With use cases like computer vision, natural language processing, predictive modeling, and much more, deep learning (DL) provides the kinds of far-reaching applications that change the way technology can impact human existence. The possibilities are limitless, and we’ve just scratched the surface of its potential.
But designing an infrastructure for DL creates a unique set of challenges. Even the training and inferences steps of DL feature separate requirements. You typically want to run a proof of concept (POC) for the training phase of the project and a separate one for the inference portion, as the requirements for each are quite different.
There are three significant obstacles for you to be aware of when designing a deep learning infrastructure: scalability, customizing for each workload, and optimizing workload performance.
The hardware-related steps required to stand up a DL technology cluster each have unique challenges. Moving from POC to production often results in failure, due to additional scale, complexity, user adoption, and other issues. You need to design scalability into the hardware at the start.
Specific workloads require specific customizations. You can run ML on a non-GPU-accelerated cluster, but DL typically requires GPU-based systems. And training requires the ability to support ingest, egress, and processing of massive datasets.
One of the most crucial factors of your hardware build is optimizing performance for your workload. Your cluster should be a modular design, allowing customization to meet your key concerns, such as networking speed, processing power, etc. This build can grow with you and your workloads and adapt as new technologies or needs arise.
Training an artificial neural network requires you to curate huge quantities of data into a designated structure, then feed that massive training dataset into a DL framework. Once the DL framework is trained, it can leverage this training when exposed to new data and make inferences about the new data. But each of these processes features different infrastructure requirements for optimal performance.
Training is the process of learning a new capability from existing data based on exposure to related data, usually in very large quantities. These factors should be considered in your training infrastructure:
Inference is the application of what has been learned to new data (usually via an application or service) and making an informed decision regarding the data and its attributes. Once your framework is trained, it can then make educated assumptions about new data based on the training it has received. These factors should be considered in your inference infrastructure:
There are several special considerations for inference on the edge:
Your goals for your DL technology are to drive AI applications that optimize automation and allow you a far greater level of efficiency in your organization. Learn even more about how to build the infrastructure that will accomplish these goals with this white paper from Silicon Mechanics.
Silicon Mechanics, Inc. is one of the world’s largest private providers of high-performance computing (HPC), artificial intelligence (AI), and enterprise storage solutions. Since 2001, Silicon Mechanics’ clients have relied on its custom-tailored open-source systems and professional services expertise to overcome the world’s most complex computing challenges. With thousands of clients across the aerospace and defense, education/research, financial services, government, life sciences/healthcare, and oil and gas sectors, Silicon Mechanics solutions always come with “Expert Included” SM.
Accelerate your performance on even the most challenging workloads with Silicon Mechanics systems based on 4th Gen Intel Xeon processors.READ MORE
Composable infrastructure on the edge is a big change from the fixed form factors that HPC and AI have historically relied upon.READ MORE
Our engineers are not only experts in traditional HPC and AI technologies, we also routinely build complex rack-scale solutions with today's newest innovations so that we can design and build the best solution for your unique needs.
Talk to an engineer and see how we can help solve your computing challenges today.