What is XRP and why to use it
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Best laptop for machine learning 2022
In the field of Data Science, powerful hardware with server-level performance is a necessary tool. And in the future, own capacities are much cheaper, especially considering the need for permanent storage of datasets.
2022-03-24, by ,
#Laptop || #Python || #PC ||
Table of contents:
Choice of accessories
Most of the frameworks are adapted to NVIDIA cards with their wonderful CUDA cores, which no one has yet been able to replace. As for the processor, the choice here is obvious - Intel with the maximum number of cores and the possibility of overclocking. So you can get good performance in single-threaded and multi-threaded calculations. There are also special requirements for the motherboard whe choosing the Best laptop for machine learning 2022 - this is preferably 4 memory channels (to unlock the potential of the processor) and good cooling on the main power nodes.
Excessive power will not be a burden to you. You can always provide cloud computing services to other professionals who work with machine learning. All your free time and even on your vacation, the computer will bring money. A good water cooling system will make the work silent and allow you to not interrupt the learning process 24/7.
Video card
The performance of a card in machine learning directly depends on the speed and amount of memory, as well as the number of CUDA cores. Platforms such as PyTorch, MXNet, TensorFlow, as well as hybrids based on their principles, use libraries for GPU acceleration, such as cuDNN, DALI and NCCL. This helps speed up training using one or more GPUs.
The table contains the characteristics of all top solutions from NVIDIA.
CPU
Machine learning requires a lot of RAM. To speed up access to it, you need a processor that supports four channels, and not 2, as in conventional custom solutions. At the moment, among non-server solutions on the market, there is an excellent option - Intel Core i9. Many cores, multi-threading, support for 4 memory channels, good frequency and overclocking capability.
The choice of a specific model is a matter of budget, the more cores and frequencies, the better. It is also worth noting that it is desirable to be able to overclock for a short-term increase in power.
RAM
It is best to use high frequency DDR4 memory, it is not that expensive and will give you a nice boost in power. 4 channels on the processor means 8 slots on the motherboard. The minimum that is worth putting on a computer for machine learning is 32 Gb, but the more, the better. It is better to occupy all the slots in order to provide each processor core with the fastest possible access to memory. The maximum possible amount of memory for i9 is 256 Gb.
SSD
Quick access to datasets must be ensured so that the drive does not slow down the rest of the computer. The new m.2 SSDs provide data access at speeds up to 3.5 Gb per second. It is not necessary to store all the information on them, you can put one SSD and expand the amount of memory with an additional HDD. Moreover, it is not necessary to put the HDD into the system, you can connect it to the local network, providing it with a static IP address, and you can access it from any device connected to the Internet.
