Artificial intelligence has the potential to revolutionize industries by automating specialized tasks and extracting insights from vast datasets through the use of neural networks. One fundamental aspect of AI’s success is the training of neural networks – machine learning algorithms that use a network of interconnected nodes, or neurons, to mimic how the human brain works. The training of the model used by the nodes/neurons relies on multiple passes over the dataset, with each complete pass called an epoch. During an epoch, the model undergoes multiple iterations, where it processes smaller batches of data and updates its parameters. Each epoch refines the model’s accuracy, and the efficiency of this process significantly influences both the time and cost needed to achieve the best results.
Neural network training is a process that involves three important stages – creating or modifying the neural network, loading the training data, and then testing the model’s accuracy. In the first stage, the network architecture is designed by defining layers, functions, and parameters that guide how the model learns from the data. Once the structure is in place, the model is fed with a curated training dataset, enabling it to recognize patterns and make predictions. This is a resource-intensive process requiring considerable computational power and data throughput. Afterward, the model’s performance is evaluated using test data, which allows for fine tuning and identifying areas of improvement.
Each of these cycles is referred to as an epoch, and its duration and efficiency are critical in determining how fast and effectively the model can be trained. Shorter epochs allow for more iterations within a set time, leading to faster creation of an accurate model. However, the speed of each epoch depends on several factors, and one key ingredient is the performance of the underlying hardware and data infrastructure.
Factors Impacting Epoch Duration
Three main factors influence how quickly an epoch completes: network speed, GPU performance, and storage throughput and latency.
The bandwidth and latency of network connections affect how fast data is transferred to and from storage. For environments dealing with large datasets, even minor network delays can lead to significant training time increases.
Graphics Processing Units (GPUs) have become the de facto standard for AI training, as they are well suited to delivering impressive computational throughput for the highly parallel tasks involved in neural networks. GPU manufacturers are constantly increasing the performance and scale of these devices, which has led to greater demands for data throughput from the storage layer.
The data storage layer holds the training dataset, which can range in size from 10TB to 10PBs and supply it to one or more GPUs via the high-speed network. File sharing protocols such as NFS have become pervasive, so that the same dataset can be accessed by all GPUs nodes in the cluster. The rate at which data is read from or written to the storage significantly impacts how fast the GPUs can load the data, which in turn affects the speed of their calculations.
Choosing the right storage solution becomes important in optimizing the AI training environment, especially when working within a budget. GPU performance comes at a cost, so introducing AI optimized storage solution provides an opportunity to balance cost and improve performance.
Storage Options for AI Workloads
When selecting storage for AI workloads, each option presents different trade-offs between cost and performance.
Traditional systems like Network-Attached Storage (NAS) or Storage Area Networks (SAN) are common in corporate environments. They are reliable and scalable but designed for generic office applications and enterprise workflows. Although they offer good performance, these systems are not cost-effective, energy efficient and require storage administration staff to manage the enterprise features sets not typically required for AI training. They are not specifically optimized for AI training demands that require high throughput and low latency.
High-Performance Computing (HPC) storage systems are built for massive datasets and high-compute workloads common in scientific computing. These systems can offer exceptional performance for HPC workloads usually distributed across 100/1000s of CPU nodes. They can be costly to deploy and manage and usually require in-house specialized expertise to operate and maintain.
As AI adoption has grown, other more specialized AI-specific storage solutions have emerged to meet its unique demands. These systems are optimized for large data sizes and rapid data access patterns typical in AI training. AI-specific storage solutions often blend enterprise and HPC storage characteristics, yet offer a more AI focused balance between performance and cost. Traditional storage solutions are typically focused on enterprise features like snapshots or tiering, which are more suited to enterprise workflows rather than AI-specific use cases, such as reducing training time or integrating models for inferencing at the edge.
Choosing the right storage solution involves balancing cost with performance and realistic space and energy. Opting for the highest-performing system can lead to prohibitively high costs, especially as the workloads scale. On the other hand, selecting a less expensive storage option could extend epoch durations, driving up training costs and time to solution.
To find the right balance, organizations can employ several strategies that are also related to the development stage:
Consider Cloud Adoption: Cloud-based GPU and storage offers flexibility and scalability, with many providers offering AI-optimized tiers. Cloud storage can be scaled up or down as needed, making it an attractive, cost-effective option for organizations who are at the initial stages of evaluating their project. However, costs can start to accelerate once a project reaches a particular stage. Recent studies have shown that in many cases, once a model has begun to gain traction, on-prem infrastructure can offer a ROI within 2-months.
Assess Workload Requirements: Understanding the specific demands of the AI workload is essential. High throughput need not be directly related to scale or size of data sets, today there is no need to be locked into a larger proprietary storage solution on the basis that the organization will potentially scale. AI-level performance can be found even at 50TBs – organizations should scale as needed.
Evaluate Total Cost of Ownership: When comparing storage options, it is important to consider the total cost of ownership, which includes not only the initial purchase but also ongoing maintenance, management, and energy costs. While high-performance storage may have higher upfront costs than NVMe flash costs, they could offer better long-term value by reducing epoch durations and accelerating the time-to-market of the AI model.
As AI continues to evolve, the demands on storage systems will only grow. Organizations that invest in the right storage solutions today will be better positioned to harness AI’s full potential in the future. By carefully weighing cost against performance and selecting storage solutions that align with their specific AI workloads, businesses can ensure their neural networks are trained efficiently and effectively. In this age of AI, storage is not just a supporting component, it is a critical factor to achieving success.
