Technologies / Artificial Intelligence

DeepSpeed - deep learning performance optimization

The training focuses on using the DeepSpeed framework to optimize deep learning models. During the hands-on workshop, participants will learn advanced techniques for optimizing the training performance of deep neural network models. The class is conducted in the form of interactive workshops, where theory is immediately verified in practice through the implementation of real use cases.

Issues

DeepSpeed architecture and components
Zero Redundancy Optimizer (ZeRO).
Distributed training strategies
Pipeline parallelism
Tensor parallelism
Mixed precision training
Gradient checkpointing
Memory optimization techniques
Compression model
Batch size optimization
Communication optimization
Performance debugging

Benefits

Upon completion of the training, the participant will be able to effectively use DeepSpeed to optimize the process of training deep learning models
The ability to implement advanced memory and performance optimization techniques in AI projects
To configure and manage distributed model training systems
Will be able to effectively reduce computational resource consumption while maintaining high model quality
Will gain a working knowledge of scaling deep learning training on compute clusters
Will master techniques for debugging and troubleshooting performance issues in large AI models

Who is this training for?

Machine learning engineers working on large AI models

Artificial intelligence developers looking to improve the performance of their models

MLOps specialists looking for optimization solutions

AI solution architects responsible for scalability of systems

Python programmers specializing in deep learning

Researchers and scientists working with deep learning models

DevOps engineers working on AI infrastructure

Prerequisites

Practical knowledge of Python at an intermediate level
Basic knowledge of deep learning and neural networks
Experience working with machine learning frameworks (PyTorch, TensorFlow)
Knowledge of issues related to distributed computing

Training program

Key features and capabilities
Integration with popular frameworks
Preparation of the development environment
Memory optimization techniques
Zero Redundancy Optimizer (ZeRO).
Optimization of gradients

Model compression techniques

Distributed training

Tensor parallelism
Optimization of communication between nodes
Advanced optimization techniques
Adaptive gradient methods
Mixed precision training

Dynamic batch sizing

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

Who is the DeepSpeed - deep learning performance optimization training for?

This training is designed for professionals looking to develop skills in deepspeed - deep learning performance optimization. Required level: advanced.

How long is the DeepSpeed - deep learning performance optimization training?

The training lasts 3. Available in online or on-site format.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.