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.

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.

Python 3.5+.

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 .

(Added by me - not ported from NVIDIA library)

You can call the grab_gpus(num_gpus, gpu_select) 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 memory-usage is 0%.

I have found this useful as I have a shared gpu server and like to use tensorflow which is very greedy and calls to tf.Session() grabs all available gpus.

E.g.

import py3nvml
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)

import py3nvml
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 gpu_fraction parameter (default is 1):

import py3nvml
# Will allocate a GPU if less than 10% of its memory is being used
py3nvml.grab_gpus(num_gpus=2, gpu_fraction=0.9)
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.

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 return XML info on the GPUs, and then defined my own printout. I have included this as a script in 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:

For py3smi, you can specify an update period so it will refresh the feed every few seconds. I.e., similar to watch -n5 nvidia-smi, you can run py3smi -l 5.

(below here is everything ported from pynvml)

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

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

As stated above, the pynvml 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:

• Instead of returning error codes, failing error codes are raised as Python exceptions. E.g. They could be wrapped with exception handlers.

  try:
      nvmlDeviceGetCount()
  except NVMLError as error:
      print(error)

• C function output parameters are returned from the corresponding Python function left to right. Eg the C function:

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

  Can be called like so:

  nvmlInit()
  handle = nvmlDeviceGetHandleByIndex(0)
  (current, pending) = nvmlDeviceGetEccMode(handle)

• C structs are converted into Python classes. E.g. the C struct:

  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:

  info = nvmlDeviceGetMemoryInfo(handle)
  print("Total memory: {}".format(info.total))
  # will print:
  #   Total memory: 5636292608
  print("Free memory: {}".format(info.free))
  # will print:
  #   Free memory: 5578420224
  print("Used memory: ".format(info.used))
  # will print:
  #   Used memory: 57872384

• Python handles string buffer creation. E.g. the C function:

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

  Can be called like so:

  version = nvmlSystemGetDriverVersion()
  nvmlShutdown()

For usage information see the NVML documentation.

All meaningful NVML constants and enums are exposed in Python.

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

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

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