Deep Speed: High-Performance Training with Game-Changing Optimization

Deep learning has transformed fields ranging from computer vision to natural language processing. However, as models become more extensive and sophisticated, training them becomes more difficult due to memory restrictions and computing constraints. Deep Speed, a game-changing optimization package developed by Microsoft, tackles these issues by enabling efficient and scalable deep-learning model training.

This blog post will define Deep Speed and explain how it can be used to accomplish high-performance training.

What is Deep Speed?

Microsoft Research created Deep Speed, an open-source deep learning optimization library. It offers a comprehensive range of tools and strategies for optimizing the training process, particularly on large-scale models and distributed systems. Researchers and practitioners can use Deep Speed to overcome memory and performance limitations while training huge models by integrating them into deep learning frameworks like PyTorch.

What are the key features and benefits of Deep Speed?

Deep Speed has numerous major characteristics and advantages that make it an invaluable tool for deep learning practitioners:

• Deep Speed uses memory optimization techniques such as activation checkpointing and zero redundancy optimizer (ZeRO) to reduce memory usage during training. Activation checkpointing selectively saves the required activations, allowing larger models to fit within memory restrictions. Zero saves memory by distributing model weights and optimizer states among devices.
• Training Acceleration: To accelerate the training process, the library employs techniques such as gradient accumulation, tensor parallelism, and pipeline parallelism. Gradient accumulation accumulates gradients across many mini-batches, minimizing optimizer steps. Tensor parallelism and pipeline parallelism distribute model parameters and calculations across several devices, maximizing resource utilization.
• Deep Speed enables effective distributed training across numerous nodes and GPUs, allowing researchers to handle more significant problems and use parallelism for faster convergence. It connects effortlessly with distributed training systems such as Horovod and NCCL for increased scalability.

How to use Deep Speed?

1. Installation: Deep Speed, which is accessible as a Python package, is to be installed first. Depending on your needs, it can be readily installed via pip or building from the source.
2. Model Selection: Modify your PyTorch code to add Deep Speeds API and functionality. Importing the Deep Speed library, wrapping the model in Deep Speeds engine, and defining configuration options are common steps.
3. Configuration: Deep Speed offers several configuration options for optimizing memory utilization, enabling gradient accumulation, and configuring parallelism techniques. Fine-tune these settings based on your models needs and available resources.
4. Training Execution: Run your training script with Deep Speed enabled to take advantage of memory optimization, speedup approaches, and scalability. Monitor the training process and iterate as needed to attain the best results.

How to implement Deep Speed?

Microsoft Research created DeepSpeed, a deep learning optimization library. Its goal is to speed up the training of large deep learning models by incorporating memory optimization approaches, mixed precision training, and gradient accumulation.

Here is an example of how DeepSpeed can be used with PyTorch.

1. Install DeepSpeed:

pip install deepspeed

2. Import the necessary libraries:

import torch


import deepspeed

3. Define your model and optimizer using PyTorch:

model = YourModel()


optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

4. Wrap your model and optimizer with DeepSpeed:

model, optimizer, _, _ = deepspeed.initialize(model=model, optimizer=optimizer)

The deepspeed.initialize() method in this example encapsulates the model and optimizer to enable DeepSpeed optimizations. The gradient computation and parameter update are handled by the model is backward and step procedures, respectively. DeepSpeed functionalities such as optimizer and learning rate scheduler steps can also be included.

DeepSpeed includes several extra features, including zero redundancy optimizer (ZeRO) approaches and memory optimization methodologies. More information on using these capabilities and customizing their behavior based on your specific needs may be found in the DeepSpeed documentation.

What is DeepSpeed-Chat?

DeepSpeed-Chat is a simple method for training and inference of powerful ChatGPT-like models? It solves existing system limitations by offering an end-to-end RLHF (Reinforcement Learning with Human Feedback) pipeline that supports the complex training process of ChatGPT models. DeepSpeed-Chat provides a single script for training models, mirroring InstructGPT three-step workflow and giving data abstraction and blending features.

It also presents the DeepSpeed-RLHF system, which integrates DeepSpeed training and inference capabilities into a single Hybrid Engine. This solution makes RLHF training efficient, inexpensive, and scalable to the AI community by leveraging multiple optimizations, such as tensor-parallelism and memory optimization methods.

Conclusion

Deep Speed is a sophisticated optimization library that dramatically enhances large-scale deep learning models training efficiency and scalability. Deep Speed enables academics and practitioners to push the frontiers of deep learning applications and achieve state-of-the-art performance by effectively managing memory, speeding training speed, and enabling distributed training.