Deep learning and AI (artificial intelligence) are two of the latest hot topics within the IT industry, and the number of systems and software
aimed at AI workloads has exploded over the last few years, but what exactly do we mean by these terms?
This guide will take you through the principles, terminology and processes of deep learning and AI to help provide a clearer understanding of these technologies, what drives them and how businesses can take advantage.
Deep Learning or AI?
AI (Artificial Intelligence) is the umbrella term for the designing of systems to mimic human intelligence. It is broken down into subsets including machine learning and deep learning. With machine learning, the goal is to create a simulation of human learning so that an application can adapt to uncertain or unexpected conditions. To perform this task, machine learning relies on algorithms to analyse huge datasets and perform predictive analytics far faster than any human can. Machine Learning uses various techniques including statistical analysis, finding analogies in data, using logic, and identifying symbols. In contrast, deep learning processes data using computing units, called neurones, arranged into ordered sections, called layers. This technique, at the foundation of deep learning, is called a neural network, and it is intended to mimic how the human brain learns.
Neural networks and deep learning
Unlike machine learning algorithms, whose performance plateaus with scale and volume of data, larger neural networks with greater quantities of data see their performance continue to increase. This is because the computer uses the many multitude of layers to solve the problem one small step at a time. This is referred to as a Convolutional Neural Network (CNN), where each artificial neurone is connected to a small window over the input or previous layer. For example, in a visual task, each neurone in the first convolution layer will only see a small part of the image, maybe only a few pixels. This convolution layer consists of multiple maps, each searching for a different feature, and each neurone in a map searching for that feature in a slightly different location.
This first layer will come (after some training) to identify useful low level features in the image, such as lines, edges, and gradients in different orientations. This convolution layer is then sub-sampled in what is called a pooling layer, before the whole process starts again with another convolution layer this time finding combinations of the features of the previous layer (lines, corners, curves etc). Once this process has occurred multiple times, fully connected layers look at the complete output of the previous layer and identify the major features together to give a final result or classification.
Alternatively a Recurrent Neural Network (RNN) can be employed. This works in the same way as a CNN but adds a built-in feedback loop where the output from one layer is fed back into the layer preceding it. This enables them to have an internal memory where they’re able to remember the input received, facilitating their accuracy in predicting the next event. RNNs are particularly designed to recognise patterns in a sequence of data, such as text, genomes, handwriting or spoken words.
As with most neural networks, the parameters or weights of the system start out randomly, and the network will perform poorly. However during training, you can program the network what the correct classification of an image is, and over many iterations the network parameters, weights and bias are slowly modified to give the correct classification.
Powering Deep Learning
Having understood the principles behind deep learning, it is clear that there are many small tasks occurring at the same time in parallel. This type of parallel computing is very specific and does not suit all types of processor. The CPU (central processing unit) is typically seen as the brain of a computer and it is very adept at doing tasks very quickly and often at the same time. However it does reach a limit as to how much concurrent, or parallel, tasks can be achieved without bottlenecks forming. GPUs (graphics processing unit) are designed for the rendering of high resolution images and video concurrently - both hugely parallel workloads. Because GPUs can perform parallel operations on multiple sets of data, they are also perfect for non-graphical tasks such as machine learning and scientific computation.
Adding such GPUs to a system is referred to as GPU-accelerated computing, due to the fact that intensive parallel workloads are off-loaded from the CPU and moved to the GPU for better performance. Designed with thousands of cores running simultaneously, GPUs enable massive parallelism where each core is focused on making efficient calculations.
How GPU-acceleration works
To leverage this multi-core design even more, GPUs are often grouped together to deliver even more parallel processing power, this is done by two technologies called NVLink and NVSwitch - both proprietary NVIDIA interconnects that allows GPU - GPU communication at much faster rates than the PCIe bus can provide, boosting performance in multi-GPU system configurations. In some configurations you can pool the memory of two GPUs together to form a single large unified memory, ideal for working with large datasets.
Depending on budget and performance required NVIDIA produce a wide range of GPUs perfect for scientific research, high performance computing (HPC) and deep learning uses.
Languages, Libraries and Frameworks
So we understand the hardware required to begin deep learning and the relative performance they may give, but how is deep learning actually done? It is carried out by using a programming language. To avoid learning an entire language, deep learning frameworks or libraries or are then employed to automate and structure some of the tasks.
