Windows vs mac for data science
![windows vs mac for data science windows vs mac for data science](https://i.pinimg.com/originals/37/7e/8b/377e8b269cb261e4dfe2f9bcc001165f.jpg)
- #Windows vs mac for data science for mac os#
- #Windows vs mac for data science mac os#
- #Windows vs mac for data science install#
- #Windows vs mac for data science windows 10#
#Windows vs mac for data science mac os#
Linux directories also always return file lists in sorted form, while Mac OS returns files in access order. In Linux, you would use the POSIX standard implementation of `/dev/shm` shared memory, but that doesn’t exist in Mac OS. Most of the time, it is easiest to optimize a pipeline for fast inter-cloud bucket access that writes directly to RAM without any mass-storage access besides the cloud buckets. 2) The problem with file-I/O-heavy solutionsįile-I/O-heavy processes depend on a few basic tricks: sorting the data and pushing it to the memory file system from HDD or SSD (or Hadoop) drives. Mac OS is different in places, which means that what is well CPU-optimized on Mac OS might perform poorly on a Linux system. Also, if you need to do some more fine-grained multithreading stuff at programming language level, Linux is a reliable companion, because the server machine thinks about all these concepts in the same terms. So you basically just develop with this paradigm and the OS pipeline will do all the multiprocessing and multithreading for you. In my opinion, this guides you towards good software architecture choices (modularization of code). Once you learn the paradigm, it is easy to use and guarantees high CPU utilization. In Linux, you should always build small programs that can be piped together into a bigger process. 1) The problem with processing incentive solutionsĬPU-heavy processes need to be solved with multithreaded or multiprocessing optimization. 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. 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.
![windows vs mac for data science windows vs mac for data science](https://www.compsmag.com/wp-content/uploads/2018/10/windows-vs-mac-vs-linux.png)
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.
#Windows vs mac for data science 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.
#Windows vs mac for data science 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.
![windows vs mac for data science windows vs mac for data science](https://i.pinimg.com/originals/de/ce/3b/dece3b75eedb7055eee18ee48fbe777b.jpg)
![windows vs mac for data science windows vs mac for data science](https://i.pinimg.com/originals/70/17/40/70174017dc567e0b61d0278414b782f2.jpg)
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.
#Windows vs mac for data science windows 10#
In this article series, I will explain the benefits of using Windows 10 with Windows Subsystem Linux 2 for ML problems.