from __future__ import absolute_import from __future__ import print_function from __future__ import division import logging import os import warnings from py3nvml import py3nvml def grab_gpus(num_gpus=1, gpu_select=None, gpu_fraction=0.95, max_procs=-1, env_set_ok=False, gpu_min_memory = None): """ Checks for gpu availability and sets CUDA_VISIBLE_DEVICES as such. Note that this function does not do anything to 'reserve' gpus, it only limits what GPUS your program can see by altering the CUDA_VISIBLE_DEVICES variable. Other programs can still come along and snatch your gpu. This function is more about preventing **you** from stealing someone else's GPU. If more than 1 GPU is requested but not all were available, then it will set the CUDA_VISIBLE_DEVICES variable to see all the available GPUs. A warning is generated in this case. If one or more GPUs were requested and none were available, a Warning will be raised. Before raising it, the CUDA_VISIBLE_DEVICES will be set to a blank string. This means the calling function can ignore this warning and proceed if it chooses to only use the CPU, and it should still be protected against putting processes on a busy GPU. You can call this function with num_gpus=0 to blank out the CUDA_VISIBLE_DEVICES environment variable. Parameters ---------- num_gpus : int How many gpus your job needs (optional). Can set to -1 to take all remaining available GPUs. gpu_select : iterable A single int or an iterable of ints indicating gpu numbers to search through. If None, will search through all gpus. gpu_fraction : float The fractional of a gpu memory that must be free for the script to see the gpu as free. Defaults to 1. Useful if someone has grabbed a tiny amount of memory on a gpu but isn't using it. max_procs : int Maximum number of processes allowed on a GPU (as well as memory restriction). env_set_ok : bool If false, will complain if CUDA_VISIBLE_DEVICES is already set. gpu_min_memory: int The minimal allowed graphics card memory amount in MiB. Returns ------- success : int Number of gpus 'grabbed' Raises ------ RuntimeWarning If couldn't connect with NVIDIA drivers. If 1 or more gpus were requested and none were available. Will NOT raise a RuntimeWarning for mismatch in GPU availability if `num_gpus` is -1. ValueError If the gpu_select option was not understood (can fix by leaving this field blank, providing an int or an iterable of ints). """ if not env_set_ok and 'CUDA_VISIBLE_DEVICES' in os.environ.keys(): raise ValueError('Trying to set CUDA_VISIBLE_DEVICES but it has been ' 'set already. Specify env_set_ok=True to avoid this ' 'error.') # Set the visible devices to blank. os.environ['CUDA_VISIBLE_DEVICES'] = "" if num_gpus == 0: return 0 # Try connect with NVIDIA drivers logger = logging.getLogger(__name__) try: py3nvml.nvmlInit() except: str_ = "Couldn't connect to nvml drivers. Check they are installed " \ "correctly.\nProceeding on cpu only..." warnings.warn(str_, RuntimeWarning) logger.warning(str_) return 0 numDevices = py3nvml.nvmlDeviceGetCount() gpu_free = [False]*numDevices warn_about_fewer_gpus = True if num_gpus == -1: num_gpus = numDevices warn_about_fewer_gpus = False # Flag which gpus we can check if gpu_select is None: gpu_check = [True] * numDevices else: gpu_check = [False] * numDevices try: gpu_check[gpu_select] = True except TypeError: try: for i in gpu_select: gpu_check[i] = True except: raise ValueError('Please set gpu_select to None, an int or an' 'iterable of ints.') # Print out GPU device info. Useful for debugging. for i in range(numDevices): # If the gpu was specified, examine it if not gpu_check[i]: continue handle = py3nvml.nvmlDeviceGetHandleByIndex(i) info = py3nvml.nvmlDeviceGetMemoryInfo(handle) str_ = "GPU {}:\t".format(i) + \ "Used Mem: {:>6}MB\t".format(info.used/(1024*1024)) + \ "Total Mem: {:>6}MB".format(info.total/(1024*1024)) logger.debug(str_) # Check the number of procs running on each gpu if max_procs >= 0: procs_ok = get_free_gpus(max_procs=max_procs) else: procs_ok = [True, ] * numDevices # Now check if any devices are suitable for i in range(numDevices): # If the gpu was not specified, skip it if gpu_check[i] and procs_ok[i]: handle = py3nvml.nvmlDeviceGetHandleByIndex(i) info = py3nvml.nvmlDeviceGetMemoryInfo(handle) # Sometimes we want to avoid using weaker GPUs overall if gpu_min_memory: if (info.total/(1024*1024) < gpu_min_memory): logger.info('GPU {} does not have enough RAM. Skipping.'