T7610 Drivers

[Tisha]

Thelist below indicates the recommended order to install the drivers for your system and provides links to additional information about each driver. Drivers are available on the Dell Resource DVD (if ordered) and for download on the Dell Support Website for your system. Before choosing your drivers, ensure the correct Operating System version is selected from the drop down list (ie Windows 7 64 bit) from the drop down list.

Install the Intel chipset update from the Dell Support Website or by inserting the Dell Resource DVD. If you are installing from the DVD, choose Windows 7>Chipset Drivers>Intel Chipset Software Installation Utility.

You can check under several categories in Device Manager to ensure the drivers installed correctly. Under the IDE ATA/ATAPI controllers category, make sure the Intel C600 series chipset controller drivers are installed and the multiple channel support is added:

You can install the Intel Unified AMT 8 Management Engine Interface form the Dell Support Site or from the Resource DVD. The location of the drivers on the DVD is Windows 7 > Chipset Drivers > Intel Unified AMT 8 Management Interface.

You can install the drivers from the Dell Support Site, or from the Resource DVD under the Windows 7 > Video Drivers category. Once installed correctly the specific video card will show under Display adapters in Device Manager, as in the case of the nVidia Quadro K2000 below:

Install the Realtek ALC 3220 audio driver from the Dell Support Website or by inserting the Dell Resource DVD. If you are installing from the DVD, choose Windows 7>Audio Drivers>Realtek ALC 3220 HD Audio.

I talked at length about GPU choice in my GPU recommendations blog post, and 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.

Be careful about the memory requirements when you pick your GPU. RTX cards, which can run in 16-bits, can train models which are twice as big with the same memory compared to GTX cards. As such RTX cards have a memory advantage and picking RTX cards and learn how to use 16-bit models effectively will carry you a long way. In general, the requirements for memory are roughly the following:

Another problem to watch out for, especially if you buy multiple RTX cards is cooling. If you want to stick GPUs into PCIe slots which are next to each other you should make sure that you get GPUs with a blower-style fan. Otherwise you might run into temperature issues and your GPUs will be slower (about 30%) and die faster.

RAM size does not affect deep learning performance. However, it might hinder you from executing your GPU code comfortably (without swapping to disk). You should have enough RAM to comfortable work with your GPU. This means you should have at least the amount of RAM that matches your biggest GPU. For example, if you have a Titan RTX with 24 GB of memory you should have at least 24 GB of RAM. However, if you have more GPUs you do not necessarily need more RAM.

A different strategy is influenced by psychology: Psychology tells us that concentration is a resource that is depleted over time. RAM is one of the few hardware pieces that allows you to conserve your concentration resource for more difficult programming problems. Rather than spending lots of time on circumnavigating RAM bottlenecks, you can invest your concentration on more pressing matters if you have more RAM. With a lot of RAM you can avoid those bottlenecks, save time and increase productivity on more pressing problems. Especially in Kaggle competitions, I found additional RAM very useful for feature engineering. So if you have the money and do a lot of pre-processing then additional RAM might be a good choice. So with this strategy, you want to have more, cheap RAM now rather than later.

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. The second most common mistake is to get a CPU which is too powerful.

When you select CPU PCIe lanes and motherboard PCIe lanes make sure that you select a combination which supports the desired number of GPUs. If you buy a motherboard that supports 2 GPUs, and you want to have 2 GPUs eventually, make sure that you buy a CPU that supports 2 GPUs, but do not necessarily look at PCIe lanes.

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.

While this reasoning seems sensible, there is the fact that the CPU has 100% usage when I run deep learning programs, so what is the issue here? I did some CPU core rate underclocking experiments to find out.

However, I recommend an SSD for comfort and productivity: Programs start and respond more quickly, and pre-processing with large files is quite a bit faster. If you buy an NVMe SSD you will have an even smoother experience when compared to a regular SSD.

Generally, you want a PSU that is sufficient to accommodate all your future GPUs. GPUs typically get more energy efficient over time; so while other components will need to be replaced, a PSU should last a long while so a good PSU is a good investment.

You can calculate the required watts by adding up the watt of your CPU and GPUs with an additional 10% of watts for other components and as a buffer for power spikes. For example, if you have 4 GPUs with each 250 watts TDP and a CPU with 150 watts TDP, then you will need a PSU with a minimum of 4250 + 150 + 100 = 1250 watts. I would usually add another 10% just to be sure everything works out, which in this case would result in a total of 1375 Watts. I would round up in this case an get a 1400 watts PSU.

One important part to be aware of is that even if a PSU has the required wattage, it might not have enough PCIe 8-pin or 6-pin connectors. Make sure you have enough connectors on the PSU to support all your GPUs!

Cooling is important and it can be a significant bottleneck which reduces performance more than poor hardware choices do. You should be fine with a standard heat sink or all-in-one (AIO) water cooling solution for your CPU, but what for your GPU you will need to make special considerations.

Air cooling is safe and solid for a single GPU or if you have multiple GPUs with space between them (2 GPUs in a 3-4 GPU case). However, one of the biggest mistakes can be made when you try to cool 3-4 GPUs and you need to think carefully about your options in this case.

However, typical pre-programmed schedules for fan speeds are badly designed for deep learning programs, so that this temperature threshold is reached within seconds after starting a deep learning program. The result is a decreased performance (0-10%) which can be significant for multiple GPUs (10-25%) where the GPU heat up each other.

Since NVIDIA GPUs are first and foremost gaming GPUs, they are optimized for Windows. You can change the fan schedule with a few clicks in Windows, but not so in Linux, and as most deep learning libraries are written for Linux this is a problem.

Another, more costly, and craftier option is to use water cooling. I do not recommend water cooling if you have a single GPU or if you have space between your two GPUs (2 GPUs in 3-4 GPU board). However, water cooling makes sure that even the beefiest GPU stay cool in a 4 GPU setup which is not possible when you cool with air. Another advantage of water cooling is that it operates much more silently, which is a big plus if you run multiple GPUs in an area where other people work. Water cooling will cost you about $100 for each GPU and some additional upfront costs (something like $50). Water cooling will also require some additional effort to assemble your computer, but there are many detailed guides on that and it should only require a few more hours of time in total. Maintenance should not be that complicated or effortful.

Your motherboard should have enough PCIe ports to support the number of GPUs you want to run (usually limited to four GPUs, even if you have more PCIe slots); remember that most GPUs have a width of two PCIe slots, so buy a motherboard that has enough space between PCIe slots if you intend to use multiple GPUs. Make sure your motherboard not only has the PCIe slots, but actually supports the GPU setup that you want to run. You can usually find information in this if you search your motherboard of choice on newegg and look at PCIe section on the specification page.

When you select a case, you should make sure that it supports full length GPUs that sit on top of your motherboard. Most cases support full length GPUs, but you should be suspicious if you buy a small case. Check its dimensions and specifications; you can also try a google image search of that model and see if you find pictures with GPUs in them.

Given that video card add-ins for desktops for 3000 series RTX cards seem to start at $1000 it seems to me I should bide my time with a good entry level laptop with an RTX GPU that has much fairer prices until the video card price gouging is done for.

I had a gaming laptop for deep learning. However I think desktop is still a better choice . Using Laptop for deep learning tend to overheat the laptop and battery appears to degrade much faster.

Moreover, the largest gpu memory in a laptop is 8gb but note that not all 8gb can be allocated for deep learning, which may not be sufficient if you are trying a very deep network or dual network. Mobile gpu is also less efficient than desktop gpu. Computing speed (cpu and etc) can also slower than a gaming desktop.

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