Technologies / Artificial Intelligence

Distributed Deep Learning From Horovod

The training introduces participants to the world of distributed deep learning model training using the Horovod framework. The program is designed to show how to effectively scale neural model training on computing clusters. During the intensive workshop, participants learn to implement distributed training algorithms, optimize performance and manage resources in a distributed environment. The class combines theory with practical exercises on real computing infrastructure.

Issues

The training program covers key aspects of distributed deep learning model training. Participants will learn about Horovod architecture, parallelization techniques, communication optimization, resource management, performance monitoring and operational best practices. Special emphasis is placed on the practical aspects of implementing and optimizing distributed training.

Benefits

Implementing and optimizing distributed deep learning training
They will gain knowledge of distributed systems architecture, parallelization techniques, communication optimization and resource management
Methods for efficient training scaling, debugging techniques and best practices in distributed ML system development

Who is this training for?

It is particularly valuable for MLOps specialists responsible for training infrastructure, AI engineers optimizing training processes, and solution architects designing scalable machine learning systems

The program is also suitable for teams implementing distributed ML solutions in production environments

Prerequisites

Prior to training, participants should have hands-on experience in training deep learning models and basic knowledge of distributed systems
Familiarity with Python and basic systems administration is also advisable

Training program

Integration with ML frameworks

MPI Basics
Implementation of distributed training
Parallelization strategies
Synchronous and asynchronous SGD
Optimization of communication

Performance optimization

Bandwidth and latency
Performance profiling

Integration and implementation
Integration with orchestration systems
Monitoring of distributed training

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 Distributed Deep Learning From Horovod training for?

The training is particularly valuable for MLOps specialists responsible for training infrastructure, AI engineers optimizing training processes, and solution architects designing scalable machine learning systems. Level: intermediate.

How long is the Distributed Deep Learning From Horovod training?

The training lasts 1 day. It is delivered online or on-site.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT is ISO 9001 certified.

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.