Automatic GPU Allocation

gpu_allocation.py

# EDIT 10/04/2022 - This version was provided by @jayelm who fixed some bugs and made the function much more robust

import os
import subprocess
import time

def assign_free_gpus(threshold_vram_usage=1500, max_gpus=2, wait=False, sleep_time=10):
    """
    Assigns free gpus to the current process via the CUDA_AVAILABLE_DEVICES env variable
    This function should be called after all imports,
    in case you are setting CUDA_AVAILABLE_DEVICES elsewhere

    Borrowed and fixed from https://gist.github.com/afspies/7e211b83ca5a8902849b05ded9a10696

    Args:
        threshold_vram_usage (int, optional): A GPU is considered free if the vram usage is below the threshold
                                              Defaults to 1500 (MiB).
        max_gpus (int, optional): Max GPUs is the maximum number of gpus to assign.
                                  Defaults to 2.
        wait (bool, optional): Whether to wait until a GPU is free. Default False.
        sleep_time (int, optional): Sleep time (in seconds) to wait before checking GPUs, if wait=True. Default 10.
    """

    def _check():
        # Get the list of GPUs via nvidia-smi
        smi_query_result = subprocess.check_output(
            "nvidia-smi -q -d Memory | grep -A4 GPU", shell=True
        )
        # Extract the usage information
        gpu_info = smi_query_result.decode("utf-8").split("\n")
        gpu_info = list(filter(lambda info: "Used" in info, gpu_info))
        gpu_info = [
            int(x.split(":")[1].replace("MiB", "").strip()) for x in gpu_info
        ]  # Remove garbage
        # Keep gpus under threshold only
        free_gpus = [
            str(i) for i, mem in enumerate(gpu_info) if mem < threshold_vram_usage
        ]
        free_gpus = free_gpus[: min(max_gpus, len(free_gpus))]
        gpus_to_use = ",".join(free_gpus)
        return gpus_to_use

    while True:
        gpus_to_use = _check()
        if gpus_to_use or not wait:
            break
        print(f"No free GPUs found, retrying in {sleep_time}s")
        time.sleep(sleep_time)

    if not gpus_to_use:
        raise RuntimeError("No free GPUs found")
    os.environ["CUDA_VISIBLE_DEVICES"] = gpus_to_use
    logger.info(f"Using GPU(s): {gpus_to_use}")

I wrote a version which fixes the above bug and additionally has support for waiting until a free GPU is available:

import os
import subprocess
import time

def assign_free_gpus(threshold_vram_usage=1500, max_gpus=2, wait=False, sleep_time=10):
    """
    Assigns free gpus to the current process via the CUDA_AVAILABLE_DEVICES env variable
    This function should be called after all imports,
    in case you are setting CUDA_AVAILABLE_DEVICES elsewhere

    Borrowed and fixed from https://gist.github.com/afspies/7e211b83ca5a8902849b05ded9a10696

    Args:
        threshold_vram_usage (int, optional): A GPU is considered free if the vram usage is below the threshold
                                              Defaults to 1500 (MiB).
        max_gpus (int, optional): Max GPUs is the maximum number of gpus to assign.
                                  Defaults to 2.
        wait (bool, optional): Whether to wait until a GPU is free. Default False.
        sleep_time (int, optional): Sleep time (in seconds) to wait before checking GPUs, if wait=True. Default 10.
    """

    def _check():
        # Get the list of GPUs via nvidia-smi
        smi_query_result = subprocess.check_output(
            "nvidia-smi -q -d Memory | grep -A4 GPU", shell=True
        )
        # Extract the usage information
        gpu_info = smi_query_result.decode("utf-8").split("\n")
        gpu_info = list(filter(lambda info: "Used" in info, gpu_info))
        gpu_info = [
            int(x.split(":")[1].replace("MiB", "").strip()) for x in gpu_info
        ]  # Remove garbage
        # Keep gpus under threshold only
        free_gpus = [
            str(i) for i, mem in enumerate(gpu_info) if mem < threshold_vram_usage
        ]
        free_gpus = free_gpus[: min(max_gpus, len(free_gpus))]
        gpus_to_use = ",".join(free_gpus)
        return gpus_to_use

    while True:
        gpus_to_use = _check()
        if gpus_to_use or not wait:
            break
        print(f"No free GPUs found, retrying in {sleep_time}s")
        time.sleep(sleep_time)

    if not gpus_to_use:
        raise RuntimeError("No free GPUs found")
    os.environ["CUDA_VISIBLE_DEVICES"] = gpus_to_use
    logger.info(f"Using GPU(s): {gpus_to_use}")

Oh thank you for these changes, that is vastly superior!

If we really wanted to perfect this, we'd probably want to use pynvml which is more efficient and direct. I have a (very crude) script for a different setting which does this. Will merge these at some point.

import pynvml as pynvml
import psutil

def check_gpu_usage(process_exceptions=['Xorg'], user_exceptions=['bla123'], min_memory=5, base_on_memory=True, base_on_process=True):
    # Process exceptions -> we don't care about such procs
    # User exceptions -> we care ONLY about procs of this user
    pynvml.nvmlInit()
    # print ("Driver Version:", pynvml.nvmlSystemGetDriverVersion())
    deviceCount = pynvml.nvmlDeviceGetCount()
    free_gpus = []
    for i in range(deviceCount):

        handle = pynvml.nvmlDeviceGetHandleByIndex(i)
        mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
        free_memory = mem.free/(1024**3)
        if base_on_memory and free_memory < min_memory:
            continue

        free = True 
        if base_on_process:
            procs = [*pynvml.nvmlDeviceGetComputeRunningProcesses(handle), *pynvml.nvmlDeviceGetGraphicsRunningProcesses(handle)]
            for p in procs:
                try:
                    process = psutil.Process(p.pid)
                except psutil.NoSuchProcess:
                    continue

                if process.name not in process_exceptions and process.username() in user_exceptions:
                    free = False
                    break
        if free:
            free_gpus.append(str(i))

    print(f"[[GPU INFO]] [{','.join(free_gpus)}] Free")
    pynvml.nvmlShutdown()