Frameworks are pre-compiled collections of pre-scripted libraries and models and provide the easiest way to start an AI project. NVIDIA provides multiple GPU-accelerated frameworks via its NGC (NVIDIA GPU Cloud) web portal.
Alternatively, you can download libraries such as CuDNN and NCCL from NGC which require more coding experience, but provide greater control than pre-scripted frameworks.
If you prefer full control of the AI model creation then you can program your own code using popular languages such as Python, R, Java and C++. Python is the most popular programming language due to its syntaxes being very simple and can be easily learnt, which makes algorithms easy to implement.
Libraries and Frameworks
These vary dependent on which programming language you are using and there is a large variety to choose from - some better suited to certain AI projects than others. The most popular frameworks are TensorFlow, Caffe2 and PyTorch. CuDNN and NCCL are two NVIDIA libraries - the first containing NVIDIA-optimised versions of many of these frameworks; and the second being aimed at multi-GPU applications.
You may also come across Docker or container when dealing with frameworks. Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow a developer to package up an application with all of the parts it needs, such as data, libraries and other dependencies, and deploy it as one package. Containers also allow multiple models to be deployed but in segregation from one another. This also means that any changes or optimisations made within a container will not impact the host operating system or other containers.FIND OUT MORE
Data for Deep Learning
We’ve mentioned that deep learning relies on huge datasets (often referred to as Big Data), being fed to the GPUs that then perform the parallel processing in the framework of your choice. However, this data needs to be prepared prior to ingest into the system. Data preparation is the process of readying data for the training, testing, and implementation of an algorithm. It’s a multi-step process that involves data collection, structuring and cleaning, feature engineering, and labelling. These steps play an important role in the overall quality of your deep learning model, as they build on each other to ensure a model performs to expectations.
Once you have collected the data, you would start by preprocessing and cleaning it. This includes organising and formatting, standardising, and dealing with any missing data. While data preprocessing is a way of refining data, feature engineering is the process of creating features to enhance it. Feature engineering allows you to define the most important information in your dataset, and utilise domain expertise to get the most out of it. This might mean breaking data into multiple parts to clarify particular relationships.
Finally data labelling is a key part of data preparation for deep learning because it specifies which parts of the data the model will learn from. Though improvements in unsupervised learning have resulted in deep learning projects that do not require labeled data, many systems still rely on labeled data to learn and perform their given tasks.
As we’ve mentioned to get the most from deep learning the datasets are better the larger they are. However this presents problems as the entire dataset cannot be fed into the system all at once, so batches are used. Batch sizes will usually be defined by the capabilities of your hardware, most importantly the available GPU memory, as you will not want to overload the system. Each time a batch of data is fed into the system this is referred to as an iteration, and when all batches have been though the system once it is referred to as an epoch. It is common that full training of even an average size dataset may require several batches per epoch and many epochs.
There are multiple software packages available that are specifically designed to help with data cleansing, feature engineering, visualisation, data labelling and more.
The Stages of Deep Learning
Deep learning can be broken down into three stages - development, training and inferencing. Each of these stages has different hardware requirements and involves different approaches.
Development of an AI model starts out with the data collection and preparation steps outlined above. Once the data is ready is can be subjected to the libraries and frameworks to begin building your CNN and AI model. As this stage is experimental and the CNN will involve only a few layers as it is best to use small datasets so models can be created quickly and scrapped if they aren’t what is required. This stage can be carried out on relatively low powered hardware – typically one or two GPUs is sufficient. However development workstations with four GPUs provide many models to be started at once so time to your ideal version is reduced.
Once you think you’re on the right lines the small model can be scaled up and fully trained, requiring a much greater hardware demand.
When training a neural network, training data is put into the first layer of the network, and individual neurones assign a weighting to the input — how correct or incorrect it is — based on the task being performed. In an image recognition network, the first layer might look for edges. The next might look for how these edges form shapes — rectangles or circles. The third might look for particular features — such as shiny eyes and button noses. Each layer passes the image to the next, until the final layer and the final output determined by the total of all those weightings is produced.
