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

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

NVIDIA source

py3nvml.py3nvml

Finally, I will remove the

py3nvml.nvidia_smi

nvidia-smi -q -x

__ 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)

CUDA_VISIBLE_DEVICES

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()

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

.. 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() 

You can select the graphics card based on its capacity. Specify minimal amount of graphics card memory in MiB in order to exclude the weaker graphics cards.

.. code:: python

# Will allocate a GPU only if it has more than 4000 MiB of memory
py3nvml.grab_gpus(num_gpus=2, gpu_min_memory=4000)
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

.. 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

.. 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

py3nvml/nvidia_smi.py

scripts/py3smi

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

py3smi -l 5

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

py3smi
-f

Regular Usage

Visit

NVML reference

__ 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

nvidia-smi

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

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.