How To Train Large Language Models LLM like GPT 4 on PyTorch 2.0 | Distributed Model Training on GPU

Hi, thanks for watching our video about training large language models on PyTorch 2.0
In this video we’ll walk you through:
- GPT4
- GPU
- Multimodal
- LLM
- Model Training
- Model Parallelism
- Distributed Data Parallel DDP
- Deep Learning
- ChatGPT
- palm-e Models
- VALLE
- GOOGLE LAMDA
Mode Parallel, Gradient Accumulation, Data Parallel with PyTorch, Larger Batches

The PyTorch Distributed Stack is a set of PyTorch features that facilitate training models in distributed systems. PyTorch has introduced multiple new features in the distributed package to help support larger scale and higher efficiency for both data and model parallel. In this talk, Yanli Zhao (Software Engineer, Meta AI) shares tips on how to reduce memory footprint, fit larger models and achieve significant speedup with distributed systems with features like Zero Redundancy Optimizer, DistributedDataParallel, FullyShardedDataParallel, CUDA RDMA and ShardedTensor.

OpenAI has officially announced GPT-4 the latest version of its incredibly popular large language model powering artificial intelligence (AI) chatbots. ChatGPT is an AI chatbot that utilizes the language models of GPT-3 and GPT-4 for interactive communication. In other words you can use chatgpt with gpt4. A Generative Pre-Trained Transformer (GPT) is a sophisticated neural network architecture used to train large language models (LLMs). It makes use of large amounts of publicly available Internet text to simulate human communication.

Main improvements:
* It's multimodal (what it is is in video)
* Longer context length
* Better performance
* More safer

chatgpt,
gpt4,
google AI,
Google Lamda,
Google PALM,
artificial intelligence,
Machine learning,
transformer models,
deep learning,
Ray,Ray Tune,Hyperparameter optimization,machine learning,mlflow,open-source

TIMESTAMPS
0:00 Intro
0:55 PyTorch
1:25 Autograd
2:35 PyTorch Distributed
3:10 Types of Parallelization
5:20 DDP
6:38 FSDP
7:48 Fairscale
8:55 PyTorch Lightning
10:30 Model Parallel
11:48 Sagemaker Training



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LINKS/Sources USED:
PyTorch Distributed : https://www.youtube.com/watch?v=3XUG7cjte2U
YC: https://www.youtube.com/watch?v=hQC5O3WTmuo
Fairscale: https://www.youtube.com/watch?v=oDt7ebOwWIc
AWS Reinvent: https://www.youtube.com/watch?v=vv52RsBM8o4

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