Machine learning and deep learning workloads often involve heavy computation. Sometimes it'll be enough to run it on the CPU (Central Processing Unit), but some types of models such as neural networks and gradient boosted trees benefit greatly from running on GPU (Graphical Processing Unit). Because shorter experiment iterations are always preferred (the time of the data scientist/engineer costs money), using GPUs and making sure they are utilised efficiently is important. In this post we take a look at the tools available for monitoring the GPU usage, focusing on NVIDIA hardware and GNU/Linux.


nvidia-smi is the de facto standard tool when it comes to monitoring the utilisation of NVIDIA GPUs. It is an application that queries the NVML (NVIDIA Management Library). It's installed together with the CUDA Toolkit.

nvidia-smi screenshot
Example output of nvidia-smi command showing one GPU and a process using it.

Let's dig into the output: it seems to have a header with nvidia-smi version and CUDA driver version, both of those things can be found in other places but it's nice to have them available so quickly without going through the installed OS packages or Linux kernel module listings.

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |
| N/A   83C    P0   151W / 149W |   2259MiB / 11439MiB |     97%      Default |

The top part of the output has information on each GPU, in the example above there's just one but the tool supports displaying metrics for multiple GPUs as well. We can see that the GPU is "Tesla K80" which matches our expectations for the AWS EC2 p2.xlarge instance type. The "0" to the left of the device name is the index of the GPU, it is the same identifier as we will see in the bottom table in the output. Perf with value "P0" means that the device is set up for maximum performance, rather than for lowest power consumption. The temperature is 83 degrees Celsius which is not cold but far from being worriesome. I trust AWS to manage the cooling in their datacenters and would only pay attention to the temperature of my own hardware.

Persistence-M stands for "Persistence Mode" where "On" means that the driver will remain loaded even when no apps are using the GPU. Pwr:Usage/Cap is not something I'd pay attention to in case of cloud computing but it's funny to see the usage exceeding the capacity.

Disp.A is for "Display Active" and "Off" means that there isn't a display using the device which again makes sense for a virtual machine. Bus-Id is the GPU's PCI bus ID which by itself is of little interest to the end-user but can be used to filter nvidia-smi output and only show the stats for a particular device. Memory-Usage is one of my favourites, it shows the amount of memory allocated by the applications out of total amount of memory. Note that TensorFlow for example would by default pre-allocate most of the GPU memory without actually using it. See this post if that's not desired.

Volatile Uncorr. ECC is a counter of uncorrectable ECC memory errors since the last driver load. GPU-Util indicates that over the last polling interval the GPU was utilised 96% of the time. A low value here can indicate that GPU is underused which can be the case if the code spends a lot of time in other places (reading mini-batches from disk for example). Compute M. indicates the shared access mode where the default setting allows multiple clients to access the CPU concurrently.

| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|    0      4631      C   ...naconda3/envs/toxic-comments/bin/python  2248MiB |

The table in the bottom part of the nvidia-smi output describes the processes that are currently using the GPUs:

All of these metrics are described in more details in the nvidia-smi manual page.

Not just monitoring

nvidia-smi is more than just a monitoring tool, in fact, it supports changing some of the settings we looked at such as persistence and compute modes. It also has a daemon to speed up the queries. Refer to the man page if you want to know more.

Interactive usage

Running nvidia-smi continuously can be done in two ways:

In both cases, use Ctrl+C to quit the refresh loop.

Machine-readable output

nvidia-smi can produce files XML and CSV output. The following command produces a CSV line per second with a short summary of the processes using the GPU:

$ nvidia-smi --format=csv,noheader \
    --query-compute-apps=timestamp,gpu_name,pid,used_memory \
2018/01/21 18:54:28.513, Tesla K80, 4631, 2248 MiB
2018/01/21 18:54:29.513, Tesla K80, 4631, 2248 MiB
2018/01/21 18:54:30.513, Tesla K80, 4631, 2248 MiB
2018/01/21 18:54:31.514, Tesla K80, 4631, 2248 MiB

The output can be redicted to a file using the --filename=FILE command-line argument and then consumed by a monitoring tool of your choice that supports CSV input.


Nvtop stands for NVidia TOP, a (h)top like task monitor for NVIDIA GPUs. It can handle multiple GPUs and print information about them in a htop familiar way.

Nvtop screenshot
Nvtop screenshot showing one GPU and a process using it.


gpustat offers a minimalistic view of the GPU usage. It is a Python app using NVML and can be easily installed with pip.

gpustat screenshot
gpustat screenshot showing one GPU and a process ran by 'ubuntu' user utilising it.

I found that watch --color --no-title --interval=1 gpustat --color command is a reasonably good choice: it keeps the colour output of gpustat, refreshes every second and hides the title with current time which watch adds by default (it's not needed because gpustat prints a timestamp itself).

Other top-like utilities for NVIDIA

There are more projects aiming to have a familiar top-like interface for GPU usage stats:

Tools for AMD and Intel GPUs

Due to the lack of support of AMD and Intel GPUs in particular or OpenCL in general by modern machine learning and deep learning frameworks, I will limit the section to some references.

Both tools are available as packages in Debian and Ubuntu.