Let’s say the task was to identify images of cats. The neural network gets all these training images, does its weightings and comes to a conclusion of cat or not. What it gets in response from the training algorithm is only “right” or “wrong”. And if the algorithm informs the neural network that it was wrong, it doesn’t get informed what the right answer is. The error is propagated back through the network’s layers and it has to guess at something else. In each attempt it must consider other attributes — in this example attributes of “catness” — and weigh the attributes examined at each layer higher or lower. Then it guesses again. And again. And again. Until it has the correct weightings and gets the correct answer practically every time. It is this repeated process of hundreds or thousands of training epochs that make this stage very compute intensive, and that’s why you’ll usually see systems aimed at AI training being multi-GPU servers to provide as much power as possible, with training systems often having as many as eight GPUs
To make use of all that training in the real world, you need a speedy application that can retain the learning and apply it quickly to data the model has never seen. That is inferencing - taking smaller batches of real-world data and quickly coming back with the same correct answer. This is achieved in two ways - the first approach looks at parts of the neural network that don’t get activated after it’s trained. These sections just aren’t needed and can be pruned away. The second approach looks for ways to fuse multiple layers of the neural network into a single computational step.
It’s akin to the compression that happens to a digital image. Designers might work on these huge, beautiful, million pixel-wide and tall images, but when they go to put it online, they’ll turn into a compressed image or video. It’ll be almost exactly the same, often indistinguishable to the human eye, but at a smaller resolution. Similarly with inferencing you’ll get almost the same accuracy of prediction, but simplified, compressed and optimised for runtime performance. Because of this downsizing inferencing can be carried out by less powerful hardware - often a single GPU system or an endpoint device with an embedded GPU module - ideal for siting out in the real world rather a datacentre.
Deep Learning Performance
We’ve seen how the many processing cores in a GPU are designed to process hugely parallel workloads with the objective of delivering results in ever shorter time frames. However, there are a number of factors that define how quickly you can see results, the primary one being accuracy of the results required - or precision. Precision refers to the number of decimal places, or in computer terms ‘bits’ of any given result - for example 3.14 is less precise than 3.141592654. Having more bits or decimal places to represent each number gives scientists the flexibility to represent a larger range of values, with room for a fluctuating number of digits on either side of the decimal point during the course of a computation - this is called Floating Point (FP).
Within GPU specification sheets you will see terms like FP32, FP64 or FP16. FP32 refers to 32 decimal places and is termed single precision; FP64 - twice as precise at 64 decimal places is called double precision; and FP16 being half as precise is termed half-precision. The higher precision level a machine uses, the more computational resources, data transfer and memory storage it requires. It costs more and it consumes more power. Since not every workload requires high precision, AI researchers can benefit by mixing and matching different levels of precision.
NVIDIA A100 PCIe
PEAK 312 TFLOPS
PEAK 1,248 TOPS
PEAK 19.5 TFLOPS
Going back to GPU specifications you’ll often see a performance number written in FLOPS (floating point operations per second) - as above. It will also be caveated with an FP number - for example 312 TeraFLOPS (or TFLOPS) at FP32. This tells you that the GPU is capable of 312 trillion calculations per second delivering single precision results - to 32 decimal places. Some GPUs are designed to excel at a certain precision, however newer GPUs featuring Tensor cores are designed to excel at mixed precision calculations.
Once a model is trained and ready for inference - obtaining results from a new data set - the precision is often lowered still to 8- or even 4-bits or decimal places - this is referred to as INT8 or INT4 - Integer 8 or Integer 4. Once you know a model gives accurate results moving to a lower precision decreases power and GPU memory burden to deliver results faster.
Depending on budget and performance required, NVIDIA produce various ranges of GPUs each aimed at delivering performance at different precision levels, including the new Tensor core models ideal for mixed precision calculations.
Systems for Deep Learning
Now we’ve covered the three stages, hardware requirements and performance aspects of deep learning, it’s valuable to understand what these systems look like. As trusted AI advisors, the Scan AI team has put together a portfolio of systems that address all budgets and possible scenarios when it comes to deep learning - offering a range of NVIDIA GPUs from the GeForce, Quadro, Tesla and Jetson families in various quantities and in workstation, server and embedded options.
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Use Cases for Deep Learning
Businesses are increasingly turning to deep learning and AI to solve their greatest challenges. Just a few of the many use cases include enabling more accurate, faster diagnoses in healthcare, offering personalised customer experiences in retail, minimising downtime in manufacturing through predictive maintenance or improving traffic flow by creating smart cities. When powerful GPU-accelerated platforms are integrated into existing workflows, business is improved and industry is transformed.
Worldwide spending on AI technologies is expected to reach €52 billion in 2021 with human-centric industries, such as financial services, retail and healthcare expected to be the biggest spenders, closely followed by asset-intensive industries including manufacturing, energy and transport.FIND OUT MORE