What are the most common hardware requirements for building neural networks?

Many beginners often overlook a crucial aspect in neural network development. While leveraging robust frameworks like Tensorflow and Pytorch for built-in models and packages is essential, what often slips their mind is the use of Numpy arrays for data storage. This oversight can significantly hamper computational speed. Depending on your available hardware, it's vital to make the most of your resources. Personally, with my M1 Pro MacBook Pro, I've noticed a substantial performance boost when using Tensorflow-Metal as opposed to standard Tensorflow. Yet, simply utilizing Tensorflow-Metal isn't enough; converting your array data into tensors is equally critical for a remarkable enhancement in neural network computation speed.

Clustered computing mitigates against CPU limitations by parallelising across many nodes. However, the performance achieved with GPU hardware and accelerated libraries is hard to ignore. GPU’s are designed for data parallelism while CPU’s are designed for task parallelism. This means that they can parallelise the same operation across data elements. They are therefore able to process many more data elements per instruction compared to a CPU. To further enhance this capability modern GPU’s have thousands of cores compared 64 in the largest CPU’s. Furthermore, GPU’s are optimised for vector and matrix operations which are heavily used in NN’s giving them a significant performance uplift over a CPU - particularly for deep NN’s.

Ample storage is vital for machine learning and AI projects. Fast NVMe drives are best for quick data access, available up to 4TB. SATA SSDs (up to 8TB) handle larger datasets, while platter drives (18TB+) are for extensive data and archives. RAID setups boost storage but add complexity.

Not only is the amount of RAM important but to echo Palwasha’s point, so is speed, and latency plays a part too. RAM is utilised by both the CPU and GPU (VRAM) to read and write data much faster compared to traditional flash or disk storage. This is because RAM is faster and has dedicated lanes on the motherboard direct to the processor. Recently, RAM has seen a significant increase in speed with DDR5 and GDDR6X (VRAM) having transfer speeds up to 64 and 1000GB per second respectively compared to the fastest NVME drive which maxes out around 3.5GB per second. Interestingly, for Satya’s situation above, the latest M1 and M2 Apple chips have the RAM soldered directly to the processor with both CPU and GPU cores which does boost performance.

Cloud computing revolutionizes neural network projects. In a recent endeavor, I grappled with an extensive video dataset. Local SSD storage constraints forced me to compromise on high-resolution and high-framerate video processing. Enter cloud computing – a game-changer. It offered cost-effective, scalable storage options, allowing me to accommodate massive datasets effortlessly. Plus, since my project was short-term, I could flexibly provision and release resources, eliminating long-term costs. Platforms like AWS SageMaker are gaining traction, simplifying neural network deployment. Cloud computing empowers neural network developers to tackle resource-intensive tasks without breaking the bank.

Navigating neural network frameworks and libraries is a nuanced art. The landscape is teeming with versions, and harmonizing them for your specific use case can be a puzzle. Recently, I grappled with version conflicts; one library demanded a higher Numpy version while another insisted on a lower one – a head-scratcher indeed. In such conundrums, I recommend crafting tailored virtual environments, each housing only the essentials, instead of a one-size-fits-all approach. Moreover, when wielding these tools, don't overlook the optimization potential. Penning hardware-optimized code stands as a sage counsel for enhancing computational speed.

There are some really easy ways to ensure compatibility. One way is to use PC Part Picker which makes it easy for those keen to build their own pc’s by providing compatibility guidance on all components including the case. Using that tool allows you to focus on the important decisions like specifications vs cost. I built my own PC a couple years ago and had a very issue free experience when it came to putting everything together and booting it up for the first time. I also recommend researching bottlenecking to ensure you don’t get a really fast GPU that’s ultimately bottlenecked by the CPU or RAM. Oh and RAM slots are configured to make use of multi-channel memory architecture so pick your slots wisely and don’t forget to enable XMP.

I've particularly found Tensor Processing Units (TPUs) extremely helpful when training really complex models. TPUs are specialized hardware accelerators designed specifically for machine learning (ML) workloads. They are custom-designed by Google AI to perform large matrix operations efficiently, which is a common operation in many ML algorithms. TPUs are significantly faster and more efficient than CPUs or GPUs for ML training and inference. For example, TPUs can train a ResNet-50 image classification model in 2 hours, compared to 12 hours for a CPU and 6 hours for a GPU. If you are in the ML space, you know that extra 6/10hrs time save leads to considerable business gains.