.format(i)) continue # Sometimes GPU has a few MB used when it is actually free if (info.free+10)/info.total >= gpu_fraction: gpu_free[i] = True else: logger.info('GPU {} has processes on it. Skipping.'.format(i)) py3nvml.nvmlShutdown() # Now check whether we can create the session if sum(gpu_free) == 0: warnings.warn("Could not find enough GPUs for your job", RuntimeWarning) logger.warning(str_) return 0 else: if sum(gpu_free) >= num_gpus: # only use the first num_gpus gpus. Hide the rest from greedy # tensorflow available_gpus = [i for i, x in enumerate(gpu_free) if x] use_gpus = ','.join(list(str(s) for s in available_gpus[:num_gpus])) logger.debug('{} Gpus found free'.format(sum(gpu_free))) logger.info('Using {}'.format(use_gpus)) os.environ['CUDA_VISIBLE_DEVICES'] = use_gpus return num_gpus elif warn_about_fewer_gpus: # use everything we can. s = "Only {} GPUs found but {} ".format(sum(gpu_free), num_gpus) + \ "requested. Allocating these and continuing." warnings.warn(s, RuntimeWarning) logger.warning(s) available_gpus = [i for i, x in enumerate(gpu_free) if x] use_gpus = ','.join(list(str(s) for s in available_gpus)) logger.debug('{} Gpus found free'.format(sum(gpu_free))) logger.info('Using {}'.format(use_gpus)) os.environ['CUDA_VISIBLE_DEVICES'] = use_gpus return sum(gpu_free) def try_get_info(f, h, default='N/A'): try: v = f(h) except py3nvml.NVMLError_NotSupported: v = default return v def get_free_gpus(max_procs=0): """ Checks the number of processes running on your GPUs. Parameters ---------- max_procs : int Maximum number of procs allowed to run on a gpu for it to be considered 'available' Returns ------- availabilities : list(bool) List of length N for an N-gpu system. The nth value will be true, if the nth gpu had at most max_procs processes running on it. Set to 0 to look for gpus with no procs on it. Note ---- If function can't query the driver will return an empty list rather than raise an Exception. """ # Try connect with NVIDIA drivers logger = logging.getLogger(__name__) try: py3nvml.nvmlInit() except: str_ = """Couldn't connect to nvml drivers. Check they are installed correctly.""" warnings.warn(str_, RuntimeWarning) logger.warning(str_) return [] num_gpus = py3nvml.nvmlDeviceGetCount() gpu_free = [False]*num_gpus for i in range(num_gpus): try: h = py3nvml.nvmlDeviceGetHandleByIndex(i) except: continue procs = try_get_info(py3nvml.nvmlDeviceGetComputeRunningProcesses, h, ['something']) if len(procs) <= max_procs: gpu_free[i] = True py3nvml.nvmlShutdown() return gpu_free def get_num_procs(): """ Gets the number of processes running on each gpu Returns ------- num_procs : list(int) Number of processes running on each gpu Note ---- If function can't query the driver will return an empty list rather than raise an Exception. Note ---- If function can't get the info from the gpu will return -1 in that gpu's place """ # Try connect with NVIDIA drivers logger = logging.getLogger(__name__) try: py3nvml.nvmlInit() except: str_ = """Couldn't connect to nvml drivers. Check they are installed correctly.""" warnings.warn(str_, RuntimeWarning) logger.warning(str_) return [] num_gpus = py3nvml.nvmlDeviceGetCount() gpu_procs = [-1]*num_gpus for i in range(num_gpus): try: h = py3nvml.nvmlDeviceGetHandleByIndex(i) except: continue procs = try_get_info(py3nvml.nvmlDeviceGetComputeRunningProcesses, h, ['something']) gpu_procs[i] = len(procs) py3nvml.nvmlShutdown() return gpu_procs