2024-11-15
Summary: Explores how to effectively utilize multi-GPU environments for deep learning training. The article shares key techniques including data partitioning strategies, communication optimization, and load balancing, demonstrating how to achieve 10x training performance improvements through practical cases. Combined with industry experience, it provides an in-depth analysis of performance optimization strategies in enterprise AI training scenarios.
Deep learning is a branch of machine learning that can build accurate prediction models without relying on structured data. This approach extracts and correlates large amounts of data through algorithmic networks that simulate brain neural networks. The more training data input, the higher the model's accuracy.
While sequential processing can be used to train deep learning models, this training method is impractical or even impossible to complete without parallel processing due to the massive amount of data and lengthy processing times.
Parallel processing can handle multiple data objects simultaneously, significantly reducing training time. This parallel processing is typically achieved through Graphics Processing Units (GPUs). GPUs are processors designed specifically for parallel work, offering significant speed advantages over traditional Central Processing Units (CPUs), often achieving speeds over 10 times faster. Typically, multiple GPUs are integrated into systems alongside CPUs. CPUs can handle more complex or general tasks, while GPUs focus on specific and highly repetitive processing tasks.
After adding multiple GPUs to a system, parallelism needs to be built into the deep learning process. There are two main methods to achieve parallelism: model parallelism and data parallelism.
Model parallelism is used when the model's parameters are too large to fit within memory constraints. With this method, the model's training process is split across multiple GPUs and executed in parallel or series. Model parallelism uses the same dataset for each part of the model and requires data synchronization between partitions.
Data parallelism is a method of replicating models across multiple GPUs. This method is particularly useful when the batch size is too large to fit on a single machine or when aiming to accelerate the training process. In data parallelism, each model replica trains simultaneously on a subset of the dataset. After completion, the results from each model are merged, and training continues normally.
When working with deep learning models, various frameworks are available, including Keras, PyTorch, and TensorFlow. The implementation of multi-GPU systems varies depending on the chosen framework.
TensorFlow is an open-source framework created by Google, suitable for various machine learning operations. Its library includes multiple machine learning and deep learning algorithms and models for training. TensorFlow also includes built-in methods for distributed training using GPUs.
Through the API, tf.distribute.Strategy can be used to distribute operations across multiple GPUs, TPUs, or machines. This method supports creating and managing multiple user segments and allows easy switching between distribution strategies.
tf.distribute.Strategy extends two strategies:
TPUStrategy: Supports distributing workloads across multiple Tensor Processing Units (TPUs). TPUs are specialized units on Google Cloud Platform optimized for TensorFlow training.
The distributed data parallel process for both methods follows these steps:
Through this data parallel approach, TensorFlow can effectively utilize multiple GPUs to accelerate model training.
PyTorch is a Python-based open-source scientific computing framework that can utilize tensor computation and GPUs to train machine learning models. The framework supports distributed training through the torch.distributed
backend.
In PyTorch, three types of GPU parallelism (or distribution) methods are available:
DataParallel
class to allow model replicas to be distributed across GPUs on multiple machines. Can be combined with model_parallel
to achieve both model and data parallelism.When implementing machine learning operations with multiple GPUs, there are three main deployment models. The chosen model depends on where resources are hosted and the operation scale.
GPU servers are servers that integrate GPUs with one or more CPUs. When workloads are allocated to these servers, the CPU acts as a central management center for the GPUs, responsible for task allocation and result collection.
GPU clusters consist of computing clusters with nodes containing one or more GPUs. Clusters can be composed of nodes with identical GPUs (homogeneous) or different GPUs (heterogeneous). Data is transferred between nodes in the cluster through interconnects.
Kubernetes is an open-source platform for orchestrating and automating container deployments. The platform supports using GPUs in clusters to accelerate workloads such as deep learning.
When using GPUs in Kubernetes, heterogeneous clusters can be deployed with specified resource requirements, such as memory needs. These clusters can also be monitored to ensure reliable performance and optimize GPU utilization. The multi-GPU parallel training process follows these steps:
This approach fully utilizes Kubernetes' GPU management capabilities to accelerate model training.
Reference: Run:ai "Deep Learning with Multiple GPUs"
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Advantages of using Rise VAST AI Platform:
RiseUnion's platform simplifies AI infrastructure processes, helping enterprises improve productivity and model quality.
To learn more about RiseUnion's GPU virtualization and computing power management solutions, please contact us: contact@riseunion.io