Artificial intelligence technologies such as machine learning and deep learning involve use of large amounts of data and complex algorithms that require powerful computation hardware. This makes selecting the best machine for such tasks challenging because you have to consider many factors such as portability, processing speed, and the graphics processing capability among others. This article will help you through the grueling decision-making process.
One of the most important factors to consider when choosing a deep learning machine is the general processing unit (GPU). GPUs are microprocessing chips primarily designed for handling graphics. GPUs have become popular in deep learning field mainly due to their ability to handle simultaneous computations faster than CPUs. Essentially, GPUs have a large number of cores and high memory bandwidth and are thus suited for multiple parallel processing of large amounts of data. This has been boosted by efforts to develop AI-based GPU frameworks such as CuDNN and parallel computation APIs like CUDA by NVIDIA. Such frameworks and APIs allow scientists to leverage GPU parallelism for deep learning tasks.
Here is what to look for in a GPU:
An NVIDIA GPU is preferable because of the available frameworks and APIs (CUDA and CuDNN) compatible with major deep learning frameworks such as TensorFlow and PyTorch. The latest generations of NVIDIA GPUs such as the GeForce RTX based on Turing architecture are AI-enabled with Tensor cores which makes them suitable for deep learning.
RAM is another important factor to consider when purchasing a deep learning laptop. The larger the RAM the higher the amount of data it can handle, leading to faster processing. With more RAM you can use your machine to perform other tasks as the model trains. Although a minimum of 8GB RAM can do the job, 16GB RAM and above is recommended for most deep learning tasks.
When it comes to CPU, a minimum of 7th generation (Intel Core i7 processor) is recommended. However, getting Intel Core i5 with Turbo Boosts can do the trick. If one opts for a desktop then selecting the right combination of CPU and motherboard that match your GPU specifications is recommended. In that case, the choice of the number of PCIe lanes ( PCIe lanes determine the speed of transferring data from CPU RAM to GPU RAM) should also be taken into consideration (4-16 PCIe lanes is best for most deep learning tasks).
Storage is also an important factor, specifically due to the increasing size of deep learning datasets requiring higher storage capacity. For example, Imagenet, one of the most popular datasets for deep learning, is 150 GB in size and consists of more than 14 million images across 20,000 categories. Although SSD is recommended for its speed and efficiency, you can get an HDD at a relatively cheaper price to do the job. However, if you value speed, price and efficiency then a hybrid of the two is the best option.
There are various opinions as to how to select the best computation resources for deep learning tasks. Here are some views from Reddit and Quora users:
Here is Kenya AI's tested list of some of the best laptops for machine learning.
About the Author
Eugene Oduma is an artificial intelligence enthusiast and a member of AI Kenya. He is also involved in digital marketing, content creation, machine learning and data science.
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