Python Bindings for the NVIDIA Management Library
Provides a Python interface to GPU management and monitoring functions.
This is a wrapper around the NVML library. For information about the NVML library, see the NVML developer page http://developer.nvidia.com/nvidia-management-library-nvml
Download the latest package from: http://pypi.python.org/pypi/nvidia-ml-py/
Note this file can be run with 'python -m doctest -v README.txt' although the results are system dependent
The nvml header file contains function documentation that is relevant to this wrapper. The header file is distributed with. https://developer.nvidia.com/gpu-deployment-kit
The main difference is this library handles allocating structs and passing pointers to the functions, before returning the desired value. Non-success return codes are raised as exceptions as described in the section below.
Python 2.5, or an earlier version with the ctypes module.
Pip Installation with python3:
python3 -m pip install nvidia-ml-pyManual Installation:
$ tar -xzf nvidia-ml-py-$major-$minor-$patch.tar.gz`
$ cd nvidia-ml-py-$major-$minor-$patch
$ sudo python setup.py install
>>> from pynvml import *
>>> nvmlInit()
>>> print(f"Driver Version: {nvmlSystemGetDriverVersion()}")
Driver Version: 11.515.48
>>> deviceCount = nvmlDeviceGetCount()
>>> for i in range(deviceCount):
... handle = nvmlDeviceGetHandleByIndex(i)
... print(f"Device {i} : {nvmlDeviceGetName(handle)}")
...
Device 0 : Tesla K40c
>>> nvmlShutdown()
Python methods wrap NVML functions, implemented in a C shared library. Each function's use is the same with the following exceptions:
>>> try:
... nvmlDeviceGetCount()
... except NVMLError as error:
... print(error)
...
Uninitialized
nvmlReturn_t nvmlDeviceGetEccMode(nvmlDevice_t device,
nvmlEnableState_t *current,
nvmlEnableState_t *pending);
>>> nvmlInit()
>>> handle = nvmlDeviceGetHandleByIndex(0)
>>> (current, pending) = nvmlDeviceGetEccMode(handle)
// C Function and typedef 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;
# Python call to function and accessing members of ctype struct
>>> info = nvmlDeviceGetMemoryInfo(handle)
>>> print(f"Total memory: {info.total}")
Total memory: 5636292608
>>> print(f"Free memory:, {info.free}")
Free memory: 5578420224
>>> print(f"Used memory: {info.used}")
Used memory: 57872384
// C Function that needs character array and length
nvmlReturn_t nvmlSystemGetDriverVersion(char* version,
unsigned int length);
# Python function handles memory
>>> version = nvmlSystemGetDriverVersion()
>>> print(version)
... 11.520.75
>>> 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.
Since the C library uses return codes and python prefers exception handling, the library converts all return codes to various exceptions. The exceptions are generated automatically via a function at run time instead of being defined manually.
The list of exceptions can be found in NVMLError base class.
The example seen above in the FUNCTIONS section:
>>> try:
... nvmlDeviceGetCount()
... except NVMLError as error:
... print(error)
...
Uninitialized
Can be more accurately caught like this:
>>> try:
... nvmlDeviceGetCount()
... except NVMLError_Uninitialized as error:
... print(error)
...
Uninitialized
The conversion from name to exception is like this for all exceptions:
NVML_ERROR_UNINITIALIZED => NVMLError_UninitializedNVML_ERROR_LIBRARY_NOT_FOUND => NVMLError_LibraryNotFoundNVML_ERROR_ALREADY_INITIALIZED => NVMLError_AlreadyInitializedVersion 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
Version 10.418
Version 11.515.48
Version 11.520
Version 11.525
Copyright (c) 2011-2023, NVIDIA Corporation. All rights reserved.
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.
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