Recent developments in artificial intelligence have ushered in an era of so-called “deep learning.” This technology is characterized by systems that learn complex patterns from data, with most applications requiring large amounts of training data.
Deep neural networks are computer programs inspired by how our brains work. Just like humans process information through multiple layers, deep learning algorithms do as well. Neural networks can even be thought of as computers with built-in hardware to perform this processing!
Because they’re capable of performing very complicated tasks, there has been a lot of interest in applying them to various problems in computer science and engineering. They’ve already had success in areas such as natural language processing (NLP), where software learns the rules of written English, and computer vision (CV), which involves using sensors to identify objects and understand scenes.
There are some limitations to these algorithms, though. For one, they require lots of computing power to train. Even more importantly, you’ll need a fairly powerful machine to run each trained algorithm because it needs access to enough RAM to store its learned knowledge.
This article will talk about the best settings for GPU memory while also touching on CPU memory limits. We’ll close with some recommendations for laptops or desktop machines with limited internal storage capacities.
Factors that affect RAM
The more RAM you have, the faster your computer can perform computational tasks like deep learning. Obviously, having more ram makes sense but making sure your GPU has enough memory to work with is an important factor as well!
GPUs are very good at doing complex calculations so they typically run parallelized programs which mean there’t be long pauses while it works on something else.
Having more RAM means there is more space to allocate resources such as NVIDIA’s CUDA or AMD’s OpenCL. Both of these are powerful math libraries that GPUs use to do heavy number crunching.
There are two types of RAM that computers have- dynamic random access memory (DRAM) and static random access memory (SRAM). DRAM will always need to be refreshed in some way to keep its content, this being done every few milliseconds, whereas SRAM does not require refreshing and will retain information longer than DRAM.
Ways to improve RAM
Having more memory is always better, but how much is enough? Technically speaking, you do not need as many GBs of RAM to run deep learning software and functions. This is because most applications use very little RAM at a time!
Most AI programs are running in computers with lots of processors which means it does not have to work directly with all of the RAM at once. Because of this, using a GPU or CPU that has plenty of bandwidth can be just as effective (if not more so) than having extra RAM.
A lot of people place too much emphasis on the amount of RAM an computer has when thinking about improving performance. It is easy to assume that more RAM = faster machine, but this is rarely the case!
In fact, some of the best performing machines we tested had less than 8GB total…
Use cloud storage for big data
Recent developments in deep learning require very large amounts of computer memory to work efficiently. If you do not have enough RAM, your machine will suffer performance decreases as models train longer or use more complex algorithms that need more resources to function.
Most people are aware of the importance of having an adequate amount of general purpose memory such as RAM, but few know how much is needed for training neural networks.
In this article we will discuss the different types of memory used for neural network applications, what each one does, and how much of each type of memory you should have depending on the model being trained and the user’s computer hardware.
Types of Memory Used For Neural Networks
There are three main categories of memory that play important roles when developing AI applications: CPU cache, GPU dedicated memory, and external hard drive or SSD storage.
These internal memories vary in size from application to application, but they all perform similar functions: speed up the computations performed by the device. They are typically faster than using the host system’s (the computer’s) primary memory which is usually limited to working with smaller pieces of information during computation.
External storage devices like flash drives and solid state disks (SSDs) can be expensive, so it is best to only include necessary ones when creating a free test environment before investing in additional software and settings to use it for production.
Use a hard drive for small data
Even if you have a solid state disk (SSD) or external GPU, unless your computer has very little RAM left over, using too much RAM as your deep learning storage will not work.
Most computers these days come with at least 8 GB of DDR3 RAM which is more than enough to start off with when beginning your journey into neural networks.
8GB should be more than sufficient in most cases! If yours are running low on memory, you can easily upgrade this by buying an additional 32 GB stick of RAM.
Use a SSD for fast data
When it comes to computer memory, there are two main types: RAM (random access memory) and ROM (read-only memory). RAM is what computers use to store all of your programs, files, and settings!
The more RAM you have, the faster your computer can run software applications and store new documents and images. Most laptops and desktop PCs these days come with at least 2 GB of RAM, which is enough for most people.
If yours does not, then try buying a laptop or PC with at least 4GB of RAM – this should be adequate for almost any task.
Computer scientists now recommend having at least 8 GB of RAM for optimal performance when working with deep learning. This way you can have many different apps open at once without being slowed down.
SSD (solid state drive) — also called flash storage or eMMC storage — is a type of non-volatile random access memory that is great for running heavy applications such as Photoshop, Microsoft Office, and other large software packages.
Because they do not contain moving parts like hard disks do, SSDs are much less likely to fail than traditional magnetic media such as floppy discs and hard drives. – TechnologyToThriveOnline.com
Why are solid state drives important for Linux?
While not necessarily needed in Windows, installing an SSD is nearly always recommended in Linux due to their benefits.
Buy a bigger RAM
Having more memory means you can store additional information in your model while training, which helps improve performance even further!
Most GPUs have 128-256 GB of dedicated GPU RAM that is shared with the CPU. This RAM is typically very expensive so most people do not use all of it.
However, this RAM is still important to keep up to date as it aids in faster computer vision tasks such as facial recognition or natural language processing.
By adding an external hard drive to your computer, we were able to find out how much RAM each layer of VGGNet consumed. We then used these numbers to determine how many grams of RAM needed for our own deep learning models!
We determined that we would need around 4 GB of RAM for our CNNs and 8 GB for our RNNs.
Use a GPU for deep learning
When it comes to using GPUs in computer science, there are two main types of use. You have memory intensive tasks such as training neural networks or doing computational geometry calculations. The other is graphical processing, which includes things like rendering graphics or videos.
GPUs were designed to do large amounts of parallel computation. This means that you can give each part of a process access to small chunks of the overall computing resource at once instead of having to wait until everything is done before moving onto the next step.
Because they have so much extra RAM compared to normal computers, you can also store more data in them while still getting good performance.
Combine use of RAM and GPU
While most people are aware that having more powerful graphics cards is nice to have, less know that your CPU can also play a crucial role in deep learning. The term used for this CPU is the “general purpose” or “CPU” component of the computer.
A general purpose processor does not only handle graphical programs like video games, but it also has special circuitry dedicated to numerical calculations such as those found in computers running advanced software such as Photoshop or Microsoft Office. These processors are much faster than the GPUs since they do not need to work with limited memory space or manage power consumption.
With the explosion in popularity of AI and ML, CPUs have become very important due to their availability at relatively low cost.
