-Unis | Retail | 2.3M | $611B |
AnotherBrain | France | It Services And It Consulting | 65 | $1.1M |
Eaton | Irlande | Appliances, Electrical, And Electronics Manufacturing | 92K | $20B |
HIRINGNINJA | Inde | Human Resources Services | 13 | |
Fraunhofer-Gesellschaft | Allemagne | Non-Profit Organizations | 10K | $42M |
Samsung Food | Royaume-Uni | Technology, Information And Internet | 80 | |
CHARM Therapeutics | Royaume-Uni | Pharmaceutical Manufacturing | 58 | $1M |
Yo Hr Consultancy | Inde | Real Estate | 10 | |
Codete | Pologne | Software Development | 250 | $12M |
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Statistiques d'Utilisation Technologique et Part de Marché
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Pytorch Lightning est utilisé dans 8 pays
Il y a 76 alternatives à Pytorch Lightning
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Questions fréquemment posées
Nos données proviennent d'offres d'emploi collectées auprès de millions d'entreprises. Nous surveillons ces offres sur les sites web des entreprises, les plateformes d'emploi et d'autres plateformes de recrutement. L'analyse des offres d'emploi constitue une méthode fiable pour comprendre les technologies utilisées par les entreprises, y compris l'utilisation de leurs outils internes.
Nous actualisons nos données quotidiennement pour vous garantir un accès à l'information la plus récente disponible. Ce processus de mise à jour fréquente assure que nos insights et notre intelligence reflètent les derniers développements et tendances au sein de l'industrie.
PyTorch Lightning is a popular library built on top of PyTorch, a widely-used open-source machine learning framework. It simplifies the process of training PyTorch models by providing a high-level interface with pre-built components for common tasks, such as distributed training, mixed precision training, and logging. This technology aims to abstract the complexities of PyTorch while maintaining flexibility and control for researchers and practitioners.
PyTorch Lightning falls under the category of Machine Learning Tools, specifically catering to developers and researchers in the field of artificial intelligence. It offers a streamlined workflow for building and training machine learning models, allowing users to focus more on experimentation and model architecture rather than boilerplate code for training loops and optimizations. With its growing popularity, PyTorch Lightning has become a go-to choice for many machine learning practitioners due to its efficiency and ease of use.
Founded in 2019 by William Falcon and a team of researchers and engineers, PyTorch Lightning was motivated by the desire to enhance the PyTorch ecosystem by providing a standardized and efficient way to build and train models. Since its inception, PyTorch Lightning has gained significant traction within the machine learning community for its performance improvements and developer-friendly design. The project continues to evolve with regular updates and contributions from a thriving open-source community.
Currently, PyTorch Lightning holds a notable market share within the Machine Learning Tools category, with an increasing number of users adopting the library for their projects. Given its trajectory and continuous enhancements, it is anticipated that PyTorch Lightning will experience further growth in the future as more developers embrace its productivity-boosting features and streamlined approach to deep learning model training.
Pytorch Lightning is a powerful framework that streamlines the process of developing and training machine learning models. Its popularity stems from its ease of use, flexibility, and efficiency, making it a top choice for many companies in the industry.
Simplified Development Process
Pytorch Lightning simplifies the development process by abstracting complex components, allowing developers to focus on building models rather than dealing with low-level details. This approach saves time and effort compared to traditional Pytorch, enabling teams to iterate quickly and experiment more efficiently.
Enhanced Reproducibility
One of the key benefits of Pytorch Lightning is its focus on reproducibility. By enforcing best practices and providing standardized training loops, Pytorch Lightning ensures that experiments can be replicated easily. This level of consistency is crucial for building reliable and trustworthy machine learning models.
Scalability and Performance
Pytorch Lightning offers enhanced scalability and performance, allowing companies to train models on large datasets and distributed systems without compromising speed or efficiency. Its integration with Pytorch's backend further enhances performance, making it a superior choice for handling complex machine learning tasks.
