Often users or organizations may not want to leverage cloud services, especially if their data is sensitive, and so focus on building an on-premise deep learning environment. The major focus here should be to invest in the right type of hardware to enable maximum performance and leverage the right GPU for building deep learning models. With regards to hardware, special emphasis goes to the following:
- Processor: You can invest in an i5 or an i7 Intel CPU, or maybe an Intel Xeon if you are looking to spoil yourself!
- RAM: Invest in at least 32 GB of DDR4 or better RAM for your memory.
- Disk: A 1 TB hard disk is excellent, and also you can invest in a minimum of 128 GB or 256 GB of SSD for fast data access!
- GPU: Perhaps the most important component for deep learning. Invest in a NVIDIA GPU, anything above a GTX 1070 with 8 GB.
Other things you shouldn't neglect include a motherboard, power supply, robust case, and cooler.
Once you get your rig set up, for the software configuration, you can repeat all the steps from the previous section, excluding the cloud setup, and you should be good to go!