Need help with py3nvml?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

fbcotter
164 Stars 25 Forks BSD 3-Clause "New" or "Revised" License 79 Commits 1 Opened issues

Description

Python 3 Bindings for NVML library. Get NVIDIA GPU status inside your program.

Services available

!
?

Need anything else?

Contributors list

# 12,438
style-t...
Python
neural-...
Jupyter...
61 commits
# 569,943
HTML
Shell
Amazon ...
Python
1 commit
# 443,174
JavaScr...
HTML
Shell
1 commit
# 304,178
React
TypeScr...
HTML
Redux
1 commit

py3nvml

Documentation also available at

readthedocs
__.

Python 3 compatible bindings to the NVIDIA Management Library. Can be used to query the state of the GPUs on your system. This was ported from the NVIDIA provided python bindings

nvidia-ml-py
, which only supported python 2. I have forked from version 7.352.0. The old library was itself a wrapper around the
NVIDIA Management Library
.

__ https://py3nvml.readthedocs.io/en/latest/ __ https://pypi.python.org/pypi/nvidia-ml-py/7.352.0 __ http://developer.nvidia.com/nvidia-management-library-nvml

In addition to these NVIDIA functions to query the state of the GPU, I have written a couple functions/tools to help in using gpus (particularly for a shared gpu server). These are:

  • A function to 'restrict' the available GPUs by setting the
    CUDA_VISIBLE_DEVICES
    environment variable.
  • A script for displaying a differently formatted nvidia-smi.

See the Utils section below for more info.

Updates in Version 0.2.3

To try and keep py3nvml somewhat up-to-date with the constantly evolving nvidia drivers, I have done some work to the

py3nvml.py3nvml
module. In particular, I have updated all the constants that were missing in py3nvml and existing in the
NVIDIA source
__ as of version 418.43. In addition, I have wrapped all of these constants in Enums so it is easier to see what constants go together. Finally, for all the functions in
py3nvml.py3nvml
I have copied in the C docstring. While this will result in some strange looking docstrings which will be slightly incorrect, they should give good guidance on the scope of the function, something which was ill-defined before.

Finally, I will remove the

py3nvml.nvidia_smi
module in a future version, as I believe it was only ever meant as an example of how to use the nvml functions to query the gpus, and is now quite out of date. To get the same functionality, you can call
nvidia-smi -q -x
from python with subprocess.

__ https://github.com/NVIDIA/nvidia-settings/blob/master/src/nvml.h

Requires

Python 3.5+.

Installation

From PyPi::

$ pip install py3nvml

From GitHub::

$ pip install -e git+https://github.com/fbcotter/py3nvml#egg=py3nvml

Or, download and pip install::

$ git clone https://github.com/fbcotter/py3nvml
$ cd py3nvml
$ pip install .

.. _utils-label:

Utils

(Added by me - not ported from NVIDIA library)

grab_gpus ~~~~~~~~~

You can call the :code:

grab_gpus(num_gpus, gpu_select, gpu_fraction=.95)
function to check the available gpus and set the
CUDA_VISIBLE_DEVICES
environment variable as need be. It determines if a GPU is available by checking if the amount of free memory is below memory-usage is above/equal to the gpu_fraction value. The default of .95 allows for some small amount of memory to be taken before it deems the gpu as being 'used'.

I have found this useful as I have a shared gpu server and like to use tensorflow which is very greedy and calls to :code:

tf.Session()
grabs all available gpus.

E.g.

.. code:: python

import py3nvml
import tensorflow as tf
py3nvml.grab_gpus(3)
sess = tf.Session() # now we only grab 3 gpus!

Or the following will grab 2 gpus from the first 4 (and leave any higher gpus untouched)

.. code:: python

py3nvml.grab_gpus(num_gpus=2, gpu_select=[0,1,2,3])
sess = tf.Session() 

This will look for 3 available gpus in the range of gpus from 0 to 3. The range option is not necessary, and it only serves to restrict the search space for the grab_gpus.

You can adjust the memory threshold for determining if a GPU is free/used with the :code:

gpu_fraction
parameter (default is 1):

.. code:: python

# Will allocate a GPU if less than 20% of its memory is being used
py3nvml.grab_gpus(num_gpus=2, gpu_fraction=0.8)
sess = tf.Session() 

This function has no return codes but may raise some warnings/exceptions:

  • If the method could not connect to any NVIDIA gpus, it will raise a RuntimeWarning.
  • If it could connect to the GPUs, but there were none available, it will raise a ValueError.
  • If it could connect to the GPUs but not enough were available (i.e. more than 1 was requested), it will take everything it can and raise a RuntimeWarning.

getfreegpus ~~~~~~~~~~~~~ This tool can query the gpu status. Unlike the default for

grab_gpus
, which checks the memory usage of a gpu, this function checks if a process is running on a gpu. For a system with N gpus, returns a list of N booleans, where the nth value is True if no process was found running on gpu n. An example use is:

.. code:: python

import py3nvml
free_gpus = py3nvml.get_free_gpus()
if True not in free_gpus:
    print('No free gpus found')

getnumprocs ~~~~~~~~~~~~~ This function is called by

get_free_gpus
. It simply returns a list of integers with the number of processes running on each gpu. E.g. if you had 1 process running on gpu 5 in an 8 gpu system, you would expect to get the following:

.. code:: python

import py3nvml
num_procs = py3nvml.get_num_procs()
print(num_proces)
>>> [0, 0, 0, 0, 0, 1, 0, 0]

py3smi ~~~~~~ I found the default

nvidia-smi
output was missing some useful info, so made use of the
py3nvml/nvidia_smi.py
module to query the device and get info on the GPUs, and then defined my own printout. I have included this as a script in
scripts/py3smi
. The print code is horribly messy but the query code is very simple and should be understandable.

