gpuutils 0.0.1

Gpu Utils: Simple Tool for GPU Analysis and Allocation

GpuUtils

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.

Installation

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

Allocation

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)

Support

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

Licence

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