Skip to main content

Gpu Utils: Simple Tool for GPU Analysis and Allocation

Project description


Working on a shared environment with multiple GPUs might be problematic. Advanced frameworks apply greedy approach and they tend to allocate all GPUs and all memory of your system. GpuUtils helps you to find the best GPU on your system to allocate. It also provides a gpu related information in a structure format.


The easiest way to install GpuUtils is to install it via PyPI.

pip install gpuutils

Analyzing system

Running nvidia-smi command in the command prompt allows users to monitor GPU related information such as memory and utilization. Herein, system analysis function loads GPU related information into a pandas data frame or json array.

from gpuutils import GpuUtils
df = GpuUtils.analyzeSystem() #this will return a pandas data frame
#dict = GpuUtils.analyzeSystem(pandas_format = False) #this will return a json array

Default configuration of system analysis returns a Pandas data frame.

gpu_index total_memories_in_mb available_memories_in_mb memory_usage_percentage utilizations power_usages_in_watts power_capacities_in_watts
1 32480 32469 0.0339 0 43 300
2 32480 32469 0.0339 0 43 300
3 32480 32469 0.0339 0 44 300
4 32480 32469 0.0339 0 43 300
5 32480 32469 0.0339 0 43 300
6 32480 32469 0.0339 0 43 300
7 32480 32469 0.0339 0 43 300
0 32480 31031 4.4612 7 56 300


GpuUtils can allocate GPUs as well. Calling allocation function directly finds the available GPUs and allocate based on your demand.

from gpuutils import GpuUtils
GpuUtils.allocate() #this tries to allocate a GPU having 1GB memory
#GpuUtils.allocate(required_memory = 10000)
#GpuUtils.allocate(required_memory = 10000, gpu_count=1)

To avoid greedy approach

Advanced frameworks such as TensorFlow tend to allocate all memory. You can avoid this approach if you pass the framework argument in allocate function. In this way, the framework will use the gpu memory as much as needed. Currently, keras and tensorflow frameworks are supported in allocate function.

GpuUtils.allocate(framework = 'keras')


There are many ways to support a project - starring⭐️ the GitHub repos is just one.


GpuUtils is licensed under the MIT License - see LICENSE for more details.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gpuutils, version 0.0.2
Filename, size File type Python version Upload date Hashes
Filename, size gpuutils-0.0.2-py3-none-any.whl (4.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size gpuutils-0.0.2.tar.gz (3.8 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page