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The rapid advancement of machine learning (ML) and artificial intelligence (AI) has brought immense opportunities but also significant challenges, particularly in managing the high costs associated with training and deploying complex models. At Hopsworks, we have seen organizations grapple with escalating expenses related to computing resources, as they move their applications from experiments to production systems. What seemed like a fairly small cost in development, often blew up to completely unaffordable proportions that jeopardized the entire project.
This post will explore several key strategies to mitigate these costs, focusing on leveraging GPUs, transitioning to on-premise infrastructure, and implementing advanced resource management techniques, all while highlighting how Hopsworks can facilitate these optimizations.
Traditionally, CPUs have been the workhorses of computing. However, for machine learning workloads, GPUs have emerged as the more efficient and cost-effective solution. GPUs are designed for parallel processing, making them ideal for the matrix operations that are fundamental to deep learning algorithms. GPUs offer significant advantages over CPUs in terms of speed, efficiency, and cost-effectiveness, particularly for tasks like neural network training. They can have thousands of cores, enabling them to perform many calculations simultaneously, unlike CPUs, which have a fraction of that number. This results in much higher compute power for a lower price. Research also shows that GPU clusters are faster and more cost-effective than CPU clusters, helping to meet SLAs. Additionally, GPUs can be used for other data science tasks besides model training, such as ETL jobs and data wrangling.
While cloud computing has provided accessibility and scalability for ML workloads, the cost can quickly become prohibitive, especially for long-term, production projects with 5 nines availability requirements and mission critical governance controls that are demanded by sovereign applications. At Hopsworks, we have found that customers can hit three birds with one stone: achieve higher levels of data sovereignty, improved performance and / or reduced response times, and reduce costs at the same time.
Transitioning to on-premise infrastructure can be a strategic move for cost optimization, as on-premise ML infrastructure provides cost savings due to lower long-term costs compared to cloud services, especially for production-level, 24x7 based ML resource usage. Additionally, it allows for greater control over hardware configurations and faster network connectivity that the one that is typically offered in standard public cloud infrastructures, leading to improved performance. Dedicated, not shared, resources can ensure consistent performance, and data locality avoids latency and costs of data transfer to and from the cloud.
When deciding between on-premise and cloud solutions, Hopsworks always recommends considering the long term needs and project requirements to maximize cost efficiency. For stable, long-running processes, on-premise solutions often come out as cheaper over time.
Once you have a GPU-based, on-premise machine learning infrastructure, implementing advanced resource management and scheduling techniques is essential to maximize resource utilization and reduce costs.
For example, there are several strategies available for helping enterprises optimize GPU usage and performance. These strategies include time-slicing to share GPUs among workloads, using Multi-Instance GPU (MIG) to partition GPUs for isolation, dynamic energy optimization to adjust energy use based on workload, batch processing to leverage parallel processing, right-sizing GPU instances to match workload demands, monitoring and analysis to track performance, checkpointing to assess and potentially terminate unproductive training, and mixed precision training to improve performance with lower precision arithmetic.
With the Hopsworks AI Lakehouse, users receive not just the feature store foundation, but they can also implement a centralized MLOps platform built around that feature store, designed for both cloud and on-premise deployments, offering flexibility in governance and cost management. Hopsworks provides essential resource management and scheduling features, allowing users to optimize their ML infrastructure. These features include things like
The implementation of the AI Lakehouse, therefore, how the GPU and on-premise cost optimizations can be brought to life, by allowing the management staff to dig into the details and do the optimizations that really contribute to the bottom line.
Optimizing machine learning costs requires a strategic approach that encompasses the choice of hardware, infrastructure, and resource management techniques. By adopting GPUs, moving to on-premise infrastructure, implementing advanced scheduling, and utilizing platforms like the Hopsworks AI Lakehouse, organizations can significantly reduce their ML expenses while maintaining high performance. Continuous monitoring, experimentation, and adaptation are key to achieving the most efficient and cost-effective machine learning workflows.
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