Distributed and Scalable Deep Learning

Distributed and Scalable Deep Learning (Horovod, DeepSpeed, and Multi-GPU Training)

Executive Overview

With the increasing scale and complexity of AI models, distributed and scalable deep learning has become a cornerstone of enterprise AI infrastructure. This 7-day corporate training program empowers participants to leverage multi-GPU, multi-node, and distributed computing techniques to accelerate training and deployment. Using advanced frameworks such as Horovod, DeepSpeed, and PyTorch Distributed Data Parallel (DDP), participants will gain practical knowledge in scaling deep learning workflows efficiently across cloud and on-premise environments. The program is designed for AI teams looking to reduce model training time, optimize GPU utilization, and scale large language models and deep learning systems across clusters.

Objectives of the Training

Understand the architecture and challenges of distributed deep learning.
Implement data, model, and pipeline parallelism techniques.
Utilize frameworks like Horovod, DeepSpeed, and PyTorch DDP for multi-GPU and multi-node training.
Optimize model performance using mixed precision and gradient accumulation.
Deploy distributed workloads efficiently across cloud and hybrid environments.

Prerequisites

Intermediate understanding of Python and deep learning frameworks (TensorFlow/PyTorch).
Familiarity with GPU computing and basic Linux operations.
Understanding of neural networks and model training workflows.
Exposure to cloud platforms (AWS, Azure, or GCP) is helpful.

What You Will Learn

Distributed training concepts and parallelization strategies.
Multi-GPU training using PyTorch and TensorFlow.
Efficient scaling of large models using Horovod and DeepSpeed.
Profiling and optimizing distributed deep learning workloads.
Best practices for enterprise-scale distributed AI deployment.

Target Audience

This course is designed for AI Engineers, ML Practitioners, Research Scientists, and Cloud Architects who manage large-scale model training. It is also suited for Technical Managers and Infrastructure Leads tasked with scaling AI systems across enterprise GPU and cloud environments.

Detailed 7-Day Curriculum

Day 1 – Foundations of Distributed Deep Learning (6 Hours)

Session 1: Why Distributed Deep Learning? Scaling Challenges in Modern AI.
Session 2: Parallelization Strategies – Data, Model, and Pipeline Parallelism.
Session 3: GPU Clusters, Interconnects, and Hardware Overview.
Hands-on: Setting up a Multi-GPU Environment on Local and Cloud Platforms.

Day 2 – Multi-GPU and Data Parallel Training (6 Hours)

Session 1: Multi-GPU Training in TensorFlow and PyTorch.
Session 2: Understanding Synchronous vs. Asynchronous Training.
Session 3: NCCL and Communication Strategies for Efficient Synchronization.
Hands-on: Implementing Multi-GPU Data Parallelism in PyTorch.

Day 3 – Distributed Training with Horovod (6 Hours)

Session 1: Introduction to Horovod and Distributed Training Architecture.
Session 2: Integrating Horovod with TensorFlow, Keras, and PyTorch.
Session 3: Tuning and Scaling Deep Learning Workloads with Horovod.
Workshop: Distributed Image Classification using Horovod.

Day 4 – DeepSpeed for Efficient Large Model Training (6 Hours)

Session 1: Overview of DeepSpeed and ZeRO Optimizations.
Session 2: Gradient Accumulation, Mixed Precision, and Memory Efficiency.
Session 3: Training and Scaling Large Language Models using DeepSpeed.
Case Study: Training Transformer Models with DeepSpeed on Cloud GPUs.

Day 5 – Hybrid Parallelism and Pipeline Optimization (6 Hours)

Session 1: Combining Data, Model, and Pipeline Parallelism for Scalable Training.
Session 2: Fault Tolerance, Checkpointing, and Elastic Training.
Session 3: Profiling Distributed Training Workloads with TensorBoard and Nsight.
Hands-on: Pipeline Parallel Training for Transformer Architectures.

Day 6 – Cloud Deployment and Performance Optimization (6 Hours)

Session 1: Deploying Distributed Training on AWS, Azure, and GCP.
Session 2: Using Kubernetes and Ray for Distributed AI Workloads.
Session 3: Performance Optimization and Benchmarking for Multi-GPU Systems.
Workshop: Benchmarking and Scaling Deep Learning Across Cloud Clusters.

Day 7 – Capstone Project & Future of Distributed Deep Learning (6 Hours)

Session 1: Capstone Project – Building a Distributed Training Pipeline for an Enterprise Model.
Session 2: Group Presentation and Results Discussion.
Session 3: The Future of Scalable AI – Federated Learning and Cross-Cloud Collaboration.
Panel Discussion: AI Infrastructure Strategy for the Enterprise.

Capstone Project

Participants will implement a distributed training system for a real-world enterprise use case such as large-scale image recognition or natural language processing. The project will involve configuring multi-node environments, optimizing performance with Horovod or DeepSpeed, and analyzing training scalability metrics.

Future Trends in Distributed Deep Learning

The future of distributed deep learning will be defined by the convergence of AI supercomputing, federated training, and hybrid cloud orchestration. Emerging technologies such as Zero Redundancy Optimization (ZeRO), automatic parallelism, and energy-efficient GPU scheduling will revolutionize scalability. Enterprises investing in distributed AI infrastructure will gain unmatched agility and performance in training next-generation AI models.