Python 3 Bindings for the NVIDIA Management Library
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:
CUDA_VISIBLE_DEVICES
environment variable.See the Utils section below for more info.
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to query the gpus, and is now quite out of date.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
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 .
.. _utils-label:
(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()
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.
get_free_gpus
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')
get_num_procs
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:
.. code:: python
try:
nvmlDeviceGetCount()
except NVMLError as error:
print(error)
.. 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)
.. 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
.. code:: c
nvmlReturn_t nvmlSystemGetDriverVersion(char* version,
unsigned int length);
Can be called like so:
.. code:: python
version = nvmlSystemGetDriverVersion()
nvmlShutdown()
NVML_TEMPERATURE_GPU is available under
py3nvml.NVML_TEMPERATURE_GPUThe NVML_VALUE_NOT_AVAILABLE constant is not used. Instead None is mapped to the field.
Version 2.285.0
Version 3.295.0
Version 4.304.0
Version 4.304.3
Version 5.319.0
Version 6.340.0
Version 7.346.0
Version 7.352.0
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