- #Which mac is best for deep learning for mac os#
- #Which mac is best for deep learning mac os#
- #Which mac is best for deep learning install#
Linux is the superior development platform in all three aspects, mostly because you are always developing for Linux servers. Then there is the problem of package management, which is common to all development. There are two main problems related to data science: the CPU-heavy and file-I/O-heavy (pre)processes.
#Which mac is best for deep learning mac os#
There are three fundamental problems in Mac OS compared to Linux that might lead to false positive performance (not model, but CPU and memory performance) validation of the system you are trying to deploy. It is almost as good as cloud-native development with Chrome OS against real server machines, as WSL 2 uses a real Linux kernel locally. It is very nice to have the option of testing GPU-accelerated models locally on your laptop, but using Windows Subsystem Linux 2 also solves a few other important problems mentioned below. GPU Acceleration is nice, but there are other problems in Mac OS too Some data scientists lack full-stack development experience and are unaware that some problems can and should be fixed.
Many data scientists use more mature models and they do not seem to run into the issue of fixing the code presented in some university papers. But it does cause some persistent problems. Most data scientists (and developers in general) choose Mac OS and make do without local testing of GPU-accelerated models, which is fine I suppose.
#Which mac is best for deep learning install#
The second-best option would be Linux, but I have not been able to install it without glitches on any machine that has an Nvidia GPU, which I need for local testing of GPU-accelerated model training. As of 2021, I would choose Chrome OS because there is nothing better than developing against a cloud-native copy of the production environment, or some smaller version of that same system.
#Which mac is best for deep learning for mac os#
The best years for Mac OS seemed to be 2012–2014. I have tried all the OS’s several times during the past 15 years. The next article will be an in-depth install guide for WSL 2 in case you run into problems. Now we’ll go through the benefits of using WSL 2 and discuss why you might want to avoid Mac OS in machine learning. The article will be published in three parts: In part one we talked about what you need to know before using GPU-accelerated models on your laptop. In this article series, I will explain the benefits of using Windows 10 with Windows Subsystem Linux 2 for ML problems.
0 kommentar(er)