Running pip install will now put this script in your python's bin, and you'll be able to run it from the command line. Here is a comparison of the two outputs:

.. image:: https://i.imgur.com/TvdfkFE.png

.. image:: https://i.imgur.com/UPSHr8k.png

For py3smi, you can specify an update period so it will refresh the feed every few seconds. I.e., similar to :code:

watch -n5 nvidia-smi
, you can run :code:
py3smi -l 5
.

You can also get the full output (very similar to nvidia-smi) by running

py3smi
-f
(this shows a slightly modified process info pane below).

Regular Usage

Visit

NVML reference
__ for a list of the functions available and their help. Also the script py3smi is a bit hacky but shows examples of me querying the GPUs for info.

__ https://docs.nvidia.com/deploy/nvml-api/index.html

(below here is everything ported from pynvml)

.. code:: python

from py3nvml.py3nvml import *
nvmlInit()
print("Driver Version: {}".format(nvmlSystemGetDriverVersion()))
# e.g. will print:
#   Driver Version: 352.00
deviceCount = nvmlDeviceGetCount()
for i in range(deviceCount):
    handle = nvmlDeviceGetHandleByIndex(i)
    print("Device {}: {}".format(i, nvmlDeviceGetName(handle)))
# e.g. will print:
#  Device 0 : Tesla K40c
#  Device 1 : Tesla K40c

nvmlShutdown()

Additionally, see

py3nvml.nvidia_smi.py
. This does the equivalent of the
nvidia-smi
command::
nvidia-smi -q -x

With

.. code:: python

import py3nvml.nvidia_smi as smi
print(smi.XmlDeviceQuery())

Differences from NVML ~~~~~~~~~~~~~~~~~~~~~ The py3nvml library consists of python methods which wrap several NVML functions, implemented in a C shared library. Each function's use is the same with the following exceptions:

  1. Instead of returning error codes, failing error codes are raised as Python exceptions. I.e. They should be wrapped with exception handlers.

.. code:: python

try:
    nvmlDeviceGetCount()
except NVMLError as error:
    print(error)
  1. C function output parameters are returned from the corresponding Python function as tuples, rather than requiring pointers. Eg the C function:

.. code:: c

nvmlReturn_t nvmlDeviceGetEccMode(nvmlDevice_t device,
                                  nvmlEnableState_t *current,
                                  nvmlEnableState_t *pending);

Becomes

.. code:: python

nvmlInit()
handle = nvmlDeviceGetHandleByIndex(0)
(current, pending) = nvmlDeviceGetEccMode(handle)
  1. C structs are converted into Python classes. E.g. the C struct:

.. code:: c

nvmlReturn_t DECLDIR nvmlDeviceGetMemoryInfo(nvmlDevice_t device,
                                             nvmlMemory_t *memory);
typedef struct nvmlMemory_st {
    unsigned long long total;
    unsigned long long free;
    unsigned long long used;
} nvmlMemory_t;

Becomes:

.. code:: python

info = nvmlDeviceGetMemoryInfo(handle)
print("Total memory: {}MiB".format(info.total >> 20))
# will print:
#   Total memory: 5375MiB
print("Free memory: {}".format(info.free >> 20))
# will print:
#   Free memory: 5319MiB
print("Used memory: ".format(info.used >> 20))
# will print:
#   Used memory: 55MiB
  1. Python handles string buffer creation. E.g. the C function:

.. code:: c

nvmlReturn_t nvmlSystemGetDriverVersion(char* version,
                                        unsigned int length);

Can be called like so:

.. code:: python

version = nvmlSystemGetDriverVersion()
nvmlShutdown()
  1. All meaningful NVML constants and enums are exposed in Python. E.g. the constant
    NVML_TEMPERATURE_GPU
    is available under
    py3nvml.NVML_TEMPERATURE_GPU

The

NVML_VALUE_NOT_AVAILABLE
constant is not used. Instead None is mapped to the field.

Release Notes (for pynvml)

Version 2.285.0

  • Added new functions for NVML 2.285. See NVML documentation for more information.
  • Ported to support Python 3.0 and Python 2.0 syntax.
  • Added nvidia_smi.py tool as a sample app.

Version 3.295.0

  • Added new functions for NVML 3.295. See NVML documentation for more information.
  • Updated nvidia_smi.py tool
    • Includes additional error handling

Version 4.304.0

  • Added new functions for NVML 4.304. See NVML documentation for more information.
  • Updated nvidia_smi.py tool

Version 4.304.3

  • Fixing nvmlUnitGetDeviceCount bug

Version 5.319.0

  • Added new functions for NVML 5.319. See NVML documentation for more information.

Version 6.340.0

  • Added new functions for NVML 6.340. See NVML documentation for more information.

Version 7.346.0

  • Added new functions for NVML 7.346. See NVML documentation for more information.

Version 7.352.0

  • Added new functions for NVML 7.352. See NVML documentation for more information.

COPYRIGHT

Copyright (c) 2011-2015, NVIDIA Corporation. All rights reserved.

LICENSE

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  • Neither the name of the NVIDIA Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.