Metadata-Version: 1.1
Name: py3nvml
Version: 0.2.7
Summary: Python 3 Bindings for the NVIDIA Management Library
Home-page: https://github.com/fbcotter/py3nvml.git
Author: Fergal Cotter
Author-email: fbc23@cam.ac.uk
License: BSD
Download-URL: https://github.com/fbcotter/py3nvml/archive/0.2.7.tar.gz
Description: 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() 
        
        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:
        
        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)
        
        
        2. 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)
        
        3. 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
        
        4. 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()
        
        
        5.  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.
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: System :: Hardware
Classifier: Topic :: System :: Systems Administration
