Exclusive Content:

Haiper steps out of stealth mode, secures $13.8 million seed funding for video-generative AI

Haiper Emerges from Stealth Mode with $13.8 Million Seed...

VOXI UK Launches First AI Chatbot to Support Customers

VOXI Launches AI Chatbot to Revolutionize Customer Services in...

“Revealing Weak Infosec Practices that Open the Door for Cyber Criminals in Your Organization” • The Register

Warning: Stolen ChatGPT Credentials a Hot Commodity on the...

The process of distributed training in PyTorch: understanding distributed data-parallel and mixed-precision training

Using nn.parallel.DistributedDataParallel for Training Models in Multiple GPUs: A Comprehensive Tutorial

In this tutorial, we explored how to use nn.parallel.DistributedDataParallel for training models on multiple GPUs in Pytorch. We started with a minimal example of training an image classifier using the CIFAR10 dataset and then experimented with different parallelization techniques to speed up the training process.

We first trained the model on a single Nvidia A100 GPU for 1 epoch using standard Pytorch code. We then explored nn.DataParallel, which splits the batch and processes it independently on each GPU. However, we found that it provided no gain in performance due to CPU and hard disk bottlenecks.

Next, we dove into nn.DistributedDataParallel, which parallelizes the module by splitting the input across specified devices. We initialized the distributed learning processes, wrapped the model using DDP, and used a DistributedSampler in the DataLoader. We also discussed good practices for DDP, such as isolating data download and file I/O operations to the main process.

Additionally, we covered mixed-precision training in Pytorch, which combines FP16 and FP32 for faster training with comparable performance to FP32. We implemented mixed-precision training in our model training function and compared the results of different parallelization techniques.

Overall, we found that DistributedDataParallel with 4 GPUs provided the best performance in terms of training time, with a speedup of 2X compared to a single GPU. Mixed precision training further improved training time while maintaining performance. The results may vary based on your hardware configuration.

If you’re interested in exploring these topics further, you can access the code on GitHub and check out additional resources on distributed training and mixed-precision training. Your support through social media sharing, donations, or purchasing our book and e-course is greatly appreciated and helps us continue to produce quality AI content.

We hope you found this tutorial helpful and informative. Thank you for following along and supporting our work. Feel free to reach out on our Discord server if you have any questions or feedback. Stay tuned for more updates on deep learning and AI in production.

Latest

Comprehending the Receptive Field of Deep Convolutional Networks

Exploring the Receptive Field of Deep Convolutional Networks: From...

Using Amazon Bedrock, Planview Creates a Scalable AI Assistant for Portfolio and Project Management

Revolutionizing Project Management with AI: Planview's Multi-Agent Architecture on...

Boost your Large-Scale Machine Learning Models with RAG on AWS Glue powered by Apache Spark

Building a Scalable Retrieval Augmented Generation (RAG) Data Pipeline...

YOLOv11: Advancing Real-Time Object Detection to the Next Level

Unveiling YOLOv11: The Next Frontier in Real-Time Object Detection The...

Don't miss

Haiper steps out of stealth mode, secures $13.8 million seed funding for video-generative AI

Haiper Emerges from Stealth Mode with $13.8 Million Seed...

VOXI UK Launches First AI Chatbot to Support Customers

VOXI Launches AI Chatbot to Revolutionize Customer Services in...

Microsoft launches new AI tool to assist finance teams with generative tasks

Microsoft Launches AI Copilot for Finance Teams in Microsoft...

Investing in digital infrastructure key to realizing generative AI’s potential for driving economic growth | articles

Challenges Hindering the Widescale Deployment of Generative AI: Legal,...

Comprehending the Receptive Field of Deep Convolutional Networks

Exploring the Receptive Field of Deep Convolutional Networks: From Human Vision to Deep Learning Architectures In this article, we delved into the concept of receptive...

Boost your Large-Scale Machine Learning Models with RAG on AWS Glue...

Building a Scalable Retrieval Augmented Generation (RAG) Data Pipeline on LangChain with AWS Glue and Amazon OpenSearch Serverless Large language models (LLMs) are revolutionizing the...

Utilizing Python Debugger and the Logging Module for Debugging in Machine...

Debugging, Logging, and Schema Validation in Deep Learning: A Comprehensive Guide Have you ever found yourself stuck on an error for way too long? It...