How To Optimise Distributed Machine Learning Training with Horovod, Kubernetes, and GPU Acceleration

by Olakunle Ebenezer Aribisala

6th August 2023

As machine learning models grow larger and more complex, training them on a single machine often takes days or even weeks. Distributed training has become a key solution, enabling models to process massive datasets efficiently while reducing training time. This article explores how combining Horovod, Kubernetes, and GPU acceleration can create powerful, scalable, and cost-effective training pipelines for modern machine learning workloads.
Training deep neural networks on a single machine can be painfully slow. Distributed training solves this problem by splitting the data and computations across multiple nodes and GPUs. This approach speeds up training, improves hardware utilisation, and enables the development of increasingly sophisticated models.
Two main methods exist in distributed machine learning: data parallelism, where each node processes a portion of the data and synchronises results, and model parallelism, where different parts of the model are trained on different nodes. For most use cases, data parallelism is the go-to method because it is simpler to implement and widely supported.

Horovod Simplifies Distributed Machine Learning

Horovod, an open-source library, has made distributed training easier by allowing popular machine learning frameworks like TensorFlow and PyTorch to scale across multiple GPUs and nodes with minimal code changes.

Horovod uses an efficient communication technique called ring-allreduce to share gradients among GPUs, reducing network bottlenecks. It supports both synchronous and asynchronous training, fault tolerance, and elastic scaling, making it adaptable to various workloads.

To get the most out of Horovod, practitioners often use hierarchical allreduce for very large clusters to reduce communication overhead, adjust batch sizes to balance memory and throughput, and fine-tune gradient aggregation settings for better performance.

Kubernetes Orchestrates Training at Scale

Kubernetes, or K8s, provides a platform for automating the deployment, scaling, and management of containerised applications, which makes it well-suited for distributed machine learning training.

With Kubernetes, training jobs can be deployed in a reproducible way, GPU resources can be scaled dynamically, and integrations with cloud or on-premises hardware accelerators become seamless. Tools like Kubeflow and MPI Operator further simplify the setup of distributed training jobs. Enabling GPU scheduling through NVIDIA plugins and configuring workloads to run efficiently across nodes ensures that the system performs at its best.
GPU Acceleration Powers Faster Training

GPUs are the hardware backbone of modern machine learning training because they handle large-scale mathematical computations far more efficiently than CPUs. When combined with distributed training frameworks like Horovod and orchestration tools like Kubernetes, GPUs enable massive speed-ups for training complex models.

Maximising GPU performance involves using mixed-precision training to reduce memory usage, monitoring GPU activity to avoid idle resources, and leveraging high-bandwidth interconnects like NVLink to speed up data transfer between GPUs.

Building an Optimised Training Pipeline

Creating a robust distributed machine learning workflow involves several steps. First, containerise the machine learning code with all dependencies included. Next, write training scripts that are compatible with Horovod. Then, define Kubernetes job configurations specifying the number of nodes, GPU requirements, and storage locations for datasets and logs. Deploying this setup on a GPU-enabled Kubernetes cluster allows teams to monitor performance and refine training parameters iteratively for better results.

Combining Horovod for distributed training, Kubernetes for orchestration, and GPUs for acceleration delivers an efficient and flexible training infrastructure. This approach reduces costs, improves resource utilisation, and enables teams to train state-of-the-art models at scale.
As machine learning workloads become more complex, adopting distributed training is no longer optional; it is essential for keeping pace in today’s data-driven world.