Metadata-Version: 1.1
Name: cuthon
Version: 0.5
Summary: A simple tool to select the first available GPU(s) and run Python
Home-page: https://github.com/awni/cuthon
Author: Awni Hannun
Author-email: awni@stanford.edu
License: MIT
Description: ======
        cuthon
        ======
        
        Cuthon is a simple Python script to avoid setting `CUDA_VISIBLE_DEVICES` when
        running python programs on a GPU. The script will find the first *unused*
        GPU(s) then run the program as usual. At its simplest::
        
          cuthon my_program.py
        
        This tool is intended for a fairly niche use case: running python programs on
        an interactive node which has more than one GPU. For those that have done this
        often you may be relieved at never having to run `nvidia-smi` followed by
        setting `CUDA_VISIBLE_DEVICES` again (when running a python program that is).
        
        -------
        Install
        -------
        Install with `pip`::
        
            pip install cuthon
        
        -----
        Usage
        -----
        
        In general, use `cuthon` just like you would use `python`.
        
        - `cuthon` to launch a python repl.
        - `cuthon -V` to see the python version number.
        - `cuthon train_model.py` to run your program.
        
        For help on available `cuthon` options type::
        
            cuthon -h --
        
        The output will be::
        
            usage: cuthon.py [-h] [-n NUM_GPUS] [-l]
        
            Select the first unused GPU(s) and run Python. To pass the script arguments
            specify '--' between cuthon arguments and arguments to be passed through to
            your script. If '--' is not specified, then all arguments will be passed
            through.
        
            optional arguments:
              -h, --help            show this help message and exit
              -n NUM_GPUS, --num_gpus NUM_GPUS
                                    The number of GPUs to use.
              -l, --least_used      Switch from an unused to a least-used policy.
        
        For example, to run on two available GPUs execute::
        
            cuthon -n 2 -- train_model.py
        
Keywords: gpu development cuda
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