Community Support
Pytorch Lightning boasts a vibrant community of developers and researchers who actively contribute to its growth and improvement. This active support system provides companies with access to a wealth of resources, tutorials, and expertise, ensuring they can leverage the full potential of the framework.
In conclusion, Pytorch Lightning's ease of use, reproducibility, scalability, performance, and strong community support make it a standout choice for companies looking to accelerate their machine learning projects with cutting-edge technology.
PyTorch Lightning has gained significant popularity among companies in the Machine Learning Tools category, with several notable industry players leveraging its capabilities to enhance their AI and machine learning projects. Here are a few case studies showcasing how some companies have successfully implemented PyTorch Lightning:
1. Facebook: Facebook, a global social media giant, has been using PyTorch Lightning for optimizing and streamlining their deep learning models for various applications. They started incorporating PyTorch Lightning into their frameworks in early 2019, aiming to improve training efficiency and model scalability across their platform. By leveraging PyTorch Lightning's structured and efficient design patterns, Facebook has been able to accelerate their model development cycles and achieve better performance outcomes in real-world scenarios.
2. NVIDIA: NVIDIA, a leading technology company renowned for its graphics processing units (GPUs), has integrated PyTorch Lightning into their AI research and development processes. NVIDIA adopted PyTorch Lightning to simplify the deployment of complex deep learning models on their hardware infrastructure, enhancing the overall performance and efficiency of their AI solutions. Since incorporating PyTorch Lightning into their workflow in mid-2020, NVIDIA has reported significant gains in productivity and model training speed, further solidifying their position as a key player in the AI hardware market.
3. Spotify: Spotify, a prominent music streaming service, has embraced PyTorch Lightning to empower their data scientists and engineers in building advanced recommendation systems and content personalization algorithms. By implementing PyTorch Lightning in early 2021, Spotify aimed to standardize their machine learning workflows and accelerate the development of innovative AI-driven features for their users. Through the use of PyTorch Lightning's modular and extensible architecture, Spotify has been able to iterate rapidly on their machine learning models and deliver personalized music recommendations at scale, enriching the user experience on their platform.
These case studies highlight how top companies like Facebook, NVIDIA, and Spotify have strategically adopted PyTorch Lightning to drive advancements in AI, enhance model performance, and achieve operational efficiency in their respective domains within the Machine Learning Tools category.
Vous pouvez accéder à une liste actualisée des entreprises utilisant Pytorch Lightning en visitant TheirStack.com. Notre plateforme fournit une base de données complète des entreprises utilisant diverses technologies et outils internes.
À ce jour, nous disposons de données sur 44 entreprises qui utilisent Pytorch Lightning.
Pytorch Lightning est utilisé par une large gamme d'organisations dans divers secteurs, y compris "Software Development", "Software Development", "Software Development", "Retail", "It Services And It Consulting", "Appliances, Electrical, And Electronics Manufacturing", "Human Resources Services", "Non-Profit Organizations", "Technology, Information And Internet", "Pharmaceutical Manufacturing". Pour une liste complète de tous les secteurs utilisant Pytorch Lightning, veuillez visiter TheirStack.com.
Certaines des entreprises qui utilisent Pytorch Lightning incluent Scale AI, Matterport, Weights & Biases, Walmart, AnotherBrain, Eaton, HIRINGNINJA, Fraunhofer-Gesellschaft, Samsung Food, CHARM Therapeutics et bien d'autres encore. Vous pouvez trouver une liste complète des 44 entreprises qui utilisent Pytorch Lightning sur TheirStack.com.
Selon nos données, Pytorch Lightning est le plus populaire dans États-Unis (21 companies), Allemagne (2 companies), Inde (2 companies), Irlande (2 companies), Royaume-Uni (2 companies), Chypre (1 companies), France (1 companies), Pologne (1 companies). Toutefois, il est utilisé par des entreprises du monde entier.
Vous pouvez trouver des entreprises utilisant Pytorch Lightning en le recherchant sur TheirStack.com. Nous suivons les offres d'emploi de millions d'entreprises et les utilisons pour découvrir quelles technologies et outils internes elles emploient.