A gaming laptop for Data Science and ML?

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Gaming laptops now include super-powerful GPUs that could be just perfect for Deep Learning and Data Science. Image source: Future

For a long time, gaming laptops were known for being extravagant, expensive, large, noisy, prone to overheating, and expensive. Crafted for a specific market of consumers that value the video game experience more than reality, these powerful machines had really no other use. However, since the time of the first Alienware laptops, a lot has changed. We now find models such as the Asus Zephyrus series, the Razer 15, and the not-really-a-gaming-laptop Dell XPS 15, all of which actually deliver a lot of power in compact and somehow aesthetic designs. Did I mention they are expensive?

In a somehow unrelated field, Machine learning has become the standard to model large datasets. From business to academia, ML models are constantly proving to be extremely useful and everyone wants to use them (even if they are not necessary for specific data projects). Therefore, as a Data Scientist, one of the skills you definitely want to have is a good knowledge of Machine Learning methods. One of the most popular implementations for ML and Deep learning is Tensorflow along with its high-level wrapper Keras, which is intuitive and user-friendly. Running in both Python and R, Keras brings the power of Deep Learning to the people. So Data Science is now available for everyone, just read a few Towards Data Science tutorials, watch some YouTube videos of a professor from a university in India and you are ready to go! Wait, you still need a computer.

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NVIDIA GPUs are the most common for Deep Learning since the setup for Tensorflow or Pytorch is straightforward. Image: nvidia.com

So what do Data Science and gaming gave in common? You guessed it right: linear algebra, parallel computations, and usage of Artificial Intelligence. This brings us to the most important component that you want in a laptop these days: a GPU. And not just any GPU but you might want to stick to Nvidia. Setting up a GPU for machine learning either in Ubuntu or Windows 10 is just easier with Nvidia as compared to AMD. If you are brave you can try AMD but you might have a hard time finding instructions on how to set up correctly and the community is smaller which means fewer Stack OverFlow posts to the rescue.

But Alejandro, what about the amazing MacBook Pro 16 with the new 9th gen Intel CPU processors and an AMD Graphics car…. Right. If you want a MacBook, the best you will get is an AMD graphics card. Though to be fair, it was about time Apple stepped up their game by adding dedicated graphics to their expensive pro laptops. So this is an important thing you have to consider, as an Apple user it might be hard to get those Deep Learning models to run on your AMD graphics card.

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AMD Ryzen CPUs are just killing it. Source: cpubenchmark.net/

Having said that, a GPU might not be absolutely essential if you don’t work with large (and I mean large) datasets and complex models. A good CPU could in principle do the heavy lifting in a reasonable time. Especially if your gaming laptop has an AMD Ryzen CPU like the Lenovo Legion 5 or Asus ROG Zephyrus G14 (seriously, look at the benchmarks online, the Ryzen is just a beast). The M1 Apple CPUs are doing pretty well on benchmarks as well and could probably get your back when it comes to doing serious data work. On the other hand, the Dell XPS is powered by 10th gen Intel CPUs and includes an Nvidia GTX 1650 (which is not up for the task when it comes to serious gaming/Deep Learning but could complement the CPU). Again, think about the work you’ll be doing with this laptop and decide whether you need a GPU at all. However, if you want to do some gaming or video edition besides Data Science, then a GPU is a must.

A good combination of CPU and RAM memory could in principle do the job, right? RAM memory is usually overlooked when buying a computer. We think: “come on that there is no way I am going to use more than 16GB”. The next thing, you realized that you have 50 tabs of Chrome open, you are playing music on YouTube, training a neural network on Python, and doing some bioinformatics in R, and you have 0% available RAM. Your computer crashes and we all know how this ends. So for Data Science, I would advise getting 32Gb. Really, you never know when you will have to load a massive dataset on your laptop, and you want to be ready for that. Or perhaps multiple Jupyter notebooks, each one with a large dataset in it. Also, Windows 10 is not great at managing your RAM. Ubuntu would do a great job and you could probably be fine with 16GB, but if you want to use Windows 10, consider the upgrade.

If you are a Data Scientist, you are basically typing for money. Coding efficiently and happily requires entering the zone, which requires not only a great Cercle set playing in the background but also a great typing experience. No one wants a small, inaccurate keyboard with no feeling. So this part is very important: you want to try the keyboard before committing completely to the laptop. As for the screen, you will probably connect an external monitor anyway but, hey, it is always nice to be able to work or play directly on your laptop, when you are away or just want to sit on the couch as opposed to your desk. Gaming laptops usually come with a high refresh rate but not so high resolution (Full HD is the standard). I would say that if you are not a gamer and want the computer mostly for work try to get a higher resolution screen even at the cost of lower refresh rates.

A minor point that you also want to consider is the design. Bringing a Dell Area 51M R2 to the office (whenever that happens again) and using it to present your data analysis to your colleagues is just not… let’s say appropriate. One thing I don’t like about gaming laptops is their design. I don’t blame the manufacturers, they need to fit a GPU, fans, or maybe a vapor chamber and other multiple components. At the same time, gaming laptop manufactures seem to be obsessed with neon lights and spec-ship-inspired designs. Thus finding a discrete, elegant gaming laptop is probably out of the table. Or is it? Here is where Razer comes in to save the day. The Razer 15 line provides a great trade-off between power and design, though at a high cost. Consider also the Asus Zephyrus G15 which has a decent design as well.

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My two top picks based on power and design: ASUS ROG Zephyrus G15 starts at $1,199 and the Razer Blade 15 starts at $1,429. Source: laptopvslaptop.com

Finally, you went for it and you decided to get a gaming laptop. Now what? Can I just work from Windows 10? Do I have to install Ubuntu as a partition? Well, you can actually do either or both. But an efficient way to do serious work from Windows 10 is to install the WSL so you can run Ubuntu from Windows. From Windows, you will have control over your GPU using the apps provided by Nvidia and your laptop manufacturer. So even though you do the partition and install Ubuntu (or any other Linux distro) keeping Windows 10 will bring out the full potential of your new machine. See my post on how to setup WSL2 on Windows 10 for Data Science.

Conclusion

If you are getting into Machine Learning, Deep learning, and large datasets, you need a powerful computer. While you can always put together a beast on your desktop, some people prefer the portability of a laptop (even if by portability you mean moving your laptop around from your desk to your couch and your bed). Gaming laptops offer serious computational power with a few drawbacks. Probably the main drawback is the price, the Razer 15 Advanced 2020 sells for around $3k, which is insane! The new Asus ROG Zephyrus G15 including an RTX 3080 (!) will cost you around $2,499. However, depending on the configuration, you can get a great gaming laptop for about $1,200. But if you want to invest in a powerful machine that will be able to handle all your Data needs, casual gaming, and creative tasks, consider getting a gaming (or an almost-gaming computer). My top selection (as you probably already know and in no particular order): Asus Zephyrus G15, Lenovo Legion 5, Razer 15, and Dell XPS 15.

Computational biologist and data scientist. Data narrative on molecular biology, machine learning and finance.

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