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
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DeepSpeed architecture and components
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Zero Redundancy Optimizer (ZeRO).
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Distributed training strategies
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Pipeline parallelism
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Tensor parallelism
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Mixed precision training
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Gradient checkpointing
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Memory optimization techniques
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Compression model
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Batch size optimization
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Communication optimization
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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?
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
Architecture and basic components
- Key features and capabilities
- Integration with popular frameworks
- Preparation of the development environment
- Memory optimization techniques
- Zero Redundancy Optimizer (ZeRO).
- Optimization of gradients
GPU memory management
- Model compression techniques
Parallel processing
- Distributed training
Pipeline parallelism
- Tensor parallelism
- Optimization of communication between nodes
- Advanced optimization techniques
- Adaptive gradient methods
- Mixed precision training
Checkpoint optimization
- 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?
The target audience includes machine learning engineers working on large AI models, artificial intelligence developers looking to improve the performance of their models, and MLOps specialists looking for optimization solutions. Level: advanced.
How long is the DeepSpeed - Deep Learning Performance Optimization training?
The training lasts 3 days. 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.
Request a quote
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