Distributed Deep Learning With Jax Large–Scale Training
Free Download Distributed Deep Learning With Jax Large–Scale Training
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 232.64 MB | Duration: 0h 30m
Implement Multi-Device Parallel Training Strategies for Efficient Large Model Training - FSDP/TP/PP/DP
What you'll learn
Distributed Training of Large Scale Machine Learning Models
How to parallelize models over workers (tensor, pipeline, data, sequence)
Collective communication in training large scale models
Using Jax and XLA to distribute models over thousands of workers
Requirements
Intermediate Python, Entry level Machine Learning knowledge, Some Jax knowledge preferred
Description
Master the art and science of distributed training for GPT-style language models in this comprehensive course using JAX and XLA. Learn how to scale up autoregressive transformer training from scratch, implementing the same techniques used by leading AI labs to train large language models efficiently across multiple devices.What You'll Learn:Distributed Training FundamentalsUnderstanding distributed computing for language modelsJAX's transformation and parallelization primitivesXLA (Accelerated Linear Algebra) compilation optimizationMastering collective communication primitives: • All-reduce for gradient aggregation • All-gather for distributed data collection • Broadcast for parameter synchronization • Scatter/Gather for efficient token distributionAdvanced Parallelization Strategies for GPTTensor Parallelism: Splitting attention heads and MLP layersPipeline Parallelism: Optimal transformer layer distributionSequence Parallelism: Handling long sequences efficientlyData Parallelism: Scaling batch processingHybrid parallelism approaches specific to GPT modelsGSPMD and Automated ShardingImplementing GSPMD for GPT model componentsAutomatic sharding strategies for attention layersOptimizing communication patterns for autoregressive modelsCustom sharding annotations for transformer blocksPractical ImplementationBuilding distributed GPT architectures from scratchEfficient parameter sharding and synchronizationHandling distributed attention computationManaging causal masks in distributed settingsImplementing efficient parameter serversPerformance Optimization and DebuggingProfiling distributed GPT trainingOptimizing attention computationImplementation of gradient checkpointingMemory optimization techniquesHandling numerical stability in distributed settingsHands-on Projects:Implement a mini-GPT from scratch with distributed trainingBuild hybrid parallelism strategies for GPT trainingCreate custom sharding strategies using GSPMDOptimize collective communication patterns for transformer blocksDeploy and manage distributed GPT training across multiple nodesWho This Course is For:Machine Learning Engineers working on language modelsDeep Learning Researchers scaling up GPT-style modelsSoftware Engineers transitioning to large-scale MLAnyone interested in training large language modelsPrerequisites:Strong Python programming skillsBasic understanding of transformer architecturesFamiliarity with language modeling conceptsBasic linear algebra and calculusExperience with any deep learning frameworkBy the end of this course, you'll understand how to implement and scale GPT model training across distributed systems. You'll master the intricacies of parallel training strategies, communication primitives, and optimization techniques specific to large language models.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Introduction to Mesh
Lecture 3 Collective Communication
Lecture 4 Data Parallelism
Lecture 5 FSDP (Fully Sharded Data Parallel)
Lecture 6 FSDP+TP (Fully Sharded Data Parallel plus Tensor Parallel
Lecture 7 Pipeline Parallel
Anyone who wants to learn how to parallelize and scale machine learning models over thousands of workers using Jax and XLA.
Screenshot
Homepage
https://www.udemy.com/course/distributed-deep-learning-with-jax-large-scale-training/No Password - Links are Interchangeable
