MLOps use case – overclocking a GPU – MLOps and DataOps

MLOps use case – overclocking a GPU

In this modern age of AI art, image generation at the highest levels can require a lot of processing power. For any kind of graphical rendering, CPUs are only used when no other options are available and are usually not recommended for larger renderings. For machine learning TensorFlow algorithms, Google’s proprietary TPUs are the norm. But again, for anything concerning image generation or manipulation, a good GPU is good to have. And if the rare case comes up where that GPU needs a bit of extra juice to get things done, overclocking can be necessary.

A lot of the time, GPU processors have their own drivers and with their drivers come their own command-line tools. Executing these before and after the use of overclocking or another GPU feature can be a hassle. Instead, using Python’s in-built subprocess module, we can automatically overclock or perform any other GPU processes that we would like. For this example, we are going to use the CLI tools for NVIDIA, which is probably the most popular GPU brand available at the moment. NVIDIA has a command-line tool called nvidia-smi, which also contains an overclocking feature and is what we are going to invoke. Now, let’s write the code block that will help us overclock our GPUs:

import subprocessdef overclock_gpu():    # Set the new clock frequency for memory and graphics    new_clock_memory= <your_clock_frequency_in_MHz>    new_clock_graphics= <your_clock_frequency_in_MHz>    # Run NVIDIA command to overclock GPU    command = “nvidia-smi –i 0 –applications-clocks {new_clock_memory},{new_clock_graphics}”    subprocess.run(command, shell=True)if __name__ == “__main__”:    overclock_gpu()

The preceding code, when run, will overclock whichever NVIDIA GPU has been set up on your device. This, in turn, will make processes such as image processing and generation faster. This can be useful when there is a higher demand for these resources, and it isn’t possible to shift those demands to other resources. So, this code can be used to temporarily overclock a GPU based on some condition that may cause it to be called. Once it has been overclocked, you can set it back to its default by running the following command (in or out of script):

nvidia-smi –- reset-applications-clocks

So, this is how you would manipulate GPUs using Python. A lot of this section has involved learning how to manipulate data and the aspects surrounding data. However, the data itself can be difficult to work with for a variety of other reasons as well. One of the primary reasons can be just how much data is there, which can be a lot. The next section will be all about finding ways to not be overwhelmed by all of the data that comes from various sources that you may have to deal with.