Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high-performance system.
1. GPU:- This blog post assumes that you will use a GPU for deep learning. If you are building or upgrading your system for deep learning, it is not sensible to leave out the GPU. The GPU is just the heart of deep learning applications – the improvement in processing speed is just too huge to ignore. the choice of your GPU is probably the most critical choice for your deep learning system. There are three main mistakes that you can make when choosing a GPU: (1) bad cost/performance, (2) not enough memory, (3) poor cooling.
For good cost/performance, I usually recommend an RTX 2070 or an RTX 2080 Ti. If you use these cards you should use 16-bit models. Otherwise, GTX 1070, GTX 1080, GTX 1070 Ti, and GTX 1080 Ti from eBay are fair choices and you can use these GPUs with 32-bit (but not 16-bit). In general, the requirements for memory are roughly the following:
For good cost/performance, I usually recommend an RTX 2070 or an RTX 2080 Ti. If you use these cards you should use 16-bit models. Otherwise, GTX 1070, GTX 1080, GTX 1070 Ti, and GTX 1080 Ti from eBay are fair choices and you can use these GPUs with 32-bit (but not 16-bit). In general, the requirements for memory are roughly the following:
Research that is hunting state-of-the-art scores: >=11 GB
Research that is hunting for interesting architectures: >=8 GB
Any other research: 8 GB
Kaggle: 4 – 8 GB
Startups: 8 GB (but check the specific application area for model sizes)
Companies: 8 GB for prototyping, >=11 GB for training
2. RAM:- The main mistakes with RAM is to buy RAM with a too high clock rate. The second mistake is to buy not enough RAM to have a smooth prototyping experience.
3. Needed RAM clock Rate :- RAM clock rates are marketing stints where RAM companies lure you into buying “faster” RAM which actually yields little to no performance gains.
4. CPU :- The main mistake that people make is that people pay too much attention to PCIe lanes of a CPU. You should not care much about PCIe lanes. Instead, just look up if your CPU and motherboard combination supports the number of GPUs that you want to run.
5. Needed CPU core :-To be able to make a wise choice for the CPU we first need to understand the CPU and how it relates to deep learning. What does the CPU do for deep learning? The CPU does little computation when you run your deep nets on a GPU. Mostly it (1) initiates GPU function calls, (2) executes CPU functions.
By far the most useful application for your CPU is data reprocessing. There are two different common data processing strategies which have different CPU needs.
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